refactor: update session cleanup logic and extend command timeout

- Changed the command timeout duration from 60 seconds to 1 hour for improved session stability.
- Refactored session cleanup logic to utilize the CLI API session object instead of session ID, enhancing clarity and maintainability.

.gitignore

@@ -209,6 +209,7 @@ api/.vscode
 .history
 
 .idea/
+web/migration/
 
 # pnpm
 /.pnpm-store

api/.env.example

@@ -717,3 +717,13 @@ SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_SIZE=1000
 SANDBOX_EXPIRED_RECORDS_RETENTION_DAYS=30
 SANDBOX_EXPIRED_RECORDS_CLEAN_TASK_LOCK_TTL=90000
 
+# Sandbox Dify CLI configuration
+# Directory containing dify CLI binaries (dify-cli-<os>-<arch>). Defaults to api/bin when unset.
+SANDBOX_DIFY_CLI_ROOT=
+
+# CLI API URL for sandbox (dify-sandbox or e2b) to call back to Dify API.
+# This URL must be accessible from the sandbox environment.
+# For local development: use http://localhost:5001 or http://127.0.0.1:5001
+# For Docker deployment: use http://api:5001 (internal Docker network)
+# For external sandbox (e.g., e2b): use a publicly accessible URL
+CLI_API_URL=http://localhost:5001

api/agent-notes/core/app_assets/packager/zip_packager.py.md

@@ -0,0 +1,14 @@
+# Zip Packager Notes
+
+## Purpose
+- Builds a ZIP archive of asset contents stored via the configured storage backend.
+
+## Key Decisions
+- Packaging writes assets into an in-memory zip buffer returned as bytes.
+- Asset fetch + zip writing are executed via a thread pool with a lock guarding `ZipFile` writes.
+
+## Edge Cases
+- ZIP writes are serialized by the lock; storage reads still run in parallel.
+
+## Tests/Verification
+- None yet.

api/agent-notes/core/virtual_environment/providers/e2b_sandbox.py.md

@@ -0,0 +1,16 @@
+# E2B Sandbox Provider Notes
+
+## Purpose
+- Implements the E2B-backed `VirtualEnvironment` provider and bootstraps sandbox metadata, file I/O, and command execution.
+
+## Key Decisions
+- Sandbox metadata is gathered during `_construct_environment` using the E2B SDK before returning `Metadata`.
+- Architecture/OS detection uses a single `uname -m -s` call split by whitespace to reduce round-trips.
+- Command execution streams stdout/stderr through `QueueTransportReadCloser`; stdin is unsupported.
+
+## Edge Cases
+- `release_environment` raises when sandbox termination fails.
+- `execute_command` runs in a background thread; consumers must read stdout/stderr until EOF.
+
+## Tests/Verification
+- None yet. Add targeted service tests when behavior changes.

api/commands.py

@@ -23,7 +23,8 @@ from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.index_processor.constant.built_in_field import BuiltInField
 from core.rag.models.document import Document
-from core.tools.utils.system_oauth_encryption import encrypt_system_oauth_params
+from core.sandbox import SandboxBuilder, SandboxType
+from core.tools.utils.system_encryption import encrypt_system_params
 from events.app_event import app_was_created
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -1245,7 +1246,7 @@ def remove_orphaned_files_on_storage(force: bool):
             click.echo(click.style(f"- Scanning files on storage path {storage_path}", fg="white"))
             files = storage.scan(path=storage_path, files=True, directories=False)
             all_files_on_storage.extend(files)
-        except FileNotFoundError as e:
+        except FileNotFoundError:
             click.echo(click.style(f"  -> Skipping path {storage_path} as it does not exist.", fg="yellow"))
             continue
         except Exception as e:
@@ -1493,6 +1494,57 @@ def file_usage(
             click.echo(click.style(f"Use --offset {offset + limit} to see next page", fg="white"))
 
 
+@click.command("setup-sandbox-system-config", help="Setup system-level sandbox provider configuration.")
+@click.option(
+    "--provider-type", prompt=True, type=click.Choice(["e2b", "docker", "local"]), help="Sandbox provider type"
+)
+@click.option("--config", prompt=True, help='Configuration JSON (e.g., {"api_key": "xxx"} for e2b)')
+def setup_sandbox_system_config(provider_type: str, config: str):
+    """
+    Setup system-level sandbox provider configuration.
+
+    Examples:
+        flask setup-sandbox-system-config --provider-type e2b --config '{"api_key": "e2b_xxx"}'
+        flask setup-sandbox-system-config --provider-type docker --config '{"docker_sock": "unix:///var/run/docker.sock"}'
+        flask setup-sandbox-system-config --provider-type local --config '{}'
+    """
+    from models.sandbox import SandboxProviderSystemConfig
+
+    try:
+        click.echo(click.style(f"Validating config: {config}", fg="yellow"))
+        config_dict = TypeAdapter(dict[str, Any]).validate_json(config)
+        click.echo(click.style("Config validated successfully.", fg="green"))
+
+        click.echo(click.style(f"Validating config schema for provider type: {provider_type}", fg="yellow"))
+        SandboxBuilder.validate(SandboxType(provider_type), config_dict)
+        click.echo(click.style("Config schema validated successfully.", fg="green"))
+
+        click.echo(click.style("Encrypting config...", fg="yellow"))
+        click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
+        encrypted_config = encrypt_system_params(config_dict)
+        click.echo(click.style("Config encrypted successfully.", fg="green"))
+    except Exception as e:
+        click.echo(click.style(f"Error validating/encrypting config: {str(e)}", fg="red"))
+        return
+
+    deleted_count = db.session.query(SandboxProviderSystemConfig).filter_by(provider_type=provider_type).delete()
+    if deleted_count > 0:
+        click.echo(
+            click.style(
+                f"Deleted {deleted_count} existing system config for provider type: {provider_type}", fg="yellow"
+            )
+        )
+
+    system_config = SandboxProviderSystemConfig(
+        provider_type=provider_type,
+        encrypted_config=encrypted_config,
+    )
+    db.session.add(system_config)
+    db.session.commit()
+    click.echo(click.style(f"Sandbox system config setup successfully. id: {system_config.id}", fg="green"))
+    click.echo(click.style(f"Provider type: {provider_type}", fg="green"))
+
+
 @click.command("setup-system-tool-oauth-client", help="Setup system tool oauth client.")
 @click.option("--provider", prompt=True, help="Provider name")
 @click.option("--client-params", prompt=True, help="Client Params")
@@ -1512,7 +1564,7 @@ def setup_system_tool_oauth_client(provider, client_params):
 
         click.echo(click.style(f"Encrypting client params: {client_params}", fg="yellow"))
         click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        oauth_client_params = encrypt_system_oauth_params(client_params_dict)
+        oauth_client_params = encrypt_system_params(client_params_dict)
         click.echo(click.style("Client params encrypted successfully.", fg="green"))
     except Exception as e:
         click.echo(click.style(f"Error parsing client params: {str(e)}", fg="red"))
@@ -1561,7 +1613,7 @@ def setup_system_trigger_oauth_client(provider, client_params):
 
         click.echo(click.style(f"Encrypting client params: {client_params}", fg="yellow"))
         click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        oauth_client_params = encrypt_system_oauth_params(client_params_dict)
+        oauth_client_params = encrypt_system_params(client_params_dict)
         click.echo(click.style("Client params encrypted successfully.", fg="green"))
     except Exception as e:
         click.echo(click.style(f"Error parsing client params: {str(e)}", fg="red"))

api/configs/app_config.py

@@ -2,6 +2,7 @@ import logging
 from pathlib import Path
 from typing import Any
 
+from pydantic import Field
 from pydantic.fields import FieldInfo
 from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, SettingsConfigDict, TomlConfigSettingsSource
 
@@ -82,6 +83,14 @@ class DifyConfig(
         extra="ignore",
     )
 
+    SANDBOX_DIFY_CLI_ROOT: str | None = Field(
+        default=None,
+        description=(
+            "Filesystem directory containing dify CLI binaries named dify-cli-<os>-<arch>. "
+            "Defaults to api/bin when unset."
+        ),
+    )
+
     # Before adding any config,
     # please consider to arrange it in the proper config group of existed or added
     # for better readability and maintainability.

api/configs/feature/__init__.py

@@ -244,6 +244,17 @@ class PluginConfig(BaseSettings):
     )
 
 
+class CliApiConfig(BaseSettings):
+    """
+    Configuration for CLI API (for dify-cli to call back from external sandbox environments)
+    """
+
+    CLI_API_URL: str = Field(
+        description="CLI API URL for external sandbox (e.g., e2b) to call back.",
+        default="http://localhost:5001",
+    )
+
+
 class MarketplaceConfig(BaseSettings):
     """
     Configuration for marketplace
@@ -1313,6 +1324,7 @@ class FeatureConfig(
     TriggerConfig,
     AsyncWorkflowConfig,
     PluginConfig,
+    CliApiConfig,
     MarketplaceConfig,
     DataSetConfig,
     EndpointConfig,

api/controllers/cli_api/__init__.py

@@ -0,0 +1,27 @@
+from flask import Blueprint
+from flask_restx import Namespace
+
+from libs.external_api import ExternalApi
+
+bp = Blueprint("cli_api", __name__, url_prefix="/cli/api")
+
+api = ExternalApi(
+    bp,
+    version="1.0",
+    title="CLI API",
+    description="APIs for Dify CLI to call back from external sandbox environments (e.g., e2b)",
+)
+
+# Create namespace
+cli_api_ns = Namespace("cli_api", description="CLI API operations", path="/")
+
+from .plugin import plugin as _plugin
+
+api.add_namespace(cli_api_ns)
+
+__all__ = [
+    "_plugin",
+    "api",
+    "bp",
+    "cli_api_ns",
+]

api/controllers/cli_api/plugin/plugin.py

@@ -0,0 +1,137 @@
+from flask_restx import Resource
+
+from controllers.cli_api import cli_api_ns
+from controllers.cli_api.plugin.wraps import get_cli_user_tenant, plugin_data
+from controllers.cli_api.wraps import cli_api_only
+from controllers.console.wraps import setup_required
+from core.file.helpers import get_signed_file_url_for_plugin
+from core.plugin.backwards_invocation.app import PluginAppBackwardsInvocation
+from core.plugin.backwards_invocation.base import BaseBackwardsInvocationResponse
+from core.plugin.backwards_invocation.model import PluginModelBackwardsInvocation
+from core.plugin.backwards_invocation.tool import PluginToolBackwardsInvocation
+from core.plugin.entities.request import (
+    RequestInvokeApp,
+    RequestInvokeLLM,
+    RequestInvokeTool,
+    RequestRequestUploadFile,
+)
+from core.tools.entities.tool_entities import ToolProviderType
+from libs.helper import length_prefixed_response
+from models import Account, Tenant
+from models.model import EndUser
+
+
+@cli_api_ns.route("/invoke/llm")
+class CliInvokeLLMApi(Resource):
+    @get_cli_user_tenant
+    @setup_required
+    @cli_api_only
+    @plugin_data(payload_type=RequestInvokeLLM)
+    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeLLM):
+        def generator():
+            response = PluginModelBackwardsInvocation.invoke_llm(user_model.id, tenant_model, payload)
+            return PluginModelBackwardsInvocation.convert_to_event_stream(response)
+
+        return length_prefixed_response(0xF, generator())
+
+
+@cli_api_ns.route("/invoke/tool")
+class CliInvokeToolApi(Resource):
+    @get_cli_user_tenant
+    @setup_required
+    @cli_api_only
+    @plugin_data(payload_type=RequestInvokeTool)
+    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeTool):
+        def generator():
+            return PluginToolBackwardsInvocation.convert_to_event_stream(
+                PluginToolBackwardsInvocation.invoke_tool(
+                    tenant_id=tenant_model.id,
+                    user_id=user_model.id,
+                    tool_type=ToolProviderType.value_of(payload.tool_type),
+                    provider=payload.provider,
+                    tool_name=payload.tool,
+                    tool_parameters=payload.tool_parameters,
+                    credential_id=payload.credential_id,
+                ),
+            )
+
+        return length_prefixed_response(0xF, generator())
+
+
+@cli_api_ns.route("/invoke/app")
+class CliInvokeAppApi(Resource):
+    @get_cli_user_tenant
+    @setup_required
+    @cli_api_only
+    @plugin_data(payload_type=RequestInvokeApp)
+    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeApp):
+        response = PluginAppBackwardsInvocation.invoke_app(
+            app_id=payload.app_id,
+            user_id=user_model.id,
+            tenant_id=tenant_model.id,
+            conversation_id=payload.conversation_id,
+            query=payload.query,
+            stream=payload.response_mode == "streaming",
+            inputs=payload.inputs,
+            files=payload.files,
+        )
+
+        return length_prefixed_response(0xF, PluginAppBackwardsInvocation.convert_to_event_stream(response))
+
+
+@cli_api_ns.route("/upload/file/request")
+class CliUploadFileRequestApi(Resource):
+    @get_cli_user_tenant
+    @setup_required
+    @cli_api_only
+    @plugin_data(payload_type=RequestRequestUploadFile)
+    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestRequestUploadFile):
+        # generate signed url
+        url = get_signed_file_url_for_plugin(
+            filename=payload.filename,
+            mimetype=payload.mimetype,
+            tenant_id=tenant_model.id,
+            user_id=user_model.id,
+        )
+        return BaseBackwardsInvocationResponse(data={"url": url}).model_dump()
+
+
+@cli_api_ns.route("/fetch/tools/list")
+class CliFetchToolsListApi(Resource):
+    @get_cli_user_tenant
+    @setup_required
+    @cli_api_only
+    def post(self, user_model: Account | EndUser, tenant_model: Tenant):
+        from sqlalchemy.orm import Session
+
+        from extensions.ext_database import db
+        from services.tools.api_tools_manage_service import ApiToolManageService
+        from services.tools.builtin_tools_manage_service import BuiltinToolManageService
+        from services.tools.mcp_tools_manage_service import MCPToolManageService
+        from services.tools.workflow_tools_manage_service import WorkflowToolManageService
+
+        providers = []
+
+        # Get builtin tools
+        builtin_providers = BuiltinToolManageService.list_builtin_tools(user_model.id, tenant_model.id)
+        for provider in builtin_providers:
+            providers.append(provider.to_dict())
+
+        # Get API tools
+        api_providers = ApiToolManageService.list_api_tools(tenant_model.id)
+        for provider in api_providers:
+            providers.append(provider.to_dict())
+
+        # Get workflow tools
+        workflow_providers = WorkflowToolManageService.list_tenant_workflow_tools(user_model.id, tenant_model.id)
+        for provider in workflow_providers:
+            providers.append(provider.to_dict())
+
+        # Get MCP tools
+        with Session(db.engine) as session:
+            mcp_service = MCPToolManageService(session)
+            mcp_providers = mcp_service.list_providers(tenant_id=tenant_model.id, for_list=True)
+            for provider in mcp_providers:
+                providers.append(provider.to_dict())
+
+        return BaseBackwardsInvocationResponse(data={"providers": providers}).model_dump()

api/controllers/cli_api/plugin/wraps.py

@@ -0,0 +1,146 @@
+from collections.abc import Callable
+from functools import wraps
+from typing import ParamSpec, TypeVar
+
+from flask import current_app, request
+from flask_login import user_logged_in
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+from core.session.cli_api import CliApiSession, CliApiSessionManager
+from extensions.ext_database import db
+from libs.login import current_user
+from models.account import Tenant
+from models.model import DefaultEndUserSessionID, EndUser
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+class TenantUserPayload(BaseModel):
+    tenant_id: str
+    user_id: str
+
+
+def get_user(tenant_id: str, user_id: str | None) -> EndUser:
+    """
+    Get current user
+
+    NOTE: user_id is not trusted, it could be maliciously set to any value.
+    As a result, it could only be considered as an end user id.
+    """
+    if not user_id:
+        user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
+    is_anonymous = user_id == DefaultEndUserSessionID.DEFAULT_SESSION_ID
+    try:
+        with Session(db.engine) as session:
+            user_model = None
+
+            if is_anonymous:
+                user_model = (
+                    session.query(EndUser)
+                    .where(
+                        EndUser.session_id == user_id,
+                        EndUser.tenant_id == tenant_id,
+                    )
+                    .first()
+                )
+            else:
+                user_model = (
+                    session.query(EndUser)
+                    .where(
+                        EndUser.id == user_id,
+                        EndUser.tenant_id == tenant_id,
+                    )
+                    .first()
+                )
+
+            if not user_model:
+                user_model = EndUser(
+                    tenant_id=tenant_id,
+                    type="service_api",
+                    is_anonymous=is_anonymous,
+                    session_id=user_id,
+                )
+                session.add(user_model)
+                session.commit()
+                session.refresh(user_model)
+
+    except Exception:
+        raise ValueError("user not found")
+
+    return user_model
+
+
+def get_cli_user_tenant(view_func: Callable[P, R]):
+    @wraps(view_func)
+    def decorated_view(*args: P.args, **kwargs: P.kwargs):
+        session_id = request.headers.get("X-Cli-Api-Session-Id")
+
+        if session_id:
+            session: CliApiSession | None = CliApiSessionManager().get(session_id)
+            if not session:
+                raise ValueError("session not found")
+            user_id = session.user_id
+            tenant_id = session.tenant_id
+
+        else:
+            payload = TenantUserPayload.model_validate(request.get_json(silent=True) or {})
+            user_id = payload.user_id
+            tenant_id = payload.tenant_id
+
+        if not tenant_id:
+            raise ValueError("tenant_id is required")
+
+        if not user_id:
+            user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
+
+        try:
+            tenant_model = (
+                db.session.query(Tenant)
+                .where(
+                    Tenant.id == tenant_id,
+                )
+                .first()
+            )
+        except Exception:
+            raise ValueError("tenant not found")
+
+        if not tenant_model:
+            raise ValueError("tenant not found")
+
+        kwargs["tenant_model"] = tenant_model
+
+        user = get_user(tenant_id, user_id)
+        kwargs["user_model"] = user
+
+        current_app.login_manager._update_request_context_with_user(user)  # type: ignore
+        user_logged_in.send(current_app._get_current_object(), user=current_user)  # type: ignore
+
+        return view_func(*args, **kwargs)
+
+    return decorated_view
+
+
+def plugin_data(view: Callable[P, R] | None = None, *, payload_type: type[BaseModel]):
+    def decorator(view_func: Callable[P, R]):
+        def decorated_view(*args: P.args, **kwargs: P.kwargs):
+            try:
+                data = request.get_json()
+            except Exception:
+                raise ValueError("invalid json")
+
+            try:
+                payload = payload_type.model_validate(data)
+            except Exception as e:
+                raise ValueError(f"invalid payload: {str(e)}")
+
+            kwargs["payload"] = payload
+            return view_func(*args, **kwargs)
+
+        return decorated_view
+
+    if view is None:
+        return decorator
+    else:
+        return decorator(view)

api/controllers/cli_api/wraps.py

@@ -0,0 +1,54 @@
+import hashlib
+import hmac
+import time
+from collections.abc import Callable
+from functools import wraps
+from typing import ParamSpec, TypeVar
+
+from flask import abort, request
+
+from core.session.cli_api import CliApiSessionManager
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+SIGNATURE_TTL_SECONDS = 300
+
+
+def _verify_signature(session_secret: str, timestamp: str, body: bytes, signature: str) -> bool:
+    expected = hmac.new(
+        session_secret.encode(),
+        f"{timestamp}.".encode() + body,
+        hashlib.sha256,
+    ).hexdigest()
+    return hmac.compare_digest(f"sha256={expected}", signature)
+
+
+def cli_api_only(view: Callable[P, R]):
+    @wraps(view)
+    def decorated(*args: P.args, **kwargs: P.kwargs):
+        session_id = request.headers.get("X-Cli-Api-Session-Id")
+        timestamp = request.headers.get("X-Cli-Api-Timestamp")
+        signature = request.headers.get("X-Cli-Api-Signature")
+
+        if not session_id or not timestamp or not signature:
+            abort(401)
+
+        try:
+            ts = int(timestamp)
+            if abs(time.time() - ts) > SIGNATURE_TTL_SECONDS:
+                abort(401)
+        except ValueError:
+            abort(401)
+
+        session = CliApiSessionManager().get(session_id)
+        if not session:
+            abort(401)
+
+        body = request.get_data()
+        if not _verify_signature(session.secret, timestamp, body, signature):
+            abort(401)
+
+        return view(*args, **kwargs)
+
+    return decorated

api/controllers/console/__init__.py

@@ -50,6 +50,7 @@ from .app import (
     agent,
     annotation,
     app,
+    app_asset,
     audio,
     completion,
     conversation,
@@ -126,6 +127,7 @@ from .workspace import (
     model_providers,
     models,
     plugin,
+    sandbox_providers,
     tool_providers,
     trigger_providers,
     workspace,
@@ -144,6 +146,7 @@ __all__ = [
     "api",
     "apikey",
     "app",
+    "app_asset",
     "audio",
     "billing",
     "bp",
@@ -191,6 +194,7 @@ __all__ = [
     "rag_pipeline_import",
     "rag_pipeline_workflow",
     "recommended_app",
+    "sandbox_providers",
     "saved_message",
     "setup",
     "site",

api/controllers/console/app/app_asset.py

@@ -0,0 +1,274 @@
+from flask import request
+from flask_restx import Resource
+from pydantic import BaseModel, Field, field_validator
+
+from controllers.console import console_ns
+from controllers.console.app.error import (
+    AppAssetFileRequiredError,
+    AppAssetNodeNotFoundError,
+    AppAssetPathConflictError,
+)
+from controllers.console.app.wraps import get_app_model
+from controllers.console.wraps import account_initialization_required, setup_required
+from libs.login import current_account_with_tenant, login_required
+from models import App
+from models.model import AppMode
+from services.app_asset_service import AppAssetService
+from services.errors.app_asset import (
+    AppAssetNodeNotFoundError as ServiceNodeNotFoundError,
+)
+from services.errors.app_asset import (
+    AppAssetParentNotFoundError,
+)
+from services.errors.app_asset import (
+    AppAssetPathConflictError as ServicePathConflictError,
+)
+
+DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
+
+
+class CreateFolderPayload(BaseModel):
+    name: str = Field(..., min_length=1, max_length=255)
+    parent_id: str | None = None
+
+
+class CreateFilePayload(BaseModel):
+    name: str = Field(..., min_length=1, max_length=255)
+    parent_id: str | None = None
+
+    @field_validator("name", mode="before")
+    @classmethod
+    def strip_name(cls, v: str) -> str:
+        return v.strip() if isinstance(v, str) else v
+
+    @field_validator("parent_id", mode="before")
+    @classmethod
+    def empty_to_none(cls, v: str | None) -> str | None:
+        return v or None
+
+
+class UpdateFileContentPayload(BaseModel):
+    content: str
+
+
+class RenameNodePayload(BaseModel):
+    name: str = Field(..., min_length=1, max_length=255)
+
+
+class MoveNodePayload(BaseModel):
+    parent_id: str | None = None
+
+
+class ReorderNodePayload(BaseModel):
+    after_node_id: str | None = Field(default=None, description="Place after this node, None for first position")
+
+
+def reg(cls: type[BaseModel]) -> None:
+    console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
+
+
+reg(CreateFolderPayload)
+reg(CreateFilePayload)
+reg(UpdateFileContentPayload)
+reg(RenameNodePayload)
+reg(MoveNodePayload)
+reg(ReorderNodePayload)
+
+
+@console_ns.route("/apps/<string:app_id>/assets/tree")
+class AppAssetTreeResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+        tree = AppAssetService.get_asset_tree(app_model, current_user.id)
+        return {"children": [view.model_dump() for view in tree.transform()]}
+
+
+@console_ns.route("/apps/<string:app_id>/assets/folders")
+class AppAssetFolderResource(Resource):
+    @console_ns.expect(console_ns.models[CreateFolderPayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+        payload = CreateFolderPayload.model_validate(console_ns.payload or {})
+
+        try:
+            node = AppAssetService.create_folder(app_model, current_user.id, payload.name, payload.parent_id)
+            return node.model_dump(), 201
+        except AppAssetParentNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/files")
+class AppAssetFileResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+
+        file = request.files.get("file")
+        if not file:
+            raise AppAssetFileRequiredError()
+
+        payload = CreateFilePayload.model_validate(request.form.to_dict())
+        content = file.read()
+
+        try:
+            node = AppAssetService.create_file(app_model, current_user.id, payload.name, content, payload.parent_id)
+            return node.model_dump(), 201
+        except AppAssetParentNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/files/<string:node_id>")
+class AppAssetFileDetailResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        try:
+            content = AppAssetService.get_file_content(app_model, current_user.id, node_id)
+            return {"content": content.decode("utf-8", errors="replace")}
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+    @console_ns.expect(console_ns.models[UpdateFileContentPayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def put(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+
+        file = request.files.get("file")
+        if file:
+            content = file.read()
+        else:
+            payload = UpdateFileContentPayload.model_validate(console_ns.payload or {})
+            content = payload.content.encode("utf-8")
+
+        try:
+            node = AppAssetService.update_file_content(app_model, current_user.id, node_id, content)
+            return node.model_dump()
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>")
+class AppAssetNodeResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def delete(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        try:
+            AppAssetService.delete_node(app_model, current_user.id, node_id)
+            return {"result": "success"}, 200
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/rename")
+class AppAssetNodeRenameResource(Resource):
+    @console_ns.expect(console_ns.models[RenameNodePayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        payload = RenameNodePayload.model_validate(console_ns.payload or {})
+
+        try:
+            node = AppAssetService.rename_node(app_model, current_user.id, node_id, payload.name)
+            return node.model_dump()
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/move")
+class AppAssetNodeMoveResource(Resource):
+    @console_ns.expect(console_ns.models[MoveNodePayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        payload = MoveNodePayload.model_validate(console_ns.payload or {})
+
+        try:
+            node = AppAssetService.move_node(app_model, current_user.id, node_id, payload.parent_id)
+            return node.model_dump()
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except AppAssetParentNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/reorder")
+class AppAssetNodeReorderResource(Resource):
+    @console_ns.expect(console_ns.models[ReorderNodePayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        payload = ReorderNodePayload.model_validate(console_ns.payload or {})
+
+        try:
+            node = AppAssetService.reorder_node(app_model, current_user.id, node_id, payload.after_node_id)
+            return node.model_dump()
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/publish")
+class AppAssetPublishResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+        published = AppAssetService.publish(app_model, current_user.id)
+        return {
+            "id": published.id,
+            "version": published.version,
+            "asset_tree": published.asset_tree.model_dump(),
+        }, 201
+
+
+@console_ns.route("/apps/<string:app_id>/assets/files/<string:node_id>/download-url")
+class AppAssetFileDownloadUrlResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        try:
+            download_url = AppAssetService.get_file_download_url(app_model, current_user.id, node_id)
+            return {"download_url": download_url}
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()

api/controllers/console/app/error.py

@@ -110,8 +110,24 @@ class TracingConfigCheckError(BaseHTTPException):
 
 
 class InvokeRateLimitError(BaseHTTPException):
-    """Raised when the Invoke returns rate limit error."""
-
     error_code = "rate_limit_error"
     description = "Rate Limit Error"
     code = 429
+
+
+class AppAssetNodeNotFoundError(BaseHTTPException):
+    error_code = "app_asset_node_not_found"
+    description = "App asset node not found."
+    code = 404
+
+
+class AppAssetFileRequiredError(BaseHTTPException):
+    error_code = "app_asset_file_required"
+    description = "File is required."
+    code = 400
+
+
+class AppAssetPathConflictError(BaseHTTPException):
+    error_code = "app_asset_path_conflict"
+    description = "Path already exists."
+    code = 409

api/controllers/console/app/generator.py

@@ -55,6 +55,35 @@ class InstructionTemplatePayload(BaseModel):
     type: str = Field(..., description="Instruction template type")
 
 
+class ContextGeneratePayload(BaseModel):
+    """Payload for generating extractor code node."""
+
+    workflow_id: str = Field(..., description="Workflow ID")
+    node_id: str = Field(..., description="Current tool/llm node ID")
+    parameter_name: str = Field(..., description="Parameter name to generate code for")
+    language: str = Field(default="python3", description="Code language (python3/javascript)")
+    prompt_messages: list[dict[str, Any]] = Field(
+        ..., description="Multi-turn conversation history, last message is the current instruction"
+    )
+    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
+
+
+class SuggestedQuestionsPayload(BaseModel):
+    """Payload for generating suggested questions."""
+
+    workflow_id: str = Field(..., description="Workflow ID")
+    node_id: str = Field(..., description="Current tool/llm node ID")
+    parameter_name: str = Field(..., description="Parameter name")
+    language: str = Field(
+        default="English", description="Language for generated questions (e.g. English, Chinese, Japanese)"
+    )
+    model_config_data: dict[str, Any] | None = Field(
+        default=None,
+        alias="model_config",
+        description="Model configuration (optional, uses system default if not provided)",
+    )
+
+
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -64,6 +93,8 @@ reg(RuleCodeGeneratePayload)
 reg(RuleStructuredOutputPayload)
 reg(InstructionGeneratePayload)
 reg(InstructionTemplatePayload)
+reg(ContextGeneratePayload)
+reg(SuggestedQuestionsPayload)
 
 
 @console_ns.route("/rule-generate")
@@ -278,3 +309,74 @@ class InstructionGenerationTemplateApi(Resource):
                 return {"data": INSTRUCTION_GENERATE_TEMPLATE_CODE}
             case _:
                 raise ValueError(f"Invalid type: {args.type}")
+
+
+@console_ns.route("/context-generate")
+class ContextGenerateApi(Resource):
+    @console_ns.doc("generate_with_context")
+    @console_ns.doc(description="Generate with multi-turn conversation context")
+    @console_ns.expect(console_ns.models[ContextGeneratePayload.__name__])
+    @console_ns.response(200, "Content generated successfully")
+    @console_ns.response(400, "Invalid request parameters or workflow not found")
+    @console_ns.response(402, "Provider quota exceeded")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        from core.llm_generator.utils import deserialize_prompt_messages
+
+        args = ContextGeneratePayload.model_validate(console_ns.payload)
+        _, current_tenant_id = current_account_with_tenant()
+
+        prompt_messages = deserialize_prompt_messages(args.prompt_messages)
+
+        try:
+            return LLMGenerator.generate_with_context(
+                tenant_id=current_tenant_id,
+                workflow_id=args.workflow_id,
+                node_id=args.node_id,
+                parameter_name=args.parameter_name,
+                language=args.language,
+                prompt_messages=prompt_messages,
+                model_config=args.model_config_data,
+            )
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+
+
+@console_ns.route("/context-generate/suggested-questions")
+class SuggestedQuestionsApi(Resource):
+    @console_ns.doc("generate_suggested_questions")
+    @console_ns.doc(description="Generate suggested questions for context generation")
+    @console_ns.expect(console_ns.models[SuggestedQuestionsPayload.__name__])
+    @console_ns.response(200, "Questions generated successfully")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        args = SuggestedQuestionsPayload.model_validate(console_ns.payload)
+        _, current_tenant_id = current_account_with_tenant()
+
+        try:
+            return LLMGenerator.generate_suggested_questions(
+                tenant_id=current_tenant_id,
+                workflow_id=args.workflow_id,
+                node_id=args.node_id,
+                parameter_name=args.parameter_name,
+                language=args.language,
+                model_config=args.model_config_data,
+            )
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)

api/controllers/console/app/message.py

@@ -202,6 +202,7 @@ message_detail_model = console_ns.model(
         "status": fields.String,
         "error": fields.String,
         "parent_message_id": fields.String,
+        "generation_detail": fields.Raw,
     },
 )
 

api/controllers/console/app/workflow.py

@@ -46,6 +46,8 @@ from models.workflow import Workflow
 from services.app_generate_service import AppGenerateService
 from services.errors.app import WorkflowHashNotEqualError
 from services.errors.llm import InvokeRateLimitError
+from services.workflow.entities import MentionGraphRequest, MentionParameterSchema
+from services.workflow.mention_graph_service import MentionGraphService
 from services.workflow_service import DraftWorkflowDeletionError, WorkflowInUseError, WorkflowService
 
 logger = logging.getLogger(__name__)
@@ -188,6 +190,15 @@ class DraftWorkflowTriggerRunAllPayload(BaseModel):
     node_ids: list[str]
 
 
+class MentionGraphPayload(BaseModel):
+    """Request payload for generating mention graph."""
+
+    parent_node_id: str = Field(description="ID of the parent node that uses the extracted value")
+    parameter_key: str = Field(description="Key of the parameter being extracted")
+    context_source: list[str] = Field(description="Variable selector for the context source")
+    parameter_schema: dict[str, Any] = Field(description="Schema of the parameter to extract")
+
+
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -205,6 +216,7 @@ reg(WorkflowListQuery)
 reg(WorkflowUpdatePayload)
 reg(DraftWorkflowTriggerRunPayload)
 reg(DraftWorkflowTriggerRunAllPayload)
+reg(MentionGraphPayload)
 
