a3cfc1216d
Implements the third Stage 4 backend piece: a producer-organized read view of
declared outputs per workflow run that backs PRD §Node Output Inspector.
Three console REST endpoints under
``/apps/{app_id}/workflows/draft/runs/{run_id}/node-outputs[/...]``:
- ``GET .../node-outputs`` — full per-node snapshot of the run
- ``GET .../node-outputs/{node_id}`` — single node detail
- ``GET .../node-outputs/{node_id}/{output_name}/preview`` — full value with
signed download URL for file refs
Design choices baked in (per Stage 4 design doc §8 / decisions):
- **No new tables.** Topology reads from ``WorkflowRun.graph`` (snapshot at
execution time so the view stays stable even if the draft was edited
after the run started). Execution facts come from
``WorkflowNodeExecutionModel`` rows already produced by the runtime.
- **D-1 draft only.** Service 404s ``triggered_from != DEBUGGING`` with
``published_run_inspector_not_implemented``; published-run support stays
deferred to stage 4.1.
- **Declared outputs per node kind.** Agent v2 nodes resolve their declared
list via the existing ``WorkflowAgentBindingResolver`` (so PRD defaults
flow through too). Non-agent nodes don't have a declared schema yet —
the service surfaces produced payload keys with ``type=None`` so the
panel can still render them.
- **Per-output status** derives from the metadata the §5/§6 stack already
records (``output_type_check`` / ``output_check`` blobs in
``execution_metadata``). Precedence: type-check failure > output-check
failure > not-produced > ready.
- **File preview URLs** are minted via ``graphon.file.helpers.get_signed_file_url``
and merged into the value dict so the panel doesn't need a second round
trip.
- **Retries** read from ``execution_metadata.attempt`` so the frontend can
show the retry count without separate plumbing.
**SSE stream from §8.5 is intentionally deferred to a follow-up PR** — the
event bus integration + envelope shared with babysit chat warrants its own
review. The three snapshot endpoints unblock the frontend's polling MVP.
Auth pattern reuses ``@get_app_model`` (workflow / advanced-chat apps only),
matching ``workflow_run.py`` controllers.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Dify Cloud · Self-hosting · Documentation · Dify edition overview
Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including Opik, Langfuse, and Arize Phoenix) and more, letting you quickly go from prototype to production. Here's a list of the core features:
Quick start
Before installing Dify, make sure your machine meets the following minimum system requirements:
- CPU >= 2 Core
- RAM >= 4 GiB
The easiest way to start the Dify server is through Docker Compose. Before running Dify with the following commands, make sure that Docker and Docker Compose are installed on your machine:
cd dify
cd docker
cp .env.example .env
docker compose up -d
After running, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process.
Seeking help
Please refer to our FAQ if you encounter problems setting up Dify. Reach out to the community and us if you are still having issues.
If you'd like to contribute to Dify or do additional development, refer to our guide to deploying from source code
Key features
1. Workflow: Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found here.
3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
5. Agent capabilities: You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.
6. LLMOps: Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
Using Dify
-
Cloud
We host a Dify Cloud service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan. -
Self-hosting Dify Community Edition
Quickly get Dify running in your environment with this starter guide. Use our documentation for further references and more in-depth instructions. -
Dify for enterprise / organizations
We provide additional enterprise-centric features. Send us an email to discuss your enterprise needs.For startups and small businesses using AWS, check out Dify Premium on AWS Marketplace and deploy it to your own AWS VPC with one click. It's an affordable AMI offering with the option to create apps with custom logo and branding.
Staying ahead
Star Dify on GitHub and be instantly notified of new releases.
Advanced Setup
Custom configurations
If you need to customize the configuration, edit docker/.env. The essential startup defaults live in docker/.env.example, and optional advanced variables are split under docker/envs/ by theme. After making any changes, re-run docker compose up -d from the docker directory. You can find the full list of available environment variables here.
Metrics Monitoring with Grafana
Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.
Deployment with Kubernetes
If you'd like to configure a highly available setup, there are community-contributed Helm Charts and YAML files which allow Dify to be deployed on Kubernetes.
- Helm Chart by @LeoQuote
- Helm Chart by @BorisPolonsky
- Helm Chart by @magicsong
- YAML file by @Winson-030
- YAML file by @wyy-holding
- 🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym
Using Terraform for Deployment
Deploy Dify to Cloud Platform with a single click using terraform
Azure Global
Google Cloud
Using AWS CDK for Deployment
Deploy Dify to AWS with CDK
AWS
Using Alibaba Cloud Computing Nest
Quickly deploy Dify to Alibaba cloud with Alibaba Cloud Computing Nest
Using Alibaba Cloud Data Management
One-Click deploy Dify to Alibaba Cloud with Alibaba Cloud Data Management
Deploy to AKS with Azure Devops Pipeline
One-Click deploy Dify to AKS with Azure Devops Pipeline Helm Chart by @LeoZhang
Contributing
For those who'd like to contribute code, see our Contribution Guide. At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.
We are looking for contributors to help translate Dify into languages other than Mandarin or English. If you are interested in helping, please see the i18n README for more information, and leave us a comment in the
global-userschannel of our Discord Community Server.
Community & contact
- GitHub Discussion. Best for: sharing feedback and asking questions.
- GitHub Issues. Best for: bugs you encounter using Dify.AI, and feature proposals. See our Contribution Guide.
- Discord. Best for: sharing your applications and hanging out with the community.
- X(Twitter). Best for: sharing your applications and hanging out with the community.
Contributors
Star history
Security disclosure
To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to security@dify.ai, and our team will respond with detailed answer.
License
This repository is licensed under the Dify Open Source License, based on Apache 2.0 with additional conditions.