langchain-acp 0.9.0

Expose LangChain and LangGraph agents through ACP Kit.

langchain-acp

langchain-acp exposes LangChain, LangGraph, and DeepAgents graphs through ACP Kit.

It keeps ACP Kit's adapter architecture intact while staying graph-centric on the LangChain side:

• graph=...
• graph_factory=...
• graph_source=...

Install

uv add langchain-acp

pip install langchain-acp

With optional DeepAgents compatibility:

uv add "langchain-acp[deepagents]"

pip install "langchain-acp[deepagents]"

Contributor setup from the monorepo root:

uv sync --extra dev --extra langchain

Quickstart

from langchain.agents import create_agent
from langchain_acp import run_acp

graph = create_agent(model="openai:gpt-5", tools=[])

run_acp(graph=graph)

If ACP session state should affect graph construction, use graph_factory=:

from langchain.agents import create_agent
from langchain_acp import AcpSessionContext, create_acp_agent


def graph_from_session(session: AcpSessionContext):
    mode_name = session.session_mode_id or "default"
    return create_agent(model="openai:gpt-5", tools=[], name=f"graph-{mode_name}")


acp_agent = create_acp_agent(graph_factory=graph_from_session)

What The Adapter Covers

langchain-acp carries the same ACP Kit seams that matter elsewhere in the repo, but mapped onto graph ownership instead of agent ownership:

• session stores and transcript replay
• model, mode, and config-option providers
• native plan state through TaskPlan
• approval bridging from HumanInTheLoopMiddleware
• capability bridges and graph-build contributions
• tool projection maps and event projection maps
• graph, graph_factory, and graph_source
• DeepAgents compatibility helpers where they add truthful ACP behavior

That means the adapter can expose:

• plain LangChain create_agent(...) graphs
• compiled LangGraph graphs
• DeepAgents graphs

without collapsing everything into a bespoke ACP runtime.

Session-owned Graph Rebuilds

If ACP session state should decide which graph gets built, graph_factory= is the intended seam:

from langchain.agents import create_agent
from langchain_acp import AcpSessionContext, AdapterConfig, MemorySessionStore, run_acp


def graph_from_session(session: AcpSessionContext):
    mode_name = session.session_mode_id or "default"
    model_name = session.session_model_id or "openai:gpt-5-mini"
    return create_agent(
        model=model_name,
        tools=[],
        name=f"graph-{mode_name}",
        system_prompt=f"Operate in {mode_name} mode.",
    )


run_acp(
    graph_factory=graph_from_session,
    config=AdapterConfig(session_store=MemorySessionStore()),
)

Use this when workspace path, mode, model, or session metadata should rebuild the graph dynamically.

DeepAgents Compatibility

DeepAgents graphs are supported as compiled LangGraph targets.

Use the compatibility helpers only when they add real value:

• DeepAgentsCompatibilityBridge
• DeepAgentsProjectionMap

Maintained examples:

• workspace_graph.py
• deepagents_graph.py

Docs:

• https://vcoderun.github.io/acpkit/langchain-acp/
• https://vcoderun.github.io/acpkit/getting-started/langchain-quickstart/
• https://vcoderun.github.io/acpkit/examples/langchain-workspace/
• https://vcoderun.github.io/acpkit/examples/deepagents/