 
 # TODO(QuantumGhost): Refactor existing node run API to handle file parameter parsing
@@ -1166,3 +1178,54 @@ class DraftWorkflowTriggerRunAllApi(Resource):
                     "status": "error",
                 }
             ), 400
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflows/draft/mention-graph")
+class MentionGraphApi(Resource):
+    """
+    API for generating Mention LLM node graph structures.
+
+    This endpoint creates a complete graph structure containing an LLM node
+    configured to extract values from list[PromptMessage] variables.
+    """
+
+    @console_ns.doc("generate_mention_graph")
+    @console_ns.doc(description="Generate a Mention LLM node graph structure")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.expect(console_ns.models[MentionGraphPayload.__name__])
+    @console_ns.response(200, "Mention graph generated successfully")
+    @console_ns.response(400, "Invalid request parameters")
+    @console_ns.response(403, "Permission denied")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @edit_permission_required
+    def post(self, app_model: App):
+        """
+        Generate a Mention LLM node graph structure.
+
+        Returns a complete graph structure containing a single LLM node
+        configured for extracting values from list[PromptMessage] context.
+        """
+
+        payload = MentionGraphPayload.model_validate(console_ns.payload or {})
+
+        parameter_schema = MentionParameterSchema(
+            name=payload.parameter_schema.get("name", payload.parameter_key),
+            type=payload.parameter_schema.get("type", "string"),
+            description=payload.parameter_schema.get("description", ""),
+        )
+
+        request = MentionGraphRequest(
+            parent_node_id=payload.parent_node_id,
+            parameter_key=payload.parameter_key,
+            context_source=payload.context_source,
+            parameter_schema=parameter_schema,
+        )
+
+        with Session(db.engine) as session:
+            service = MentionGraphService(session)
+            response = service.generate_mention_graph(tenant_id=app_model.tenant_id, request=request)
+
+        return response.model_dump()

api/controllers/console/app/workflow_draft_variable.py

@@ -17,7 +17,7 @@ from controllers.console.wraps import account_initialization_required, edit_perm
 from controllers.web.error import InvalidArgumentError, NotFoundError
 from core.file import helpers as file_helpers
 from core.variables.segment_group import SegmentGroup
-from core.variables.segments import ArrayFileSegment, FileSegment, Segment
+from core.variables.segments import ArrayFileSegment, ArrayPromptMessageSegment, FileSegment, Segment
 from core.variables.types import SegmentType
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID, SYSTEM_VARIABLE_NODE_ID
 from extensions.ext_database import db
@@ -58,6 +58,8 @@ def _convert_values_to_json_serializable_object(value: Segment):
         return value.value.model_dump()
     elif isinstance(value, ArrayFileSegment):
         return [i.model_dump() for i in value.value]
+    elif isinstance(value, ArrayPromptMessageSegment):
+        return value.to_object()
     elif isinstance(value, SegmentGroup):
         return [_convert_values_to_json_serializable_object(i) for i in value.value]
     else:

api/controllers/console/workspace/dsl.py

@@ -0,0 +1,65 @@
+import json
+
+import httpx
+import yaml
+from flask_restx import Resource, reqparse
+from sqlalchemy.orm import Session
+from werkzeug.exceptions import Forbidden
+
+from controllers.console import console_ns
+from controllers.console.wraps import account_initialization_required, setup_required
+from core.plugin.impl.exc import PluginPermissionDeniedError
+from extensions.ext_database import db
+from libs.login import current_account_with_tenant, login_required
+from models.model import App
+from models.workflow import Workflow
+from services.app_dsl_service import AppDslService
+
+
+@console_ns.route("/workspaces/current/dsl/predict")
+class DSLPredictApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        user, _ = current_account_with_tenant()
+        if not user.is_admin_or_owner:
+            raise Forbidden()
+
+        parser = (
+            reqparse.RequestParser()
+            .add_argument("app_id", type=str, required=True, location="json")
+            .add_argument("current_node_id", type=str, required=True, location="json")
+        )
+        args = parser.parse_args()
+
+        app_id: str = args["app_id"]
+        current_node_id: str = args["current_node_id"]
+
+        with Session(db.engine) as session:
+            app = session.query(App).filter_by(id=app_id).first()
+            workflow = session.query(Workflow).filter_by(app_id=app_id, version=Workflow.VERSION_DRAFT).first()
+
+        if not app:
+            raise ValueError("App not found")
+        if not workflow:
+            raise ValueError("Workflow not found")
+
+        try:
+            i = 0
+            for node_id, _ in workflow.walk_nodes():
+                if node_id == current_node_id:
+                    break
+                i += 1
+
+            dsl = yaml.safe_load(AppDslService.export_dsl(app_model=app))
+
+            response = httpx.post(
+                "http://spark-832c:8000/predict",
+                json={"graph_data": dsl, "source_node_index": i},
+            )
+            return {
+                "nodes": json.loads(response.json()),
+            }
+        except PluginPermissionDeniedError as e:
+            raise ValueError(e.description) from e

api/controllers/console/workspace/sandbox_providers.py

@@ -0,0 +1,103 @@
+import logging
+
+from flask_restx import Resource, fields, reqparse
+
+from controllers.console import console_ns
+from controllers.console.wraps import account_initialization_required, setup_required
+from core.model_runtime.utils.encoders import jsonable_encoder
+from libs.login import current_account_with_tenant, login_required
+from services.sandbox.sandbox_provider_service import SandboxProviderService
+
+logger = logging.getLogger(__name__)
+
+
+@console_ns.route("/workspaces/current/sandbox-providers")
+class SandboxProviderListApi(Resource):
+    @console_ns.doc("list_sandbox_providers")
+    @console_ns.doc(description="Get list of available sandbox providers with configuration status")
+    @console_ns.response(200, "Success", fields.List(fields.Raw(description="Sandbox provider information")))
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        _, current_tenant_id = current_account_with_tenant()
+        providers = SandboxProviderService.list_providers(current_tenant_id)
+        return jsonable_encoder([p.model_dump() for p in providers])
+
+
+config_parser = reqparse.RequestParser()
+config_parser.add_argument("config", type=dict, required=True, location="json")
+config_parser.add_argument("activate", type=bool, required=False, default=False, location="json")
+
+
+@console_ns.route("/workspaces/current/sandbox-provider/<string:provider_type>/config")
+class SandboxProviderConfigApi(Resource):
+    @console_ns.doc("save_sandbox_provider_config")
+    @console_ns.doc(description="Save or update configuration for a sandbox provider")
+    @console_ns.expect(config_parser)
+    @console_ns.response(200, "Success")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider_type: str):
+        _, current_tenant_id = current_account_with_tenant()
+        args = config_parser.parse_args()
+
+        try:
+            result = SandboxProviderService.save_config(
+                tenant_id=current_tenant_id,
+                provider_type=provider_type,
+                config=args["config"],
+                activate=args["activate"],
+            )
+            return result
+        except ValueError as e:
+            return {"message": str(e)}, 400
+
+    @console_ns.doc("delete_sandbox_provider_config")
+    @console_ns.doc(description="Delete configuration for a sandbox provider")
+    @console_ns.response(200, "Success")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def delete(self, provider_type: str):
+        _, current_tenant_id = current_account_with_tenant()
+
+        try:
+            result = SandboxProviderService.delete_config(
+                tenant_id=current_tenant_id,
+                provider_type=provider_type,
+            )
+            return result
+        except ValueError as e:
+            return {"message": str(e)}, 400
+
+
+activate_parser = reqparse.RequestParser()
+activate_parser.add_argument("type", type=str, required=True, location="json")
+
+
+@console_ns.route("/workspaces/current/sandbox-provider/<string:provider_type>/activate")
+class SandboxProviderActivateApi(Resource):
+    """Activate a sandbox provider."""
+
+    @console_ns.doc("activate_sandbox_provider")
+    @console_ns.doc(description="Activate a sandbox provider for the current workspace")
+    @console_ns.response(200, "Success")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider_type: str):
+        """Activate a sandbox provider."""
+        _, current_tenant_id = current_account_with_tenant()
+
+        try:
+            args = activate_parser.parse_args()
+            result = SandboxProviderService.activate_provider(
+                tenant_id=current_tenant_id,
+                provider_type=provider_type,
+                type=args["type"],
+            )
+            return result
+        except ValueError as e:
+            return {"message": str(e)}, 400

api/controllers/files/__init__.py

@@ -14,7 +14,7 @@ api = ExternalApi(
 
 files_ns = Namespace("files", description="File operations", path="/")
 
-from . import image_preview, tool_files, upload
+from . import image_preview, storage_download, tool_files, upload
 
 api.add_namespace(files_ns)
 
@@ -23,6 +23,7 @@ __all__ = [
     "bp",
     "files_ns",
     "image_preview",
+    "storage_download",
     "tool_files",
     "upload",
 ]

api/controllers/files/storage_download.py

@@ -0,0 +1,56 @@
+from urllib.parse import quote, unquote
+
+from flask import Response, request
+from flask_restx import Resource
+from pydantic import BaseModel, Field
+from werkzeug.exceptions import Forbidden, NotFound
+
+from controllers.files import files_ns
+from extensions.ext_storage import storage
+from extensions.storage.file_presign_storage import FilePresignStorage
+
+DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
+
+
+class StorageDownloadQuery(BaseModel):
+    timestamp: str = Field(..., description="Unix timestamp used in the signature")
+    nonce: str = Field(..., description="Random string for signature")
+    sign: str = Field(..., description="HMAC signature")
+
+
+files_ns.schema_model(
+    StorageDownloadQuery.__name__,
+    StorageDownloadQuery.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
+)
+
+
+@files_ns.route("/storage/<path:filename>/download")
+class StorageFileDownloadApi(Resource):
+    def get(self, filename: str):
+        filename = unquote(filename)
+
+        args = StorageDownloadQuery.model_validate(request.args.to_dict(flat=True))
+
+        if not FilePresignStorage.verify_signature(
+            filename=filename,
+            timestamp=args.timestamp,
+            nonce=args.nonce,
+            sign=args.sign,
+        ):
+            raise Forbidden("Invalid or expired download link")
+
+        try:
+            generator = storage.load_stream(filename)
+        except FileNotFoundError:
+            raise NotFound("File not found")
+
+        encoded_filename = quote(filename.split("/")[-1])
+
+        return Response(
+            generator,
+            mimetype="application/octet-stream",
+            direct_passthrough=True,
+            headers={
+                "Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}",
+            },
+        )

api/controllers/inner_api/plugin/plugin.py

@@ -448,3 +448,53 @@ class PluginFetchAppInfoApi(Resource):
         return BaseBackwardsInvocationResponse(
             data=PluginAppBackwardsInvocation.fetch_app_info(payload.app_id, tenant_model.id)
         ).model_dump()
+
+
+@inner_api_ns.route("/fetch/tools/list")
+class PluginFetchToolsListApi(Resource):
+    @get_user_tenant
+    @setup_required
+    @plugin_inner_api_only
+    @inner_api_ns.doc("plugin_fetch_tools_list")
+    @inner_api_ns.doc(description="Fetch all available tools through plugin interface")
+    @inner_api_ns.doc(
+        responses={
+            200: "Tools list retrieved successfully",
+            401: "Unauthorized - invalid API key",
+            404: "Service not available",
+        }
+    )
+    def post(self, user_model: Account | EndUser, tenant_model: Tenant):
+        from sqlalchemy.orm import Session
+
+        from extensions.ext_database import db
+        from services.tools.api_tools_manage_service import ApiToolManageService
+        from services.tools.builtin_tools_manage_service import BuiltinToolManageService
+        from services.tools.mcp_tools_manage_service import MCPToolManageService
+        from services.tools.workflow_tools_manage_service import WorkflowToolManageService
+
+        providers = []
+
+        # Get builtin tools
+        builtin_providers = BuiltinToolManageService.list_builtin_tools(user_model.id, tenant_model.id)
+        for provider in builtin_providers:
+            providers.append(provider.to_dict())
+
+        # Get API tools
+        api_providers = ApiToolManageService.list_api_tools(tenant_model.id)
+        for provider in api_providers:
+            providers.append(provider.to_dict())
+
+        # Get workflow tools
+        workflow_providers = WorkflowToolManageService.list_tenant_workflow_tools(user_model.id, tenant_model.id)
+        for provider in workflow_providers:
+            providers.append(provider.to_dict())
+
+        # Get MCP tools
+        with Session(db.engine) as session:
+            mcp_service = MCPToolManageService(session)
+            mcp_providers = mcp_service.list_providers(tenant_id=tenant_model.id, for_list=True)
+            for provider in mcp_providers:
+                providers.append(provider.to_dict())
+
+        return BaseBackwardsInvocationResponse(data={"providers": providers}).model_dump()

api/controllers/inner_api/plugin/wraps.py

@@ -75,7 +75,6 @@ def get_user_tenant(view_func: Callable[P, R]):
     @wraps(view_func)
     def decorated_view(*args: P.args, **kwargs: P.kwargs):
         payload = TenantUserPayload.model_validate(request.get_json(silent=True) or {})
-
         user_id = payload.user_id
         tenant_id = payload.tenant_id
 

api/controllers/inner_api/wraps.py

@@ -5,14 +5,15 @@ from hashlib import sha1
 from hmac import new as hmac_new
 from typing import ParamSpec, TypeVar
 
-P = ParamSpec("P")
-R = TypeVar("R")
 from flask import abort, request
 
 from configs import dify_config
 from extensions.ext_database import db
 from models.model import EndUser
 
+P = ParamSpec("P")
+R = TypeVar("R")
+
 
 def billing_inner_api_only(view: Callable[P, R]):
     @wraps(view)
@@ -88,11 +89,11 @@ def plugin_inner_api_only(view: Callable[P, R]):
         if not dify_config.PLUGIN_DAEMON_KEY:
             abort(404)
 
-        # get header 'X-Inner-Api-Key'
+        # validate using inner api key
         inner_api_key = request.headers.get("X-Inner-Api-Key")
-        if not inner_api_key or inner_api_key != dify_config.INNER_API_KEY_FOR_PLUGIN:
-            abort(404)
+        if inner_api_key and inner_api_key == dify_config.INNER_API_KEY_FOR_PLUGIN:
+            return view(*args, **kwargs)
 
-        return view(*args, **kwargs)
+        abort(401)
 
     return decorated

api/core/agent/agent_app_runner.py

@@ -0,0 +1,380 @@
+import logging
+from collections.abc import Generator
+from copy import deepcopy
+from typing import Any
+
+from core.agent.base_agent_runner import BaseAgentRunner
+from core.agent.entities import AgentEntity, AgentLog, AgentResult
+from core.agent.patterns.strategy_factory import StrategyFactory
+from core.app.apps.base_app_queue_manager import PublishFrom
+from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
+from core.file import file_manager
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageContentType,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
+from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
+from core.tools.__base.tool import Tool
+from core.tools.entities.tool_entities import ToolInvokeMeta
+from core.tools.tool_engine import ToolEngine
+from models.model import Message
+
+logger = logging.getLogger(__name__)
+
+
+class AgentAppRunner(BaseAgentRunner):
+    def _create_tool_invoke_hook(self, message: Message):
+        """
+        Create a tool invoke hook that uses ToolEngine.agent_invoke.
+        This hook handles file creation and returns proper meta information.
+        """
+        # Get trace manager from app generate entity
+        trace_manager = self.application_generate_entity.trace_manager
+
+        def tool_invoke_hook(
+            tool: Tool, tool_args: dict[str, Any], tool_name: str
+        ) -> tuple[str, list[str], ToolInvokeMeta]:
+            """Hook that uses agent_invoke for proper file and meta handling."""
+            tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
+                tool=tool,
+                tool_parameters=tool_args,
+                user_id=self.user_id,
+                tenant_id=self.tenant_id,
+                message=message,
+                invoke_from=self.application_generate_entity.invoke_from,
+                agent_tool_callback=self.agent_callback,
+                trace_manager=trace_manager,
+                app_id=self.application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                conversation_id=self.conversation.id,
+            )
+
+            # Publish files and track IDs
+            for message_file_id in message_files:
+                self.queue_manager.publish(
+                    QueueMessageFileEvent(message_file_id=message_file_id),
+                    PublishFrom.APPLICATION_MANAGER,
+                )
+                self._current_message_file_ids.append(message_file_id)
+
+            return tool_invoke_response, message_files, tool_invoke_meta
+
+        return tool_invoke_hook
+
+    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
+        """
+        Run Agent application
+        """
+        self.query = query
+        app_generate_entity = self.application_generate_entity
+
+        app_config = self.app_config
+        assert app_config is not None, "app_config is required"
+        assert app_config.agent is not None, "app_config.agent is required"
+
+        # convert tools into ModelRuntime Tool format
+        tool_instances, _ = self._init_prompt_tools()
+
+        assert app_config.agent
+
+        # Create tool invoke hook for agent_invoke
+        tool_invoke_hook = self._create_tool_invoke_hook(message)
+
+        # Get instruction for ReAct strategy
+        instruction = self.app_config.prompt_template.simple_prompt_template or ""
+
+        # Use factory to create appropriate strategy
+        strategy = StrategyFactory.create_strategy(
+            model_features=self.model_features,
+            model_instance=self.model_instance,
+            tools=list(tool_instances.values()),
+            files=list(self.files),
+            max_iterations=app_config.agent.max_iteration,
+            context=self.build_execution_context(),
+            agent_strategy=self.config.strategy,
+            tool_invoke_hook=tool_invoke_hook,
+            instruction=instruction,
+        )
+
+        # Initialize state variables
+        current_agent_thought_id = None
+        has_published_thought = False
+        current_tool_name: str | None = None
+        self._current_message_file_ids: list[str] = []
+
+        # organize prompt messages
+        prompt_messages = self._organize_prompt_messages()
+
+        # Run strategy
+        generator = strategy.run(
+            prompt_messages=prompt_messages,
+            model_parameters=app_generate_entity.model_conf.parameters,
+            stop=app_generate_entity.model_conf.stop,
+            stream=True,
+        )
+
+        # Consume generator and collect result
+        result: AgentResult | None = None
+        try:
+            while True:
+                try:
+                    output = next(generator)
+                except StopIteration as e:
+                    # Generator finished, get the return value
+                    result = e.value
+                    break
+
+                if isinstance(output, LLMResultChunk):
+                    # Handle LLM chunk
+                    if current_agent_thought_id and not has_published_thought:
+                        self.queue_manager.publish(
+                            QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                            PublishFrom.APPLICATION_MANAGER,
+                        )
+                        has_published_thought = True
+
+                    yield output
+
+                elif isinstance(output, AgentLog):
+                    # Handle Agent Log using log_type for type-safe dispatch
+                    if output.status == AgentLog.LogStatus.START:
+                        if output.log_type == AgentLog.LogType.ROUND:
+                            # Start of a new round
+                            message_file_ids: list[str] = []
+                            current_agent_thought_id = self.create_agent_thought(
+                                message_id=message.id,
+                                message="",
+                                tool_name="",
+                                tool_input="",
+                                messages_ids=message_file_ids,
+                            )
+                            has_published_thought = False
+
+                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Tool call start - extract data from structured fields
+                            current_tool_name = output.data.get("tool_name", "")
+                            tool_input = output.data.get("tool_args", {})
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=current_tool_name,
+                                tool_input=tool_input,
+                                thought=None,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=None,
+                                messages_ids=[],
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                    elif output.status == AgentLog.LogStatus.SUCCESS:
+                        if output.log_type == AgentLog.LogType.THOUGHT:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            thought_text = output.data.get("thought")
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=thought_text,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=None,
+                                messages_ids=[],
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Tool call finished
+                            tool_output = output.data.get("output")
+                            # Get meta from strategy output (now properly populated)
+                            tool_meta = output.data.get("meta")
+
+                            # Wrap tool_meta with tool_name as key (required by agent_service)
+                            if tool_meta and current_tool_name:
+                                tool_meta = {current_tool_name: tool_meta}
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=None,
+                                observation=tool_output,
+                                tool_invoke_meta=tool_meta,
+                                answer=None,
+                                messages_ids=self._current_message_file_ids,
+                            )
+                            # Clear message file ids after saving
+                            self._current_message_file_ids = []
+                            current_tool_name = None
+
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                        elif output.log_type == AgentLog.LogType.ROUND:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Round finished - save LLM usage and answer
+                            llm_usage = output.metadata.get(AgentLog.LogMetadata.LLM_USAGE)
+                            llm_result = output.data.get("llm_result")
+                            final_answer = output.data.get("final_answer")
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=llm_result,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=final_answer,
+                                messages_ids=[],
+                                llm_usage=llm_usage,
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+        except Exception:
+            # Re-raise any other exceptions
+            raise
+
+        # Process final result
+        if isinstance(result, AgentResult):
+            final_answer = result.text
+            usage = result.usage or LLMUsage.empty_usage()
+
+            # Publish end event
+            self.queue_manager.publish(
+                QueueMessageEndEvent(
+                    llm_result=LLMResult(
+                        model=self.model_instance.model,
+                        prompt_messages=prompt_messages,
+                        message=AssistantPromptMessage(content=final_answer),
+                        usage=usage,
+                        system_fingerprint="",
+                    )
+                ),
+                PublishFrom.APPLICATION_MANAGER,
+            )
+
+    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Initialize system message
+        """
+        if not prompt_template:
+            return prompt_messages or []
+
+        prompt_messages = prompt_messages or []
+
+        if prompt_messages and isinstance(prompt_messages[0], SystemPromptMessage):
+            prompt_messages[0] = SystemPromptMessage(content=prompt_template)
+            return prompt_messages
+
+        if not prompt_messages:
+            return [SystemPromptMessage(content=prompt_template)]
+
+        prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
+        return prompt_messages
+
+    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Organize user query
+        """
+        if self.files:
+            # get image detail config
+            image_detail_config = (
+                self.application_generate_entity.file_upload_config.image_config.detail
+                if (
+                    self.application_generate_entity.file_upload_config
+                    and self.application_generate_entity.file_upload_config.image_config
+                )
+                else None
+            )
+            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+            for file in self.files:
+                prompt_message_contents.append(
+                    file_manager.to_prompt_message_content(
+                        file,
+                        image_detail_config=image_detail_config,
+                    )
+                )
+            prompt_message_contents.append(TextPromptMessageContent(data=query))
+
+            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+        else:
+            prompt_messages.append(UserPromptMessage(content=query))
+
+        return prompt_messages
+
+    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        As for now, gpt supports both fc and vision at the first iteration.
+        We need to remove the image messages from the prompt messages at the first iteration.
+        """
+        prompt_messages = deepcopy(prompt_messages)
+
+        for prompt_message in prompt_messages:
+            if isinstance(prompt_message, UserPromptMessage):
+                if isinstance(prompt_message.content, list):
+                    prompt_message.content = "\n".join(
+                        [
+                            content.data
+                            if content.type == PromptMessageContentType.TEXT
+                            else "[image]"
+                            if content.type == PromptMessageContentType.IMAGE
+                            else "[file]"
+                            for content in prompt_message.content
+                        ]
+                    )
+
+        return prompt_messages
+
+    def _organize_prompt_messages(self):
+        # For ReAct strategy, use the agent prompt template
+        if self.config.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT and self.config.prompt:
+            prompt_template = self.config.prompt.first_prompt
+        else:
+            prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
+
+        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
+        query_prompt_messages = self._organize_user_query(self.query or "", [])
+
+        self.history_prompt_messages = AgentHistoryPromptTransform(
+            model_config=self.model_config,
+            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
+            history_messages=self.history_prompt_messages,
+            memory=self.memory,
+        ).get_prompt()
+
+        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
+        if len(self._current_thoughts) != 0:
+            # clear messages after the first iteration
+            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
+        return prompt_messages

api/core/agent/base_agent_runner.py

@@ -6,7 +6,7 @@ from typing import Union, cast
 
 from sqlalchemy import select
 
-from core.agent.entities import AgentEntity, AgentToolEntity
+from core.agent.entities import AgentEntity, AgentToolEntity, ExecutionContext
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager
@@ -116,9 +116,20 @@ class BaseAgentRunner(AppRunner):
         features = model_schema.features if model_schema and model_schema.features else []
         self.stream_tool_call = ModelFeature.STREAM_TOOL_CALL in features
         self.files = application_generate_entity.files if ModelFeature.VISION in features else []
+        self.model_features = features
         self.query: str | None = ""
         self._current_thoughts: list[PromptMessage] = []
 
+    def build_execution_context(self) -> ExecutionContext:
+        """Build execution context."""
+        return ExecutionContext(
+            user_id=self.user_id,
+            app_id=self.app_config.app_id,
+            conversation_id=self.conversation.id,
+            message_id=self.message.id,
+            tenant_id=self.tenant_id,
+        )
+
     def _repack_app_generate_entity(
         self, app_generate_entity: AgentChatAppGenerateEntity
     ) -> AgentChatAppGenerateEntity:

api/core/agent/cot_agent_runner.py

@@ -1,437 +0,0 @@
-import json
-import logging
-from abc import ABC, abstractmethod
-from collections.abc import Generator, Mapping, Sequence
-from typing import Any
-
-from core.agent.base_agent_runner import BaseAgentRunner
-from core.agent.entities import AgentScratchpadUnit
-from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
-from core.app.apps.base_app_queue_manager import PublishFrom
-from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    PromptMessageTool,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-from core.ops.ops_trace_manager import TraceQueueManager
-from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
-from core.tools.__base.tool import Tool
-from core.tools.entities.tool_entities import ToolInvokeMeta
-from core.tools.tool_engine import ToolEngine
-from core.workflow.nodes.agent.exc import AgentMaxIterationError
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class CotAgentRunner(BaseAgentRunner, ABC):
-    _is_first_iteration = True
-    _ignore_observation_providers = ["wenxin"]
-    _historic_prompt_messages: list[PromptMessage]
-    _agent_scratchpad: list[AgentScratchpadUnit]
-    _instruction: str
-    _query: str
-    _prompt_messages_tools: Sequence[PromptMessageTool]
-
-    def run(
-        self,
-        message: Message,
-        query: str,
-        inputs: Mapping[str, str],
-    ) -> Generator:
-        """
-        Run Cot agent application
-        """
-
-        app_generate_entity = self.application_generate_entity
-        self._repack_app_generate_entity(app_generate_entity)
-        self._init_react_state(query)
-
-        trace_manager = app_generate_entity.trace_manager
-
-        # check model mode
-        if "Observation" not in app_generate_entity.model_conf.stop:
-            if app_generate_entity.model_conf.provider not in self._ignore_observation_providers:
-                app_generate_entity.model_conf.stop.append("Observation")
-
-        app_config = self.app_config
-        assert app_config.agent
-
-        # init instruction
-        inputs = inputs or {}
-        instruction = app_config.prompt_template.simple_prompt_template or ""
-        self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
-
-        iteration_step = 1
-        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
-
-        # convert tools into ModelRuntime Tool format
-        tool_instances, prompt_messages_tools = self._init_prompt_tools()
-        self._prompt_messages_tools = prompt_messages_tools
-
-        function_call_state = True
-        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
-        final_answer = ""
-        prompt_messages: list = []  # Initialize prompt_messages
-        agent_thought_id = ""  # Initialize agent_thought_id
-
-        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
-            if not final_llm_usage_dict["usage"]:
-                final_llm_usage_dict["usage"] = usage
-            else:
-                llm_usage = final_llm_usage_dict["usage"]
-                llm_usage.prompt_tokens += usage.prompt_tokens
-                llm_usage.completion_tokens += usage.completion_tokens
-                llm_usage.total_tokens += usage.total_tokens
-                llm_usage.prompt_price += usage.prompt_price
-                llm_usage.completion_price += usage.completion_price
-                llm_usage.total_price += usage.total_price
-
-        model_instance = self.model_instance
-
-        while function_call_state and iteration_step <= max_iteration_steps:
-            # continue to run until there is not any tool call
-            function_call_state = False
-
-            if iteration_step == max_iteration_steps:
-                # the last iteration, remove all tools
-                self._prompt_messages_tools = []
-
-            message_file_ids: list[str] = []
-
-            agent_thought_id = self.create_agent_thought(
-                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
-            )
-
-            if iteration_step > 1:
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            # recalc llm max tokens
-            prompt_messages = self._organize_prompt_messages()
-            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
-            # invoke model
-            chunks = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters=app_generate_entity.model_conf.parameters,
-                tools=[],
-                stop=app_generate_entity.model_conf.stop,
-                stream=True,
-                user=self.user_id,
-                callbacks=[],
-            )
-
-            usage_dict: dict[str, LLMUsage | None] = {}
-            react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks, usage_dict)
-            scratchpad = AgentScratchpadUnit(
-                agent_response="",
-                thought="",
-                action_str="",
-                observation="",
-                action=None,
-            )
-
-            # publish agent thought if it's first iteration
-            if iteration_step == 1:
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            for chunk in react_chunks:
-                if isinstance(chunk, AgentScratchpadUnit.Action):
-                    action = chunk
-                    # detect action
-                    assert scratchpad.agent_response is not None
-                    scratchpad.agent_response += json.dumps(chunk.model_dump())
-                    scratchpad.action_str = json.dumps(chunk.model_dump())
-                    scratchpad.action = action
-                else:
-                    assert scratchpad.agent_response is not None
-                    scratchpad.agent_response += chunk
-                    assert scratchpad.thought is not None
-                    scratchpad.thought += chunk
-                    yield LLMResultChunk(
-                        model=self.model_config.model,
-                        prompt_messages=prompt_messages,
-                        system_fingerprint="",
-                        delta=LLMResultChunkDelta(index=0, message=AssistantPromptMessage(content=chunk), usage=None),
-                    )
-
-            assert scratchpad.thought is not None
-            scratchpad.thought = scratchpad.thought.strip() or "I am thinking about how to help you"
-            self._agent_scratchpad.append(scratchpad)
-
-            # Check if max iteration is reached and model still wants to call tools
-            if iteration_step == max_iteration_steps and scratchpad.action:
-                if scratchpad.action.action_name.lower() != "final answer":
-                    raise AgentMaxIterationError(app_config.agent.max_iteration)
-
-            # get llm usage
-            if "usage" in usage_dict:
-                if usage_dict["usage"] is not None:
-                    increase_usage(llm_usage, usage_dict["usage"])
-            else:
-                usage_dict["usage"] = LLMUsage.empty_usage()
-
-            self.save_agent_thought(
-                agent_thought_id=agent_thought_id,
-                tool_name=(scratchpad.action.action_name if scratchpad.action and not scratchpad.is_final() else ""),
-                tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
-                tool_invoke_meta={},
-                thought=scratchpad.thought or "",
-                observation="",
-                answer=scratchpad.agent_response or "",
-                messages_ids=[],
-                llm_usage=usage_dict["usage"],
-            )
-
-            if not scratchpad.is_final():
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            if not scratchpad.action:
-                # failed to extract action, return final answer directly
-                final_answer = ""
-            else:
-                if scratchpad.action.action_name.lower() == "final answer":
-                    # action is final answer, return final answer directly
-                    try:
-                        if isinstance(scratchpad.action.action_input, dict):
-                            final_answer = json.dumps(scratchpad.action.action_input, ensure_ascii=False)
-                        elif isinstance(scratchpad.action.action_input, str):
-                            final_answer = scratchpad.action.action_input
-                        else:
-                            final_answer = f"{scratchpad.action.action_input}"
-                    except TypeError:
-                        final_answer = f"{scratchpad.action.action_input}"
-                else:
-                    function_call_state = True
-                    # action is tool call, invoke tool
-                    tool_invoke_response, tool_invoke_meta = self._handle_invoke_action(
-                        action=scratchpad.action,
-                        tool_instances=tool_instances,
-                        message_file_ids=message_file_ids,
-                        trace_manager=trace_manager,
-                    )
-                    scratchpad.observation = tool_invoke_response
-                    scratchpad.agent_response = tool_invoke_response
-
-                    self.save_agent_thought(
-                        agent_thought_id=agent_thought_id,
-                        tool_name=scratchpad.action.action_name,
-                        tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
-                        thought=scratchpad.thought or "",
-                        observation={scratchpad.action.action_name: tool_invoke_response},
-                        tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
-                        answer=scratchpad.agent_response,
-                        messages_ids=message_file_ids,
-                        llm_usage=usage_dict["usage"],
-                    )
-
-                    self.queue_manager.publish(
-                        QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                    )
-
-                # update prompt tool message
-                for prompt_tool in self._prompt_messages_tools:
-                    self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
-
-            iteration_step += 1
-
-        yield LLMResultChunk(
-            model=model_instance.model,
-            prompt_messages=prompt_messages,
-            delta=LLMResultChunkDelta(
-                index=0, message=AssistantPromptMessage(content=final_answer), usage=llm_usage["usage"]
-            ),
-            system_fingerprint="",
-        )
-
-        # save agent thought
-        self.save_agent_thought(
-            agent_thought_id=agent_thought_id,
-            tool_name="",
-            tool_input={},
-            tool_invoke_meta={},
-            thought=final_answer,
-            observation={},
-            answer=final_answer,
-            messages_ids=[],
-        )
-        # publish end event
-        self.queue_manager.publish(
-            QueueMessageEndEvent(
-                llm_result=LLMResult(
-                    model=model_instance.model,
-                    prompt_messages=prompt_messages,
-                    message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
-                    system_fingerprint="",
-                )
-            ),
-            PublishFrom.APPLICATION_MANAGER,
-        )
-
-    def _handle_invoke_action(
-        self,
-        action: AgentScratchpadUnit.Action,
-        tool_instances: Mapping[str, Tool],
-        message_file_ids: list[str],
-        trace_manager: TraceQueueManager | None = None,
-    ) -> tuple[str, ToolInvokeMeta]:
-        """
-        handle invoke action
-        :param action: action
-        :param tool_instances: tool instances
-        :param message_file_ids: message file ids
-        :param trace_manager: trace manager
-        :return: observation, meta
-        """
-        # action is tool call, invoke tool
-        tool_call_name = action.action_name
-        tool_call_args = action.action_input
-        tool_instance = tool_instances.get(tool_call_name)
-
-        if not tool_instance:
-            answer = f"there is not a tool named {tool_call_name}"
-            return answer, ToolInvokeMeta.error_instance(answer)
-
-        if isinstance(tool_call_args, str):
-            try:
-                tool_call_args = json.loads(tool_call_args)
-            except json.JSONDecodeError:
-                pass
-
-        # invoke tool
-        tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
-            tool=tool_instance,
-            tool_parameters=tool_call_args,
-            user_id=self.user_id,
-            tenant_id=self.tenant_id,
-            message=self.message,
-            invoke_from=self.application_generate_entity.invoke_from,
-            agent_tool_callback=self.agent_callback,
-            trace_manager=trace_manager,
-        )
-
-        # publish files
-        for message_file_id in message_files:
-            # publish message file
-            self.queue_manager.publish(
-                QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
-            )
-            # add message file ids
-            message_file_ids.append(message_file_id)
-
-        return tool_invoke_response, tool_invoke_meta
-
-    def _convert_dict_to_action(self, action: dict) -> AgentScratchpadUnit.Action:
-        """
-        convert dict to action
-        """
-        return AgentScratchpadUnit.Action(action_name=action["action"], action_input=action["action_input"])
-
-    def _fill_in_inputs_from_external_data_tools(self, instruction: str, inputs: Mapping[str, Any]) -> str:
-        """
-        fill in inputs from external data tools
-        """
-        for key, value in inputs.items():
-            try:
-                instruction = instruction.replace(f"{{{{{key}}}}}", str(value))
-            except Exception:
-                continue
-
-        return instruction
-
-    def _init_react_state(self, query):
-        """
-        init agent scratchpad
-        """
-        self._query = query
-        self._agent_scratchpad = []
-        self._historic_prompt_messages = self._organize_historic_prompt_messages()
-
-    @abstractmethod
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        organize prompt messages
-        """
-
-    def _format_assistant_message(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
-        """
-        format assistant message
-        """
-        message = ""
-        for scratchpad in agent_scratchpad:
-            if scratchpad.is_final():
-                message += f"Final Answer: {scratchpad.agent_response}"
-            else:
-                message += f"Thought: {scratchpad.thought}\n\n"
-                if scratchpad.action_str:
-                    message += f"Action: {scratchpad.action_str}\n\n"
-                if scratchpad.observation:
-                    message += f"Observation: {scratchpad.observation}\n\n"
-
-        return message
-
-    def _organize_historic_prompt_messages(
-        self, current_session_messages: list[PromptMessage] | None = None
-    ) -> list[PromptMessage]:
-        """
-        organize historic prompt messages
-        """
-        result: list[PromptMessage] = []
-        scratchpads: list[AgentScratchpadUnit] = []
-        current_scratchpad: AgentScratchpadUnit | None = None
-
-        for message in self.history_prompt_messages:
-            if isinstance(message, AssistantPromptMessage):
-                if not current_scratchpad:
-                    assert isinstance(message.content, str)
-                    current_scratchpad = AgentScratchpadUnit(
-                        agent_response=message.content,
-                        thought=message.content or "I am thinking about how to help you",
-                        action_str="",
-                        action=None,
-                        observation=None,
-                    )
-                    scratchpads.append(current_scratchpad)
-                if message.tool_calls:
-                    try:
-                        current_scratchpad.action = AgentScratchpadUnit.Action(
-                            action_name=message.tool_calls[0].function.name,
-                            action_input=json.loads(message.tool_calls[0].function.arguments),
-                        )
-                        current_scratchpad.action_str = json.dumps(current_scratchpad.action.to_dict())
-                    except Exception:
-                        logger.exception("Failed to parse tool call from assistant message")
-            elif isinstance(message, ToolPromptMessage):
-                if current_scratchpad:
-                    assert isinstance(message.content, str)
-                    current_scratchpad.observation = message.content
-                else:
-                    raise NotImplementedError("expected str type")
-            elif isinstance(message, UserPromptMessage):
-                if scratchpads:
-                    result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
-                    scratchpads = []
-                    current_scratchpad = None
-
-                result.append(message)
-
-        if scratchpads:
-            result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
-
-        historic_prompts = AgentHistoryPromptTransform(
-            model_config=self.model_config,
-            prompt_messages=current_session_messages or [],
-            history_messages=result,
-            memory=self.memory,
-        ).get_prompt()
-        return historic_prompts

