diff --git a/vllm/entrypoints/context.py b/vllm/entrypoints/context.py
new file mode 100644
index 0000000000..6292306e7c
--- /dev/null
+++ b/vllm/entrypoints/context.py
@@ -0,0 +1,177 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import logging
+from abc import ABC, abstractmethod
+
+from openai_harmony import Message, Role, StreamState
+
+from vllm.entrypoints.harmony_utils import (
+    get_encoding, get_streamable_parser_for_assistant, render_for_completion)
+from vllm.entrypoints.tool import Tool
+from vllm.outputs import RequestOutput
+
+logger = logging.getLogger(__name__)
+
+
+class ConversationContext(ABC):
+
+    @abstractmethod
+    def append_output(self, output) -> None:
+        pass
+
+    @abstractmethod
+    async def call_tool(self) -> list[Message]:
+        pass
+
+    @abstractmethod
+    def need_builtin_tool_call(self) -> bool:
+        pass
+
+    @abstractmethod
+    def render_for_completion(self) -> list[int]:
+        pass
+
+
+class SimpleContext(ConversationContext):
+
+    def __init__(self):
+        self.last_output = None
+
+    def append_output(self, output) -> None:
+        self.last_output = output
+
+    def need_builtin_tool_call(self) -> bool:
+        return False
+
+    async def call_tool(self) -> list[Message]:
+        raise NotImplementedError("Should not be called.")
+
+    def render_for_completion(self) -> list[int]:
+        raise NotImplementedError("Should not be called.")
+
+
+class HarmonyContext(ConversationContext):
+
+    def __init__(
+        self,
+        messages: list,
+        tool_sessions: dict[str, Tool],
+    ):
+        self._messages = messages
+        self.tool_sessions = tool_sessions
+
+        self.parser = get_streamable_parser_for_assistant()
+        self.num_init_messages = len(messages)
+        # TODO(woosuk): Implement the following fields.
+        self.num_prompt_tokens = 0
+        self.num_cached_tokens = 0
+        self.num_output_tokens = 0
+        self.num_reasoning_tokens = 0
+
+    def append_output(self, output) -> None:
+        if isinstance(output, RequestOutput):
+            output_token_ids = output.outputs[0].token_ids
+            for token_id in output_token_ids:
+                self.parser.process(token_id)
+            output_msgs = self.parser.messages
+        else:
+            # Tool output.
+            output_msgs = output
+        self._messages.extend(output_msgs)
+
+    @property
+    def messages(self) -> list:
+        return self._messages
+
+    def need_builtin_tool_call(self) -> bool:
+        last_msg = self.messages[-1]
+        recipient = last_msg.recipient
+        return recipient is not None and (recipient.startswith("browser.")
+                                          or recipient.startswith("python"))
+
+    async def call_tool(self) -> list[Message]:
+        if not self.messages:
+            return []
+        last_msg = self.messages[-1]
+        recipient = last_msg.recipient
+        if recipient is not None:
+            if recipient.startswith("browser."):
+                return await self.call_search_tool(
+                    self.tool_sessions["browser"], last_msg)
+            elif recipient.startswith("python"):
+                return await self.call_python_tool(
+                    self.tool_sessions["python"], last_msg)
+        raise ValueError("No tool call found")
+
+    def render_for_completion(self) -> list[int]:
+        return render_for_completion(self.messages)
+
+    async def call_search_tool(
+        self,
+        tool_session: Tool,
+        last_msg: Message,
+    ) -> list[Message]:
+        return await tool_session.get_result(self)
+
+    async def call_python_tool(
+        self,
+        tool_session: Tool,
+        last_msg: Message,
+    ) -> list[Message]:
+        return await tool_session.get_result(self)
+
+
+class StreamingHarmonyContext(HarmonyContext):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.last_output = None
+
+        self.parser = get_streamable_parser_for_assistant()
+        self.encoding = get_encoding()
+        self.last_tok = None
+
+    @property
+    def messages(self) -> list:
+        return self.parser.messages
+
+    def append_output(self, output) -> None:
+        if isinstance(output, RequestOutput):
+            tok = output.outputs[0].token_ids[0]
+            self.parser.process(tok)
+            self.last_tok = tok
+        else:
+            # Handle the case of tool output in direct message format
+            assert len(output) == 1, "Tool output should be a single message"
+            msg = output[0]
+            # Sometimes the recipient is not set for tool messages,
+            # so we set it to "assistant"
+            if msg.author.role == Role.TOOL and msg.recipient is None:
+                msg.recipient = "assistant"
+            toks = self.encoding.render(msg)
+            for tok in toks:
+                self.parser.process(tok)
+            self.last_tok = toks[-1]
+
+    def is_expecting_start(self) -> bool:
+        return self.parser.state == StreamState.EXPECT_START
+
+    def is_assistant_action_turn(self) -> bool:
+        return self.last_tok in self.encoding.stop_tokens_for_assistant_actions(
+        )
+
+    def render_for_completion(self) -> list[int]:
+        # now this list of tokens as next turn's starting tokens
+        # `<|start|>assistant``,
+        # we need to process them in parser.
