Graph

---
description: Imported skill graph from langchain
name: graph
signature: 78e79f48788b906d2926af6511c6dce017171f4ec062e115bec6713ccc282f4d
source: /a0/tmp/skills_research/langchain/libs/deepagents/deepagents/graph.py
---

"""Deepagents come with planning, filesystem, and subagents."""

from collections.abc import Callable, Sequence
from typing import Any

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware, InterruptOnConfig, TodoListMiddleware
from langchain.agents.middleware.types import AgentMiddleware
from langchain.agents.structured_output import ResponseFormat
from langchain.chat_models import init_chat_model
from langchain_anthropic import ChatAnthropic
from langchain_anthropic.middleware import AnthropicPromptCachingMiddleware
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import SystemMessage
from langchain_core.tools import BaseTool
from langgraph.cache.base import BaseCache
from langgraph.graph.state import CompiledStateGraph
from langgraph.store.base import BaseStore
from langgraph.types import Checkpointer

from deepagents.backends import StateBackend
from deepagents.backends.protocol import BackendFactory, BackendProtocol
from deepagents.middleware.filesystem import FilesystemMiddleware
from deepagents.middleware.memory import MemoryMiddleware
from deepagents.middleware.patch_tool_calls import PatchToolCallsMiddleware
from deepagents.middleware.skills import SkillsMiddleware
from deepagents.middleware.subagents import CompiledSubAgent, SubAgent, SubAgentMiddleware
from deepagents.middleware.summarization import SummarizationMiddleware

BASE_AGENT_PROMPT = "In order to complete the objective that the user asks of you, you have access to a number of standard tools."


def get_default_model() -> ChatAnthropic:
    """Get the default model for deep agents.

    Returns:
        `ChatAnthropic` instance configured with Claude Sonnet 4.5.
    """
    return ChatAnthropic(
        model_name="claude-sonnet-4-5-20250929",
        max_tokens=20000,  # type: ignore[call-arg]
    )


def create_deep_agent(
    model: str | BaseChatModel | None = None,
    tools: Sequence[BaseTool | Callable | dict[str, Any]] | None = None,
    *,
    system_prompt: str | SystemMessage | None = None,
    middleware: Sequence[AgentMiddleware] = (),
    subagents: list[SubAgent | CompiledSubAgent] | None = None,
    skills: list[str] | None = None,
    memory: list[str] | None = None,
    response_format: ResponseFormat | None = None,
    context_schema: type[Any] | None = None,
    checkpointer: Checkpointer | None = None,
    store: BaseStore | None = None,
    backend: BackendProtocol | BackendFactory | None = None,
    interrupt_on: dict[str, bool | InterruptOnConfig] | None = None,
    debug: bool = False,
    name: str | None = None,
    cache: BaseCache | None = None,
) -> CompiledStateGraph:
    """Create a deep agent.

    !!! warning "Deep agents require a LLM that supports tool calling!"

    By default, this agent has access to the following tools:

    - `write_todos`: manage a todo list
    - `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`: file operations
    - `execute`: run shell commands
    - `task`: call subagents

    The `execute` tool allows running shell commands if the backend implements `SandboxBackendProtocol`.
    For non-sandbox backends, the `execute` tool will return an error message.

    Args:
        model: The model to use.

            Defaults to `claude-sonnet-4-5-20250929`.

            Use the `provider:model` format (e.g., `openai:gpt-5`) to quickly switch between models.
        tools: The tools the agent should have access to.

            In addition to custom tools you provide, deep agents include built-in tools for planning,
            file management, and subagent spawning.
        system_prompt: Custom system instructions to prepend before the base deep agent
            prompt.

            If a string, it's concatenated with the base prompt.
        middleware: Additional middleware to apply after the standard middleware stack
            (`TodoListMiddleware`, `FilesystemMiddleware`, `SubAgentMiddleware`,
            `SummarizationMiddleware`, `AnthropicPromptCachingMiddleware`,
            `PatchToolCallsMiddleware`).
        subagents: The subagents to use.

            Each subagent should be a `dict` with the following keys:

            - `name`
            - `description` (used by the main agent to decide whether to call the sub agent)
            - `prompt` (used as the system prompt in the subagent)
            - (optional) `tools`
            - (optional) `model` (either a `LanguageModelLike` instance or `dict` settings)
            - (optional) `middleware` (list of `AgentMiddleware`)
        skills: Optional list of skill source paths (e.g., `["/skills/user/", "/skills/project/"]`).