api/core/agent/cot_chat_agent_runner.py

@@ -1,118 +0,0 @@
-import json
-
-from core.agent.cot_agent_runner import CotAgentRunner
-from core.file import file_manager
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from core.model_runtime.utils.encoders import jsonable_encoder
-
-
-class CotChatAgentRunner(CotAgentRunner):
-    def _organize_system_prompt(self) -> SystemPromptMessage:
-        """
-        Organize system prompt
-        """
-        assert self.app_config.agent
-        assert self.app_config.agent.prompt
-
-        prompt_entity = self.app_config.agent.prompt
-        if not prompt_entity:
-            raise ValueError("Agent prompt configuration is not set")
-        first_prompt = prompt_entity.first_prompt
-
-        system_prompt = (
-            first_prompt.replace("{{instruction}}", self._instruction)
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
-            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
-        )
-
-        return SystemPromptMessage(content=system_prompt)
-
-    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Organize user query
-        """
-        if self.files:
-            # get image detail config
-            image_detail_config = (
-                self.application_generate_entity.file_upload_config.image_config.detail
-                if (
-                    self.application_generate_entity.file_upload_config
-                    and self.application_generate_entity.file_upload_config.image_config
-                )
-                else None
-            )
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-
-            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
-            for file in self.files:
-                prompt_message_contents.append(
-                    file_manager.to_prompt_message_content(
-                        file,
-                        image_detail_config=image_detail_config,
-                    )
-                )
-            prompt_message_contents.append(TextPromptMessageContent(data=query))
-
-            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-        else:
-            prompt_messages.append(UserPromptMessage(content=query))
-
-        return prompt_messages
-
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        Organize
-        """
-        # organize system prompt
-        system_message = self._organize_system_prompt()
-
-        # organize current assistant messages
-        agent_scratchpad = self._agent_scratchpad
-        if not agent_scratchpad:
-            assistant_messages = []
-        else:
-            assistant_message = AssistantPromptMessage(content="")
-            assistant_message.content = ""  # FIXME: type check tell mypy that assistant_message.content is str
-            for unit in agent_scratchpad:
-                if unit.is_final():
-                    assert isinstance(assistant_message.content, str)
-                    assistant_message.content += f"Final Answer: {unit.agent_response}"
-                else:
-                    assert isinstance(assistant_message.content, str)
-                    assistant_message.content += f"Thought: {unit.thought}\n\n"
-                    if unit.action_str:
-                        assistant_message.content += f"Action: {unit.action_str}\n\n"
-                    if unit.observation:
-                        assistant_message.content += f"Observation: {unit.observation}\n\n"
-
-            assistant_messages = [assistant_message]
-
-        # query messages
-        query_messages = self._organize_user_query(self._query, [])
-
-        if assistant_messages:
-            # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages(
-                [system_message, *query_messages, *assistant_messages, UserPromptMessage(content="continue")]
-            )
-            messages = [
-                system_message,
-                *historic_messages,
-                *query_messages,
-                *assistant_messages,
-                UserPromptMessage(content="continue"),
-            ]
-        else:
-            # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages([system_message, *query_messages])
-            messages = [system_message, *historic_messages, *query_messages]
-
-        # join all messages
-        return messages

api/core/agent/cot_completion_agent_runner.py

@@ -1,87 +0,0 @@
-import json
-
-from core.agent.cot_agent_runner import CotAgentRunner
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.utils.encoders import jsonable_encoder
-
-
-class CotCompletionAgentRunner(CotAgentRunner):
-    def _organize_instruction_prompt(self) -> str:
-        """
-        Organize instruction prompt
-        """
-        if self.app_config.agent is None:
-            raise ValueError("Agent configuration is not set")
-        prompt_entity = self.app_config.agent.prompt
-        if prompt_entity is None:
-            raise ValueError("prompt entity is not set")
-        first_prompt = prompt_entity.first_prompt
-
-        system_prompt = (
-            first_prompt.replace("{{instruction}}", self._instruction)
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
-            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
-        )
-
-        return system_prompt
-
-    def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] | None = None) -> str:
-        """
-        Organize historic prompt
-        """
-        historic_prompt_messages = self._organize_historic_prompt_messages(current_session_messages)
-        historic_prompt = ""
-
-        for message in historic_prompt_messages:
-            if isinstance(message, UserPromptMessage):
-                historic_prompt += f"Question: {message.content}\n\n"
-            elif isinstance(message, AssistantPromptMessage):
-                if isinstance(message.content, str):
-                    historic_prompt += message.content + "\n\n"
-                elif isinstance(message.content, list):
-                    for content in message.content:
-                        if not isinstance(content, TextPromptMessageContent):
-                            continue
-                        historic_prompt += content.data
-
-        return historic_prompt
-
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        Organize prompt messages
-        """
-        # organize system prompt
-        system_prompt = self._organize_instruction_prompt()
-
-        # organize historic prompt messages
-        historic_prompt = self._organize_historic_prompt()
-
-        # organize current assistant messages
-        agent_scratchpad = self._agent_scratchpad
-        assistant_prompt = ""
-        for unit in agent_scratchpad or []:
-            if unit.is_final():
-                assistant_prompt += f"Final Answer: {unit.agent_response}"
-            else:
-                assistant_prompt += f"Thought: {unit.thought}\n\n"
-                if unit.action_str:
-                    assistant_prompt += f"Action: {unit.action_str}\n\n"
-                if unit.observation:
-                    assistant_prompt += f"Observation: {unit.observation}\n\n"
-
-        # query messages
-        query_prompt = f"Question: {self._query}"
-
-        # join all messages
-        prompt = (
-            system_prompt.replace("{{historic_messages}}", historic_prompt)
-            .replace("{{agent_scratchpad}}", assistant_prompt)
-            .replace("{{query}}", query_prompt)
-        )
-
-        return [UserPromptMessage(content=prompt)]

api/core/agent/entities.py

@@ -1,3 +1,5 @@
+import uuid
+from collections.abc import Mapping
 from enum import StrEnum
 from typing import Any, Union
 
@@ -92,3 +94,96 @@ class AgentInvokeMessage(ToolInvokeMessage):
     """
 
     pass
+
+
+class ExecutionContext(BaseModel):
+    """Execution context containing trace and audit information.
+
+    This context carries all the IDs and metadata that are not part of
+    the core business logic but needed for tracing, auditing, and
+    correlation purposes.
+    """
+
+    user_id: str | None = None
+    app_id: str | None = None
+    conversation_id: str | None = None
+    message_id: str | None = None
+    tenant_id: str | None = None
+
+    @classmethod
+    def create_minimal(cls, user_id: str | None = None) -> "ExecutionContext":
+        """Create a minimal context with only essential fields."""
+        return cls(user_id=user_id)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for passing to legacy code."""
+        return {
+            "user_id": self.user_id,
+            "app_id": self.app_id,
+            "conversation_id": self.conversation_id,
+            "message_id": self.message_id,
+            "tenant_id": self.tenant_id,
+        }
+
+    def with_updates(self, **kwargs) -> "ExecutionContext":
+        """Create a new context with updated fields."""
+        data = self.to_dict()
+        data.update(kwargs)
+
+        return ExecutionContext(
+            user_id=data.get("user_id"),
+            app_id=data.get("app_id"),
+            conversation_id=data.get("conversation_id"),
+            message_id=data.get("message_id"),
+            tenant_id=data.get("tenant_id"),
+        )
+
+
+class AgentLog(BaseModel):
+    """
+    Agent Log.
+    """
+
+    class LogType(StrEnum):
+        """Type of agent log entry."""
+
+        ROUND = "round"  # A complete iteration round
+        THOUGHT = "thought"  # LLM thinking/reasoning
+        TOOL_CALL = "tool_call"  # Tool invocation
+
+    class LogMetadata(StrEnum):
+        STARTED_AT = "started_at"
+        FINISHED_AT = "finished_at"
+        ELAPSED_TIME = "elapsed_time"
+        TOTAL_PRICE = "total_price"
+        TOTAL_TOKENS = "total_tokens"
+        PROVIDER = "provider"
+        CURRENCY = "currency"
+        LLM_USAGE = "llm_usage"
+        ICON = "icon"
+        ICON_DARK = "icon_dark"
+
+    class LogStatus(StrEnum):
+        START = "start"
+        ERROR = "error"
+        SUCCESS = "success"
+
+    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="The id of the log")
+    label: str = Field(..., description="The label of the log")
+    log_type: LogType = Field(..., description="The type of the log")
+    parent_id: str | None = Field(default=None, description="Leave empty for root log")
+    error: str | None = Field(default=None, description="The error message")
+    status: LogStatus = Field(..., description="The status of the log")
+    data: Mapping[str, Any] = Field(..., description="Detailed log data")
+    metadata: Mapping[LogMetadata, Any] = Field(default={}, description="The metadata of the log")
+
+
+class AgentResult(BaseModel):
+    """
+    Agent execution result.
+    """
+
+    text: str = Field(default="", description="The generated text")
+    files: list[Any] = Field(default_factory=list, description="Files produced during execution")
+    usage: Any | None = Field(default=None, description="LLM usage statistics")
+    finish_reason: str | None = Field(default=None, description="Reason for completion")

api/core/agent/fc_agent_runner.py

@@ -1,468 +0,0 @@
-import json
-import logging
-from collections.abc import Generator
-from copy import deepcopy
-from typing import Any, Union
-
-from core.agent.base_agent_runner import BaseAgentRunner
-from core.app.apps.base_app_queue_manager import PublishFrom
-from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
-from core.file import file_manager
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    LLMResult,
-    LLMResultChunk,
-    LLMResultChunkDelta,
-    LLMUsage,
-    PromptMessage,
-    PromptMessageContentType,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
-from core.tools.entities.tool_entities import ToolInvokeMeta
-from core.tools.tool_engine import ToolEngine
-from core.workflow.nodes.agent.exc import AgentMaxIterationError
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class FunctionCallAgentRunner(BaseAgentRunner):
-    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
-        """
-        Run FunctionCall agent application
-        """
-        self.query = query
-        app_generate_entity = self.application_generate_entity
-
-        app_config = self.app_config
-        assert app_config is not None, "app_config is required"
-        assert app_config.agent is not None, "app_config.agent is required"
-
-        # convert tools into ModelRuntime Tool format
-        tool_instances, prompt_messages_tools = self._init_prompt_tools()
-
-        assert app_config.agent
-
-        iteration_step = 1
-        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
-
-        # continue to run until there is not any tool call
-        function_call_state = True
-        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
-        final_answer = ""
-        prompt_messages: list = []  # Initialize prompt_messages
-
-        # get tracing instance
-        trace_manager = app_generate_entity.trace_manager
-
-        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
-            if not final_llm_usage_dict["usage"]:
-                final_llm_usage_dict["usage"] = usage
-            else:
-                llm_usage = final_llm_usage_dict["usage"]
-                llm_usage.prompt_tokens += usage.prompt_tokens
-                llm_usage.completion_tokens += usage.completion_tokens
-                llm_usage.total_tokens += usage.total_tokens
-                llm_usage.prompt_price += usage.prompt_price
-                llm_usage.completion_price += usage.completion_price
-                llm_usage.total_price += usage.total_price
-
-        model_instance = self.model_instance
-
-        while function_call_state and iteration_step <= max_iteration_steps:
-            function_call_state = False
-
-            if iteration_step == max_iteration_steps:
-                # the last iteration, remove all tools
-                prompt_messages_tools = []
-
-            message_file_ids: list[str] = []
-            agent_thought_id = self.create_agent_thought(
-                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
-            )
-
-            # recalc llm max tokens
-            prompt_messages = self._organize_prompt_messages()
-            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
-            # invoke model
-            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters=app_generate_entity.model_conf.parameters,
-                tools=prompt_messages_tools,
-                stop=app_generate_entity.model_conf.stop,
-                stream=self.stream_tool_call,
-                user=self.user_id,
-                callbacks=[],
-            )
-
-            tool_calls: list[tuple[str, str, dict[str, Any]]] = []
-
-            # save full response
-            response = ""
-
-            # save tool call names and inputs
-            tool_call_names = ""
-            tool_call_inputs = ""
-
-            current_llm_usage = None
-
-            if isinstance(chunks, Generator):
-                is_first_chunk = True
-                for chunk in chunks:
-                    if is_first_chunk:
-                        self.queue_manager.publish(
-                            QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                        )
-                        is_first_chunk = False
-                    # check if there is any tool call
-                    if self.check_tool_calls(chunk):
-                        function_call_state = True
-                        tool_calls.extend(self.extract_tool_calls(chunk) or [])
-                        tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
-                        try:
-                            tool_call_inputs = json.dumps(
-                                {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
-                            )
-                        except TypeError:
-                            # fallback: force ASCII to handle non-serializable objects
-                            tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
-
-                    if chunk.delta.message and chunk.delta.message.content:
-                        if isinstance(chunk.delta.message.content, list):
-                            for content in chunk.delta.message.content:
-                                response += content.data
-                        else:
-                            response += str(chunk.delta.message.content)
-
-                    if chunk.delta.usage:
-                        increase_usage(llm_usage, chunk.delta.usage)
-                        current_llm_usage = chunk.delta.usage
-
-                    yield chunk
-            else:
-                result = chunks
-                # check if there is any tool call
-                if self.check_blocking_tool_calls(result):
-                    function_call_state = True
-                    tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
-                    tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
-                    try:
-                        tool_call_inputs = json.dumps(
-                            {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
-                        )
-                    except TypeError:
-                        # fallback: force ASCII to handle non-serializable objects
-                        tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
-
-                if result.usage:
-                    increase_usage(llm_usage, result.usage)
-                    current_llm_usage = result.usage
-
-                if result.message and result.message.content:
-                    if isinstance(result.message.content, list):
-                        for content in result.message.content:
-                            response += content.data
-                    else:
-                        response += str(result.message.content)
-
-                if not result.message.content:
-                    result.message.content = ""
-
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-                yield LLMResultChunk(
-                    model=model_instance.model,
-                    prompt_messages=result.prompt_messages,
-                    system_fingerprint=result.system_fingerprint,
-                    delta=LLMResultChunkDelta(
-                        index=0,
-                        message=result.message,
-                        usage=result.usage,
-                    ),
-                )
-
-            assistant_message = AssistantPromptMessage(content=response, tool_calls=[])
-            if tool_calls:
-                assistant_message.tool_calls = [
-                    AssistantPromptMessage.ToolCall(
-                        id=tool_call[0],
-                        type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name=tool_call[1], arguments=json.dumps(tool_call[2], ensure_ascii=False)
-                        ),
-                    )
-                    for tool_call in tool_calls
-                ]
-
-            self._current_thoughts.append(assistant_message)
-
-            # save thought
-            self.save_agent_thought(
-                agent_thought_id=agent_thought_id,
-                tool_name=tool_call_names,
-                tool_input=tool_call_inputs,
-                thought=response,
-                tool_invoke_meta=None,
-                observation=None,
-                answer=response,
-                messages_ids=[],
-                llm_usage=current_llm_usage,
-            )
-            self.queue_manager.publish(
-                QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-            )
-
-            final_answer += response + "\n"
-
-            # Check if max iteration is reached and model still wants to call tools
-            if iteration_step == max_iteration_steps and tool_calls:
-                raise AgentMaxIterationError(app_config.agent.max_iteration)
-
-            # call tools
-            tool_responses = []
-            for tool_call_id, tool_call_name, tool_call_args in tool_calls:
-                tool_instance = tool_instances.get(tool_call_name)
-                if not tool_instance:
-                    tool_response = {
-                        "tool_call_id": tool_call_id,
-                        "tool_call_name": tool_call_name,
-                        "tool_response": f"there is not a tool named {tool_call_name}",
-                        "meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict(),
-                    }
-                else:
-                    # invoke tool
-                    tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
-                        tool=tool_instance,
-                        tool_parameters=tool_call_args,
-                        user_id=self.user_id,
-                        tenant_id=self.tenant_id,
-                        message=self.message,
-                        invoke_from=self.application_generate_entity.invoke_from,
-                        agent_tool_callback=self.agent_callback,
-                        trace_manager=trace_manager,
-                        app_id=self.application_generate_entity.app_config.app_id,
-                        message_id=self.message.id,
-                        conversation_id=self.conversation.id,
-                    )
-                    # publish files
-                    for message_file_id in message_files:
-                        # publish message file
-                        self.queue_manager.publish(
-                            QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
-                        )
-                        # add message file ids
-                        message_file_ids.append(message_file_id)
-
-                    tool_response = {
-                        "tool_call_id": tool_call_id,
-                        "tool_call_name": tool_call_name,
-                        "tool_response": tool_invoke_response,
-                        "meta": tool_invoke_meta.to_dict(),
-                    }
-
-                tool_responses.append(tool_response)
-                if tool_response["tool_response"] is not None:
-                    self._current_thoughts.append(
-                        ToolPromptMessage(
-                            content=str(tool_response["tool_response"]),
-                            tool_call_id=tool_call_id,
-                            name=tool_call_name,
-                        )
-                    )
-
-            if len(tool_responses) > 0:
-                # save agent thought
-                self.save_agent_thought(
-                    agent_thought_id=agent_thought_id,
-                    tool_name="",
-                    tool_input="",
-                    thought="",
-                    tool_invoke_meta={
-                        tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
-                    },
-                    observation={
-                        tool_response["tool_call_name"]: tool_response["tool_response"]
-                        for tool_response in tool_responses
-                    },
-                    answer="",
-                    messages_ids=message_file_ids,
-                )
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            # update prompt tool
-            for prompt_tool in prompt_messages_tools:
-                self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
-
-            iteration_step += 1
-
-        # publish end event
-        self.queue_manager.publish(
-            QueueMessageEndEvent(
-                llm_result=LLMResult(
-                    model=model_instance.model,
-                    prompt_messages=prompt_messages,
-                    message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
-                    system_fingerprint="",
-                )
-            ),
-            PublishFrom.APPLICATION_MANAGER,
-        )
-
-    def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
-        """
-        Check if there is any tool call in llm result chunk
-        """
-        if llm_result_chunk.delta.message.tool_calls:
-            return True
-        return False
-
-    def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
-        """
-        Check if there is any blocking tool call in llm result
-        """
-        if llm_result.message.tool_calls:
-            return True
-        return False
-
-    def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
-        """
-        Extract tool calls from llm result chunk
-
-        Returns:
-            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
-        """
-        tool_calls = []
-        for prompt_message in llm_result_chunk.delta.message.tool_calls:
-            args = {}
-            if prompt_message.function.arguments != "":
-                args = json.loads(prompt_message.function.arguments)
-
-            tool_calls.append(
-                (
-                    prompt_message.id,
-                    prompt_message.function.name,
-                    args,
-                )
-            )
-
-        return tool_calls
-
-    def extract_blocking_tool_calls(self, llm_result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
-        """
-        Extract blocking tool calls from llm result
-
-        Returns:
-            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
-        """
-        tool_calls = []
-        for prompt_message in llm_result.message.tool_calls:
-            args = {}
-            if prompt_message.function.arguments != "":
-                args = json.loads(prompt_message.function.arguments)
-
-            tool_calls.append(
-                (
-                    prompt_message.id,
-                    prompt_message.function.name,
-                    args,
-                )
-            )
-
-        return tool_calls
-
-    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Initialize system message
-        """
-        if not prompt_messages and prompt_template:
-            return [
-                SystemPromptMessage(content=prompt_template),
-            ]
-
-        if prompt_messages and not isinstance(prompt_messages[0], SystemPromptMessage) and prompt_template:
-            prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
-
-        return prompt_messages or []
-
-    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Organize user query
-        """
-        if self.files:
-            # get image detail config
-            image_detail_config = (
-                self.application_generate_entity.file_upload_config.image_config.detail
-                if (
-                    self.application_generate_entity.file_upload_config
-                    and self.application_generate_entity.file_upload_config.image_config
-                )
-                else None
-            )
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-
-            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
-            for file in self.files:
-                prompt_message_contents.append(
-                    file_manager.to_prompt_message_content(
-                        file,
-                        image_detail_config=image_detail_config,
-                    )
-                )
-            prompt_message_contents.append(TextPromptMessageContent(data=query))
-
-            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-        else:
-            prompt_messages.append(UserPromptMessage(content=query))
-
-        return prompt_messages
-
-    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        As for now, gpt supports both fc and vision at the first iteration.
-        We need to remove the image messages from the prompt messages at the first iteration.
-        """
-        prompt_messages = deepcopy(prompt_messages)
-
-        for prompt_message in prompt_messages:
-            if isinstance(prompt_message, UserPromptMessage):
-                if isinstance(prompt_message.content, list):
-                    prompt_message.content = "\n".join(
-                        [
-                            content.data
-                            if content.type == PromptMessageContentType.TEXT
-                            else "[image]"
-                            if content.type == PromptMessageContentType.IMAGE
-                            else "[file]"
-                            for content in prompt_message.content
-                        ]
-                    )
-
-        return prompt_messages
-
-    def _organize_prompt_messages(self):
-        prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
-        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
-        query_prompt_messages = self._organize_user_query(self.query or "", [])
-
-        self.history_prompt_messages = AgentHistoryPromptTransform(
-            model_config=self.model_config,
-            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
-            history_messages=self.history_prompt_messages,
-            memory=self.memory,
-        ).get_prompt()
-
-        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
-        if len(self._current_thoughts) != 0:
-            # clear messages after the first iteration
-            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
-        return prompt_messages

api/core/agent/patterns/README.md

@@ -0,0 +1,55 @@
+# Agent Patterns
+
+A unified agent pattern module that powers both Agent V2 workflow nodes and agent applications. Strategies share a common execution contract while adapting to model capabilities and tool availability.
+
+## Overview
+
+The module applies a strategy pattern around LLM/tool orchestration. `StrategyFactory` auto-selects the best implementation based on model features or an explicit agent strategy, and each strategy streams logs and usage consistently.
+
+## Key Features
+
+- **Dual strategies**
+  - `FunctionCallStrategy`: uses native LLM function/tool calling when the model exposes `TOOL_CALL`, `MULTI_TOOL_CALL`, or `STREAM_TOOL_CALL`.
+  - `ReActStrategy`: ReAct (reasoning + acting) flow driven by `CotAgentOutputParser`, used when function calling is unavailable or explicitly requested.
+- **Explicit or auto selection**
+  - `StrategyFactory.create_strategy` prefers an explicit `AgentEntity.Strategy` (FUNCTION_CALLING or CHAIN_OF_THOUGHT).
+  - Otherwise it falls back to function calling when tool-call features exist, or ReAct when they do not.
+- **Unified execution contract**
+  - `AgentPattern.run` yields streaming `AgentLog` entries and `LLMResultChunk` data, returning an `AgentResult` with text, files, usage, and `finish_reason`.
+  - Iterations are configurable and hard-capped at 99 rounds; the last round forces a final answer by withholding tools.
+- **Tool handling and hooks**
+  - Tools convert to `PromptMessageTool` objects before invocation.
+  - Optional `tool_invoke_hook` lets callers override tool execution (e.g., agent apps) while workflow runs use `ToolEngine.generic_invoke`.
+  - Tool outputs support text, links, JSON, variables, blobs, retriever resources, and file attachments; `target=="self"` files are reloaded into model context, others are returned as outputs.
+- **File-aware arguments**
+  - Tool args accept `[File: <id>]` or `[Files: <id1, id2>]` placeholders that resolve to `File` objects before invocation, enabling models to reference uploaded files safely.
+- **ReAct prompt shaping**
+  - System prompts replace `{{instruction}}`, `{{tools}}`, and `{{tool_names}}` placeholders.
+  - Adds `Observation` to stop sequences and appends scratchpad text so the model sees prior Thought/Action/Observation history.
+- **Observability and accounting**
+  - Standardized `AgentLog` entries for rounds, model thoughts, and tool calls, including usage aggregation (`LLMUsage`) across streaming and non-streaming paths.
+
+## Architecture
+
+```
+agent/patterns/
+├── base.py              # Shared utilities: logging, usage, tool invocation, file handling
+├── function_call.py     # Native function-calling loop with tool execution
+├── react.py             # ReAct loop with CoT parsing and scratchpad wiring
+└── strategy_factory.py  # Strategy selection by model features or explicit override
+```
+
+## Usage
+
+- For auto-selection:
+  - Call `StrategyFactory.create_strategy(model_features, model_instance, context, tools, files, ...)` and run the returned strategy with prompt messages and model params.
+- For explicit behavior:
+  - Pass `agent_strategy=AgentEntity.Strategy.FUNCTION_CALLING` to force native calls (falls back to ReAct if unsupported), or `CHAIN_OF_THOUGHT` to force ReAct.
+- Both strategies stream chunks and logs; collect the generator output until it returns an `AgentResult`.
+
+## Integration Points
+
+- **Model runtime**: delegates to `ModelInstance.invoke_llm` for both streaming and non-streaming calls.
+- **Tool system**: defaults to `ToolEngine.generic_invoke`, with `tool_invoke_hook` for custom callers.
+- **Files**: flows through `File` objects for tool inputs/outputs and model-context attachments.
+- **Execution context**: `ExecutionContext` fields (user/app/conversation/message) propagate to tool invocations and logging.

api/core/agent/patterns/__init__.py

@@ -0,0 +1,19 @@
+"""Agent patterns module.
+
+This module provides different strategies for agent execution:
+- FunctionCallStrategy: Uses native function/tool calling
+- ReActStrategy: Uses ReAct (Reasoning + Acting) approach
+- StrategyFactory: Factory for creating strategies based on model features
+"""
+
+from .base import AgentPattern
+from .function_call import FunctionCallStrategy
+from .react import ReActStrategy
+from .strategy_factory import StrategyFactory
+
+__all__ = [
+    "AgentPattern",
+    "FunctionCallStrategy",
+    "ReActStrategy",
+    "StrategyFactory",
+]