+        rendered_tokens = super().render_for_completion()
+
+        last_n = -1
+        to_process = []
+        while rendered_tokens[last_n] != self.last_tok:
+            to_process.append(rendered_tokens[last_n])
+            last_n -= 1
+        for tok in reversed(to_process):
+            self.parser.process(tok)
+
+        return rendered_tokens
diff --git a/vllm/entrypoints/harmony_utils.py b/vllm/entrypoints/harmony_utils.py
new file mode 100644
index 0000000000..801c82b4fa
--- /dev/null
+++ b/vllm/entrypoints/harmony_utils.py
@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import datetime
+from typing import Literal, Optional
+
+from openai.types.responses.tool import Tool
+from openai_harmony import (Conversation, DeveloperContent,
+                            HarmonyEncodingName, Message, ReasoningEffort,
+                            Role, StreamableParser, SystemContent, TextContent,
+                            ToolDescription, load_harmony_encoding)
+
+REASONING_EFFORT = {
+    "high": ReasoningEffort.HIGH,
+    "medium": ReasoningEffort.MEDIUM,
+    "low": ReasoningEffort.LOW,
+}
+
+_harmony_encoding = None
+
+
+def get_encoding():
+    global _harmony_encoding
+    if _harmony_encoding is None:
+        _harmony_encoding = load_harmony_encoding(
+            HarmonyEncodingName.HARMONY_GPT_OSS)
+    return _harmony_encoding
+
+
+def get_system_message(
+    model_identity: Optional[str] = None,
+    reasoning_effort: Optional[Literal["high", "medium", "low"]] = None,
+    start_date: Optional[str] = None,
+    browser_description: Optional[str] = None,
+    python_description: Optional[str] = None,
+) -> Message:
+    sys_msg_content = SystemContent.new()
+    if model_identity is not None:
+        sys_msg_content = sys_msg_content.with_model_identity(model_identity)
+    if reasoning_effort is not None:
+        sys_msg_content = sys_msg_content.with_reasoning_effort(
+            REASONING_EFFORT[reasoning_effort])
+    if start_date is None:
+        # NOTE(woosuk): This brings non-determinism in vLLM. Be careful.
+        start_date = datetime.datetime.now().strftime("%Y-%m-%d")
+    sys_msg_content = sys_msg_content.with_conversation_start_date(start_date)
+    if browser_description is not None:
+        sys_msg_content = sys_msg_content.with_tools(browser_description)
+    if python_description is not None:
+        sys_msg_content = sys_msg_content.with_tools(python_description)
+    sys_msg = Message.from_role_and_content(Role.SYSTEM, sys_msg_content)
+    return sys_msg
+
+
+def get_developer_message(instructions: Optional[str] = None,
+                          tools: Optional[list[Tool]] = None) -> Message:
+    dev_msg_content = DeveloperContent.new()
+    if instructions is not None:
+        dev_msg_content = dev_msg_content.with_instructions(instructions)
+    if tools is not None:
+        function_tools = []
+        for tool in tools:
+            if tool.type in ("web_search_preview", "code_interpreter"):
+                # These are built-in tools that are added to the system message.