            Paths must be specified using POSIX conventions (forward slashes) and are relative
            to the backend's root. When using `StateBackend` (default), provide skill files via
            `invoke(files={...})`. With `FilesystemBackend`, skills are loaded from disk relative
            to the backend's `root_dir`. Later sources override earlier ones for skills with the
            same name (last one wins).
        memory: Optional list of memory file paths (`AGENTS.md` files) to load
            (e.g., `["/memory/AGENTS.md"]`).

            Display names are automatically derived from paths.

            Memory is loaded at agent startup and added into the system prompt.
        response_format: A structured output response format to use for the agent.
        context_schema: The schema of the deep agent.
        checkpointer: Optional `Checkpointer` for persisting agent state between runs.
        store: Optional store for persistent storage (required if backend uses `StoreBackend`).
        backend: Optional backend for file storage and execution.

            Pass either a `Backend` instance or a callable factory like `lambda rt: StateBackend(rt)`.
            For execution support, use a backend that implements `SandboxBackendProtocol`.
        interrupt_on: Mapping of tool names to interrupt configs.

            Pass to pause agent execution at specified tool calls for human approval or modification.

            Example: `interrupt_on={"edit_file": True}` pauses before every edit.
        debug: Whether to enable debug mode. Passed through to `create_agent`.
        name: The name of the agent. Passed through to `create_agent`.
        cache: The cache to use for the agent. Passed through to `create_agent`.

    Returns:
        A configured deep agent.
    """
    if model is None:
        model = get_default_model()
    elif isinstance(model, str):
        model = init_chat_model(model)

    if (
        model.profile is not None
        and isinstance(model.profile, dict)
        and "max_input_tokens" in model.profile
        and isinstance(model.profile["max_input_tokens"], int)
    ):
        trigger = ("fraction", 0.85)
        keep = ("fraction", 0.10)
    else:
        trigger = ("tokens", 170000)
        keep = ("messages", 6)

    # Build middleware stack for subagents (includes skills if provided)
    subagent_middleware: list[AgentMiddleware] = [
        TodoListMiddleware(),
    ]

    backend = backend if backend is not None else (lambda rt: StateBackend(rt))

    if skills is not None:
        subagent_middleware.append(SkillsMiddleware(backend=backend, sources=skills))
    subagent_middleware.extend(
        [
            FilesystemMiddleware(backend=backend),
            SummarizationMiddleware(
                model=model,
                backend=backend,
                trigger=trigger,
                keep=keep,
                trim_tokens_to_summarize=None,
            ),
            AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"),
            PatchToolCallsMiddleware(),
        ]
    )

    # Build main agent middleware stack
    deepagent_middleware: list[AgentMiddleware] = [
        TodoListMiddleware(),
    ]
    if memory is not None:
        deepagent_middleware.append(MemoryMiddleware(backend=backend, sources=memory))
    if skills is not None:
        deepagent_middleware.append(SkillsMiddleware(backend=backend, sources=skills))
    deepagent_middleware.extend(
        [
            FilesystemMiddleware(backend=backend),
            SubAgentMiddleware(
                default_model=model,
                default_tools=tools,
                subagents=subagents if subagents is not None else [],
                default_middleware=subagent_middleware,
                default_interrupt_on=interrupt_on,
                general_purpose_agent=True,
            ),
            SummarizationMiddleware(
                model=model,
                backend=backend,
                trigger=trigger,
                keep=keep,
                trim_tokens_to_summarize=None,
            ),
            AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"),
            PatchToolCallsMiddleware(),
        ]
    )
    if middleware:
        deepagent_middleware.extend(middleware)
    if interrupt_on is not None:
        deepagent_middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))

    # Combine system_prompt with BASE_AGENT_PROMPT
    if system_prompt is None:
        final_system_prompt: str | SystemMessage = BASE_AGENT_PROMPT
    elif isinstance(system_prompt, SystemMessage):
        # SystemMessage: append BASE_AGENT_PROMPT to content_blocks
        new_content = [
            *system_prompt.content_blocks,
            {"type": "text", "text": f"\n\n{BASE_AGENT_PROMPT}"},
        ]
        final_system_prompt = SystemMessage(content=new_content)
    else:
        # String: simple concatenation
        final_system_prompt = system_prompt + "\n\n" + BASE_AGENT_PROMPT

    return create_agent(
        model,
        system_prompt=final_system_prompt,
        tools=tools,
        middleware=deepagent_middleware,
        response_format=response_format,
        context_schema=context_schema,
        checkpointer=checkpointer,
        store=store,
        debug=debug,
        name=name,
        cache=cache,
    ).with_config({"recursion_limit": 1000})