api/core/agent/patterns/base.py

@@ -0,0 +1,474 @@
+"""Base class for agent strategies."""
+
+from __future__ import annotations
+
+import json
+import re
+import time
+from abc import ABC, abstractmethod
+from collections.abc import Callable, Generator
+from typing import TYPE_CHECKING, Any
+
+from core.agent.entities import AgentLog, AgentResult, ExecutionContext
+from core.file import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.message_entities import TextPromptMessageContent
+from core.tools.entities.tool_entities import ToolInvokeMessage, ToolInvokeMeta
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+# Type alias for tool invoke hook
+# Returns: (response_content, message_file_ids, tool_invoke_meta)
+ToolInvokeHook = Callable[["Tool", dict[str, Any], str], tuple[str, list[str], ToolInvokeMeta]]
+
+
+class AgentPattern(ABC):
+    """Base class for agent execution strategies."""
+
+    def __init__(
+        self,
+        model_instance: ModelInstance,
+        tools: list[Tool],
+        context: ExecutionContext,
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        files: list[File] = [],
+        tool_invoke_hook: ToolInvokeHook | None = None,
+    ):
+        """Initialize the agent strategy."""
+        self.model_instance = model_instance
+        self.tools = tools
+        self.context = context
+        self.max_iterations = min(max_iterations, 99)  # Cap at 99 iterations
+        self.workflow_call_depth = workflow_call_depth
+        self.files: list[File] = files
+        self.tool_invoke_hook = tool_invoke_hook
+
+    @abstractmethod
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the agent strategy."""
+        pass
+
+    def _accumulate_usage(self, total_usage: dict[str, Any], delta_usage: LLMUsage) -> None:
+        """Accumulate LLM usage statistics."""
+        if not total_usage.get("usage"):
+            # Create a copy to avoid modifying the original
+            total_usage["usage"] = LLMUsage(
+                prompt_tokens=delta_usage.prompt_tokens,
+                prompt_unit_price=delta_usage.prompt_unit_price,
+                prompt_price_unit=delta_usage.prompt_price_unit,
+                prompt_price=delta_usage.prompt_price,
+                completion_tokens=delta_usage.completion_tokens,
+                completion_unit_price=delta_usage.completion_unit_price,
+                completion_price_unit=delta_usage.completion_price_unit,
+                completion_price=delta_usage.completion_price,
+                total_tokens=delta_usage.total_tokens,
+                total_price=delta_usage.total_price,
+                currency=delta_usage.currency,
+                latency=delta_usage.latency,
+            )
+        else:
+            current: LLMUsage = total_usage["usage"]
+            current.prompt_tokens += delta_usage.prompt_tokens
+            current.completion_tokens += delta_usage.completion_tokens
+            current.total_tokens += delta_usage.total_tokens
+            current.prompt_price += delta_usage.prompt_price
+            current.completion_price += delta_usage.completion_price
+            current.total_price += delta_usage.total_price
+
+    def _extract_content(self, content: Any) -> str:
+        """Extract text content from message content."""
+        if isinstance(content, list):
+            # Content items are PromptMessageContentUnionTypes
+            text_parts = []
+            for c in content:
+                # Check if it's a TextPromptMessageContent (which has data attribute)
+                if isinstance(c, TextPromptMessageContent):
+                    text_parts.append(c.data)
+            return "".join(text_parts)
+        return str(content)
+
+    def _has_tool_calls(self, chunk: LLMResultChunk) -> bool:
+        """Check if chunk contains tool calls."""
+        # LLMResultChunk always has delta attribute
+        return bool(chunk.delta.message and chunk.delta.message.tool_calls)
+
+    def _has_tool_calls_result(self, result: LLMResult) -> bool:
+        """Check if result contains tool calls (non-streaming)."""
+        # LLMResult always has message attribute
+        return bool(result.message and result.message.tool_calls)
+
+    def _extract_tool_calls(self, chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
+        """Extract tool calls from streaming chunk."""
+        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+        if chunk.delta.message and chunk.delta.message.tool_calls:
+            for tool_call in chunk.delta.message.tool_calls:
+                if tool_call.function:
+                    try:
+                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
+                    except json.JSONDecodeError:
+                        args = {}
+                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
+        return tool_calls
+
+    def _extract_tool_calls_result(self, result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
+        """Extract tool calls from non-streaming result."""
+        tool_calls = []
+        if result.message and result.message.tool_calls:
+            for tool_call in result.message.tool_calls:
+                if tool_call.function:
+                    try:
+                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
+                    except json.JSONDecodeError:
+                        args = {}
+                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
+        return tool_calls
+
+    def _extract_text_from_message(self, message: PromptMessage) -> str:
+        """Extract text content from a prompt message."""
+        # PromptMessage always has content attribute
+        content = message.content
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            # Extract text from content list
+            text_parts = []
+            for item in content:
+                if isinstance(item, TextPromptMessageContent):
+                    text_parts.append(item.data)
+            return " ".join(text_parts)
+        return ""
+
+    def _get_tool_metadata(self, tool_instance: Tool) -> dict[AgentLog.LogMetadata, Any]:
+        """Get metadata for a tool including provider and icon info."""
+        from core.tools.tool_manager import ToolManager
+
+        metadata: dict[AgentLog.LogMetadata, Any] = {}
+        if tool_instance.entity and tool_instance.entity.identity:
+            identity = tool_instance.entity.identity
+            if identity.provider:
+                metadata[AgentLog.LogMetadata.PROVIDER] = identity.provider
+
+            # Get icon using ToolManager for proper URL generation
+            tenant_id = self.context.tenant_id
+            if tenant_id and identity.provider:
+                try:
+                    provider_type = tool_instance.tool_provider_type()
+                    icon = ToolManager.get_tool_icon(tenant_id, provider_type, identity.provider)
+                    if isinstance(icon, str):
+                        metadata[AgentLog.LogMetadata.ICON] = icon
+                    elif isinstance(icon, dict):
+                        # Handle icon dict with background/content or light/dark variants
+                        metadata[AgentLog.LogMetadata.ICON] = icon
+                except Exception:
+                    # Fallback to identity.icon if ToolManager fails
+                    if identity.icon:
+                        metadata[AgentLog.LogMetadata.ICON] = identity.icon
+            elif identity.icon:
+                metadata[AgentLog.LogMetadata.ICON] = identity.icon
+        return metadata
+
+    def _create_log(
+        self,
+        label: str,
+        log_type: AgentLog.LogType,
+        status: AgentLog.LogStatus,
+        data: dict[str, Any] | None = None,
+        parent_id: str | None = None,
+        extra_metadata: dict[AgentLog.LogMetadata, Any] | None = None,
+    ) -> AgentLog:
+        """Create a new AgentLog with standard metadata."""
+        metadata: dict[AgentLog.LogMetadata, Any] = {
+            AgentLog.LogMetadata.STARTED_AT: time.perf_counter(),
+        }
+        if extra_metadata:
+            metadata.update(extra_metadata)
+
+        return AgentLog(
+            label=label,
+            log_type=log_type,
+            status=status,
+            data=data or {},
+            parent_id=parent_id,
+            metadata=metadata,
+        )
+
+    def _finish_log(
+        self,
+        log: AgentLog,
+        data: dict[str, Any] | None = None,
+        usage: LLMUsage | None = None,
+    ) -> AgentLog:
+        """Finish an AgentLog by updating its status and metadata."""
+        log.status = AgentLog.LogStatus.SUCCESS
+
+        if data is not None:
+            log.data = data
+
+        # Calculate elapsed time
+        started_at = log.metadata.get(AgentLog.LogMetadata.STARTED_AT, time.perf_counter())
+        finished_at = time.perf_counter()
+
+        # Update metadata
+        log.metadata = {
+            **log.metadata,
+            AgentLog.LogMetadata.FINISHED_AT: finished_at,
+            # Calculate elapsed time in seconds
+            AgentLog.LogMetadata.ELAPSED_TIME: round(finished_at - started_at, 4),
+        }
+
+        # Add usage information if provided
+        if usage:
+            log.metadata.update(
+                {
+                    AgentLog.LogMetadata.TOTAL_PRICE: usage.total_price,
+                    AgentLog.LogMetadata.CURRENCY: usage.currency,
+                    AgentLog.LogMetadata.TOTAL_TOKENS: usage.total_tokens,
+                    AgentLog.LogMetadata.LLM_USAGE: usage,
+                }
+            )
+
+        return log
+
+    def _replace_file_references(self, tool_args: dict[str, Any]) -> dict[str, Any]:
+        """
+        Replace file references in tool arguments with actual File objects.
+
+        Args:
+            tool_args: Dictionary of tool arguments
+
+        Returns:
+            Updated tool arguments with file references replaced
+        """
+        # Process each argument in the dictionary
+        processed_args: dict[str, Any] = {}
+        for key, value in tool_args.items():
+            processed_args[key] = self._process_file_reference(value)
+        return processed_args
+
+    def _process_file_reference(self, data: Any) -> Any:
+        """
+        Recursively process data to replace file references.
+        Supports both single file [File: file_id] and multiple files [Files: file_id1, file_id2, ...].
+
+        Args:
+            data: The data to process (can be dict, list, str, or other types)
+
+        Returns:
+            Processed data with file references replaced
+        """
+        single_file_pattern = re.compile(r"^\[File:\s*([^\]]+)\]$")
+        multiple_files_pattern = re.compile(r"^\[Files:\s*([^\]]+)\]$")
+
+        if isinstance(data, dict):
+            # Process dictionary recursively
+            return {key: self._process_file_reference(value) for key, value in data.items()}
+        elif isinstance(data, list):
+            # Process list recursively
+            return [self._process_file_reference(item) for item in data]
+        elif isinstance(data, str):
+            # Check for single file pattern [File: file_id]
+            single_match = single_file_pattern.match(data.strip())
+            if single_match:
+                file_id = single_match.group(1).strip()
+                # Find the file in self.files
+                for file in self.files:
+                    if file.id and str(file.id) == file_id:
+                        return file
+                # If file not found, return original value
+                return data
+
+            # Check for multiple files pattern [Files: file_id1, file_id2, ...]
+            multiple_match = multiple_files_pattern.match(data.strip())
+            if multiple_match:
+                file_ids_str = multiple_match.group(1).strip()
+                # Split by comma and strip whitespace
+                file_ids = [fid.strip() for fid in file_ids_str.split(",")]
+
+                # Find all matching files
+                matched_files: list[File] = []
+                for file_id in file_ids:
+                    for file in self.files:
+                        if file.id and str(file.id) == file_id:
+                            matched_files.append(file)
+                            break
+
+                # Return list of files if any were found, otherwise return original
+                return matched_files or data
+
+            return data
+        else:
+            # Return other types as-is
+            return data
+
+    def _create_text_chunk(self, text: str, prompt_messages: list[PromptMessage]) -> LLMResultChunk:
+        """Create a text chunk for streaming."""
+        return LLMResultChunk(
+            model=self.model_instance.model,
+            prompt_messages=prompt_messages,
+            delta=LLMResultChunkDelta(
+                index=0,
+                message=AssistantPromptMessage(content=text),
+                usage=None,
+            ),
+            system_fingerprint="",
+        )
+
+    def _invoke_tool(
+        self,
+        tool_instance: Tool,
+        tool_args: dict[str, Any],
+        tool_name: str,
+    ) -> tuple[str, list[File], ToolInvokeMeta | None]:
+        """
+        Invoke a tool and collect its response.
+
+        Args:
+            tool_instance: The tool instance to invoke
+            tool_args: Tool arguments
+            tool_name: Name of the tool
+
+        Returns:
+            Tuple of (response_content, tool_files, tool_invoke_meta)
+        """
+        # Process tool_args to replace file references with actual File objects
+        tool_args = self._replace_file_references(tool_args)
+
+        # If a tool invoke hook is set, use it instead of generic_invoke
+        if self.tool_invoke_hook:
+            response_content, _, tool_invoke_meta = self.tool_invoke_hook(tool_instance, tool_args, tool_name)
+            # Note: message_file_ids are stored in DB, we don't convert them to File objects here
+            # The caller (AgentAppRunner) handles file publishing
+            return response_content, [], tool_invoke_meta
+
+        # Default: use generic_invoke for workflow scenarios
+        # Import here to avoid circular import
+        from core.tools.tool_engine import DifyWorkflowCallbackHandler, ToolEngine
+
+        tool_response = ToolEngine().generic_invoke(
+            tool=tool_instance,
+            tool_parameters=tool_args,
+            user_id=self.context.user_id or "",
+            workflow_tool_callback=DifyWorkflowCallbackHandler(),
+            workflow_call_depth=self.workflow_call_depth,
+            app_id=self.context.app_id,
+            conversation_id=self.context.conversation_id,
+            message_id=self.context.message_id,
+        )
+
+        # Collect response and files
+        response_content = ""
+        tool_files: list[File] = []
+
+        for response in tool_response:
+            if response.type == ToolInvokeMessage.MessageType.TEXT:
+                assert isinstance(response.message, ToolInvokeMessage.TextMessage)
+                response_content += response.message.text
+
+            elif response.type == ToolInvokeMessage.MessageType.LINK:
+                # Handle link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Link: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.IMAGE:
+                # Handle image URL messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Image: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK:
+                # Handle image link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Image: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.BINARY_LINK:
+                # Handle binary file link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    filename = response.meta.get("filename", "file") if response.meta else "file"
+                    response_content += f"[File: {filename} - {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.JSON:
+                # Handle JSON messages
+                if isinstance(response.message, ToolInvokeMessage.JsonMessage):
+                    response_content += json.dumps(response.message.json_object, ensure_ascii=False, indent=2)
+
+            elif response.type == ToolInvokeMessage.MessageType.BLOB:
+                # Handle blob messages - convert to text representation
+                if isinstance(response.message, ToolInvokeMessage.BlobMessage):
+                    mime_type = (
+                        response.meta.get("mime_type", "application/octet-stream")
+                        if response.meta
+                        else "application/octet-stream"
+                    )
+                    size = len(response.message.blob)
+                    response_content += f"[Binary data: {mime_type}, size: {size} bytes]"
+
+            elif response.type == ToolInvokeMessage.MessageType.VARIABLE:
+                # Handle variable messages
+                if isinstance(response.message, ToolInvokeMessage.VariableMessage):
+                    var_name = response.message.variable_name
+                    var_value = response.message.variable_value
+                    if isinstance(var_value, str):
+                        response_content += var_value
+                    else:
+                        response_content += f"[Variable {var_name}: {json.dumps(var_value, ensure_ascii=False)}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.BLOB_CHUNK:
+                # Handle blob chunk messages - these are parts of a larger blob
+                if isinstance(response.message, ToolInvokeMessage.BlobChunkMessage):
+                    response_content += f"[Blob chunk {response.message.sequence}: {len(response.message.blob)} bytes]"
+
+            elif response.type == ToolInvokeMessage.MessageType.RETRIEVER_RESOURCES:
+                # Handle retriever resources messages
+                if isinstance(response.message, ToolInvokeMessage.RetrieverResourceMessage):
+                    response_content += response.message.context
+
+            elif response.type == ToolInvokeMessage.MessageType.FILE:
+                # Extract file from meta
+                if response.meta and "file" in response.meta:
+                    file = response.meta["file"]
+                    if isinstance(file, File):
+                        # Check if file is for model or tool output
+                        if response.meta.get("target") == "self":
+                            # File is for model - add to files for next prompt
+                            self.files.append(file)
+                            response_content += f"File '{file.filename}' has been loaded into your context."
+                        else:
+                            # File is tool output
+                            tool_files.append(file)
+
+        return response_content, tool_files, None
+
+    def _find_tool_by_name(self, tool_name: str) -> Tool | None:
+        """Find a tool instance by its name."""
+        for tool in self.tools:
+            if tool.entity.identity.name == tool_name:
+                return tool
+        return None
+
+    def _convert_tools_to_prompt_format(self) -> list[PromptMessageTool]:
+        """Convert tools to prompt message format."""
+        prompt_tools: list[PromptMessageTool] = []
+        for tool in self.tools:
+            prompt_tools.append(tool.to_prompt_message_tool())
+        return prompt_tools
+
+    def _update_usage_with_empty(self, llm_usage: dict[str, Any]) -> None:
+        """Initialize usage tracking with empty usage if not set."""
+        if "usage" not in llm_usage or llm_usage["usage"] is None:
+            llm_usage["usage"] = LLMUsage.empty_usage()

api/core/agent/patterns/function_call.py

@@ -0,0 +1,299 @@
+"""Function Call strategy implementation."""
+
+import json
+from collections.abc import Generator
+from typing import Any, Union
+
+from core.agent.entities import AgentLog, AgentResult
+from core.file import File
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageTool,
+    ToolPromptMessage,
+)
+from core.tools.entities.tool_entities import ToolInvokeMeta
+
+from .base import AgentPattern
+
+
+class FunctionCallStrategy(AgentPattern):
+    """Function Call strategy using model's native tool calling capability."""
+
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the function call agent strategy."""
+        # Convert tools to prompt format
+        prompt_tools: list[PromptMessageTool] = self._convert_tools_to_prompt_format()
+
+        # Initialize tracking
+        iteration_step: int = 1
+        max_iterations: int = self.max_iterations + 1
+        function_call_state: bool = True
+        total_usage: dict[str, LLMUsage | None] = {"usage": None}
+        messages: list[PromptMessage] = list(prompt_messages)  # Create mutable copy
+        final_text: str = ""
+        finish_reason: str | None = None
+        output_files: list[File] = []  # Track files produced by tools
+
+        while function_call_state and iteration_step <= max_iterations:
+            function_call_state = False
+            round_log = self._create_log(
+                label=f"ROUND {iteration_step}",
+                log_type=AgentLog.LogType.ROUND,
+                status=AgentLog.LogStatus.START,
+                data={},
+            )
+            yield round_log
+            # On last iteration, remove tools to force final answer
+            current_tools: list[PromptMessageTool] = [] if iteration_step == max_iterations else prompt_tools
+            model_log = self._create_log(
+                label=f"{self.model_instance.model} Thought",
+                log_type=AgentLog.LogType.THOUGHT,
+                status=AgentLog.LogStatus.START,
+                data={},
+                parent_id=round_log.id,
+                extra_metadata={
+                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
+                },
+            )
+            yield model_log
+
+            # Track usage for this round only
+            round_usage: dict[str, LLMUsage | None] = {"usage": None}
+
+            # Invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
+                prompt_messages=messages,
+                model_parameters=model_parameters,
+                tools=current_tools,
+                stop=stop,
+                stream=stream,
+                user=self.context.user_id,
+                callbacks=[],
+            )
+
+            # Process response
+            tool_calls, response_content, chunk_finish_reason = yield from self._handle_chunks(
+                chunks, round_usage, model_log
+            )
+            messages.append(self._create_assistant_message(response_content, tool_calls))
+
+            # Accumulate to total usage
+            round_usage_value = round_usage.get("usage")
+            if round_usage_value:
+                self._accumulate_usage(total_usage, round_usage_value)
+
+            # Update final text if no tool calls (this is likely the final answer)
+            if not tool_calls:
+                final_text = response_content
+
+            # Update finish reason
+            if chunk_finish_reason:
+                finish_reason = chunk_finish_reason
+
+            # Process tool calls
+            tool_outputs: dict[str, str] = {}
+            if tool_calls:
+                function_call_state = True
+                # Execute tools
+                for tool_call_id, tool_name, tool_args in tool_calls:
+                    tool_response, tool_files, _ = yield from self._handle_tool_call(
+                        tool_name, tool_args, tool_call_id, messages, round_log
+                    )
+                    tool_outputs[tool_name] = tool_response
+                    # Track files produced by tools
+                    output_files.extend(tool_files)
+            yield self._finish_log(
+                round_log,
+                data={
+                    "llm_result": response_content,
+                    "tool_calls": [
+                        {"name": tc[1], "args": tc[2], "output": tool_outputs.get(tc[1], "")} for tc in tool_calls
+                    ]
+                    if tool_calls
+                    else [],
+                    "final_answer": final_text if not function_call_state else None,
+                },
+                usage=round_usage.get("usage"),
+            )
+            iteration_step += 1
+
+        # Return final result
+        from core.agent.entities import AgentResult
+
+        return AgentResult(
+            text=final_text,
+            files=output_files,
+            usage=total_usage.get("usage") or LLMUsage.empty_usage(),
+            finish_reason=finish_reason,
+        )
+
+    def _handle_chunks(
+        self,
+        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
+        llm_usage: dict[str, LLMUsage | None],
+        start_log: AgentLog,
+    ) -> Generator[
+        LLMResultChunk | AgentLog,
+        None,
+        tuple[list[tuple[str, str, dict[str, Any]]], str, str | None],
+    ]:
+        """Handle LLM response chunks and extract tool calls and content.
+
+        Returns a tuple of (tool_calls, response_content, finish_reason).
+        """
+        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+        response_content: str = ""
+        finish_reason: str | None = None
+        if isinstance(chunks, Generator):
+            # Streaming response
+            for chunk in chunks:
+                # Extract tool calls
+                if self._has_tool_calls(chunk):
+                    tool_calls.extend(self._extract_tool_calls(chunk))
+
+                # Extract content
+                if chunk.delta.message and chunk.delta.message.content:
+                    response_content += self._extract_content(chunk.delta.message.content)
+
+                # Track usage
+                if chunk.delta.usage:
+                    self._accumulate_usage(llm_usage, chunk.delta.usage)
+
+                # Capture finish reason
+                if chunk.delta.finish_reason:
+                    finish_reason = chunk.delta.finish_reason
+
+                yield chunk
+        else:
+            # Non-streaming response
+            result: LLMResult = chunks
+
+            if self._has_tool_calls_result(result):
+                tool_calls.extend(self._extract_tool_calls_result(result))
+
+            if result.message and result.message.content:
+                response_content += self._extract_content(result.message.content)
+
+            if result.usage:
+                self._accumulate_usage(llm_usage, result.usage)
+
+            # Convert to streaming format
+            yield LLMResultChunk(
+                model=result.model,
+                prompt_messages=result.prompt_messages,
+                delta=LLMResultChunkDelta(index=0, message=result.message, usage=result.usage),
+            )
+        yield self._finish_log(
+            start_log,
+            data={
+                "result": response_content,
+            },
+            usage=llm_usage.get("usage"),
+        )
+        return tool_calls, response_content, finish_reason
+
+    def _create_assistant_message(
+        self, content: str, tool_calls: list[tuple[str, str, dict[str, Any]]] | None = None
+    ) -> AssistantPromptMessage:
+        """Create assistant message with tool calls."""
+        if tool_calls is None:
+            return AssistantPromptMessage(content=content)
+        return AssistantPromptMessage(
+            content=content or "",
+            tool_calls=[
+                AssistantPromptMessage.ToolCall(
+                    id=tc[0],
+                    type="function",
+                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tc[1], arguments=json.dumps(tc[2])),
+                )
+                for tc in tool_calls
+            ],
+        )
+
+    def _handle_tool_call(
+        self,
+        tool_name: str,
+        tool_args: dict[str, Any],
+        tool_call_id: str,
+        messages: list[PromptMessage],
+        round_log: AgentLog,
+    ) -> Generator[AgentLog, None, tuple[str, list[File], ToolInvokeMeta | None]]:
+        """Handle a single tool call and return response with files and meta."""
+        # Find tool
+        tool_instance = self._find_tool_by_name(tool_name)
+        if not tool_instance:
+            raise ValueError(f"Tool {tool_name} not found")
+
+        # Get tool metadata (provider, icon, etc.)
+        tool_metadata = self._get_tool_metadata(tool_instance)
+
+        # Create tool call log
+        tool_call_log = self._create_log(
+            label=f"CALL {tool_name}",
+            log_type=AgentLog.LogType.TOOL_CALL,
+            status=AgentLog.LogStatus.START,
+            data={
+                "tool_call_id": tool_call_id,
+                "tool_name": tool_name,
+                "tool_args": tool_args,
+            },
+            parent_id=round_log.id,
+            extra_metadata=tool_metadata,
+        )
+        yield tool_call_log
+
+        # Invoke tool using base class method with error handling
+        try:
+            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args, tool_name)
+
+            yield self._finish_log(
+                tool_call_log,
+                data={
+                    **tool_call_log.data,
+                    "output": response_content,
+                    "files": len(tool_files),
+                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
+                },
+            )
+            final_content = response_content or "Tool executed successfully"
+            # Add tool response to messages
+            messages.append(
+                ToolPromptMessage(
+                    content=final_content,
+                    tool_call_id=tool_call_id,
+                    name=tool_name,
+                )
+            )
+            return response_content, tool_files, tool_invoke_meta
+        except Exception as e:
+            # Tool invocation failed, yield error log
+            error_message = str(e)
+            tool_call_log.status = AgentLog.LogStatus.ERROR
+            tool_call_log.error = error_message
+            tool_call_log.data = {
+                **tool_call_log.data,
+                "error": error_message,
+            }
+            yield tool_call_log
+
+            # Add error message to conversation
+            error_content = f"Tool execution failed: {error_message}"
+            messages.append(
+                ToolPromptMessage(
+                    content=error_content,
+                    tool_call_id=tool_call_id,
+                    name=tool_name,
+                )
+            )
+            return error_content, [], None

api/core/agent/patterns/react.py

@@ -0,0 +1,418 @@
+"""ReAct strategy implementation."""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Generator
+from typing import TYPE_CHECKING, Any, Union
+
+from core.agent.entities import AgentLog, AgentResult, AgentScratchpadUnit, ExecutionContext
+from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
+from core.file import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    PromptMessage,
+    SystemPromptMessage,
+)
+
+from .base import AgentPattern, ToolInvokeHook
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+
+class ReActStrategy(AgentPattern):
+    """ReAct strategy using reasoning and acting approach."""
+
+    def __init__(
+        self,
+        model_instance: ModelInstance,
+        tools: list[Tool],
+        context: ExecutionContext,
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        files: list[File] = [],
+        tool_invoke_hook: ToolInvokeHook | None = None,
+        instruction: str = "",
+    ):
+        """Initialize the ReAct strategy with instruction support."""
+        super().__init__(
+            model_instance=model_instance,
+            tools=tools,
+            context=context,
+            max_iterations=max_iterations,
+            workflow_call_depth=workflow_call_depth,
+            files=files,
+            tool_invoke_hook=tool_invoke_hook,
+        )
+        self.instruction = instruction
+
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the ReAct agent strategy."""
+        # Initialize tracking
+        agent_scratchpad: list[AgentScratchpadUnit] = []
+        iteration_step: int = 1
+        max_iterations: int = self.max_iterations + 1
+        react_state: bool = True
+        total_usage: dict[str, Any] = {"usage": None}
+        output_files: list[File] = []  # Track files produced by tools
+        final_text: str = ""
+        finish_reason: str | None = None
+
+        # Add "Observation" to stop sequences
+        if "Observation" not in stop:
+            stop = stop.copy()
+            stop.append("Observation")
+
+        while react_state and iteration_step <= max_iterations:
+            react_state = False
+            round_log = self._create_log(
+                label=f"ROUND {iteration_step}",
+                log_type=AgentLog.LogType.ROUND,
+                status=AgentLog.LogStatus.START,
+                data={},
+            )
+            yield round_log
+
+            # Build prompt with/without tools based on iteration
+            include_tools = iteration_step < max_iterations
+            current_messages = self._build_prompt_with_react_format(
+                prompt_messages, agent_scratchpad, include_tools, self.instruction
+            )
+
+            model_log = self._create_log(
+                label=f"{self.model_instance.model} Thought",
+                log_type=AgentLog.LogType.THOUGHT,
+                status=AgentLog.LogStatus.START,
+                data={},
+                parent_id=round_log.id,
+                extra_metadata={
+                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
+                },
+            )
+            yield model_log
+
+            # Track usage for this round only
+            round_usage: dict[str, Any] = {"usage": None}
+
+            # Use current messages directly (files are handled by base class if needed)
+            messages_to_use = current_messages
+
+            # Invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
+                prompt_messages=messages_to_use,
+                model_parameters=model_parameters,
+                stop=stop,
+                stream=stream,
+                user=self.context.user_id or "",
+                callbacks=[],
+            )
+
+            # Process response
+            scratchpad, chunk_finish_reason = yield from self._handle_chunks(
+                chunks, round_usage, model_log, current_messages
+            )
+            agent_scratchpad.append(scratchpad)
+
+            # Accumulate to total usage
+            round_usage_value = round_usage.get("usage")
+            if round_usage_value:
+                self._accumulate_usage(total_usage, round_usage_value)
+
+            # Update finish reason
+            if chunk_finish_reason:
+                finish_reason = chunk_finish_reason
+
+            # Check if we have an action to execute
+            if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
+                react_state = True
+                # Execute tool
+                observation, tool_files = yield from self._handle_tool_call(
+                    scratchpad.action, current_messages, round_log
+                )
+                scratchpad.observation = observation
+                # Track files produced by tools
+                output_files.extend(tool_files)
+
+                # Add observation to scratchpad for display
+                yield self._create_text_chunk(f"\nObservation: {observation}\n", current_messages)
+            else:
+                # Extract final answer
+                if scratchpad.action and scratchpad.action.action_input:
+                    final_answer = scratchpad.action.action_input
+                    if isinstance(final_answer, dict):
+                        final_answer = json.dumps(final_answer, ensure_ascii=False)
+                    final_text = str(final_answer)
+                elif scratchpad.thought:
+                    # If no action but we have thought, use thought as final answer
+                    final_text = scratchpad.thought
+
+            yield self._finish_log(
+                round_log,
+                data={
+                    "thought": scratchpad.thought,
+                    "action": scratchpad.action_str if scratchpad.action else None,
+                    "observation": scratchpad.observation or None,
+                    "final_answer": final_text if not react_state else None,
+                },
+                usage=round_usage.get("usage"),
+            )
+            iteration_step += 1
+
+        # Return final result
+
+        from core.agent.entities import AgentResult
+
+        return AgentResult(
+            text=final_text, files=output_files, usage=total_usage.get("usage"), finish_reason=finish_reason
+        )
+
+    def _build_prompt_with_react_format(
+        self,
+        original_messages: list[PromptMessage],
+        agent_scratchpad: list[AgentScratchpadUnit],
+        include_tools: bool = True,
+        instruction: str = "",
+    ) -> list[PromptMessage]:
+        """Build prompt messages with ReAct format."""
+        # Copy messages to avoid modifying original
+        messages = list(original_messages)
+
+        # Find and update the system prompt that should already exist
+        system_prompt_found = False
+        for i, msg in enumerate(messages):
+            if isinstance(msg, SystemPromptMessage):
+                system_prompt_found = True
+                # The system prompt from frontend already has the template, just replace placeholders
+
+                # Format tools
+                tools_str = ""
+                tool_names = []
+                if include_tools and self.tools:
+                    # Convert tools to prompt message tools format
+                    prompt_tools = [tool.to_prompt_message_tool() for tool in self.tools]
+                    tool_names = [tool.name for tool in prompt_tools]
+
+                    # Format tools as JSON for comprehensive information
+                    from core.model_runtime.utils.encoders import jsonable_encoder
+
+                    tools_str = json.dumps(jsonable_encoder(prompt_tools), indent=2)
+                    tool_names_str = ", ".join(f'"{name}"' for name in tool_names)
+                else:
+                    tools_str = "No tools available"
+                    tool_names_str = ""
+
+                # Replace placeholders in the existing system prompt
+                updated_content = msg.content
+                assert isinstance(updated_content, str)
+                updated_content = updated_content.replace("{{instruction}}", instruction)
+                updated_content = updated_content.replace("{{tools}}", tools_str)
+                updated_content = updated_content.replace("{{tool_names}}", tool_names_str)
+
+                # Create new SystemPromptMessage with updated content
+                messages[i] = SystemPromptMessage(content=updated_content)
+                break
+
+        # If no system prompt found, that's unexpected but add scratchpad anyway
+        if not system_prompt_found:
+            # This shouldn't happen if frontend is working correctly
+            pass
+
+        # Format agent scratchpad
+        scratchpad_str = ""
+        if agent_scratchpad:
+            scratchpad_parts: list[str] = []
+            for unit in agent_scratchpad:
+                if unit.thought:
+                    scratchpad_parts.append(f"Thought: {unit.thought}")
+                if unit.action_str:
+                    scratchpad_parts.append(f"Action:\n```\n{unit.action_str}\n```")
+                if unit.observation:
+                    scratchpad_parts.append(f"Observation: {unit.observation}")
+            scratchpad_str = "\n".join(scratchpad_parts)
+
+        # If there's a scratchpad, append it to the last message
+        if scratchpad_str:
+            messages.append(AssistantPromptMessage(content=scratchpad_str))
+
+        return messages
+
+    def _handle_chunks(
+        self,
+        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
+        llm_usage: dict[str, Any],
+        model_log: AgentLog,
+        current_messages: list[PromptMessage],
+    ) -> Generator[
+        LLMResultChunk | AgentLog,
+        None,
+        tuple[AgentScratchpadUnit, str | None],
+    ]:
+        """Handle LLM response chunks and extract action/thought.
+
+        Returns a tuple of (scratchpad_unit, finish_reason).
+        """
+        usage_dict: dict[str, Any] = {}
+
+        # Convert non-streaming to streaming format if needed
+        if isinstance(chunks, LLMResult):
+            # Create a generator from the LLMResult
+            def result_to_chunks() -> Generator[LLMResultChunk, None, None]:
+                yield LLMResultChunk(
+                    model=chunks.model,
+                    prompt_messages=chunks.prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=chunks.message,
+                        usage=chunks.usage,
+                        finish_reason=None,  # LLMResult doesn't have finish_reason, only streaming chunks do
+                    ),
+                    system_fingerprint=chunks.system_fingerprint or "",
+                )
+
+            streaming_chunks = result_to_chunks()
+        else:
+            streaming_chunks = chunks
+
+        react_chunks = CotAgentOutputParser.handle_react_stream_output(streaming_chunks, usage_dict)
+
+        # Initialize scratchpad unit
+        scratchpad = AgentScratchpadUnit(
+            agent_response="",
+            thought="",
+            action_str="",
+            observation="",
+            action=None,
+        )
+
+        finish_reason: str | None = None
+
+        # Process chunks
+        for chunk in react_chunks:
+            if isinstance(chunk, AgentScratchpadUnit.Action):
+                # Action detected
+                action_str = json.dumps(chunk.model_dump())
+                scratchpad.agent_response = (scratchpad.agent_response or "") + action_str
+                scratchpad.action_str = action_str
+                scratchpad.action = chunk
+
+                yield self._create_text_chunk(json.dumps(chunk.model_dump()), current_messages)
+            else:
+                # Text chunk
+                chunk_text = str(chunk)
+                scratchpad.agent_response = (scratchpad.agent_response or "") + chunk_text
+                scratchpad.thought = (scratchpad.thought or "") + chunk_text
+
+                yield self._create_text_chunk(chunk_text, current_messages)
+
+        # Update usage
+        if usage_dict.get("usage"):
+            if llm_usage.get("usage"):
+                self._accumulate_usage(llm_usage, usage_dict["usage"])
+            else:
+                llm_usage["usage"] = usage_dict["usage"]
+
+        # Clean up thought
+        scratchpad.thought = (scratchpad.thought or "").strip() or "I am thinking about how to help you"
+
+        # Finish model log
+        yield self._finish_log(
+            model_log,
+            data={
+                "thought": scratchpad.thought,
+                "action": scratchpad.action_str if scratchpad.action else None,
+            },
+            usage=llm_usage.get("usage"),
+        )
+
+        return scratchpad, finish_reason
+
+    def _handle_tool_call(
+        self,
+        action: AgentScratchpadUnit.Action,
+        prompt_messages: list[PromptMessage],
+        round_log: AgentLog,
+    ) -> Generator[AgentLog, None, tuple[str, list[File]]]:
+        """Handle tool call and return observation with files."""
+        tool_name = action.action_name
+        tool_args: dict[str, Any] | str = action.action_input
+
+        # Find tool instance first to get metadata
+        tool_instance = self._find_tool_by_name(tool_name)
+        tool_metadata = self._get_tool_metadata(tool_instance) if tool_instance else {}
+
+        # Start tool log with tool metadata
+        tool_log = self._create_log(
+            label=f"CALL {tool_name}",
+            log_type=AgentLog.LogType.TOOL_CALL,
+            status=AgentLog.LogStatus.START,
+            data={
+                "tool_name": tool_name,
+                "tool_args": tool_args,
+            },
+            parent_id=round_log.id,
+            extra_metadata=tool_metadata,
+        )
+        yield tool_log
+
+        if not tool_instance:
+            # Finish tool log with error
+            yield self._finish_log(
+                tool_log,
+                data={
+                    **tool_log.data,
+                    "error": f"Tool {tool_name} not found",
+                },
+            )
+            return f"Tool {tool_name} not found", []
+
+        # Ensure tool_args is a dict
+        tool_args_dict: dict[str, Any]
+        if isinstance(tool_args, str):
+            try:
+                tool_args_dict = json.loads(tool_args)
+            except json.JSONDecodeError:
+                tool_args_dict = {"input": tool_args}
+        elif not isinstance(tool_args, dict):
+            tool_args_dict = {"input": str(tool_args)}
+        else:
+            tool_args_dict = tool_args
+
+        # Invoke tool using base class method with error handling
+        try:
+            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args_dict, tool_name)
+
+            # Finish tool log
+            yield self._finish_log(
+                tool_log,
+                data={
+                    **tool_log.data,
+                    "output": response_content,
+                    "files": len(tool_files),
+                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
+                },
+            )
+
+            return response_content or "Tool executed successfully", tool_files
+        except Exception as e:
+            # Tool invocation failed, yield error log
+            error_message = str(e)
+            tool_log.status = AgentLog.LogStatus.ERROR
+            tool_log.error = error_message
+            tool_log.data = {
+                **tool_log.data,
+                "error": error_message,
+            }
+            yield tool_log
+
+            return f"Tool execution failed: {error_message}", []

api/core/agent/patterns/strategy_factory.py

@@ -0,0 +1,107 @@
+"""Strategy factory for creating agent strategies."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from core.agent.entities import AgentEntity, ExecutionContext
+from core.file.models import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.model_entities import ModelFeature
+
+from .base import AgentPattern, ToolInvokeHook
+from .function_call import FunctionCallStrategy
+from .react import ReActStrategy
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+
+class StrategyFactory:
+    """Factory for creating agent strategies based on model features."""
+
+    # Tool calling related features
+    TOOL_CALL_FEATURES = {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL}
+
+    @staticmethod
+    def create_strategy(
+        model_features: list[ModelFeature],
+        model_instance: ModelInstance,
+        context: ExecutionContext,
+        tools: list[Tool],
+        files: list[File],
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        agent_strategy: AgentEntity.Strategy | None = None,
+        tool_invoke_hook: ToolInvokeHook | None = None,
+        instruction: str = "",
+    ) -> AgentPattern:
+        """
+        Create an appropriate strategy based on model features.
+
+        Args:
+            model_features: List of model features/capabilities
+            model_instance: Model instance to use
+            context: Execution context containing trace/audit information
+            tools: Available tools
+            files: Available files
+            max_iterations: Maximum iterations for the strategy
+            workflow_call_depth: Depth of workflow calls
+            agent_strategy: Optional explicit strategy override
+            tool_invoke_hook: Optional hook for custom tool invocation (e.g., agent_invoke)
+            instruction: Optional instruction for ReAct strategy
+
+        Returns:
+            AgentStrategy instance
+        """
+        # If explicit strategy is provided and it's Function Calling, try to use it if supported
+        if agent_strategy == AgentEntity.Strategy.FUNCTION_CALLING:
+            if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
+                return FunctionCallStrategy(
+                    model_instance=model_instance,
+                    context=context,
+                    tools=tools,
+                    files=files,
+                    max_iterations=max_iterations,
+                    workflow_call_depth=workflow_call_depth,
+                    tool_invoke_hook=tool_invoke_hook,
+                )
+            # Fallback to ReAct if FC is requested but not supported
+
+        # If explicit strategy is Chain of Thought (ReAct)
+        if agent_strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
+            return ReActStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+                instruction=instruction,
+            )
+
+        # Default auto-selection logic
+        if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
+            # Model supports native function calling
+            return FunctionCallStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+            )
+        else:
+            # Use ReAct strategy for models without function calling
+            return ReActStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+                instruction=instruction,
+            )

api/core/app/apps/advanced_chat/app_generator.py

@@ -24,11 +24,13 @@ from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
 from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
+from core.app.layers.sandbox_layer import SandboxLayer
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
 from core.repositories import DifyCoreRepositoryFactory
+from core.sandbox import Sandbox, SandboxManager
 from core.workflow.repositories.draft_variable_repository import (
     DraftVariableSaverFactory,
 )
@@ -40,7 +42,9 @@ from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, Conversation, EndUser, Message, Workflow, WorkflowNodeExecutionTriggeredFrom
 from models.enums import WorkflowRunTriggeredFrom
+from models.workflow_features import WorkflowFeatures
 from services.conversation_service import ConversationService
+from services.sandbox.sandbox_provider_service import SandboxProviderService
 from services.workflow_draft_variable_service import (
     DraftVarLoader,
     WorkflowDraftVariableService,
@@ -512,6 +516,31 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 if workflow is None:
                     raise ValueError("Workflow not found")
 
+                sandbox: Sandbox | None = None
+                graph_engine_layers: tuple = ()
+                if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
+                    sandbox_provider = SandboxProviderService.get_sandbox_provider(
+                        application_generate_entity.app_config.tenant_id
+                    )
+                    if workflow.version == Workflow.VERSION_DRAFT:
+                        sandbox = SandboxManager.create_draft(
+                            tenant_id=application_generate_entity.app_config.tenant_id,
+                            app_id=application_generate_entity.app_config.app_id,
+                            user_id=application_generate_entity.user_id,
+                            sandbox_provider=sandbox_provider,
+                        )
+                    else:
+                        if application_generate_entity.workflow_run_id is None:
+                            raise ValueError("workflow_run_id is required when sandbox is enabled")
+                        sandbox = SandboxManager.create(
+                            tenant_id=application_generate_entity.app_config.tenant_id,
+                            app_id=application_generate_entity.app_config.app_id,
+                            user_id=application_generate_entity.user_id,
+                            workflow_execution_id=application_generate_entity.workflow_run_id,
+                            sandbox_provider=sandbox_provider,
+                        )
+                    graph_engine_layers = (SandboxLayer(sandbox=sandbox),)
+
                 # Determine system_user_id based on invocation source
                 is_external_api_call = application_generate_entity.invoke_from in {
                     InvokeFrom.WEB_APP,
@@ -542,6 +571,8 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 app=app,
                 workflow_execution_repository=workflow_execution_repository,
                 workflow_node_execution_repository=workflow_node_execution_repository,
+                graph_engine_layers=graph_engine_layers,
+                sandbox=sandbox,
             )
 
             try:

api/core/app/apps/advanced_chat/app_runner.py

@@ -24,6 +24,7 @@ from core.app.layers.conversation_variable_persist_layer import ConversationVari
 from core.db.session_factory import session_factory
 from core.moderation.base import ModerationError
 from core.moderation.input_moderation import InputModeration
+from core.sandbox import Sandbox
 from core.variables.variables import Variable
 from core.workflow.enums import WorkflowType
 from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
@@ -66,6 +67,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
+        sandbox: Sandbox | None = None,
     ):
         super().__init__(
             queue_manager=queue_manager,
@@ -82,6 +84,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         self._app = app
         self._workflow_execution_repository = workflow_execution_repository
         self._workflow_node_execution_repository = workflow_node_execution_repository
+        self._sandbox = sandbox
 
