langchain-acp 0.9.3

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 you are using Codex-backed LangChain models through codex-auth-helper, you must pass the LangChain system behavior through the helper's instructions= argument. The same repo policy now applies on the Pydantic path too: Codex-backed model factories take explicit instructions instead of inventing an implicit default.

from codex_auth_helper import create_codex_chat_openai
from langchain.agents import create_agent

model = create_codex_chat_openai(
    "gpt-5.4",
    instructions="You are a careful assistant that explains concrete workspace observations.",
)
graph = create_agent(model=model, tools=[], name="codex-graph")

That instructions string is required and is forwarded to the OpenAI Responses request behind ChatOpenAI. See the maintained example at https://github.com/vcoderun/acpkit/blob/main/examples/langchain/codex_graph.py.

Use the same pattern inside graph_factory= paths too. If the graph is rebuilt per session, keep the Codex system behavior explicit in the factory instead of relying on an implicit default:

from codex_auth_helper import create_codex_chat_openai
from langchain.agents import create_agent
from langchain_acp import AcpSessionContext


def graph_from_session(session: AcpSessionContext):
    mode_name = session.session_mode_id or "ask"
    model_name = session.session_model_id or "gpt-5.4-mini"
    model = create_codex_chat_openai(
        model_name,
        instructions=f"Operate in {mode_name} mode and explain concrete workspace observations.",
    )
    return create_agent(model=model, tools=[], name=f"codex-{mode_name}")

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
• prompt capability advertisement through prompt_capabilities
• native plan state through TaskPlan
• approval bridging from HumanInTheLoopMiddleware
• remembered approval policies and permission card rendering on NativeApprovalBridge
• capability bridges and graph-build contributions
• built-in and host-defined slash commands
• tool projection maps and event projection maps
• external hook/event projection through ExternalHookEventBridge
• 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.

Runtime Controls

The adapter now owns a small ACP-native slash-command layer instead of leaving that surface entirely to the graph:

• mode commands such as /ask or /review when the session publishes modes
• /model for ACP-owned model selection
• /tools for the active graph tool node
• /mcp-servers for attached session MCP servers
• custom host commands through slash_command_provider

Example:

from acp.schema import AvailableCommand
from langchain_acp import (
    AdapterConfig,
    SlashCommandResult,
    StaticSlashCommand,
    StaticSlashCommandProvider,
)

config = AdapterConfig(
    slash_command_provider=StaticSlashCommandProvider(
        commands=[
            StaticSlashCommand(
                command=AvailableCommand(name="ping", description="Return pong."),
                handler=lambda _request: SlashCommandResult(text="pong"),
            )
        ]
    )
)

Prompt capability advertisement is also explicit now:

from langchain_acp import AdapterConfig, AdapterPromptCapabilities

config = AdapterConfig(
    prompt_capabilities=AdapterPromptCapabilities(
        audio=False,
        image=False,
        embedded_context=True,
    )
)

If the graph uses approval middleware, remembered choices and ACP permission presentation stay on NativeApprovalBridge, not on AdapterConfig:

from langchain_acp import NativeApprovalBridge

config = AdapterConfig(
    approval_bridge=NativeApprovalBridge(enable_persistent_choices=True),
)

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.

The maintained examples under examples/langchain/ also expose ACP-visible model and mode choices through available_models, available_modes, default_model_id, and default_mode_id, then consume session.session_model_id and session.session_mode_id inside graph_factory=....

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/