+                pass
+            elif tool.type == "function":
+                function_tools.append(tool)
+            else:
+                raise ValueError(f"tool type {tool.type} not supported")
+        if function_tools:
+            function_tool_descriptions = [
+                ToolDescription.new(
+                    name=tool.name,
+                    description=tool.description,
+                    parameters=tool.parameters,
+                ) for tool in function_tools
+            ]
+            dev_msg_content = dev_msg_content.with_function_tools(
+                function_tool_descriptions)
+    dev_msg = Message.from_role_and_content(Role.DEVELOPER, dev_msg_content)
+    return dev_msg
+
+
+def get_user_message(content: str) -> Message:
+    return Message.from_role_and_content(Role.USER, content)
+
+
+def parse_chat_input(chat_msg) -> Message:
+    role = chat_msg["role"]
+    content = chat_msg["content"]
+    if isinstance(content, str):
+        contents = [TextContent(text=content)]
+    else:
+        # TODO: Support refusal.
+        contents = [TextContent(text=c["text"]) for c in content]
+    msg = Message.from_role_and_contents(role, contents)
+    return msg
+
+
+def render_for_completion(messages: list[Message]) -> list[int]:
+    conversation = Conversation.from_messages(messages)
+    token_ids = get_encoding().render_conversation_for_completion(
+        conversation, Role.ASSISTANT)
+    return token_ids
+
+
+def get_stop_tokens_for_assistant_actions() -> list[int]:
+    return get_encoding().stop_tokens_for_assistant_actions()
+
+
+def get_streamable_parser_for_assistant() -> StreamableParser:
+    return StreamableParser(get_encoding(), role=Role.ASSISTANT)
diff --git a/vllm/entrypoints/tool.py b/vllm/entrypoints/tool.py
new file mode 100644
index 0000000000..01ee77414f
--- /dev/null
+++ b/vllm/entrypoints/tool.py
@@ -0,0 +1,87 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any
+
+from vllm.logger import init_logger
+
+if TYPE_CHECKING:
+    # Avoid circular import.
+    from vllm.entrypoints.context import ConversationContext
+
+logger = init_logger(__name__)
+
+
+class Tool(ABC):
+
+    @abstractmethod
+    async def get_result(self, context: "ConversationContext") -> Any:
+        pass
+
+
+class HarmonyBrowserTool(Tool):
+
+    def __init__(self):
+        self.enabled = True
+        exa_api_key = os.getenv("EXA_API_KEY")
+        if not exa_api_key:
+            self.enabled = False
+            logger.warning_once("EXA_API_KEY is not set, browsing is disabled")
+            return
+
+        try:
+            from gpt_oss.tools.simple_browser import SimpleBrowserTool
+            from gpt_oss.tools.simple_browser.backend import ExaBackend
+        except ImportError:
+            self.enabled = False
+            logger.warning_once(
+                "gpt_oss is not installed, browsing is disabled")
+            return
+
+        browser_backend = ExaBackend(source="web", api_key=exa_api_key)
+        self.browser_tool = SimpleBrowserTool(backend=browser_backend)
+        logger.info_once("Browser tool initialized")
+
+    async def get_result(self, context: "ConversationContext") -> Any:
+        from vllm.entrypoints.context import HarmonyContext
+        assert isinstance(context, HarmonyContext)
+        last_msg = context.messages[-1]
+        tool_output_msgs = []
+        async for msg in self.browser_tool.process(last_msg):
+            tool_output_msgs.append(msg)
+        return tool_output_msgs
+
+    @property
+    def tool_config(self) -> Any:
+        return self.browser_tool.tool_config
+
+
+class HarmonyPythonTool(Tool):
+
+    def __init__(self):
+        self.enabled = True
+
+        try:
+            from gpt_oss.tools.python_docker.docker_tool import PythonTool
+        except ImportError:
+            self.enabled = False
+            logger.warning_once(
+                "gpt_oss is not installed, code interpreter is disabled")
+            return
+
+        self.python_tool = PythonTool()
+        logger.info_once("Code interpreter tool initialized")
+
+    async def get_result(self, context: "ConversationContext") -> Any:
+        from vllm.entrypoints.context import HarmonyContext
+        assert isinstance(context, HarmonyContext)
+        last_msg = context.messages[-1]
+        tool_output_msgs = []
+        async for msg in self.python_tool.process(last_msg):
+            tool_output_msgs.append(msg)
+        return tool_output_msgs
+
+    @property
+    def tool_config(self) -> Any:
+        return self.python_tool.tool_config