     @trace_span(WorkflowAppRunnerHandler)
     def run(self):
@@ -156,6 +159,10 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
 
             # init graph
             graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.time())
+
+            if self._sandbox:
+                graph_runtime_state.set_sandbox(self._sandbox)
+
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,
                 graph_runtime_state=graph_runtime_state,

api/core/app/apps/advanced_chat/generate_response_converter.py

@@ -82,7 +82,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -110,7 +110,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -120,6 +120,6 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -4,6 +4,7 @@ import re
 import time
 from collections.abc import Callable, Generator, Mapping
 from contextlib import contextmanager
+from dataclasses import dataclass, field
 from threading import Thread
 from typing import Any, Union
 
@@ -19,6 +20,7 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
 )
 from core.app.entities.queue_entities import (
+    ChunkType,
     MessageQueueMessage,
     QueueAdvancedChatMessageEndEvent,
     QueueAgentLogEvent,
@@ -70,13 +72,131 @@ from core.workflow.runtime import GraphRuntimeState
 from core.workflow.system_variable import SystemVariable
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models import Account, Conversation, EndUser, Message, MessageFile
+from models import Account, Conversation, EndUser, LLMGenerationDetail, Message, MessageFile
 from models.enums import CreatorUserRole
 from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
 
+@dataclass
+class StreamEventBuffer:
+    """
+    Buffer for recording stream events in order to reconstruct the generation sequence.
+    Records the exact order of text chunks, thoughts, and tool calls as they stream.
+    """
+
+    # Accumulated reasoning content (each thought block is a separate element)
+    reasoning_content: list[str] = field(default_factory=list)
+    # Current reasoning buffer (accumulates until we see a different event type)
+    _current_reasoning: str = ""
+    # Tool calls with their details
+    tool_calls: list[dict] = field(default_factory=list)
+    # Tool call ID to index mapping for updating results
+    _tool_call_id_map: dict[str, int] = field(default_factory=dict)
+    # Sequence of events in stream order
+    sequence: list[dict] = field(default_factory=list)
+    # Current position in answer text
+    _content_position: int = 0
+    # Track last event type to detect transitions
+    _last_event_type: str | None = None
+
+    def _flush_current_reasoning(self) -> None:
+        """Flush accumulated reasoning to the list and add to sequence."""
+        if self._current_reasoning.strip():
+            self.reasoning_content.append(self._current_reasoning.strip())
+            self.sequence.append({"type": "reasoning", "index": len(self.reasoning_content) - 1})
+            self._current_reasoning = ""
+
+    def record_text_chunk(self, text: str) -> None:
+        """Record a text chunk event."""
+        if not text:
+            return
+
+        # Flush any pending reasoning first
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+        text_len = len(text)
+        start_pos = self._content_position
+
+        # If last event was also content, extend it; otherwise create new
+        if self.sequence and self.sequence[-1].get("type") == "content":
+            self.sequence[-1]["end"] = start_pos + text_len
+        else:
+            self.sequence.append({"type": "content", "start": start_pos, "end": start_pos + text_len})
+
+        self._content_position += text_len
+        self._last_event_type = "content"
+
+    def record_thought_chunk(self, text: str) -> None:
+        """Record a thought/reasoning chunk event."""
+        if not text:
+            return
+
+        # Accumulate thought content
+        self._current_reasoning += text
+        self._last_event_type = "thought"
+
+    def record_tool_call(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        tool_arguments: str,
+        tool_icon: str | dict | None = None,
+        tool_icon_dark: str | dict | None = None,
+    ) -> None:
+        """Record a tool call event."""
+        if not tool_call_id:
+            return
+
+        # Flush any pending reasoning first
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+        # Check if this tool call already exists (we might get multiple chunks)
+        if tool_call_id in self._tool_call_id_map:
+            idx = self._tool_call_id_map[tool_call_id]
+            # Update arguments if provided
+            if tool_arguments:
+                self.tool_calls[idx]["arguments"] = tool_arguments
+        else:
+            # New tool call
+            tool_call = {
+                "id": tool_call_id or "",
+                "name": tool_name or "",
+                "arguments": tool_arguments or "",
+                "result": "",
+                "elapsed_time": None,
+                "icon": tool_icon,
+                "icon_dark": tool_icon_dark,
+            }
+            self.tool_calls.append(tool_call)
+            idx = len(self.tool_calls) - 1
+            self._tool_call_id_map[tool_call_id] = idx
+            self.sequence.append({"type": "tool_call", "index": idx})
+
+        self._last_event_type = "tool_call"
+
+    def record_tool_result(self, tool_call_id: str, result: str, tool_elapsed_time: float | None = None) -> None:
+        """Record a tool result event (update existing tool call)."""
+        if not tool_call_id:
+            return
+        if tool_call_id in self._tool_call_id_map:
+            idx = self._tool_call_id_map[tool_call_id]
+            self.tool_calls[idx]["result"] = result
+            self.tool_calls[idx]["elapsed_time"] = tool_elapsed_time
+
+    def finalize(self) -> None:
+        """Finalize the buffer, flushing any pending data."""
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+    def has_data(self) -> bool:
+        """Check if there's any meaningful data recorded."""
+        return bool(self.reasoning_content or self.tool_calls or self.sequence)
+
+
 class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
     """
     AdvancedChatAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
@@ -144,6 +264,8 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         self._workflow_run_id: str = ""
         self._draft_var_saver_factory = draft_var_saver_factory
         self._graph_runtime_state: GraphRuntimeState | None = None
+        # Stream event buffer for recording generation sequence
+        self._stream_buffer = StreamEventBuffer()
         self._seed_graph_runtime_state_from_queue_manager()
 
     def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
@@ -383,7 +505,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         queue_message: Union[WorkflowQueueMessage, MessageQueueMessage] | None = None,
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
-        """Handle text chunk events."""
+        """Handle text chunk events and record to stream buffer for sequence reconstruction."""
         delta_text = event.text
         if delta_text is None:
             return
@@ -405,9 +527,53 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        self._task_state.answer += delta_text
+        tool_call = event.tool_call
+        tool_result = event.tool_result
+        tool_payload = tool_call or tool_result
+        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else ""
+        tool_name = tool_payload.name if tool_payload and tool_payload.name else ""
+        tool_arguments = tool_call.arguments if tool_call and tool_call.arguments else ""
+        tool_files = tool_result.files if tool_result else []
+        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
+        tool_icon = tool_payload.icon if tool_payload else None
+        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
+        # Record stream event based on chunk type
+        chunk_type = event.chunk_type or ChunkType.TEXT
+        match chunk_type:
+            case ChunkType.TEXT:
+                self._stream_buffer.record_text_chunk(delta_text)
+                self._task_state.answer += delta_text
+            case ChunkType.THOUGHT:
+                # Reasoning should not be part of final answer text
+                self._stream_buffer.record_thought_chunk(delta_text)
+            case ChunkType.TOOL_CALL:
+                self._stream_buffer.record_tool_call(
+                    tool_call_id=tool_call_id,
+                    tool_name=tool_name,
+                    tool_arguments=tool_arguments,
+                    tool_icon=tool_icon,
+                    tool_icon_dark=tool_icon_dark,
+                )
+            case ChunkType.TOOL_RESULT:
+                self._stream_buffer.record_tool_result(
+                    tool_call_id=tool_call_id,
+                    result=delta_text,
+                    tool_elapsed_time=tool_elapsed_time,
+                )
+            case _:
+                pass
         yield self._message_cycle_manager.message_to_stream_response(
-            answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
+            answer=delta_text,
+            message_id=self._message_id,
+            from_variable_selector=event.from_variable_selector,
+            chunk_type=event.chunk_type.value if event.chunk_type else None,
+            tool_call_id=tool_call_id or None,
+            tool_name=tool_name or None,
+            tool_arguments=tool_arguments or None,
+            tool_files=tool_files,
+            tool_elapsed_time=tool_elapsed_time,
+            tool_icon=tool_icon,
+            tool_icon_dark=tool_icon_dark,
         )
 
     def _handle_iteration_start_event(
@@ -775,6 +941,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
 
         # If there are assistant files, remove markdown image links from answer
         answer_text = self._task_state.answer
+        answer_text = self._strip_think_blocks(answer_text)
         if self._recorded_files:
             # Remove markdown image links since we're storing files separately
             answer_text = re.sub(r"!\[.*?\]\(.*?\)", "", answer_text).strip()
@@ -826,6 +993,54 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         ]
         session.add_all(message_files)
 
+        # Save generation detail (reasoning/tool calls/sequence) from stream buffer
+        self._save_generation_detail(session=session, message=message)
+
+    @staticmethod
+    def _strip_think_blocks(text: str) -> str:
+        """Remove <think>...</think> blocks (including their content) from text."""
+        if not text or "<think" not in text.lower():
+            return text
+
+        clean_text = re.sub(r"<think[^>]*>.*?</think>", "", text, flags=re.IGNORECASE | re.DOTALL)
+        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
+        return clean_text
+
+    def _save_generation_detail(self, *, session: Session, message: Message) -> None:
+        """
+        Save LLM generation detail for Chatflow using stream event buffer.
+        The buffer records the exact order of events as they streamed,
+        allowing accurate reconstruction of the generation sequence.
+        """
+        # Finalize the stream buffer to flush any pending data
+        self._stream_buffer.finalize()
+
+        # Only save if there's meaningful data
+        if not self._stream_buffer.has_data():
+            return
+
+        reasoning_content = self._stream_buffer.reasoning_content
+        tool_calls = self._stream_buffer.tool_calls
+        sequence = self._stream_buffer.sequence
+
+        # Check if generation detail already exists for this message
+        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
+
+        if existing:
+            existing.reasoning_content = json.dumps(reasoning_content) if reasoning_content else None
+            existing.tool_calls = json.dumps(tool_calls) if tool_calls else None
+            existing.sequence = json.dumps(sequence) if sequence else None
+        else:
+            generation_detail = LLMGenerationDetail(
+                tenant_id=self._application_generate_entity.app_config.tenant_id,
+                app_id=self._application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                reasoning_content=json.dumps(reasoning_content) if reasoning_content else None,
+                tool_calls=json.dumps(tool_calls) if tool_calls else None,
+                sequence=json.dumps(sequence) if sequence else None,
+            )
+            session.add(generation_detail)
+
     def _seed_graph_runtime_state_from_queue_manager(self) -> None:
         """Bootstrap the cached runtime state from the queue manager when present."""
         candidate = self._base_task_pipeline.queue_manager.graph_runtime_state

api/core/app/apps/agent_chat/app_runner.py

@@ -3,10 +3,8 @@ from typing import cast
 
 from sqlalchemy import select
 
-from core.agent.cot_chat_agent_runner import CotChatAgentRunner
-from core.agent.cot_completion_agent_runner import CotCompletionAgentRunner
+from core.agent.agent_app_runner import AgentAppRunner
 from core.agent.entities import AgentEntity
-from core.agent.fc_agent_runner import FunctionCallAgentRunner
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.apps.base_app_runner import AppRunner
@@ -14,8 +12,7 @@ from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity
 from core.app.entities.queue_entities import QueueAnnotationReplyEvent
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
-from core.model_runtime.entities.llm_entities import LLMMode
-from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
+from core.model_runtime.entities.model_entities import ModelFeature
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.moderation.base import ModerationError
 from extensions.ext_database import db
@@ -194,22 +191,7 @@ class AgentChatAppRunner(AppRunner):
             raise ValueError("Message not found")
         db.session.close()
 
-        runner_cls: type[FunctionCallAgentRunner] | type[CotChatAgentRunner] | type[CotCompletionAgentRunner]
-        # start agent runner
-        if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
-            # check LLM mode
-            if model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT:
-                runner_cls = CotChatAgentRunner
-            elif model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.COMPLETION:
-                runner_cls = CotCompletionAgentRunner
-            else:
-                raise ValueError(f"Invalid LLM mode: {model_schema.model_properties.get(ModelPropertyKey.MODE)}")
-        elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
-            runner_cls = FunctionCallAgentRunner
-        else:
-            raise ValueError(f"Invalid agent strategy: {agent_entity.strategy}")
-
-        runner = runner_cls(
+        runner = AgentAppRunner(
             tenant_id=app_config.tenant_id,
             application_generate_entity=application_generate_entity,
             conversation=conversation_result,

api/core/app/apps/agent_chat/generate_response_converter.py

@@ -81,7 +81,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

api/core/app/apps/chat/generate_response_converter.py

@@ -81,7 +81,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

api/core/app/apps/common/workflow_response_converter.py

@@ -70,6 +70,8 @@ class _NodeSnapshot:
     """Empty string means the node is not executing inside an iteration."""
     loop_id: str = ""
     """Empty string means the node is not executing inside a loop."""
+    mention_parent_id: str = ""
+    """Empty string means the node is not an extractor node."""
 
 
 class WorkflowResponseConverter:
@@ -131,6 +133,7 @@ class WorkflowResponseConverter:
             start_at=event.start_at,
             iteration_id=event.in_iteration_id or "",
             loop_id=event.in_loop_id or "",
+            mention_parent_id=event.in_mention_parent_id or "",
         )
         node_execution_id = NodeExecutionId(event.node_execution_id)
         self._node_snapshots[node_execution_id] = snapshot
@@ -287,6 +290,7 @@ class WorkflowResponseConverter:
                 created_at=int(snapshot.start_at.timestamp()),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                mention_parent_id=event.in_mention_parent_id,
                 agent_strategy=event.agent_strategy,
             ),
         )
@@ -373,6 +377,7 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                mention_parent_id=event.in_mention_parent_id,
             ),
         )
 
@@ -422,6 +427,7 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                mention_parent_id=event.in_mention_parent_id,
                 retry_index=event.retry_index,
             ),
         )
@@ -671,7 +677,7 @@ class WorkflowResponseConverter:
             task_id=task_id,
             data=AgentLogStreamResponse.Data(
                 node_execution_id=event.node_execution_id,
-                id=event.id,
+                message_id=event.id,
                 parent_id=event.parent_id,
                 label=event.label,
                 error=event.error,

api/core/app/apps/completion/generate_response_converter.py

@@ -79,7 +79,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -106,7 +106,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 if not isinstance(metadata, dict):
                     metadata = {}
@@ -116,6 +116,6 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

api/core/app/apps/pipeline/generate_response_converter.py

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(cast(dict, data))
             else:
-                response_chunk.update(sub_stream_response.model_dump())
+                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(cast(dict, sub_stream_response.to_ignore_detail_dict()))
             else:
-                response_chunk.update(sub_stream_response.model_dump())
+                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
             yield response_chunk

api/core/app/apps/workflow/app_generator.py

@@ -23,11 +23,13 @@ from core.app.apps.workflow.generate_response_converter import WorkflowAppGenera
 from core.app.apps.workflow.generate_task_pipeline import WorkflowAppGenerateTaskPipeline
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import WorkflowAppBlockingResponse, WorkflowAppStreamResponse
+from core.app.layers.sandbox_layer import SandboxLayer
 from core.db.session_factory import session_factory
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.repositories import DifyCoreRepositoryFactory
+from core.sandbox import Sandbox, SandboxManager
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
@@ -38,6 +40,8 @@ from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, EndUser, Workflow, WorkflowNodeExecutionTriggeredFrom
 from models.enums import WorkflowRunTriggeredFrom
+from models.workflow_features import WorkflowFeatures
+from services.sandbox.sandbox_provider_service import SandboxProviderService
 from services.workflow_draft_variable_service import DraftVarLoader, WorkflowDraftVariableService
 
 SKIP_PREPARE_USER_INPUTS_KEY = "_skip_prepare_user_inputs"
@@ -488,6 +492,31 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 if workflow is None:
                     raise ValueError("Workflow not found")
 
+                sandbox: Sandbox | None = None
+                if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
+                    sandbox_provider = SandboxProviderService.get_sandbox_provider(
+                        application_generate_entity.app_config.tenant_id
+                    )
+                    if workflow.version == Workflow.VERSION_DRAFT:
+                        sandbox = SandboxManager.create_draft(
+                            tenant_id=application_generate_entity.app_config.tenant_id,
+                            app_id=application_generate_entity.app_config.app_id,
+                            user_id=application_generate_entity.user_id,
+                            sandbox_provider=sandbox_provider,
+                        )
+                    else:
+                        sandbox = SandboxManager.create(
+                            tenant_id=application_generate_entity.app_config.tenant_id,
+                            app_id=application_generate_entity.app_config.app_id,
+                            user_id=application_generate_entity.user_id,
+                            workflow_execution_id=application_generate_entity.workflow_execution_id,
+                            sandbox_provider=sandbox_provider,
+                        )
+                    graph_engine_layers = (
+                        *graph_engine_layers,
+                        SandboxLayer(sandbox=sandbox),
+                    )
+
                 # Determine system_user_id based on invocation source
                 is_external_api_call = application_generate_entity.invoke_from in {
                     InvokeFrom.WEB_APP,
@@ -512,6 +541,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 workflow_node_execution_repository=workflow_node_execution_repository,
                 root_node_id=root_node_id,
                 graph_engine_layers=graph_engine_layers,
+                sandbox=sandbox,
             )
 
             try:

api/core/app/apps/workflow/app_runner.py

@@ -7,6 +7,7 @@ from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.apps.workflow.app_config_manager import WorkflowAppConfig
 from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
+from core.sandbox import Sandbox
 from core.workflow.enums import WorkflowType
 from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
@@ -42,6 +43,7 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
+        sandbox: Sandbox | None = None,
     ):
         super().__init__(
             queue_manager=queue_manager,
@@ -55,6 +57,7 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         self._root_node_id = root_node_id
         self._workflow_execution_repository = workflow_execution_repository
         self._workflow_node_execution_repository = workflow_node_execution_repository
+        self._sandbox = sandbox
 
     @trace_span(WorkflowAppRunnerHandler)
     def run(self):
@@ -99,6 +102,9 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
 
             graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
 
+            if self._sandbox:
+                graph_runtime_state.set_sandbox(self._sandbox)
+
             # init graph
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,

api/core/app/apps/workflow/generate_response_converter.py

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -13,6 +13,7 @@ from core.app.apps.common.workflow_response_converter import WorkflowResponseCon
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.queue_entities import (
     AppQueueEvent,
+    ChunkType,
     MessageQueueMessage,
     QueueAgentLogEvent,
     QueueErrorEvent,
@@ -483,11 +484,33 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if delta_text is None:
             return
 
+        tool_call = event.tool_call
+        tool_result = event.tool_result
+        tool_payload = tool_call or tool_result
+        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else None
+        tool_name = tool_payload.name if tool_payload and tool_payload.name else None
+        tool_arguments = tool_call.arguments if tool_call else None
+        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
+        tool_files = tool_result.files if tool_result else []
+        tool_icon = tool_payload.icon if tool_payload else None
+        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
+
         # only publish tts message at text chunk streaming
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        yield self._text_chunk_to_stream_response(delta_text, from_variable_selector=event.from_variable_selector)
+        yield self._text_chunk_to_stream_response(
+            text=delta_text,
+            from_variable_selector=event.from_variable_selector,
+            chunk_type=event.chunk_type,
+            tool_call_id=tool_call_id,
+            tool_name=tool_name,
+            tool_arguments=tool_arguments,
+            tool_files=tool_files,
+            tool_elapsed_time=tool_elapsed_time,
+            tool_icon=tool_icon,
+            tool_icon_dark=tool_icon_dark,
+        )
 
     def _handle_agent_log_event(self, event: QueueAgentLogEvent, **kwargs) -> Generator[StreamResponse, None, None]:
         """Handle agent log events."""
@@ -650,16 +673,61 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         session.add(workflow_app_log)
 
     def _text_chunk_to_stream_response(
-        self, text: str, from_variable_selector: list[str] | None = None
+        self,
+        text: str,
+        from_variable_selector: list[str] | None = None,
+        chunk_type: ChunkType | None = None,
+        tool_call_id: str | None = None,
+        tool_name: str | None = None,
+        tool_arguments: str | None = None,
+        tool_files: list[str] | None = None,
+        tool_error: str | None = None,
+        tool_elapsed_time: float | None = None,
+        tool_icon: str | dict | None = None,
+        tool_icon_dark: str | dict | None = None,
     ) -> TextChunkStreamResponse:
         """
         Handle completed event.
         :param text: text
         :return:
         """
+        from core.app.entities.task_entities import ChunkType as ResponseChunkType
+
+        response_chunk_type = ResponseChunkType(chunk_type.value) if chunk_type else ResponseChunkType.TEXT
+
+        data = TextChunkStreamResponse.Data(
+            text=text,
+            from_variable_selector=from_variable_selector,
+            chunk_type=response_chunk_type,
+        )
+
+        if response_chunk_type == ResponseChunkType.TOOL_CALL:
+            data = data.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+        elif response_chunk_type == ResponseChunkType.TOOL_RESULT:
+            data = data.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_files": tool_files,
+                    "tool_error": tool_error,
+                    "tool_elapsed_time": tool_elapsed_time,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+
         response = TextChunkStreamResponse(
             task_id=self._application_generate_entity.task_id,
-            data=TextChunkStreamResponse.Data(text=text, from_variable_selector=from_variable_selector),
+            data=data,
         )
 
         return response

api/core/app/apps/workflow_app_runner.py

@@ -385,6 +385,7 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                     inputs=inputs,
                     process_data=process_data,
                     outputs=outputs,
@@ -405,6 +406,7 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                     agent_strategy=event.agent_strategy,
                     provider_type=event.provider_type,
                     provider_id=event.provider_id,
@@ -428,6 +430,7 @@ class WorkflowBasedAppRunner:
                     execution_metadata=execution_metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunFailedEvent):
@@ -444,6 +447,7 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunExceptionEvent):
@@ -460,15 +464,25 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunStreamChunkEvent):
+            from core.app.entities.queue_entities import ChunkType as QueueChunkType
+
+            if event.is_final and not event.chunk:
+                return
+
             self._publish_event(
                 QueueTextChunkEvent(
                     text=event.chunk,
                     from_variable_selector=list(event.selector),
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    chunk_type=QueueChunkType(event.chunk_type.value),
+                    tool_call=event.tool_call,
+                    tool_result=event.tool_result,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunRetrieverResourceEvent):
@@ -477,6 +491,7 @@ class WorkflowBasedAppRunner:
                     retriever_resources=event.retriever_resources,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunAgentLogEvent):

api/core/app/entities/app_asset_entities.py

@@ -0,0 +1,294 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Generator
+from enum import StrEnum
+
+from pydantic import BaseModel, Field
+
+
+class AssetNodeType(StrEnum):
+    FILE = "file"
+    FOLDER = "folder"
+
+
+class AppAssetNode(BaseModel):
+    id: str = Field(description="Unique identifier for the node")
+    node_type: AssetNodeType = Field(description="Type of node: file or folder")
+    name: str = Field(description="Name of the file or folder")
+    parent_id: str | None = Field(default=None, description="Parent folder ID, None for root level")
+    order: int = Field(default=0, description="Sort order within parent folder, lower values first")
+    extension: str = Field(default="", description="File extension without dot, empty for folders")
+    size: int = Field(default=0, description="File size in bytes, 0 for folders")
+    checksum: str = Field(default="", description="SHA-256 checksum of file content, empty for folders")
+
+    @classmethod
+    def create_folder(cls, node_id: str, name: str, parent_id: str | None = None) -> AppAssetNode:
+        return cls(id=node_id, node_type=AssetNodeType.FOLDER, name=name, parent_id=parent_id)
+
+    @classmethod
+    def create_file(
+        cls, node_id: str, name: str, parent_id: str | None = None, size: int = 0, checksum: str = ""
+    ) -> AppAssetNode:
+        return cls(
+            id=node_id,
+            node_type=AssetNodeType.FILE,
+            name=name,
+            parent_id=parent_id,
+            extension=name.rsplit(".", 1)[-1] if "." in name else "",
+            size=size,
+            checksum=checksum,
+        )
+
+
+class AppAssetNodeView(BaseModel):
+    id: str = Field(description="Unique identifier for the node")
+    node_type: str = Field(description="Type of node: 'file' or 'folder'")
+    name: str = Field(description="Name of the file or folder")
+    path: str = Field(description="Full path from root, e.g. '/folder/file.txt'")
+    extension: str = Field(default="", description="File extension without dot")
+    size: int = Field(default=0, description="File size in bytes")
+    checksum: str = Field(default="", description="SHA-256 checksum of file content")
+    children: list[AppAssetNodeView] = Field(default_factory=list, description="Child nodes for folders")
+
+
+class TreeNodeNotFoundError(Exception):
+    """Tree internal: node not found"""
+
+    pass
+
+
+class TreeParentNotFoundError(Exception):
+    """Tree internal: parent folder not found"""
+
+    pass
+
+
+class TreePathConflictError(Exception):
+    """Tree internal: path already exists"""
+
+    pass
+
+
+class AppAssetFileTree(BaseModel):
+    """
+    File tree structure for app assets using adjacency list pattern.
+
+    Design:
+    - Storage: Flat list with parent_id references (adjacency list)
+    - Path: Computed dynamically via get_path(), not stored
+    - Order: Integer field for user-defined sorting within each folder
+    - API response: transform() builds nested tree with computed paths
+
+    Why adjacency list over nested tree or materialized path:
+    - Simpler CRUD: move/rename only updates one node's parent_id
+    - No path cascade: renaming parent doesn't require updating all descendants
+    - JSON-friendly: flat list serializes cleanly to database JSON column
+    - Trade-off: path lookup is O(depth), acceptable for typical file trees
+    """
+
+    nodes: list[AppAssetNode] = Field(default_factory=list, description="Flat list of all nodes in the tree")
+
+    def get(self, node_id: str) -> AppAssetNode | None:
+        return next((n for n in self.nodes if n.id == node_id), None)
+
+    def get_children(self, parent_id: str | None) -> list[AppAssetNode]:
+        return [n for n in self.nodes if n.parent_id == parent_id]
+
+    def has_child_named(self, parent_id: str | None, name: str) -> bool:
+        return any(n.name == name and n.parent_id == parent_id for n in self.nodes)
+
+    def get_path(self, node_id: str) -> str:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+        parts: list[str] = []
+        current: AppAssetNode | None = node
+        while current:
+            parts.append(current.name)
+            current = self.get(current.parent_id) if current.parent_id else None
+        return "/" + "/".join(reversed(parts))
+
+    def relative_path(self, a: AppAssetNode, b: AppAssetNode) -> str:
+        """
+        Calculate relative path from node a to node b for Markdown references.
+        Path is computed from a's parent directory (where the file resides).
+
+        Examples:
+            /foo/a.md -> /foo/b.md         => ./b.md
+            /foo/a.md -> /foo/sub/b.md     => ./sub/b.md
+            /foo/sub/a.md -> /foo/b.md     => ../b.md
+            /foo/sub/deep/a.md -> /foo/b.md => ../../b.md
+        """
+
+        def get_ancestor_ids(node_id: str | None) -> list[str]:
+            chain: list[str] = []
+            current_id = node_id
+            while current_id:
+                chain.append(current_id)
+                node = self.get(current_id)
+                current_id = node.parent_id if node else None
+            return chain
+
+        a_dir_ancestors = get_ancestor_ids(a.parent_id)
+        b_ancestors = [b.id] + get_ancestor_ids(b.parent_id)
+        a_dir_set = set(a_dir_ancestors)
+
+        lca_id: str | None = None
+        lca_index_in_b = -1
+        for idx, ancestor_id in enumerate(b_ancestors):
+            if ancestor_id in a_dir_set or (a.parent_id is None and b_ancestors[idx:] == []):
+                lca_id = ancestor_id
+                lca_index_in_b = idx
+                break
+
+        if a.parent_id is None:
+            steps_up = 0
+            lca_index_in_b = len(b_ancestors)
+        elif lca_id is None:
+            steps_up = len(a_dir_ancestors)
+            lca_index_in_b = len(b_ancestors)
+        else:
+            steps_up = 0
+            for ancestor_id in a_dir_ancestors:
+                if ancestor_id == lca_id:
+                    break
+                steps_up += 1
+
+        path_down: list[str] = []
+        for i in range(lca_index_in_b - 1, -1, -1):
+            node = self.get(b_ancestors[i])
+            if node:
+                path_down.append(node.name)
+
+        if steps_up == 0:
+            return "./" + "/".join(path_down)
+
+        parts: list[str] = [".."] * steps_up + path_down
+        return "/".join(parts)
+
+    def get_descendant_ids(self, node_id: str) -> list[str]:
+        result: list[str] = []
+        stack = [node_id]
+        while stack:
+            current_id = stack.pop()
+            for child in self.nodes:
+                if child.parent_id == current_id:
+                    result.append(child.id)
+                    stack.append(child.id)
+        return result
+
+    def add(self, node: AppAssetNode) -> AppAssetNode:
+        if self.get(node.id):
+            raise TreePathConflictError(node.id)
+        if self.has_child_named(node.parent_id, node.name):
+            raise TreePathConflictError(node.name)
+        if node.parent_id:
+            parent = self.get(node.parent_id)
+            if not parent or parent.node_type != AssetNodeType.FOLDER:
+                raise TreeParentNotFoundError(node.parent_id)
+        siblings = self.get_children(node.parent_id)
+        node.order = max((s.order for s in siblings), default=-1) + 1
+        self.nodes.append(node)
+        return node
+
+    def update(self, node_id: str, size: int, checksum: str) -> AppAssetNode:
+        node = self.get(node_id)
+        if not node or node.node_type != AssetNodeType.FILE:
+            raise TreeNodeNotFoundError(node_id)
+        node.size = size
+        node.checksum = checksum
+        return node
+
+    def rename(self, node_id: str, new_name: str) -> AppAssetNode:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+        if node.name != new_name and self.has_child_named(node.parent_id, new_name):
+            raise TreePathConflictError(new_name)
+        node.name = new_name
+        if node.node_type == AssetNodeType.FILE:
+            node.extension = new_name.rsplit(".", 1)[-1] if "." in new_name else ""
+        return node
+
+    def move(self, node_id: str, new_parent_id: str | None) -> AppAssetNode:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+        if new_parent_id:
+            parent = self.get(new_parent_id)
+            if not parent or parent.node_type != AssetNodeType.FOLDER:
+                raise TreeParentNotFoundError(new_parent_id)
+        if self.has_child_named(new_parent_id, node.name):
+            raise TreePathConflictError(node.name)
+        node.parent_id = new_parent_id
+        siblings = self.get_children(new_parent_id)
+        node.order = max((s.order for s in siblings if s.id != node_id), default=-1) + 1
+        return node
+
+    def reorder(self, node_id: str, after_node_id: str | None) -> AppAssetNode:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+
+        siblings = sorted(self.get_children(node.parent_id), key=lambda x: x.order)
+        siblings = [s for s in siblings if s.id != node_id]
+
+        if after_node_id is None:
+            insert_idx = 0
+        else:
+            after_node = self.get(after_node_id)
+            if not after_node or after_node.parent_id != node.parent_id:
+                raise TreeNodeNotFoundError(after_node_id)
+            insert_idx = next((i for i, s in enumerate(siblings) if s.id == after_node_id), -1) + 1
+
+        siblings.insert(insert_idx, node)
+        for idx, sibling in enumerate(siblings):
+            sibling.order = idx
+
+        return node
+
+    def remove(self, node_id: str) -> list[str]:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+        ids_to_remove = [node_id] + self.get_descendant_ids(node_id)
+        self.nodes = [n for n in self.nodes if n.id not in ids_to_remove]
+        return ids_to_remove
+
+    def walk_files(self) -> Generator[AppAssetNode, None, None]:
+        return (n for n in self.nodes if n.node_type == AssetNodeType.FILE)
+
+    def transform(self) -> list[AppAssetNodeView]:
+        by_parent: dict[str | None, list[AppAssetNode]] = defaultdict(list)
+        for n in self.nodes:
+            by_parent[n.parent_id].append(n)
+
+        for children in by_parent.values():
+            children.sort(key=lambda x: x.order)
+
+        paths: dict[str, str] = {}
+        tree_views: dict[str, AppAssetNodeView] = {}
+
+        def build_view(node: AppAssetNode, parent_path: str) -> None:
+            path = f"{parent_path}/{node.name}"
+            paths[node.id] = path
+            child_views: list[AppAssetNodeView] = []
+            for child in by_parent.get(node.id, []):
+                build_view(child, path)
+                child_views.append(tree_views[child.id])
+            tree_views[node.id] = AppAssetNodeView(
+                id=node.id,
+                node_type=node.node_type.value,
+                name=node.name,
+                path=path,
+                extension=node.extension,
+                size=node.size,
+                checksum=node.checksum,
+                children=child_views,
+            )
+
+        for root_node in by_parent.get(None, []):
+            build_view(root_node, "")
+
+        return [tree_views[n.id] for n in by_parent.get(None, [])]

api/core/app/entities/app_invoke_entities.py

@@ -36,6 +36,9 @@ class InvokeFrom(StrEnum):
     # this is used for plugin trigger and webhook trigger.
     TRIGGER = "trigger"
 
+    # AGENT indicates that this invocation is from an agent.
+    AGENT = "agent"
+
     # EXPLORE indicates that this invocation is from
     # the workflow (or chatflow) explore page.
     EXPLORE = "explore"

api/core/app/entities/llm_generation_entities.py

@@ -0,0 +1,72 @@
+"""
+LLM Generation Detail entities.
+
+Defines the structure for storing and transmitting LLM generation details
+including reasoning content, tool calls, and their sequence.
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class ContentSegment(BaseModel):
+    """Represents a content segment in the generation sequence."""
+
+    type: Literal["content"] = "content"
+    start: int = Field(..., description="Start position in the text")
+    end: int = Field(..., description="End position in the text")
+
+
+class ReasoningSegment(BaseModel):
+    """Represents a reasoning segment in the generation sequence."""
+
+    type: Literal["reasoning"] = "reasoning"
+    index: int = Field(..., description="Index into reasoning_content array")
+
+
+class ToolCallSegment(BaseModel):
+    """Represents a tool call segment in the generation sequence."""
+
+    type: Literal["tool_call"] = "tool_call"
+    index: int = Field(..., description="Index into tool_calls array")
+
+
+SequenceSegment = ContentSegment | ReasoningSegment | ToolCallSegment
+
+
+class ToolCallDetail(BaseModel):
+    """Represents a tool call with its arguments and result."""
+
+    id: str = Field(default="", description="Unique identifier for the tool call")
+    name: str = Field(..., description="Name of the tool")
+    arguments: str = Field(default="", description="JSON string of tool arguments")
+    result: str = Field(default="", description="Result from the tool execution")
+    elapsed_time: float | None = Field(default=None, description="Elapsed time in seconds")
+    icon: str | dict | None = Field(default=None, description="Icon of the tool")
+    icon_dark: str | dict | None = Field(default=None, description="Dark theme icon of the tool")
+
+
+class LLMGenerationDetailData(BaseModel):
+    """
+    Domain model for LLM generation detail.
+
+    Contains the structured data for reasoning content, tool calls,
+    and their display sequence.
+    """
+
+    reasoning_content: list[str] = Field(default_factory=list, description="List of reasoning segments")
+    tool_calls: list[ToolCallDetail] = Field(default_factory=list, description="List of tool call details")
+    sequence: list[SequenceSegment] = Field(default_factory=list, description="Display order of segments")
+
+    def is_empty(self) -> bool:
+        """Check if there's any meaningful generation detail."""
+        return not self.reasoning_content and not self.tool_calls
+
+    def to_response_dict(self) -> dict:
+        """Convert to dictionary for API response."""
+        return {
+            "reasoning_content": self.reasoning_content,
+            "tool_calls": [tc.model_dump() for tc in self.tool_calls],
+            "sequence": [seg.model_dump() for seg in self.sequence],
+        }

api/core/app/entities/queue_entities.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities import AgentNodeStrategyInit
+from core.workflow.entities import AgentNodeStrategyInit, ToolCall, ToolResult
 from core.workflow.enums import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes import NodeType
 
@@ -177,6 +177,17 @@ class QueueLoopCompletedEvent(AppQueueEvent):
     error: str | None = None
 
 
+class ChunkType(StrEnum):
+    """Stream chunk type for LLM-related events."""
+
+    TEXT = "text"  # Normal text streaming
+    TOOL_CALL = "tool_call"  # Tool call arguments streaming
+    TOOL_RESULT = "tool_result"  # Tool execution result
+    THOUGHT = "thought"  # Agent thinking process (ReAct)
+    THOUGHT_START = "thought_start"  # Agent thought start
+    THOUGHT_END = "thought_end"  # Agent thought end
+
+
 class QueueTextChunkEvent(AppQueueEvent):
     """
     QueueTextChunkEvent entity
@@ -190,6 +201,18 @@ class QueueTextChunkEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
+
+    # Extended fields for Agent/Tool streaming
+    chunk_type: ChunkType = ChunkType.TEXT
+    """type of the chunk"""
+
+    # Tool streaming payloads
+    tool_call: ToolCall | None = None
+    """structured tool call info"""
+    tool_result: ToolResult | None = None
+    """structured tool result info"""
 
 
 class QueueAgentMessageEvent(AppQueueEvent):
@@ -229,6 +252,8 @@ class QueueRetrieverResourcesEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
 
 
 class QueueAnnotationReplyEvent(AppQueueEvent):
@@ -306,6 +331,8 @@ class QueueNodeStartedEvent(AppQueueEvent):
     node_run_index: int = 1  # FIXME(-LAN-): may not used
     in_iteration_id: str | None = None
     in_loop_id: str | None = None
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
     agent_strategy: AgentNodeStrategyInit | None = None
 
@@ -328,6 +355,8 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -383,6 +412,8 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -407,6 +438,8 @@ class QueueNodeFailedEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)

api/core/app/entities/task_entities.py

@@ -113,6 +113,38 @@ class MessageStreamResponse(StreamResponse):
     answer: str
     from_variable_selector: list[str] | None = None
 
+    # Extended fields for Agent/Tool streaming (imported at runtime to avoid circular import)
+    chunk_type: str | None = None
+    """type of the chunk: text, tool_call, tool_result, thought"""
+
+    # Tool call fields (when chunk_type == "tool_call")
+    tool_call_id: str | None = None
+    """unique identifier for this tool call"""
+    tool_name: str | None = None
+    """name of the tool being called"""
+    tool_arguments: str | None = None
+    """accumulated tool arguments JSON"""
+
+    # Tool result fields (when chunk_type == "tool_result")
+    tool_files: list[str] | None = None
+    """file IDs produced by tool"""
+    tool_error: str | None = None
+    """error message if tool failed"""
+    tool_elapsed_time: float | None = None
+    """elapsed time spent executing the tool"""
+    tool_icon: str | dict | None = None
+    """icon of the tool"""
+    tool_icon_dark: str | dict | None = None
+    """dark theme icon of the tool"""
+
+    def model_dump(self, *args, **kwargs) -> dict[str, object]:
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump(*args, **kwargs)
+
+    def model_dump_json(self, *args, **kwargs) -> str:
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump_json(*args, **kwargs)
+
 
 class MessageAudioStreamResponse(StreamResponse):
     """
@@ -262,6 +294,7 @@ class NodeStartStreamResponse(StreamResponse):
         extras: dict[str, object] = Field(default_factory=dict)
         iteration_id: str | None = None
         loop_id: str | None = None
+        mention_parent_id: str | None = None
         agent_strategy: AgentNodeStrategyInit | None = None
 
     event: StreamEvent = StreamEvent.NODE_STARTED
@@ -285,6 +318,7 @@ class NodeStartStreamResponse(StreamResponse):
                 "extras": {},
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "mention_parent_id": self.data.mention_parent_id,
             },
         }
 
@@ -320,6 +354,7 @@ class NodeFinishStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
+        mention_parent_id: str | None = None
 
     event: StreamEvent = StreamEvent.NODE_FINISHED
     workflow_run_id: str
@@ -349,6 +384,7 @@ class NodeFinishStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "mention_parent_id": self.data.mention_parent_id,
             },
         }
 
@@ -384,6 +420,7 @@ class NodeRetryStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
+        mention_parent_id: str | None = None
         retry_index: int = 0
 
     event: StreamEvent = StreamEvent.NODE_RETRY
@@ -414,6 +451,7 @@ class NodeRetryStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "mention_parent_id": self.data.mention_parent_id,
                 "retry_index": self.data.retry_index,
             },
         }
@@ -582,6 +620,17 @@ class LoopNodeCompletedStreamResponse(StreamResponse):
     data: Data
 
 
+class ChunkType(StrEnum):
+    """Stream chunk type for LLM-related events."""
+
+    TEXT = "text"  # Normal text streaming
+    TOOL_CALL = "tool_call"  # Tool call arguments streaming
+    TOOL_RESULT = "tool_result"  # Tool execution result
+    THOUGHT = "thought"  # Agent thinking process (ReAct)
+    THOUGHT_START = "thought_start"  # Agent thought start
+    THOUGHT_END = "thought_end"  # Agent thought end
+
+
 class TextChunkStreamResponse(StreamResponse):
     """
     TextChunkStreamResponse entity
@@ -595,6 +644,36 @@ class TextChunkStreamResponse(StreamResponse):
         text: str
         from_variable_selector: list[str] | None = None
 
+        # Extended fields for Agent/Tool streaming
+        chunk_type: ChunkType = ChunkType.TEXT
+        """type of the chunk"""
+
+        # Tool call fields (when chunk_type == TOOL_CALL)
+        tool_call_id: str | None = None
+        """unique identifier for this tool call"""
+        tool_name: str | None = None
+        """name of the tool being called"""
+        tool_arguments: str | None = None
+        """accumulated tool arguments JSON"""
+
+        # Tool result fields (when chunk_type == TOOL_RESULT)
+        tool_files: list[str] | None = None
+        """file IDs produced by tool"""
+        tool_error: str | None = None
+        """error message if tool failed"""
+
+        # Tool elapsed time fields (when chunk_type == TOOL_RESULT)
+        tool_elapsed_time: float | None = None
+        """elapsed time spent executing the tool"""
+
+        def model_dump(self, *args, **kwargs) -> dict[str, object]:
+            kwargs.setdefault("exclude_none", True)
+            return super().model_dump(*args, **kwargs)
+
+        def model_dump_json(self, *args, **kwargs) -> str:
+            kwargs.setdefault("exclude_none", True)
+            return super().model_dump_json(*args, **kwargs)
+
     event: StreamEvent = StreamEvent.TEXT_CHUNK
     data: Data
 
@@ -743,7 +822,7 @@ class AgentLogStreamResponse(StreamResponse):
         """
 
         node_execution_id: str
-        id: str
+        message_id: str
         label: str
         parent_id: str | None = None
         error: str | None = None

api/core/app/layers/sandbox_layer.py

@@ -0,0 +1,22 @@
+import logging
+
+from core.sandbox import Sandbox
+from core.workflow.graph_engine.layers.base import GraphEngineLayer
+from core.workflow.graph_events.base import GraphEngineEvent
+
+logger = logging.getLogger(__name__)
+
+
+class SandboxLayer(GraphEngineLayer):
+    def __init__(self, sandbox: Sandbox) -> None:
+        super().__init__()
+        self._sandbox = sandbox
+
+    def on_graph_start(self) -> None:
+        pass
+
+    def on_event(self, event: GraphEngineEvent) -> None:
+        pass
+
+    def on_graph_end(self, error: Exception | None) -> None:
+        self._sandbox.release()

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -1,4 +1,5 @@
 import logging
+import re
 import time
 from collections.abc import Generator
 from threading import Thread
@@ -58,7 +59,7 @@ from core.prompt.utils.prompt_template_parser import PromptTemplateParser
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models.model import AppMode, Conversation, Message, MessageAgentThought
+from models.model import AppMode, Conversation, LLMGenerationDetail, Message, MessageAgentThought
 
 logger = logging.getLogger(__name__)
 
@@ -68,6 +69,8 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
     EasyUIBasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
+    _THINK_PATTERN = re.compile(r"<think[^>]*>(.*?)</think>", re.IGNORECASE | re.DOTALL)
+
     _task_state: EasyUITaskState
     _application_generate_entity: Union[ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity]
 
@@ -409,11 +412,136 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
                 )
             )
 
+        # Save LLM generation detail if there's reasoning_content
+        self._save_generation_detail(session=session, message=message, llm_result=llm_result)
+
         message_was_created.send(
             message,
             application_generate_entity=self._application_generate_entity,
         )
 
+    def _save_generation_detail(self, *, session: Session, message: Message, llm_result: LLMResult) -> None:
+        """
+        Save LLM generation detail for Completion/Chat/Agent-Chat applications.
+        For Agent-Chat, also merges MessageAgentThought records.
+        """
+        import json
+
+        reasoning_list: list[str] = []
+        tool_calls_list: list[dict] = []
+        sequence: list[dict] = []
+        answer = message.answer or ""
+
+        # Check if this is Agent-Chat mode by looking for agent thoughts
+        agent_thoughts = (
+            session.query(MessageAgentThought)
+            .filter_by(message_id=message.id)
+            .order_by(MessageAgentThought.position.asc())
+            .all()
+        )
+
+        if agent_thoughts:
+            # Agent-Chat mode: merge MessageAgentThought records
+            content_pos = 0
+            cleaned_answer_parts: list[str] = []
+            for thought in agent_thoughts:
+                # Add thought/reasoning
+                if thought.thought:
+                    reasoning_text = thought.thought
+                    if "<think" in reasoning_text.lower():
+                        clean_text, extracted_reasoning = self._split_reasoning_from_answer(reasoning_text)
+                        if extracted_reasoning:
+                            reasoning_text = extracted_reasoning
+                            thought.thought = clean_text or extracted_reasoning
+                    reasoning_list.append(reasoning_text)
+                    sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
+
+                # Add tool calls
+                if thought.tool:
+                    tool_calls_list.append(
+                        {
+                            "name": thought.tool,
+                            "arguments": thought.tool_input or "",
+                            "result": thought.observation or "",
+                        }
+                    )
+                    sequence.append({"type": "tool_call", "index": len(tool_calls_list) - 1})
+
+                # Add answer content if present
+                if thought.answer:
+                    content_text = thought.answer
+                    if "<think" in content_text.lower():
+                        clean_answer, extracted_reasoning = self._split_reasoning_from_answer(content_text)
+                        if extracted_reasoning:
+                            reasoning_list.append(extracted_reasoning)
+                            sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
+                        content_text = clean_answer
+                        thought.answer = clean_answer or content_text
+
+                    if content_text:
+                        start = content_pos
+                        end = content_pos + len(content_text)
+                        sequence.append({"type": "content", "start": start, "end": end})
+                        content_pos = end
+                        cleaned_answer_parts.append(content_text)
+
+            if cleaned_answer_parts:
+                merged_answer = "".join(cleaned_answer_parts)
+                message.answer = merged_answer
+                llm_result.message.content = merged_answer
+        else:
+            # Completion/Chat mode: use reasoning_content from llm_result
+            reasoning_content = llm_result.reasoning_content
+            if not reasoning_content and answer:
+                # Extract reasoning from <think> blocks and clean the final answer
+                clean_answer, reasoning_content = self._split_reasoning_from_answer(answer)
+                if reasoning_content:
+                    answer = clean_answer
+                    llm_result.message.content = clean_answer
+                    llm_result.reasoning_content = reasoning_content
+                    message.answer = clean_answer
+            if reasoning_content:
+                reasoning_list = [reasoning_content]
+                # Content comes first, then reasoning
+                if answer:
+                    sequence.append({"type": "content", "start": 0, "end": len(answer)})
+                sequence.append({"type": "reasoning", "index": 0})
+
+        # Only save if there's meaningful generation detail
+        if not reasoning_list and not tool_calls_list:
+            return
+
+        # Check if generation detail already exists
+        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
+
+        if existing:
+            existing.reasoning_content = json.dumps(reasoning_list) if reasoning_list else None
+            existing.tool_calls = json.dumps(tool_calls_list) if tool_calls_list else None
+            existing.sequence = json.dumps(sequence) if sequence else None
+        else:
+            generation_detail = LLMGenerationDetail(
+                tenant_id=self._application_generate_entity.app_config.tenant_id,
+                app_id=self._application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                reasoning_content=json.dumps(reasoning_list) if reasoning_list else None,
+                tool_calls=json.dumps(tool_calls_list) if tool_calls_list else None,
+                sequence=json.dumps(sequence) if sequence else None,
+            )
+            session.add(generation_detail)
+
+    @classmethod
+    def _split_reasoning_from_answer(cls, text: str) -> tuple[str, str]:
+        """
+        Extract reasoning segments from <think> blocks and return (clean_text, reasoning).
+        """
+        matches = cls._THINK_PATTERN.findall(text)
+        reasoning_content = "\n".join(match.strip() for match in matches) if matches else ""
+
+        clean_text = cls._THINK_PATTERN.sub("", text)
+        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
+
+        return clean_text, reasoning_content or ""
+
     def _handle_stop(self, event: QueueStopEvent):
         """
         Handle stop.

api/core/app/task_pipeline/message_cycle_manager.py

@@ -232,15 +232,31 @@ class MessageCycleManager:
         answer: str,
         message_id: str,
         from_variable_selector: list[str] | None = None,
+        chunk_type: str | None = None,
+        tool_call_id: str | None = None,
+        tool_name: str | None = None,
+        tool_arguments: str | None = None,
+        tool_files: list[str] | None = None,
+        tool_error: str | None = None,
+        tool_elapsed_time: float | None = None,
+        tool_icon: str | dict | None = None,
+        tool_icon_dark: str | dict | None = None,
         event_type: StreamEvent | None = None,
     ) -> MessageStreamResponse:
         """
         Message to stream response.
         :param answer: answer
         :param message_id: message id
+        :param from_variable_selector: from variable selector
+        :param chunk_type: type of the chunk (text, function_call, tool_result, thought)
+        :param tool_call_id: unique identifier for this tool call
+        :param tool_name: name of the tool being called
+        :param tool_arguments: accumulated tool arguments JSON
+        :param tool_files: file IDs produced by tool
+        :param tool_error: error message if tool failed
         :return:
         """
-        return MessageStreamResponse(
+        response = MessageStreamResponse(
             task_id=self._application_generate_entity.task_id,
             id=message_id,
             answer=answer,
@@ -248,6 +264,35 @@ class MessageCycleManager:
             event=event_type or StreamEvent.MESSAGE,
         )
 
+        if chunk_type:
+            response = response.model_copy(update={"chunk_type": chunk_type})
+
+        if chunk_type == "tool_call":
+            response = response.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+        elif chunk_type == "tool_result":
+            response = response.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_files": tool_files,
+                    "tool_error": tool_error,
+                    "tool_elapsed_time": tool_elapsed_time,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+
+        return response
+
     def message_replace_to_stream_response(self, answer: str, reason: str = "") -> MessageReplaceStreamResponse:
         """
         Message replace to stream response.

api/core/app_assets/__init__.py

@@ -0,0 +1,31 @@
+from .entities import (
+    AssetItem,
+    FileAsset,
+    FileReference,
+    SkillAsset,
+    SkillMetadata,
+    ToolConfiguration,
+    ToolFieldConfig,
+    ToolReference,
+)
+from .packager import AssetPackager, ZipPackager
+from .parser import AssetItemParser, AssetParser, FileAssetParser, SkillAssetParser
+from .paths import AssetPaths
+
+__all__ = [
+    "AssetItem",
+    "AssetItemParser",
+    "AssetPackager",
+    "AssetParser",
+    "AssetPaths",
+    "FileAsset",
+    "FileAssetParser",
+    "FileReference",
+    "SkillAsset",
+    "SkillAssetParser",
+    "SkillMetadata",
+    "ToolConfiguration",
+    "ToolFieldConfig",
+    "ToolReference",
+    "ZipPackager",
+]

api/core/app_assets/entities/__init__.py

@@ -0,0 +1,20 @@
+from .assets import AssetItem, FileAsset
+from .skill import (
+    FileReference,
+    SkillAsset,
+    SkillMetadata,
+    ToolConfiguration,
+    ToolFieldConfig,
+    ToolReference,
+)
+
+__all__ = [
+    "AssetItem",
+    "FileAsset",
+    "FileReference",
+    "SkillAsset",
+    "SkillMetadata",
+    "ToolConfiguration",
+    "ToolFieldConfig",
+    "ToolReference",
+]

api/core/app_assets/entities/assets.py

@@ -0,0 +1,22 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+
+
+@dataclass
+class AssetItem(ABC):
+    node_id: str
+    path: str
+    file_name: str
+    extension: str
+
+    @abstractmethod
+    def get_storage_key(self) -> str:
+        raise NotImplementedError
+
+
+@dataclass
+class FileAsset(AssetItem):
+    storage_key: str
+
+    def get_storage_key(self) -> str:
+        return self.storage_key

api/core/app_assets/entities/skill.py

@@ -0,0 +1,59 @@
+from dataclasses import dataclass
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from core.tools.entities.tool_entities import ToolProviderType
+
+from .assets import AssetItem
+
+
+class ToolFieldConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    id: str
+    value: Any
+    auto: bool = False
+
+
+class ToolConfiguration(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    fields: list[ToolFieldConfig] = Field(default_factory=list)
+
+    def default_values(self) -> dict[str, Any]:
+        return {field.id: field.value for field in self.fields if field.value is not None}
+
+
+class ToolReference(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    uuid: str = Field(description="Unique identifier for this tool reference")
+    type: ToolProviderType = Field(description="Tool provider type")
+    provider: str = Field(description="Tool provider")
+    tool_name: str = Field(description="Tool name")
+    credential_id: str | None = Field(default=None, description="Credential ID")
+    configuration: ToolConfiguration | None = Field(default=None, description="Tool configuration")
+
+
+class FileReference(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    source: str = Field(description="Source location or identifier of the file")
+    uuid: str = Field(description="Unique identifier for this file reference")
+
+
+class SkillMetadata(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    tools: dict[str, ToolReference] = Field(default_factory=dict, description="Map of tool references by UUID")
+    files: list[FileReference] = Field(default_factory=list, description="List of file references")
+
+
+@dataclass
+class SkillAsset(AssetItem):
+    storage_key: str
+    metadata: SkillMetadata
+
+    def get_storage_key(self) -> str:
+        return self.storage_key

api/core/app_assets/packager/__init__.py

@@ -0,0 +1,7 @@
+from .base import AssetPackager
+from .zip_packager import ZipPackager
+
+__all__ = [
+    "AssetPackager",
+    "ZipPackager",
+]

api/core/app_assets/packager/base.py

@@ -0,0 +1,9 @@
+from abc import ABC, abstractmethod
+
+from core.app_assets.entities import AssetItem
+
+
+class AssetPackager(ABC):
+    @abstractmethod
+    def package(self, assets: list[AssetItem]) -> bytes:
+        raise NotImplementedError

api/core/app_assets/packager/zip_packager.py

@@ -0,0 +1,42 @@
+import io
+import zipfile
+from concurrent.futures import Future, ThreadPoolExecutor
+from threading import Lock
+from typing import TYPE_CHECKING
+
+from core.app_assets.entities import AssetItem
+
+from .base import AssetPackager
+
+if TYPE_CHECKING:
+    from extensions.ext_storage import Storage
+
+
+class ZipPackager(AssetPackager):
+    _storage: "Storage"
+
+    def __init__(self, storage: "Storage") -> None:
+        self._storage = storage
+
+    def package(self, assets: list[AssetItem]) -> bytes:
+        zip_buffer = io.BytesIO()
+
+        with zipfile.ZipFile(zip_buffer, "w", zipfile.ZIP_DEFLATED) as zf:
+            lock = Lock()
+            # FOR DELVELPMENT AND TESTING ONLY, TODO: optimize
+            with ThreadPoolExecutor(max_workers=8) as executor:
+                futures: list[Future[None]] = []
+                for asset in assets:
+
+                    def _write_asset(a: AssetItem) -> None:
+                        content = self._storage.load_once(a.get_storage_key())
+                        with lock:
+                            zf.writestr(a.path, content)
+
+                    futures.append(executor.submit(_write_asset, asset))
+
+                # Wait for all futures to complete
+                for future in futures:
+                    future.result()
+
+        return zip_buffer.getvalue()

api/core/app_assets/parser/__init__.py

@@ -0,0 +1,10 @@
+from .asset_parser import AssetParser
+from .base import AssetItemParser, FileAssetParser
+from .skill_parser import SkillAssetParser
+
+__all__ = [
+    "AssetItemParser",
+    "AssetParser",
+    "FileAssetParser",
+    "SkillAssetParser",
+]

api/core/app_assets/parser/asset_parser.py

@@ -0,0 +1,36 @@
+from core.app.entities.app_asset_entities import AppAssetFileTree
+from core.app_assets.entities import AssetItem
+from core.app_assets.paths import AssetPaths
+
+from .base import AssetItemParser, FileAssetParser
+
+
+class AssetParser:
+    def __init__(
+        self,
+        tree: AppAssetFileTree,
+        tenant_id: str,
+        app_id: str,
+    ) -> None:
+        self._tree = tree
+        self._tenant_id = tenant_id
+        self._app_id = app_id
+        self._parsers = {}
+        self._default_parser = FileAssetParser()
+
+    def register(self, extension: str, parser: AssetItemParser) -> None:
+        self._parsers[extension] = parser
+
+    def parse(self) -> list[AssetItem]:
+        assets: list[AssetItem] = []
+
+        for node in self._tree.walk_files():
+            path = self._tree.get_path(node.id).lstrip("/")
+            storage_key = AssetPaths.draft_file(self._tenant_id, self._app_id, node.id)
+            extension = node.extension or ""
+
+            parser = self._parsers.get(extension, self._default_parser)
+            asset = parser.parse(node.id, path, node.name, extension, storage_key)
+            assets.append(asset)
+
+        return assets

api/core/app_assets/parser/base.py

@@ -0,0 +1,34 @@
+from abc import ABC, abstractmethod
+
+from core.app_assets.entities import AssetItem, FileAsset
+
+
+class AssetItemParser(ABC):
+    @abstractmethod
+    def parse(
+        self,
+        node_id: str,
+        path: str,
+        file_name: str,
+        extension: str,
+        storage_key: str,
+    ) -> AssetItem:
+        raise NotImplementedError
+
+
+class FileAssetParser(AssetItemParser):
+    def parse(
+        self,
+        node_id: str,
+        path: str,
+        file_name: str,
+        extension: str,
+        storage_key: str,
+    ) -> FileAsset:
+        return FileAsset(
+            node_id=node_id,
+            path=path,
+            file_name=file_name,
+            extension=extension,
+            storage_key=storage_key,
+        )

api/core/app_assets/parser/skill_parser.py

@@ -0,0 +1,161 @@
+import json
+import logging
+import re
+from typing import Any
+
+from core.app.entities.app_asset_entities import AppAssetFileTree, AppAssetNode
+from core.app_assets.entities import (
+    SkillAsset,
+    SkillMetadata,
+)
+from core.app_assets.entities.skill import FileReference, ToolConfiguration, ToolReference
+from core.app_assets.paths import AssetPaths
+from core.tools.entities.tool_entities import ToolProviderType
+from extensions.ext_storage import storage
+
+from .base import AssetItemParser
+
+TOOL_REFERENCE_PATTERN = re.compile(r"§\[tool\]\.\[([^\]]+)\]\.\[([^\]]+)\]\.\[([^\]]+)\]§")
+FILE_REFERENCE_PATTERN = re.compile(r"§\[file\]\.\[([^\]]+)\]\.\[([^\]]+)\]§")
+
+logger = logging.getLogger(__name__)
+
+
+class SkillAssetParser(AssetItemParser):
+    def __init__(
+        self,
+        tenant_id: str,
+        app_id: str,
+        assets_id: str,
+        tree: AppAssetFileTree,
+    ) -> None:
+        self._tenant_id = tenant_id
+        self._app_id = app_id
+        self._assets_id = assets_id
+        self._tree = tree
+
+    def parse(
+        self,
+        node_id: str,
+        path: str,
+        file_name: str,
+        extension: str,
+        storage_key: str,
+    ) -> SkillAsset:
+        try:
+            return self._parse_skill_asset(node_id, path, file_name, extension, storage_key)
+        except Exception:
+            logger.exception("Failed to parse skill asset %s", node_id)
+            # handle as plain text
+            return SkillAsset(
+                node_id=node_id,
+                path=path,
+                file_name=file_name,
+                extension=extension,
+                storage_key=storage_key,
+                metadata=SkillMetadata(),
+            )
+
+    def _parse_skill_asset(
+        self, node_id: str, path: str, file_name: str, extension: str, storage_key: str
+    ) -> SkillAsset:
+        try:
+            data = json.loads(storage.load_once(storage_key))
+        except (json.JSONDecodeError, UnicodeDecodeError):
+            # handle as plain text
+            return SkillAsset(
+                node_id=node_id,
+                path=path,
+                file_name=file_name,
+                extension=extension,
+                storage_key=storage_key,
+                metadata=SkillMetadata(),
+            )
+
+        if not isinstance(data, dict):
+            raise ValueError(f"Skill document {node_id} must be a JSON object")
+
+        data_dict: dict[str, Any] = data
+        metadata_raw = data_dict.get("metadata", {})
+        content = data_dict.get("content", "")
+
+        if not isinstance(content, str):
+            raise ValueError(f"Skill document {node_id} 'content' must be a string")
+
+        resolved_key = AssetPaths.build_resolved_file(self._tenant_id, self._app_id, self._assets_id, node_id)
+        current_file = self._tree.get(node_id)
+        if current_file is None:
+            raise ValueError(f"File not found for id={node_id}")
+
+        metadata = self._resolve_metadata(content, metadata_raw)
+        storage.save(resolved_key, self._resolve_content(current_file, content, metadata).encode("utf-8"))
+
+        return SkillAsset(
+            node_id=node_id,
+            path=path,
+            file_name=file_name,
+            extension=extension,
+            storage_key=resolved_key,
+            metadata=metadata,
+        )
+
+    def _resolve_content(self, current_file: AppAssetNode, content: str, metadata: SkillMetadata) -> str:
+        for match in FILE_REFERENCE_PATTERN.finditer(content):
+            # replace with file relative path
+            file_id = match.group(2)
+            file = self._tree.get(file_id)
+            if file is None:
+                logger.warning("File not found for id=%s, skipping", file_id)
+                # replace with file not found placeholder
+                content = content.replace(match.group(0), "[File not found]")
+                continue
+            content = content.replace(match.group(0), self._tree.relative_path(current_file, file))
+
+        for match in TOOL_REFERENCE_PATTERN.finditer(content):
+            tool_id = match.group(3)
+            tool = metadata.tools.get(tool_id)
+            if tool is None:
+                logger.warning("Tool not found for id=%s, skipping", tool_id)
+                # replace with tool not found placeholder
+                content = content.replace(match.group(0), f"[Tool not found: {tool_id}]")
+                continue
+            content = content.replace(match.group(0), f"[Bash Command: {tool.tool_name}_{tool_id}]")
+        return content
+
+    def _resolve_file_references(self, content: str) -> list[FileReference]:
+        file_references: list[FileReference] = []
+        for match in FILE_REFERENCE_PATTERN.finditer(content):
+            file_references.append(
+                FileReference(
+                    source=match.group(1),
+                    uuid=match.group(2),
+                )
+            )
+        return file_references
+
+    def _resolve_tool_references(self, content: str, tools: dict[str, Any]) -> dict[str, ToolReference]:
+        tool_references: dict[str, ToolReference] = {}
+        for match in TOOL_REFERENCE_PATTERN.finditer(content):
+            tool_id = match.group(3)
+            tool_name = match.group(2)
+            tool_provider = match.group(1)
+            metadata = tools.get(tool_id)
+            if metadata is None:
+                raise ValueError(f"Tool metadata for {tool_id} not found")
+
+            configuration = ToolConfiguration.model_validate(metadata.get("configuration", {}))
+            tool_references[tool_id] = ToolReference(
+                uuid=tool_id,
+                type=ToolProviderType.value_of(metadata.get("type", None)),
+                provider=tool_provider,
+                tool_name=tool_name,
+                credential_id=metadata.get("credential_id", None),
+                configuration=configuration,
+            )
+        return tool_references
+
+    def _resolve_metadata(self, content: str, metadata: dict[str, Any]) -> SkillMetadata:
+        return SkillMetadata(
+            files=self._resolve_file_references(content=content),
+            tools=self._resolve_tool_references(content=content, tools=metadata.get("tools", {})),
+        )

api/core/app_assets/paths.py

@@ -0,0 +1,18 @@
+class AssetPaths:
+    _BASE = "app_assets"
+
+    @staticmethod
+    def draft_file(tenant_id: str, app_id: str, node_id: str) -> str:
+        return f"{AssetPaths._BASE}/{tenant_id}/{app_id}/draft/{node_id}"
+
+    @staticmethod
+    def build_zip(tenant_id: str, app_id: str, assets_id: str) -> str:
+        return f"{AssetPaths._BASE}/{tenant_id}/{app_id}/artifacts/{assets_id}.zip"
+
+    @staticmethod
+    def build_resolved_file(tenant_id: str, app_id: str, assets_id: str, node_id: str) -> str:
+        return f"{AssetPaths._BASE}/{tenant_id}/{app_id}/artifacts/{assets_id}/resolved/{node_id}"
+
+    @staticmethod
+    def build_tool_artifact(tenant_id: str, app_id: str, assets_id: str) -> str:
+        return f"{AssetPaths._BASE}/{tenant_id}/{app_id}/artifacts/{assets_id}/tool_artifact.json"

api/core/callback_handler/index_tool_callback_handler.py

@@ -5,7 +5,6 @@ from sqlalchemy import select
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.rag.index_processor.constant.index_type import IndexStructureType
 from core.rag.models.document import Document
@@ -90,6 +89,8 @@ class DatasetIndexToolCallbackHandler:
     # TODO(-LAN-): Improve type check
     def return_retriever_resource_info(self, resource: Sequence[RetrievalSourceMetadata]):
         """Handle return_retriever_resource_info."""
+        from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
+
         self._queue_manager.publish(
             QueueRetrieverResourcesEvent(retriever_resources=resource), PublishFrom.APPLICATION_MANAGER
         )

api/core/file/file_manager.py

@@ -1,4 +1,5 @@
 import base64
+import logging
 from collections.abc import Mapping
 
 from configs import dify_config
@@ -10,7 +11,10 @@ from core.model_runtime.entities import (
     TextPromptMessageContent,
     VideoPromptMessageContent,
 )
-from core.model_runtime.entities.message_entities import PromptMessageContentUnionTypes
+from core.model_runtime.entities.message_entities import (
+    MultiModalPromptMessageContent,
+    PromptMessageContentUnionTypes,
+)
 from core.tools.signature import sign_tool_file
 from extensions.ext_storage import storage
 
@@ -18,6 +22,8 @@ from . import helpers
 from .enums import FileAttribute
 from .models import File, FileTransferMethod, FileType
 
+logger = logging.getLogger(__name__)
+
 
 def get_attr(*, file: File, attr: FileAttribute):
     match attr:
@@ -89,6 +95,8 @@ def to_prompt_message_content(
         "format": f.extension.removeprefix("."),
         "mime_type": f.mime_type,
         "filename": f.filename or "",
+        # Encoded file reference for context restoration: "transfer_method:related_id" or "remote:url"
+        "file_ref": _encode_file_ref(f),
     }
     if f.type == FileType.IMAGE:
         params["detail"] = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
@@ -96,6 +104,17 @@ def to_prompt_message_content(
     return prompt_class_map[f.type].model_validate(params)
 
 
+def _encode_file_ref(f: File) -> str | None:
+    """Encode file reference as 'transfer_method:id_or_url' string."""
+    if f.transfer_method == FileTransferMethod.REMOTE_URL:
+        return f"remote:{f.remote_url}" if f.remote_url else None
+    elif f.transfer_method == FileTransferMethod.LOCAL_FILE:
+        return f"local:{f.related_id}" if f.related_id else None
+    elif f.transfer_method == FileTransferMethod.TOOL_FILE:
+        return f"tool:{f.related_id}" if f.related_id else None
+    return None
+
+
 def download(f: File, /):
     if f.transfer_method in (
         FileTransferMethod.TOOL_FILE,
@@ -164,3 +183,128 @@ def _to_url(f: File, /):
         return sign_tool_file(tool_file_id=f.related_id, extension=f.extension)
     else:
         raise ValueError(f"Unsupported transfer method: {f.transfer_method}")
+
+
+def restore_multimodal_content(
+    content: MultiModalPromptMessageContent,
+) -> MultiModalPromptMessageContent:
+    """
+    Restore base64_data or url for multimodal content from file_ref.
+
+    file_ref format: "transfer_method:id_or_url" (e.g., "local:abc123", "remote:https://...")
+
+    Args:
+        content: MultiModalPromptMessageContent with file_ref field
+
+    Returns:
+        MultiModalPromptMessageContent with restored base64_data or url
+    """
+    # Skip if no file reference or content already has data
+    if not content.file_ref:
+        return content
+    if content.base64_data or content.url:
+        return content
+
+    try:
+        file = _build_file_from_ref(
+            file_ref=content.file_ref,
+            file_format=content.format,
+            mime_type=content.mime_type,
+            filename=content.filename,
+        )
+        if not file:
+            return content
+
+        # Restore content based on config
+        if dify_config.MULTIMODAL_SEND_FORMAT == "base64":
+            restored_base64 = _get_encoded_string(file)
+            return content.model_copy(update={"base64_data": restored_base64})
+        else:
+            restored_url = _to_url(file)
+            return content.model_copy(update={"url": restored_url})
+
+    except Exception as e:
+        logger.warning("Failed to restore multimodal content: %s", e)
+        return content
+
+
+def _build_file_from_ref(
+    file_ref: str,
+    file_format: str | None,
+    mime_type: str | None,
+    filename: str | None,
+) -> File | None:
+    """
+    Build a File object from encoded file_ref string.
+
+    Args:
+        file_ref: Encoded reference "transfer_method:id_or_url"
+        file_format: The file format/extension (without dot)
+        mime_type: The mime type
+        filename: The filename
+
+    Returns:
+        File object with storage_key loaded, or None if not found
+    """
+    from sqlalchemy import select
+    from sqlalchemy.orm import Session
+
+    from extensions.ext_database import db
+    from models.model import UploadFile
+    from models.tools import ToolFile
+
+    # Parse file_ref: "method:value"
+    if ":" not in file_ref:
+        logger.warning("Invalid file_ref format: %s", file_ref)
+        return None
+
+    method, value = file_ref.split(":", 1)
+    extension = f".{file_format}" if file_format else None
+
+    if method == "remote":
+        return File(
+            tenant_id="",
+            type=FileType.IMAGE,
+            transfer_method=FileTransferMethod.REMOTE_URL,
+            remote_url=value,
+            extension=extension,
+            mime_type=mime_type,
+            filename=filename,
+            storage_key="",
+        )
+
+    # Query database for storage_key
+    with Session(db.engine) as session:
+        if method == "local":
+            stmt = select(UploadFile).where(UploadFile.id == value)
+            upload_file = session.scalar(stmt)
+            if upload_file:
+                return File(
+                    tenant_id=upload_file.tenant_id,
+                    type=FileType(upload_file.extension)
+                    if hasattr(FileType, upload_file.extension.upper())
+                    else FileType.IMAGE,
+                    transfer_method=FileTransferMethod.LOCAL_FILE,
+                    related_id=value,
+                    extension=extension or ("." + upload_file.extension if upload_file.extension else None),
+                    mime_type=mime_type or upload_file.mime_type,
+                    filename=filename or upload_file.name,
+                    storage_key=upload_file.key,
+                )
+        elif method == "tool":
+            stmt = select(ToolFile).where(ToolFile.id == value)
+            tool_file = session.scalar(stmt)
+            if tool_file:
+                return File(
+                    tenant_id=tool_file.tenant_id,
+                    type=FileType.IMAGE,
+                    transfer_method=FileTransferMethod.TOOL_FILE,
+                    related_id=value,
+                    extension=extension,
+                    mime_type=mime_type or tool_file.mimetype,
+                    filename=filename or tool_file.name,
+                    storage_key=tool_file.file_key,
+                )
+
+    logger.warning("File not found for file_ref: %s", file_ref)
+    return None

api/core/llm_generator/llm_generator.py

@@ -1,11 +1,16 @@
 import json
 import logging
 import re
-from collections.abc import Sequence
-from typing import Protocol, cast
+from collections.abc import Mapping, Sequence
+from typing import Any, Protocol, cast
 
 import json_repair
 
+from core.llm_generator.output_models import (
+    CodeNodeStructuredOutput,
+    InstructionModifyOutput,
+    SuggestedQuestionsOutput,
+)
 from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
 from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
 from core.llm_generator.prompts import (
@@ -393,6 +398,432 @@ class LLMGenerator:
             logger.exception("Failed to invoke LLM model, model: %s", model_config.get("name"))
             return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
 
+    @classmethod
+    def generate_with_context(
+        cls,
+        tenant_id: str,
+        workflow_id: str,
+        node_id: str,
+        parameter_name: str,
+        language: str,
+        prompt_messages: list[PromptMessage],
+        model_config: dict,
+    ) -> dict:
+        """
+        Generate extractor code node based on conversation context.
+
+        Args:
+            tenant_id: Tenant/workspace ID
+            workflow_id: Workflow ID
+            node_id: Current tool/llm node ID
+            parameter_name: Parameter name to generate code for
+            language: Code language (python3/javascript)
+            prompt_messages: Multi-turn conversation history (last message is instruction)
+            model_config: Model configuration (provider, name, completion_params)
+
+        Returns:
+            dict with CodeNodeData format:
+            - variables: Input variable selectors
+            - code_language: Code language
+            - code: Generated code
+            - outputs: Output definitions
+            - message: Explanation
+            - error: Error message if any
+        """
+        from sqlalchemy import select
+        from sqlalchemy.orm import Session
+
+        from services.workflow_service import WorkflowService
+
+        # Get workflow
+        with Session(db.engine) as session:
+            stmt = select(App).where(App.id == workflow_id)
+            app = session.scalar(stmt)
+            if not app:
+                return cls._error_response(f"App {workflow_id} not found")
+
+            workflow = WorkflowService().get_draft_workflow(app_model=app)
+            if not workflow:
+                return cls._error_response(f"Workflow for app {workflow_id} not found")
+
+        # Get upstream nodes via edge backtracking
+        upstream_nodes = cls._get_upstream_nodes(workflow.graph_dict, node_id)
+
+        # Get current node info
+        current_node = cls._get_node_by_id(workflow.graph_dict, node_id)
+        if not current_node:
+            return cls._error_response(f"Node {node_id} not found")
+
+        # Get parameter info
+        parameter_info = cls._get_parameter_info(
+            tenant_id=tenant_id,
+            node_data=current_node.get("data", {}),
+            parameter_name=parameter_name,
+        )
+
+        # Build system prompt
+        system_prompt = cls._build_extractor_system_prompt(
+            upstream_nodes=upstream_nodes,
+            current_node=current_node,
+            parameter_info=parameter_info,
+            language=language,
+        )
+
+        # Construct complete prompt_messages with system prompt
+        complete_messages: list[PromptMessage] = [
+            SystemPromptMessage(content=system_prompt),
+            *prompt_messages,
+        ]
+
+        from core.llm_generator.output_parser.structured_output import invoke_llm_with_pydantic_model
+
+        # Get model instance and schema
+        provider = model_config.get("provider", "")
+        model_name = model_config.get("name", "")
+        model_instance = ModelManager().get_model_instance(
+            tenant_id=tenant_id,
+            model_type=ModelType.LLM,
+            provider=provider,
+            model=model_name,
+        )
+
+        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
+        if not model_schema:
+            return cls._error_response(f"Model schema not found for {model_name}")
+
+        model_parameters = model_config.get("completion_params", {})
+        try:
+            response = invoke_llm_with_pydantic_model(
+                provider=provider,
+                model_schema=model_schema,
+                model_instance=model_instance,
+                prompt_messages=complete_messages,
+                output_model=CodeNodeStructuredOutput,
+                model_parameters=model_parameters,
+                stream=False,
+                tenant_id=tenant_id,
+            )
+
+            return cls._parse_code_node_output(
+                response.structured_output, language, parameter_info.get("type", "string")
+            )
+
+        except InvokeError as e:
+            return cls._error_response(str(e))
+        except Exception as e:
+            logger.exception("Failed to generate with context, model: %s", model_config.get("name"))
+            return cls._error_response(f"An unexpected error occurred: {str(e)}")
+
+    @classmethod
+    def _error_response(cls, error: str) -> dict:
+        """Return error response in CodeNodeData format."""
+        return {
+            "variables": [],
+            "code_language": "python3",
+            "code": "",
+            "outputs": {},
+            "message": "",
+            "error": error,
+        }
+
+    @classmethod
+    def generate_suggested_questions(
+        cls,
+        tenant_id: str,
+        workflow_id: str,
+        node_id: str,
+        parameter_name: str,
+        language: str,
+        model_config: dict | None = None,
+    ) -> dict:
+        """
+        Generate suggested questions for context generation.
+
+        Returns dict with questions array and error field.
+        """
+        from sqlalchemy import select
+        from sqlalchemy.orm import Session
+
+        from core.llm_generator.output_parser.structured_output import invoke_llm_with_pydantic_model
+        from services.workflow_service import WorkflowService
+
+        # Get workflow context (reuse existing logic)
+        with Session(db.engine) as session:
+            stmt = select(App).where(App.id == workflow_id)
+            app = session.scalar(stmt)
+            if not app:
+                return {"questions": [], "error": f"App {workflow_id} not found"}
+
+            workflow = WorkflowService().get_draft_workflow(app_model=app)
+            if not workflow:
+                return {"questions": [], "error": f"Workflow for app {workflow_id} not found"}
+
+        upstream_nodes = cls._get_upstream_nodes(workflow.graph_dict, node_id)
+        current_node = cls._get_node_by_id(workflow.graph_dict, node_id)
+        if not current_node:
+            return {"questions": [], "error": f"Node {node_id} not found"}
+
+        parameter_info = cls._get_parameter_info(
+            tenant_id=tenant_id,
+            node_data=current_node.get("data", {}),
+            parameter_name=parameter_name,
+        )
+
+        # Build prompt
+        system_prompt = cls._build_suggested_questions_prompt(
+            upstream_nodes=upstream_nodes,
+            current_node=current_node,
+            parameter_info=parameter_info,
+            language=language,
+        )
+
+        prompt_messages: list[PromptMessage] = [
+            SystemPromptMessage(content=system_prompt),
+        ]
+
+        # Get model instance - use default if model_config not provided
+        model_manager = ModelManager()
+        if model_config:
+            provider = model_config.get("provider", "")
+            model_name = model_config.get("name", "")
+            model_instance = model_manager.get_model_instance(
+                tenant_id=tenant_id,
+                model_type=ModelType.LLM,
+                provider=provider,
+                model=model_name,
+            )
+        else:
+            model_instance = model_manager.get_default_model_instance(
+                tenant_id=tenant_id,
+                model_type=ModelType.LLM,
+            )
+            model_name = model_instance.model
+
+        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
+        if not model_schema:
+            return {"questions": [], "error": f"Model schema not found for {model_name}"}
+
+        completion_params = model_config.get("completion_params", {}) if model_config else {}
+        model_parameters = {**completion_params, "max_tokens": 256}
+        try:
+            response = invoke_llm_with_pydantic_model(
+                provider=model_instance.provider,
+                model_schema=model_schema,
+                model_instance=model_instance,
+                prompt_messages=prompt_messages,
+                output_model=SuggestedQuestionsOutput,
+                model_parameters=model_parameters,
+                stream=False,
+                tenant_id=tenant_id,
+            )
+
+            questions = response.structured_output.get("questions", []) if response.structured_output else []
+            return {"questions": questions, "error": ""}
+
+        except InvokeError as e:
+            return {"questions": [], "error": str(e)}
+        except Exception as e:
+            logger.exception("Failed to generate suggested questions, model: %s", model_name)
+            return {"questions": [], "error": f"An unexpected error occurred: {str(e)}"}
+
+    @classmethod
+    def _build_suggested_questions_prompt(
+        cls,
+        upstream_nodes: list[dict],
+        current_node: dict,
+        parameter_info: dict,
+        language: str = "English",
+    ) -> str:
+        """Build minimal prompt for suggested questions generation."""
+        # Simplify upstream nodes to reduce tokens
+        sources = [f"{n['title']}({','.join(n.get('outputs', {}).keys())})" for n in upstream_nodes[:5]]
+        param_type = parameter_info.get("type", "string")
+        param_desc = parameter_info.get("description", "")[:100]
+
+        return f"""Suggest 3 code generation questions for extracting data.
+Sources: {", ".join(sources)}
+Target: {parameter_info.get("name")}({param_type}) - {param_desc}
+Output 3 short, practical questions in {language}."""
+
+    @classmethod
+    def _get_upstream_nodes(cls, graph_dict: Mapping[str, Any], node_id: str) -> list[dict]:
+        """
+        Get all upstream nodes via edge backtracking.
+
+        Traverses the graph backwards from node_id to collect all reachable nodes.
+        """
+        from collections import defaultdict
+
+        nodes = {n["id"]: n for n in graph_dict.get("nodes", [])}
+        edges = graph_dict.get("edges", [])
+
+        # Build reverse adjacency list
+        reverse_adj: dict[str, list[str]] = defaultdict(list)
+        for edge in edges:
+            reverse_adj[edge["target"]].append(edge["source"])
+
+        # BFS to find all upstream nodes
+        visited: set[str] = set()
+        queue = [node_id]
+        upstream: list[dict] = []
+
+        while queue:
+            current = queue.pop(0)
+            for source in reverse_adj.get(current, []):
+                if source not in visited:
+                    visited.add(source)
+                    queue.append(source)
+                    if source in nodes:
+                        upstream.append(cls._extract_node_info(nodes[source]))
+
+        return upstream
+
+    @classmethod
+    def _get_node_by_id(cls, graph_dict: Mapping[str, Any], node_id: str) -> dict | None:
+        """Get node by ID from graph."""
+        for node in graph_dict.get("nodes", []):
+            if node["id"] == node_id:
+                return node
+        return None
+
+    @classmethod
+    def _extract_node_info(cls, node: dict) -> dict:
+        """Extract minimal node info with outputs based on node type."""
+        node_type = node["data"]["type"]
+        node_data = node.get("data", {})
+
+        # Build outputs based on node type (only type, no description to reduce tokens)
+        outputs: dict[str, str] = {}
+        match node_type:
+            case "start":
+                for var in node_data.get("variables", []):
+                    name = var.get("variable", var.get("name", ""))
+                    outputs[name] = var.get("type", "string")
+            case "llm":
+                outputs["text"] = "string"
+            case "code":
+                for name, output in node_data.get("outputs", {}).items():
+                    outputs[name] = output.get("type", "string")
+            case "http-request":
+                outputs = {"body": "string", "status_code": "number", "headers": "object"}
+            case "knowledge-retrieval":
+                outputs["result"] = "array[object]"
+            case "tool":
+                outputs = {"text": "string", "json": "object"}
+            case _:
+                outputs["output"] = "string"
+
+        info: dict = {
+            "id": node["id"],
+            "title": node_data.get("title", node["id"]),
+            "outputs": outputs,
+        }
+        # Only include description if not empty
+        desc = node_data.get("desc", "")
+        if desc:
+            info["desc"] = desc
+
+        return info
+
+    @classmethod
+    def _get_parameter_info(cls, tenant_id: str, node_data: dict, parameter_name: str) -> dict:
+        """Get parameter info from tool schema using ToolManager."""
+        default_info = {"name": parameter_name, "type": "string", "description": ""}
+
+        if node_data.get("type") != "tool":
+            return default_info
+
+        try:
+            from core.app.entities.app_invoke_entities import InvokeFrom
+            from core.tools.entities.tool_entities import ToolProviderType
+            from core.tools.tool_manager import ToolManager
+
+            provider_type_str = node_data.get("provider_type", "")
+            provider_type = ToolProviderType(provider_type_str) if provider_type_str else ToolProviderType.BUILT_IN
+
+            tool_runtime = ToolManager.get_tool_runtime(
+                provider_type=provider_type,
+                provider_id=node_data.get("provider_id", ""),
+                tool_name=node_data.get("tool_name", ""),
+                tenant_id=tenant_id,
+                invoke_from=InvokeFrom.DEBUGGER,
+            )
+
+            parameters = tool_runtime.get_merged_runtime_parameters()
+            for param in parameters:
+                if param.name == parameter_name:
+                    return {
+                        "name": param.name,
+                        "type": param.type.value if hasattr(param.type, "value") else str(param.type),
+                        "description": param.llm_description
+                        or (param.human_description.en_US if param.human_description else ""),
+                        "required": param.required,
+                    }
+        except Exception as e:
+            logger.debug("Failed to get parameter info from ToolManager: %s", e)
+
+        return default_info
+
+    @classmethod
+    def _build_extractor_system_prompt(
+        cls,
+        upstream_nodes: list[dict],
+        current_node: dict,
+        parameter_info: dict,
+        language: str,
+    ) -> str:
+        """Build system prompt for extractor code generation."""
+        upstream_json = json.dumps(upstream_nodes, indent=2, ensure_ascii=False)
+        param_type = parameter_info.get("type", "string")
+        return f"""You are a code generator for workflow automation.
+
+Generate {language} code to extract/transform upstream node outputs for the target parameter.
+
+## Upstream Nodes
+{upstream_json}
+
+## Target
+Node: {current_node["data"].get("title", current_node["id"])}
+Parameter: {parameter_info.get("name")} ({param_type}) - {parameter_info.get("description", "")}
+
+## Requirements
+- Write a main function that returns type: {param_type}
+- Use value_selector format: ["node_id", "output_name"]
+"""
+
+    @classmethod
+    def _parse_code_node_output(cls, content: Mapping[str, Any] | None, language: str, parameter_type: str) -> dict:
+        """
+        Parse structured output to CodeNodeData format.
+
+        Args:
+            content: Structured output dict from invoke_llm_with_structured_output
+            language: Code language
+            parameter_type: Expected parameter type
+
+        Returns dict with variables, code_language, code, outputs, message, error.
+        """
+        if content is None:
+            return cls._error_response("Empty or invalid response from LLM")
+
+        # Validate and normalize variables
+        variables = [
+            {"variable": v.get("variable", ""), "value_selector": v.get("value_selector", [])}
+            for v in content.get("variables", [])
+            if isinstance(v, dict)
+        ]
+
+        outputs = content.get("outputs", {"result": {"type": parameter_type}})
+
+        return {
+            "variables": variables,
+            "code_language": language,
+            "code": content.get("code", ""),
+            "outputs": outputs,
+            "message": content.get("explanation", ""),
+            "error": "",
+        }
+
     @staticmethod
     def instruction_modify_legacy(
         tenant_id: str, flow_id: str, current: str, instruction: str, model_config: dict, ideal_output: str | None
@@ -529,6 +960,10 @@ class LLMGenerator:
             provider=model_config.get("provider", ""),
             model=model_config.get("name", ""),
         )
+        model_name = model_config.get("name", "")
+        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
+        if not model_schema:
+            return {"error": f"Model schema not found for {model_name}"}
         match node_type:
             case "llm" | "agent":
                 system_prompt = LLM_MODIFY_PROMPT_SYSTEM
@@ -552,20 +987,18 @@ class LLMGenerator:
         model_parameters = {"temperature": 0.4}
 
         try:
-            response: LLMResult = model_instance.invoke_llm(
-                prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
-            )
+            from core.llm_generator.output_parser.structured_output import invoke_llm_with_pydantic_model
 
-            generated_raw = response.message.get_text_content()
-            first_brace = generated_raw.find("{")
-            last_brace = generated_raw.rfind("}")
-            if first_brace == -1 or last_brace == -1 or last_brace < first_brace:
-                raise ValueError(f"Could not find a valid JSON object in response: {generated_raw}")
-            json_str = generated_raw[first_brace : last_brace + 1]
-            data = json_repair.loads(json_str)
-            if not isinstance(data, dict):
-                raise TypeError(f"Expected a JSON object, but got {type(data).__name__}")
-            return data
+            response = invoke_llm_with_pydantic_model(
+                provider=model_instance.provider,
+                model_schema=model_schema,
+                model_instance=model_instance,
+                prompt_messages=list(prompt_messages),
+                output_model=InstructionModifyOutput,
+                model_parameters=model_parameters,
+                stream=False,
+            )
+            return response.structured_output or {}
         except InvokeError as e:
             error = str(e)
             return {"error": f"Failed to generate code. Error: {error}"}

api/core/llm_generator/output_models.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from core.variables.types import SegmentType
+from core.workflow.nodes.base.entities import VariableSelector
+
+
+class SuggestedQuestionsOutput(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    questions: list[str] = Field(min_length=3, max_length=3)
+
+
+class CodeNodeOutput(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    type: SegmentType
+
+
+class CodeNodeStructuredOutput(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    variables: list[VariableSelector]
+    code: str
+    outputs: dict[str, CodeNodeOutput]
+    explanation: str
+
+
+class InstructionModifyOutput(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    modified: str
+    message: str

api/core/llm_generator/output_parser/file_ref.py

@@ -0,0 +1,188 @@
+"""
+File reference detection and conversion for structured output.
+
+This module provides utilities to:
+1. Detect file reference fields in JSON Schema (format: "dify-file-ref")
+2. Convert file ID strings to File objects after LLM returns
+"""
+
+import uuid
+from collections.abc import Mapping
+from typing import Any
+
+from core.file import File
+from core.variables.segments import ArrayFileSegment, FileSegment
+from factories.file_factory import build_from_mapping
+
+FILE_REF_FORMAT = "dify-file-ref"
+
+
+def is_file_ref_property(schema: dict) -> bool:
+    """Check if a schema property is a file reference."""
+    return schema.get("type") == "string" and schema.get("format") == FILE_REF_FORMAT
+
+
+def detect_file_ref_fields(schema: Mapping[str, Any], path: str = "") -> list[str]:
+    """
+    Recursively detect file reference fields in schema.
+
+    Args:
+        schema: JSON Schema to analyze
+        path: Current path in the schema (used for recursion)
+
+    Returns:
+        List of JSON paths containing file refs, e.g., ["image_id", "files[*]"]
+    """
+    file_ref_paths: list[str] = []
+    schema_type = schema.get("type")
+
+    if schema_type == "object":
+        for prop_name, prop_schema in schema.get("properties", {}).items():
+            current_path = f"{path}.{prop_name}" if path else prop_name
+
+            if is_file_ref_property(prop_schema):
+                file_ref_paths.append(current_path)
+            elif isinstance(prop_schema, dict):
+                file_ref_paths.extend(detect_file_ref_fields(prop_schema, current_path))
+
+    elif schema_type == "array":
+        items_schema = schema.get("items", {})
+        array_path = f"{path}[*]" if path else "[*]"
+
+        if is_file_ref_property(items_schema):
+            file_ref_paths.append(array_path)
+        elif isinstance(items_schema, dict):
+            file_ref_paths.extend(detect_file_ref_fields(items_schema, array_path))
+
+    return file_ref_paths
+
+
+def convert_file_refs_in_output(
+    output: Mapping[str, Any],
+    json_schema: Mapping[str, Any],
+    tenant_id: str,
+) -> dict[str, Any]:
+    """
+    Convert file ID strings to File objects based on schema.
+
+    Args:
+        output: The structured_output from LLM result
+        json_schema: The original JSON schema (to detect file ref fields)
+        tenant_id: Tenant ID for file lookup
+
+    Returns:
+        Output with file references converted to File objects
+    """
+    file_ref_paths = detect_file_ref_fields(json_schema)
+    if not file_ref_paths:
+        return dict(output)
+
+    result = _deep_copy_dict(output)
+
+    for path in file_ref_paths:
+        _convert_path_in_place(result, path.split("."), tenant_id)
+
+    return result
+
+
+def _deep_copy_dict(obj: Mapping[str, Any]) -> dict[str, Any]:
+    """Deep copy a mapping to a mutable dict."""
+    result: dict[str, Any] = {}
+    for key, value in obj.items():
+        if isinstance(value, Mapping):
+            result[key] = _deep_copy_dict(value)
+        elif isinstance(value, list):
+            result[key] = [_deep_copy_dict(item) if isinstance(item, Mapping) else item for item in value]
+        else:
+            result[key] = value
+    return result
+
+
+def _convert_path_in_place(obj: dict, path_parts: list[str], tenant_id: str) -> None:
+    """Convert file refs at the given path in place, wrapping in Segment types."""
+    if not path_parts:
+        return
+
+    current = path_parts[0]
+    remaining = path_parts[1:]
+
+    # Handle array notation like "files[*]"
+    if current.endswith("[*]"):
+        key = current[:-3] if current != "[*]" else None
+        target = obj.get(key) if key else obj
+
+        if isinstance(target, list):
+            if remaining:
+                # Nested array with remaining path - recurse into each item
+                for item in target:
+                    if isinstance(item, dict):
+                        _convert_path_in_place(item, remaining, tenant_id)
+            else:
+                # Array of file IDs - convert all and wrap in ArrayFileSegment
+                files: list[File] = []
+                for item in target:
+                    file = _convert_file_id(item, tenant_id)
+                    if file is not None:
+                        files.append(file)
+                # Replace the array with ArrayFileSegment
+                if key:
+                    obj[key] = ArrayFileSegment(value=files)
+        return
+
+    if not remaining:
+        # Leaf node - convert the value and wrap in FileSegment
+        if current in obj:
+            file = _convert_file_id(obj[current], tenant_id)
+            if file is not None:
+                obj[current] = FileSegment(value=file)
+            else:
+                obj[current] = None
+    else:
+        # Recurse into nested object
+        if current in obj and isinstance(obj[current], dict):
+            _convert_path_in_place(obj[current], remaining, tenant_id)
+
+
+def _convert_file_id(file_id: Any, tenant_id: str) -> File | None:
+    """
+    Convert a file ID string to a File object.
+
+    Tries multiple file sources in order:
+    1. ToolFile (files generated by tools/workflows)
+    2. UploadFile (files uploaded by users)
+    """
+    if not isinstance(file_id, str):
+        return None
+
+    # Validate UUID format
+    try:
+        uuid.UUID(file_id)
+    except ValueError:
+        return None
+
+    # Try ToolFile first (files generated by tools/workflows)
+    try:
+        return build_from_mapping(
+            mapping={
+                "transfer_method": "tool_file",
+                "tool_file_id": file_id,
+            },
+            tenant_id=tenant_id,
+        )
+    except ValueError:
+        pass
+
+    # Try UploadFile (files uploaded by users)
+    try:
+        return build_from_mapping(
+            mapping={
+                "transfer_method": "local_file",
+                "upload_file_id": file_id,
+            },
+            tenant_id=tenant_id,
+        )
+    except ValueError:
+        pass
+
+    # File not found in any source
+    return None

api/core/llm_generator/output_parser/structured_output.py

@@ -2,12 +2,13 @@ import json
 from collections.abc import Generator, Mapping, Sequence
 from copy import deepcopy
 from enum import StrEnum
-from typing import Any, Literal, cast, overload
+from typing import Any, Literal, TypeVar, cast, overload
 
 import json_repair
-from pydantic import TypeAdapter, ValidationError
+from pydantic import BaseModel, TypeAdapter, ValidationError
 
 from core.llm_generator.output_parser.errors import OutputParserError
+from core.llm_generator.output_parser.file_ref import convert_file_refs_in_output
 from core.llm_generator.prompts import STRUCTURED_OUTPUT_PROMPT
 from core.model_manager import ModelInstance
 from core.model_runtime.callbacks.base_callback import Callback
@@ -43,6 +44,9 @@ class SpecialModelType(StrEnum):
     OLLAMA = "ollama"
 
 
+T = TypeVar("T", bound=BaseModel)
+
+
 @overload
 def invoke_llm_with_structured_output(
     *,
@@ -57,6 +61,7 @@ def invoke_llm_with_structured_output(
     stream: Literal[True],
     user: str | None = None,
     callbacks: list[Callback] | None = None,
+    tenant_id: str | None = None,
 ) -> Generator[LLMResultChunkWithStructuredOutput, None, None]: ...
 @overload
 def invoke_llm_with_structured_output(
@@ -72,6 +77,7 @@ def invoke_llm_with_structured_output(
     stream: Literal[False],
     user: str | None = None,
     callbacks: list[Callback] | None = None,
+    tenant_id: str | None = None,
 ) -> LLMResultWithStructuredOutput: ...
 @overload
 def invoke_llm_with_structured_output(
@@ -87,6 +93,7 @@ def invoke_llm_with_structured_output(
     stream: bool = True,
     user: str | None = None,
     callbacks: list[Callback] | None = None,
+    tenant_id: str | None = None,
 ) -> LLMResultWithStructuredOutput | Generator[LLMResultChunkWithStructuredOutput, None, None]: ...
 def invoke_llm_with_structured_output(
     *,
@@ -101,23 +108,30 @@ def invoke_llm_with_structured_output(
     stream: bool = True,
     user: str | None = None,
     callbacks: list[Callback] | None = None,
+    tenant_id: str | None = None,
 ) -> LLMResultWithStructuredOutput | Generator[LLMResultChunkWithStructuredOutput, None, None]:
     """
-    Invoke large language model with structured output
-    1. This method invokes model_instance.invoke_llm with json_schema
-    2. Try to parse the result as structured output
+    Invoke large language model with structured output.
 
+    This method invokes model_instance.invoke_llm with json_schema and parses
+    the result as structured output.
+
+    :param provider: model provider name
+    :param model_schema: model schema entity
+    :param model_instance: model instance to invoke
     :param prompt_messages: prompt messages
-    :param json_schema: json schema
+    :param json_schema: json schema for structured output
     :param model_parameters: model parameters
     :param tools: tools for tool calling
     :param stop: stop words
     :param stream: is stream response
     :param user: unique user id
     :param callbacks: callbacks
+    :param tenant_id: tenant ID for file reference conversion. When provided and
+                      json_schema contains file reference fields (format: "dify-file-ref"),
+                      file IDs in the output will be automatically converted to File objects.
     :return: full response or stream response chunk generator result
     """
-
     # handle native json schema
     model_parameters_with_json_schema: dict[str, Any] = {
         **(model_parameters or {}),
@@ -153,8 +167,18 @@ def invoke_llm_with_structured_output(
                 f"Failed to parse structured output, LLM result is not a string: {llm_result.message.content}"
             )
 
+        structured_output = _parse_structured_output(llm_result.message.content)
+
+        # Convert file references if tenant_id is provided
+        if tenant_id is not None:
+            structured_output = convert_file_refs_in_output(
+                output=structured_output,
+                json_schema=json_schema,
+                tenant_id=tenant_id,
+            )
+
         return LLMResultWithStructuredOutput(
-            structured_output=_parse_structured_output(llm_result.message.content),
+            structured_output=structured_output,
             model=llm_result.model,
             message=llm_result.message,
             usage=llm_result.usage,
@@ -186,8 +210,18 @@ def invoke_llm_with_structured_output(
                     delta=event.delta,
                 )
 
+            structured_output = _parse_structured_output(result_text)
+
+            # Convert file references if tenant_id is provided
+            if tenant_id is not None:
+                structured_output = convert_file_refs_in_output(
+                    output=structured_output,
+                    json_schema=json_schema,
+                    tenant_id=tenant_id,
+                )
+
             yield LLMResultChunkWithStructuredOutput(
-                structured_output=_parse_structured_output(result_text),
+                structured_output=structured_output,
                 model=model_schema.model,
                 prompt_messages=prompt_messages,
                 system_fingerprint=system_fingerprint,
@@ -202,6 +236,87 @@ def invoke_llm_with_structured_output(
         return generator()
 
 
+@overload
+def invoke_llm_with_pydantic_model(
+    *,
+    provider: str,
+    model_schema: AIModelEntity,
+    model_instance: ModelInstance,
+    prompt_messages: Sequence[PromptMessage],
+    output_model: type[T],
+    model_parameters: Mapping | None = None,
+    tools: Sequence[PromptMessageTool] | None = None,
+    stop: list[str] | None = None,
+    stream: Literal[False] = False,
+    user: str | None = None,
+    callbacks: list[Callback] | None = None,
+    tenant_id: str | None = None,
+) -> LLMResultWithStructuredOutput: ...
+
+
+def invoke_llm_with_pydantic_model(
+    *,
+    provider: str,
+    model_schema: AIModelEntity,
+    model_instance: ModelInstance,
+    prompt_messages: Sequence[PromptMessage],
+    output_model: type[T],
+    model_parameters: Mapping | None = None,
+    tools: Sequence[PromptMessageTool] | None = None,
+    stop: list[str] | None = None,
+    stream: bool = False,
+    user: str | None = None,
+    callbacks: list[Callback] | None = None,
+    tenant_id: str | None = None,
+) -> LLMResultWithStructuredOutput:
+    """
+    Invoke large language model with a Pydantic output model.
+
+    This helper generates a JSON schema from the Pydantic model, invokes the
+    structured-output LLM path, and validates the result in non-streaming mode.
+    """
+    if stream:
+        raise ValueError("invoke_llm_with_pydantic_model only supports stream=False")
+
+    json_schema = _schema_from_pydantic(output_model)
+    result = invoke_llm_with_structured_output(
+        provider=provider,
+        model_schema=model_schema,
+        model_instance=model_instance,
+        prompt_messages=prompt_messages,
+        json_schema=json_schema,
+        model_parameters=model_parameters,
+        tools=tools,
+        stop=stop,
+        stream=False,
+        user=user,
+        callbacks=callbacks,
+        tenant_id=tenant_id,
+    )
+
+    structured_output = result.structured_output
+    if structured_output is None:
+        raise OutputParserError("Structured output is empty")
+
+    validated_output = _validate_structured_output(output_model, structured_output)
+    return result.model_copy(update={"structured_output": validated_output})
+
+
+def _schema_from_pydantic(output_model: type[BaseModel]) -> dict[str, Any]:
+    return output_model.model_json_schema()
+
+
+def _validate_structured_output(
+    output_model: type[T],
+    structured_output: Mapping[str, Any],
+) -> dict[str, Any]:
+    try:
+        validated_output = output_model.model_validate(structured_output)
+    except ValidationError as exc:
+        raise OutputParserError(f"Structured output validation failed: {exc}") from exc
+    return validated_output.model_dump(mode="python")
+
+
 def _handle_native_json_schema(
     provider: str,
     model_schema: AIModelEntity,

api/core/llm_generator/utils.py

@@ -0,0 +1,45 @@
+"""Utility functions for LLM generator."""
+
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageRole,
+    SystemPromptMessage,
+    ToolPromptMessage,
+    UserPromptMessage,
+)
+
+
+def deserialize_prompt_messages(messages: list[dict]) -> list[PromptMessage]:
+    """
+    Deserialize list of dicts to list[PromptMessage].
+
+    Expected format:
+        [
+            {"role": "user", "content": "..."},
+            {"role": "assistant", "content": "..."},
+        ]
+    """
+    result: list[PromptMessage] = []
+    for msg in messages:
+        role = PromptMessageRole.value_of(msg["role"])
+        content = msg.get("content", "")
+
+        match role:
+            case PromptMessageRole.USER:
+                result.append(UserPromptMessage(content=content))
+            case PromptMessageRole.ASSISTANT:
+                result.append(AssistantPromptMessage(content=content))
+            case PromptMessageRole.SYSTEM:
+                result.append(SystemPromptMessage(content=content))
+            case PromptMessageRole.TOOL:
+                result.append(ToolPromptMessage(content=content, tool_call_id=msg.get("tool_call_id", "")))
+
+    return result
+
+
+def serialize_prompt_messages(messages: list[PromptMessage]) -> list[dict]:
+    """
+    Serialize list[PromptMessage] to list of dicts.
+    """
+    return [{"role": msg.role.value, "content": msg.content} for msg in messages]

api/core/memory/README.md

@@ -0,0 +1,267 @@
+# Memory Module
+
+This module provides memory management for LLM conversations, enabling context retention across dialogue turns.
+
+## Overview
+
+The memory module contains two types of memory implementations:
+
+1. **TokenBufferMemory** - Conversation-level memory (existing)
+2. **NodeTokenBufferMemory** - Node-level memory (**Chatflow only**)
+
+> **Note**: `NodeTokenBufferMemory` is only available in **Chatflow** (advanced-chat mode).
+> This is because it requires both `conversation_id` and `node_id`, which are only present in Chatflow.
+> Standard Workflow mode does not have `conversation_id` and therefore cannot use node-level memory.
+
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                           Memory Architecture                               │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│  ┌─────────────────────────────────────────────────────────────────────-┐   │
+│  │                      TokenBufferMemory                               │   │
+│  │  Scope: Conversation                                                 │   │
+│  │  Storage: Database (Message table)                                   │   │
+│  │  Key: conversation_id                                                │   │
+│  └─────────────────────────────────────────────────────────────────────-┘   │
+│                                                                             │
+│  ┌─────────────────────────────────────────────────────────────────────-┐   │
+│  │                    NodeTokenBufferMemory                             │   │
+│  │  Scope: Node within Conversation                                     │   │
+│  │  Storage: WorkflowNodeExecutionModel.outputs["context"]              │   │
+│  │  Key: (conversation_id, node_id, workflow_run_id)                    │   │
+│  └─────────────────────────────────────────────────────────────────────-┘   │
+│                                                                             │
+└─────────────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## TokenBufferMemory (Existing)
+
+### Purpose
+
+`TokenBufferMemory` retrieves conversation history from the `Message` table and converts it to `PromptMessage` objects for LLM context.
+
+### Key Features
+
+- **Conversation-scoped**: All messages within a conversation are candidates
+- **Thread-aware**: Uses `parent_message_id` to extract only the current thread (supports regeneration scenarios)
+- **Token-limited**: Truncates history to fit within `max_token_limit`
+- **File support**: Handles `MessageFile` attachments (images, documents, etc.)
+
+### Data Flow
+
+```
+Message Table                    TokenBufferMemory              LLM
+     │                                  │                        │
+     │  SELECT * FROM messages          │                        │
+     │  WHERE conversation_id = ?       │                        │
+     │  ORDER BY created_at DESC        │                        │
+     ├─────────────────────────────────▶│                        │
+     │                                  │                        │
+     │                    extract_thread_messages()              │
+     │                                  │                        │
+     │                    build_prompt_message_with_files()      │
+     │                                  │                        │
+     │                    truncate by max_token_limit            │
+     │                                  │                        │
+     │                                  │  Sequence[PromptMessage]
+     │                                  ├───────────────────────▶│
+     │                                  │                        │
+```
+
+### Thread Extraction
+
+When a user regenerates a response, a new thread is created:
+
+```
+Message A (user)
+    └── Message A' (assistant)
+            └── Message B (user)
+                    └── Message B' (assistant)
+            └── Message A'' (assistant, regenerated)  ← New thread
+                    └── Message C (user)
+                            └── Message C' (assistant)
+```
+
+`extract_thread_messages()` traces back from the latest message using `parent_message_id` to get only the current thread: `[A, A'', C, C']`
+
+### Usage
+
+```python
+from core.memory.token_buffer_memory import TokenBufferMemory
+
+memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
+history = memory.get_history_prompt_messages(max_token_limit=2000, message_limit=100)
+```
+
+---
+
+## NodeTokenBufferMemory
+
+### Purpose
+
+`NodeTokenBufferMemory` provides **node-scoped memory** within a conversation. Each LLM node in a workflow can maintain its own independent conversation history.
+
+### Use Cases
+
+1. **Multi-LLM Workflows**: Different LLM nodes need separate context
+2. **Iterative Processing**: An LLM node in a loop needs to accumulate context across iterations
+3. **Specialized Agents**: Each agent node maintains its own dialogue history
+
+### Design: Zero Extra Storage
+
+**Key insight**: LLM node already saves complete context in `outputs["context"]`.
+
+Each LLM node execution outputs:
+```python
+outputs = {
+    "text": clean_text,
+    "context": self._build_context(prompt_messages, clean_text),  # Complete dialogue history!
+    ...
+}
+```
+
+This `outputs["context"]` contains:
+- All previous user/assistant messages (excluding system prompt)
+- The current assistant response
+
+**No separate storage needed** - we just read from the last execution's `outputs["context"]`.
+
+### Benefits
+
+| Aspect | Old Design (Object Storage) | New Design (outputs["context"]) |
+|--------|----------------------------|--------------------------------|
+| Storage | Separate JSON file | Already in WorkflowNodeExecutionModel |
+| Concurrency | Race condition risk | No issue (each execution is INSERT) |
+| Cleanup | Need separate cleanup task | Follows node execution lifecycle |
+| Migration | Required | None |
+| Complexity | High | Low |
+
+### Data Flow
+
+```
+WorkflowNodeExecutionModel        NodeTokenBufferMemory           LLM Node
+     │                                  │                           │
+     │                                  │◀── get_history_prompt_messages()
+     │                                  │                           │
+     │  SELECT outputs FROM             │                           │
+     │  workflow_node_executions        │                           │
+     │  WHERE workflow_run_id = ?       │                           │
+     │  AND node_id = ?                 │                           │
+     │◀─────────────────────────────────┤                           │
+     │                                  │                           │
+     │  outputs["context"]              │                           │
+     ├─────────────────────────────────▶│                           │
+     │                                  │                           │
+     │                    deserialize PromptMessages                │
+     │                                  │                           │
+     │                    truncate by max_token_limit               │
+     │                                  │                           │
+     │                                  │  Sequence[PromptMessage]  │
+     │                                  ├──────────────────────────▶│
+     │                                  │                           │
+```
+
+### Thread Tracking
+
+Thread extraction still uses `Message` table's `parent_message_id` structure:
+
+1. Query `Message` table for conversation → get thread's `workflow_run_ids`
+2. Get the last completed `workflow_run_id` in the thread
+3. Query `WorkflowNodeExecutionModel` for that execution's `outputs["context"]`
+
+### API
+
+```python
+class NodeTokenBufferMemory:
+    def __init__(
+        self,
+        app_id: str,
+        conversation_id: str,
+        node_id: str,
+        tenant_id: str,
+        model_instance: ModelInstance,
+    ):
+        """Initialize node-level memory."""
+        ...
+
+    def get_history_prompt_messages(
+        self,
+        *,
+        max_token_limit: int = 2000,
+        message_limit: int | None = None,
+    ) -> Sequence[PromptMessage]:
+        """
+        Retrieve history as PromptMessage sequence.
+        
+        Reads from last completed execution's outputs["context"].
+        """
+        ...
+
+    # Legacy methods (no-op, kept for compatibility)
+    def add_messages(self, *args, **kwargs) -> None: pass
+    def flush(self) -> None: pass
+    def clear(self) -> None: pass
+```
+
+### Configuration
+
+Add to `MemoryConfig` in `core/workflow/nodes/llm/entities.py`:
+
+```python
+class MemoryMode(StrEnum):
+    CONVERSATION = "conversation"  # Use TokenBufferMemory (default)
+    NODE = "node"                  # Use NodeTokenBufferMemory (Chatflow only)
+
+class MemoryConfig(BaseModel):
+    role_prefix: RolePrefix | None = None
+    window: MemoryWindowConfig | None = None
+    query_prompt_template: str | None = None
+    mode: MemoryMode = MemoryMode.CONVERSATION
+```
+
+**Mode Behavior:**
+
+| Mode           | Memory Class          | Scope                    | Availability  |
+| -------------- | --------------------- | ------------------------ | ------------- |
+| `conversation` | TokenBufferMemory     | Entire conversation      | All app modes |
+| `node`         | NodeTokenBufferMemory | Per-node in conversation | Chatflow only |
+
+> When `mode=node` is used in a non-Chatflow context (no conversation_id), it falls back to no memory.
+
+---
+
+## Comparison
+
+| Feature        | TokenBufferMemory        | NodeTokenBufferMemory              |
+| -------------- | ------------------------ | ---------------------------------- |
+| Scope          | Conversation             | Node within Conversation           |
+| Storage        | Database (Message table) | WorkflowNodeExecutionModel.outputs |
+| Thread Support | Yes                      | Yes                                |
+| File Support   | Yes (via MessageFile)    | Yes (via context serialization)    |
+| Token Limit    | Yes                      | Yes                                |
+| Use Case       | Standard chat apps       | Complex workflows                  |
+
+---
+
+## Extending to Other Nodes
+
+Currently only **LLM Node** outputs `context` in its outputs. To enable node memory for other nodes:
+
+1. Add `outputs["context"] = self._build_context(prompt_messages, response)` in the node
+2. The `NodeTokenBufferMemory` will automatically pick it up
+
+Nodes that could potentially support this:
+- `question_classifier`
+- `parameter_extractor`
+- `agent`
+
+---
+
+## Future Considerations
+
+1. **Cleanup**: Node memory lifecycle follows `WorkflowNodeExecutionModel`, which already has cleanup mechanisms
+2. **Compression**: For very long conversations, consider summarization strategies
+3. **Extension**: Other nodes may benefit from node-level memory

api/core/memory/__init__.py

@@ -0,0 +1,11 @@
+from core.memory.base import BaseMemory
+from core.memory.node_token_buffer_memory import (
+    NodeTokenBufferMemory,
+)
+from core.memory.token_buffer_memory import TokenBufferMemory
+
+__all__ = [
+    "BaseMemory",
+    "NodeTokenBufferMemory",
+    "TokenBufferMemory",
+]

api/core/memory/base.py

@@ -0,0 +1,83 @@
+"""
+Base memory interfaces and types.
+
+This module defines the common protocol for memory implementations.
+"""
+
+from abc import ABC, abstractmethod
+from collections.abc import Sequence
+
+from core.model_runtime.entities import ImagePromptMessageContent, PromptMessage
+
+
+class BaseMemory(ABC):
+    """
+    Abstract base class for memory implementations.
+
+    Provides a common interface for both conversation-level and node-level memory.
+    """
+
+    @abstractmethod
+    def get_history_prompt_messages(
+        self,
+        *,
+        max_token_limit: int = 2000,
+        message_limit: int | None = None,
+    ) -> Sequence[PromptMessage]:
+        """
+        Get history prompt messages.
+
+        :param max_token_limit: Maximum tokens for history
+        :param message_limit: Maximum number of messages
+        :return: Sequence of PromptMessage for LLM context
+        """
+        pass
+
+    def get_history_prompt_text(
+        self,
+        human_prefix: str = "Human",
+        ai_prefix: str = "Assistant",
+        max_token_limit: int = 2000,
+        message_limit: int | None = None,
+    ) -> str:
+        """
+        Get history prompt as formatted text.
+
+        :param human_prefix: Prefix for human messages
+        :param ai_prefix: Prefix for assistant messages
+        :param max_token_limit: Maximum tokens for history
+        :param message_limit: Maximum number of messages
+        :return: Formatted history text
+        """
+        from core.model_runtime.entities import (
+            PromptMessageRole,
+            TextPromptMessageContent,
+        )
+
+        prompt_messages = self.get_history_prompt_messages(
+            max_token_limit=max_token_limit,
+            message_limit=message_limit,
+        )
+
+        string_messages = []
+        for m in prompt_messages:
+            if m.role == PromptMessageRole.USER:
+                role = human_prefix
+            elif m.role == PromptMessageRole.ASSISTANT:
+                role = ai_prefix
+            else:
+                continue
+
+            if isinstance(m.content, list):
+                inner_msg = ""
+                for content in m.content:
+                    if isinstance(content, TextPromptMessageContent):
+                        inner_msg += f"{content.data}\n"
+                    elif isinstance(content, ImagePromptMessageContent):
+                        inner_msg += "[image]\n"
+                string_messages.append(f"{role}: {inner_msg.strip()}")
+            else:
+                message = f"{role}: {m.content}"
+                string_messages.append(message)
+
+        return "\n".join(string_messages)

api/core/memory/node_token_buffer_memory.py

@@ -0,0 +1,197 @@
+"""
+Node-level Token Buffer Memory for Chatflow.
+
+This module provides node-scoped memory within a conversation.
+Each LLM node in a workflow can maintain its own independent conversation history.
+
+Note: This is only available in Chatflow (advanced-chat mode) because it requires
+both conversation_id and node_id.
+
+Design:
+- History is read directly from WorkflowNodeExecutionModel.outputs["context"]
+- No separate storage needed - the context is already saved during node execution
+- Thread tracking leverages Message table's parent_message_id structure
+"""
+
+import logging
+from collections.abc import Sequence
+from typing import cast
+
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
+from core.file import file_manager
+from core.memory.base import BaseMemory
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    MultiModalPromptMessageContent,
+    PromptMessage,
+    PromptMessageRole,
+    SystemPromptMessage,
+    ToolPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.message_entities import PromptMessageContentUnionTypes
+from core.prompt.utils.extract_thread_messages import extract_thread_messages
+from extensions.ext_database import db
+from models.model import Message
+from models.workflow import WorkflowNodeExecutionModel
+
+logger = logging.getLogger(__name__)
+
+
+class NodeTokenBufferMemory(BaseMemory):
+    """
+    Node-level Token Buffer Memory.
+
+    Provides node-scoped memory within a conversation. Each LLM node can maintain
+    its own independent conversation history.
+
+    Key design: History is read directly from WorkflowNodeExecutionModel.outputs["context"],
+    which is already saved during node execution. No separate storage needed.
+    """
+
+    def __init__(
+        self,
+        app_id: str,
+        conversation_id: str,
+        node_id: str,
+        tenant_id: str,
+        model_instance: ModelInstance,
+    ):
+        self.app_id = app_id
+        self.conversation_id = conversation_id
+        self.node_id = node_id
+        self.tenant_id = tenant_id
+        self.model_instance = model_instance
+
+    def _get_thread_workflow_run_ids(self) -> list[str]:
+        """
+        Get workflow_run_ids for the current thread by querying Message table.
+        Returns workflow_run_ids in chronological order (oldest first).
+        """
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = (
+                select(Message)
+                .where(Message.conversation_id == self.conversation_id)
+                .order_by(Message.created_at.desc())
+                .limit(500)
+            )
+            messages = list(session.scalars(stmt).all())
+
+        if not messages:
+            return []
+
+        # Extract thread messages using existing logic
+        thread_messages = extract_thread_messages(messages)
+
+        # For newly created message, its answer is temporarily empty, skip it
+        if thread_messages and not thread_messages[0].answer and thread_messages[0].answer_tokens == 0:
+            thread_messages.pop(0)
+
+        # Reverse to get chronological order, extract workflow_run_ids
+        return [msg.workflow_run_id for msg in reversed(thread_messages) if msg.workflow_run_id]
+
+    def _deserialize_prompt_message(self, msg_dict: dict) -> PromptMessage:
+        """Deserialize a dict to PromptMessage based on role."""
+        role = msg_dict.get("role")
+        if role in (PromptMessageRole.USER, "user"):
+            return UserPromptMessage.model_validate(msg_dict)
+        elif role in (PromptMessageRole.ASSISTANT, "assistant"):
+            return AssistantPromptMessage.model_validate(msg_dict)
+        elif role in (PromptMessageRole.SYSTEM, "system"):
+            return SystemPromptMessage.model_validate(msg_dict)
+        elif role in (PromptMessageRole.TOOL, "tool"):
+            return ToolPromptMessage.model_validate(msg_dict)
+        else:
+            return PromptMessage.model_validate(msg_dict)
+
+    def _deserialize_context(self, context_data: list[dict]) -> list[PromptMessage]:
+        """Deserialize context data from outputs to list of PromptMessage."""
+        messages = []
+        for msg_dict in context_data:
+            try:
+                msg = self._deserialize_prompt_message(msg_dict)
+                msg = self._restore_multimodal_content(msg)
+                messages.append(msg)
+            except Exception as e:
+                logger.warning("Failed to deserialize prompt message: %s", e)
+        return messages
+
+    def _restore_multimodal_content(self, message: PromptMessage) -> PromptMessage:
+        """
+        Restore multimodal content (base64 or url) from file_ref.
+
+        When context is saved, base64_data is cleared to save storage space.
+        This method restores the content by parsing file_ref (format: "method:id_or_url").
+        """
+        content = message.content
+        if content is None or isinstance(content, str):
+            return message
+
+        # Process list content, restoring multimodal data from file references
+        restored_content: list[PromptMessageContentUnionTypes] = []
+        for item in content:
+            if isinstance(item, MultiModalPromptMessageContent):
+                # restore_multimodal_content preserves the concrete subclass type
+                restored_item = file_manager.restore_multimodal_content(item)
+                restored_content.append(cast(PromptMessageContentUnionTypes, restored_item))
+            else:
+                restored_content.append(item)
+
+        return message.model_copy(update={"content": restored_content})
+
+    def get_history_prompt_messages(
+        self,
+        *,
+        max_token_limit: int = 2000,
+        message_limit: int | None = None,
+    ) -> Sequence[PromptMessage]:
+        """
+        Retrieve history as PromptMessage sequence.
+        History is read directly from the last completed node execution's outputs["context"].
+        """
+        _ = message_limit  # unused, kept for interface compatibility
+
+        thread_workflow_run_ids = self._get_thread_workflow_run_ids()
+        if not thread_workflow_run_ids:
+            return []
+
+        # Get the last completed workflow_run_id (contains accumulated context)
+        last_run_id = thread_workflow_run_ids[-1]
+
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(WorkflowNodeExecutionModel).where(
+                WorkflowNodeExecutionModel.workflow_run_id == last_run_id,
+                WorkflowNodeExecutionModel.node_id == self.node_id,
+                WorkflowNodeExecutionModel.status == "succeeded",
+            )
+            execution = session.scalars(stmt).first()
+
+            if not execution:
+                return []
+
+            outputs = execution.outputs_dict
+            if not outputs:
+                return []
+
+            context_data = outputs.get("context")
+
+        if not context_data or not isinstance(context_data, list):
+            return []
+
+        prompt_messages = self._deserialize_context(context_data)
+        if not prompt_messages:
+            return []
+
+        # Truncate by token limit
+        try:
+            current_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
+            while current_tokens > max_token_limit and len(prompt_messages) > 1:
+                prompt_messages.pop(0)
+                current_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
+        except Exception as e:
+            logger.warning("Failed to count tokens for truncation: %s", e)
+
+        return prompt_messages

api/core/memory/token_buffer_memory.py

@@ -5,12 +5,12 @@ from sqlalchemy.orm import sessionmaker
 
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.file import file_manager
+from core.memory.base import BaseMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import (
     AssistantPromptMessage,
     ImagePromptMessageContent,
     PromptMessage,
-    PromptMessageRole,
     TextPromptMessageContent,
     UserPromptMessage,
 )
@@ -24,7 +24,7 @@ from repositories.api_workflow_run_repository import APIWorkflowRunRepository
 from repositories.factory import DifyAPIRepositoryFactory
 
 
-class TokenBufferMemory:
+class TokenBufferMemory(BaseMemory):
     def __init__(
         self,
         conversation: Conversation,
@@ -115,10 +115,14 @@ class TokenBufferMemory:
                 return AssistantPromptMessage(content=prompt_message_contents)
 
     def get_history_prompt_messages(
-        self, max_token_limit: int = 2000, message_limit: int | None = None
+        self,
+        *,
+        max_token_limit: int = 2000,
+        message_limit: int | None = None,
     ) -> Sequence[PromptMessage]:
         """
         Get history prompt messages.
+
         :param max_token_limit: max token limit
         :param message_limit: message limit
         """
@@ -200,44 +204,3 @@ class TokenBufferMemory:
                 curr_message_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
 
         return prompt_messages
-
-    def get_history_prompt_text(
-        self,
-        human_prefix: str = "Human",
-        ai_prefix: str = "Assistant",
-        max_token_limit: int = 2000,
-        message_limit: int | None = None,
-    ) -> str:
-        """
-        Get history prompt text.
-        :param human_prefix: human prefix
-        :param ai_prefix: ai prefix
-        :param max_token_limit: max token limit
-        :param message_limit: message limit
-        :return:
-        """
-        prompt_messages = self.get_history_prompt_messages(max_token_limit=max_token_limit, message_limit=message_limit)
-
-        string_messages = []
-        for m in prompt_messages:
-            if m.role == PromptMessageRole.USER:
-                role = human_prefix
-            elif m.role == PromptMessageRole.ASSISTANT:
-                role = ai_prefix
-            else:
-                continue
-
-            if isinstance(m.content, list):
-                inner_msg = ""
-                for content in m.content:
-                    if isinstance(content, TextPromptMessageContent):
-                        inner_msg += f"{content.data}\n"
-                    elif isinstance(content, ImagePromptMessageContent):
-                        inner_msg += "[image]\n"
-
-                string_messages.append(f"{role}: {inner_msg.strip()}")
-            else:
-                message = f"{role}: {m.content}"
-                string_messages.append(message)
-
-        return "\n".join(string_messages)

api/core/model_runtime/entities/message_entities.py

@@ -91,6 +91,9 @@ class MultiModalPromptMessageContent(PromptMessageContent):
     mime_type: str = Field(default=..., description="the mime type of multi-modal file")
     filename: str = Field(default="", description="the filename of multi-modal file")
 
+    # File reference for context restoration, format: "transfer_method:related_id" or "remote:url"
+    file_ref: str | None = Field(default=None, description="Encoded file reference for restoration")
+
     @property
     def data(self):
         return self.url or f"data:{self.mime_type};base64,{self.base64_data}"
@@ -276,7 +279,5 @@ class ToolPromptMessage(PromptMessage):
 
         :return: True if prompt message is empty, False otherwise
         """
-        if not super().is_empty() and not self.tool_call_id:
-            return False
-
-        return True
+        # ToolPromptMessage is not empty if it has content OR has a tool_call_id
+        return super().is_empty() and not self.tool_call_id

api/core/plugin/backwards_invocation/tool.py

@@ -4,6 +4,7 @@ from typing import Any
 from core.callback_handler.workflow_tool_callback_handler import DifyWorkflowCallbackHandler
 from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
 from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
+from core.tools.signature import sign_tool_file
 from core.tools.tool_engine import ToolEngine
 from core.tools.tool_manager import ToolManager
 from core.tools.utils.message_transformer import ToolFileMessageTransformer
@@ -41,6 +42,43 @@ class PluginToolBackwardsInvocation(BaseBackwardsInvocation):
                 response, user_id=user_id, tenant_id=tenant_id
             )
 
-            return response
+            return cls._sign_tool_file_urls(response)
         except Exception as e:
             raise e
+
+    # FIXME: this method should be gracefully deprecated
+    @classmethod
+    def _sign_tool_file_urls(
+        cls, messages: Generator[ToolInvokeMessage, None, None]
+    ) -> Generator[ToolInvokeMessage, None, None]:
+        """
+        Sign file URLs in tool invoke messages for external access.
+        """
+        for message in messages:
+            if message.type in {
+                ToolInvokeMessage.MessageType.IMAGE_LINK,
+                ToolInvokeMessage.MessageType.BINARY_LINK,
+                ToolInvokeMessage.MessageType.FILE,
+            }:
+                if isinstance(message.message, ToolInvokeMessage.TextMessage):
+                    url = message.message.text
+                    # Check if it's an unsigned internal path
+                    if url.startswith("/files/tools/"):
+                        parts = url.split("/")[-1]
+                        if "." in parts:
+                            file_id, ext = parts.rsplit(".", 1)
+                            extension = f".{ext}"
+                        else:
+                            file_id = parts
+                            extension = ".bin"
+
+                        signed_url = sign_tool_file(tool_file_id=file_id, extension=extension)
+
+                        yield ToolInvokeMessage(
+                            type=message.type,
+                            message=ToolInvokeMessage.TextMessage(text=signed_url),
+                            meta=message.meta,
+                        )
+                        continue
+
+            yield message

api/core/plugin/entities/request.py

@@ -282,3 +282,11 @@ class TriggerDispatchResponse(BaseModel):
             return deserialize_response(binascii.unhexlify(v.encode()))
         except Exception as e:
             raise ValueError("Failed to deserialize response from hex string") from e
+
+
+class RequestListTools(BaseModel):
+    """
+    Request to list all available tools
+    """
+
+    pass

api/core/prompt/advanced_prompt_transform.py

@@ -5,7 +5,7 @@ from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEnti
 from core.file import file_manager
 from core.file.models import File
 from core.helper.code_executor.jinja2.jinja2_formatter import Jinja2Formatter
-from core.memory.token_buffer_memory import TokenBufferMemory
+from core.memory.base import BaseMemory
 from core.model_runtime.entities import (
     AssistantPromptMessage,
     PromptMessage,
@@ -43,7 +43,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -84,7 +84,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -145,7 +145,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -270,7 +270,7 @@ class AdvancedPromptTransform(PromptTransform):
 
     def _set_histories_variable(
         self,
-        memory: TokenBufferMemory,
+        memory: BaseMemory,
         memory_config: MemoryConfig,
         raw_prompt: str,
         role_prefix: MemoryConfig.RolePrefix,

api/core/prompt/entities/advanced_prompt_entities.py

@@ -1,3 +1,4 @@
+from enum import StrEnum
 from typing import Literal
 
 from pydantic import BaseModel
@@ -5,6 +6,13 @@ from pydantic import BaseModel
 from core.model_runtime.entities.message_entities import PromptMessageRole
 
 
+class MemoryMode(StrEnum):
+    """Memory mode for LLM nodes."""
+
+    CONVERSATION = "conversation"  # Use TokenBufferMemory (default, existing behavior)
+    NODE = "node"  # Use NodeTokenBufferMemory (Chatflow only)
+
+
 class ChatModelMessage(BaseModel):
     """
     Chat Message.
@@ -48,3 +56,4 @@ class MemoryConfig(BaseModel):
     role_prefix: RolePrefix | None = None
     window: WindowConfig
     query_prompt_template: str | None = None
+    mode: MemoryMode = MemoryMode.CONVERSATION

api/core/prompt/prompt_transform.py

@@ -1,7 +1,7 @@
 from typing import Any
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
-from core.memory.token_buffer_memory import TokenBufferMemory
+from core.memory.base import BaseMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.message_entities import PromptMessage
 from core.model_runtime.entities.model_entities import ModelPropertyKey
@@ -11,7 +11,7 @@ from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 class PromptTransform:
     def _append_chat_histories(
         self,
-        memory: TokenBufferMemory,
+        memory: BaseMemory,
         memory_config: MemoryConfig,
         prompt_messages: list[PromptMessage],
         model_config: ModelConfigWithCredentialsEntity,
@@ -52,7 +52,7 @@ class PromptTransform:
 
     def _get_history_messages_from_memory(
         self,
-        memory: TokenBufferMemory,
+        memory: BaseMemory,
         memory_config: MemoryConfig,
         max_token_limit: int,
         human_prefix: str | None = None,
@@ -73,7 +73,7 @@ class PromptTransform:
         return memory.get_history_prompt_text(**kwargs)
 
     def _get_history_messages_list_from_memory(
-        self, memory: TokenBufferMemory, memory_config: MemoryConfig, max_token_limit: int
+        self, memory: BaseMemory, memory_config: MemoryConfig, max_token_limit: int
     ) -> list[PromptMessage]:
         """Get memory messages."""
         return list(

api/core/repositories/sqlalchemy_workflow_node_execution_repository.py

@@ -29,6 +29,7 @@ from models import (
     Account,
     CreatorUserRole,
     EndUser,
+    LLMGenerationDetail,
     WorkflowNodeExecutionModel,
     WorkflowNodeExecutionTriggeredFrom,
 )
@@ -457,6 +458,113 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             session.merge(db_model)
             session.flush()
 
+            # Save LLMGenerationDetail for LLM nodes with successful execution
+            if (
+                domain_model.node_type == NodeType.LLM
+                and domain_model.status == WorkflowNodeExecutionStatus.SUCCEEDED
+                and domain_model.outputs is not None
+            ):
+                self._save_llm_generation_detail(session, domain_model)
+
+    def _save_llm_generation_detail(self, session, execution: WorkflowNodeExecution) -> None:
+        """
+        Save LLM generation detail for LLM nodes.
+        Extracts reasoning_content, tool_calls, and sequence from outputs and metadata.
+        """
+        outputs = execution.outputs or {}
+        metadata = execution.metadata or {}
+
+        reasoning_list = self._extract_reasoning(outputs)
+        tool_calls_list = self._extract_tool_calls(metadata.get(WorkflowNodeExecutionMetadataKey.AGENT_LOG))
+
+        if not reasoning_list and not tool_calls_list:
+            return
+
+        sequence = self._build_generation_sequence(outputs.get("text", ""), reasoning_list, tool_calls_list)
+        self._upsert_generation_detail(session, execution, reasoning_list, tool_calls_list, sequence)
+
+    def _extract_reasoning(self, outputs: Mapping[str, Any]) -> list[str]:
+        """Extract reasoning_content as a clean list of non-empty strings."""
+        reasoning_content = outputs.get("reasoning_content")
+        if isinstance(reasoning_content, str):
+            trimmed = reasoning_content.strip()
+            return [trimmed] if trimmed else []
+        if isinstance(reasoning_content, list):
+            return [item.strip() for item in reasoning_content if isinstance(item, str) and item.strip()]
+        return []
+
+    def _extract_tool_calls(self, agent_log: Any) -> list[dict[str, str]]:
+        """Extract tool call records from agent logs."""
+        if not agent_log or not isinstance(agent_log, list):
+            return []
+
+        tool_calls: list[dict[str, str]] = []
+        for log in agent_log:
+            log_data = log.data if hasattr(log, "data") else (log.get("data", {}) if isinstance(log, dict) else {})
+            tool_name = log_data.get("tool_name")
+            if tool_name and str(tool_name).strip():
+                tool_calls.append(
+                    {
+                        "id": log_data.get("tool_call_id", ""),
+                        "name": tool_name,
+                        "arguments": json.dumps(log_data.get("tool_args", {})),
+                        "result": str(log_data.get("output", "")),
+                    }
+                )
+        return tool_calls
+
+    def _build_generation_sequence(
+        self, text: str, reasoning_list: list[str], tool_calls_list: list[dict[str, str]]
+    ) -> list[dict[str, Any]]:
+        """Build a simple content/reasoning/tool_call sequence."""
+        sequence: list[dict[str, Any]] = []
+        if text:
+            sequence.append({"type": "content", "start": 0, "end": len(text)})
+        for index in range(len(reasoning_list)):
+            sequence.append({"type": "reasoning", "index": index})
+        for index in range(len(tool_calls_list)):
+            sequence.append({"type": "tool_call", "index": index})
+        return sequence
+
+    def _upsert_generation_detail(
+        self,
+        session,
+        execution: WorkflowNodeExecution,
+        reasoning_list: list[str],
+        tool_calls_list: list[dict[str, str]],
+        sequence: list[dict[str, Any]],
+    ) -> None:
+        """Insert or update LLMGenerationDetail with serialized fields."""
+        existing = (
+            session.query(LLMGenerationDetail)
+            .filter_by(
+                workflow_run_id=execution.workflow_execution_id,
+                node_id=execution.node_id,
+            )
+            .first()
+        )
+
+        reasoning_json = json.dumps(reasoning_list) if reasoning_list else None
+        tool_calls_json = json.dumps(tool_calls_list) if tool_calls_list else None
+        sequence_json = json.dumps(sequence) if sequence else None
+
+        if existing:
+            existing.reasoning_content = reasoning_json
+            existing.tool_calls = tool_calls_json
+            existing.sequence = sequence_json
+            return
+
+        generation_detail = LLMGenerationDetail(
+            tenant_id=self._tenant_id,
+            app_id=self._app_id,
+            workflow_run_id=execution.workflow_execution_id,
+            node_id=execution.node_id,
+            reasoning_content=reasoning_json,
+            tool_calls=tool_calls_json,
+            sequence=sequence_json,
+        )
+        session.add(generation_detail)
+
     def get_db_models_by_workflow_run(
         self,
         workflow_run_id: str,

api/core/sandbox/__init__.py

@@ -0,0 +1,41 @@
+from .bash.dify_cli import (
+    DifyCliBinary,
+    DifyCliConfig,
+    DifyCliEnvConfig,
+    DifyCliLocator,
+    DifyCliToolConfig,
+)
+from .bash.session import SandboxBashSession
+from .builder import SandboxBuilder, VMConfig
+from .entities import AppAssets, DifyCli, SandboxProviderApiEntity, SandboxType
+from .initializer import AppAssetsInitializer, DifyCliInitializer, SandboxInitializer
+from .manager import SandboxManager
+from .sandbox import Sandbox
+from .storage import ArchiveSandboxStorage, SandboxStorage
+from .utils.debug import sandbox_debug
+from .utils.encryption import create_sandbox_config_encrypter, masked_config
+
+__all__ = [
+    "AppAssets",
+    "AppAssetsInitializer",
+    "ArchiveSandboxStorage",
+    "DifyCli",
+    "DifyCliBinary",
+    "DifyCliConfig",
+    "DifyCliEnvConfig",
+    "DifyCliInitializer",
+    "DifyCliLocator",
+    "DifyCliToolConfig",
+    "Sandbox",
+    "SandboxBashSession",
+    "SandboxBuilder",
+    "SandboxInitializer",
+    "SandboxManager",
+    "SandboxProviderApiEntity",
+    "SandboxStorage",
+    "SandboxType",
+    "VMConfig",
+    "create_sandbox_config_encrypter",
+    "masked_config",
+    "sandbox_debug",
+]

api/core/sandbox/bash/TODO.md

@@ -0,0 +1 @@
+# refactor the package import paths

api/core/sandbox/bash/__init__.py

@@ -0,0 +1,15 @@
+from .dify_cli import (
+    DifyCliBinary,
+    DifyCliConfig,
+    DifyCliEnvConfig,
+    DifyCliLocator,
+    DifyCliToolConfig,
+)
+
+__all__ = [
+    "DifyCliBinary",
+    "DifyCliConfig",
+    "DifyCliEnvConfig",
+    "DifyCliLocator",
+    "DifyCliToolConfig",
+]