> For the complete documentation index, see [llms.txt](https://docs.allmcp.co/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.allmcp.co/documentation/agent-frameworks/langchain.md).

# LangChain

Use AllMCP tools in LangChain agents via `langchain-mcp-adapters`.

***

## Install

{% code title="install" %}

```bash
pip install langchain-mcp-adapters langchain-anthropic langgraph
```

{% endcode %}

***

## Minimal example

{% code title="agent.py" overflow="wrap" %}

```python
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent

async def main():
    client = MultiServerMCPClient({
        "allmcp": {
            "url": "https://go.allmcp.co/mcp/",
            "transport": "streamable_http",
            "headers": {"X-API-Key": "YOUR_API_KEY"},
        }
    })
    tools = await client.get_tools()
    model = ChatAnthropic(model="claude-sonnet-4-6")
    agent = create_react_agent(model, tools)

    result = await agent.ainvoke({
        "messages": [{"role": "user", "content": "List my Bitrix24 contacts"}]
    })
    print(result["messages"][-1].content)

asyncio.run(main())
```

{% endcode %}

{% hint style="info" %}
**Auth header.** Pass your key as `X-API-Key: allmcp_xxx`. If your transport layer strips custom headers, use `Authorization: Bearer allmcp_xxx` instead — both are accepted.
{% endhint %}

***

## Multi-user setup

{% hint style="info" %}
**One agent, many users.** Append `?user_id=` to the endpoint URL so each request acts as that user and uses the providers they've connected.
{% endhint %}

{% code title="agent.py" overflow="wrap" %}

```python
async def run_for_user(user_id: str, prompt: str) -> str:
    client = MultiServerMCPClient({
        "allmcp": {
            "url": f"https://go.allmcp.co/mcp/?user_id={user_id}",
            "transport": "streamable_http",
            "headers": {"X-API-Key": "YOUR_API_KEY"},
        }
    })
    tools = await client.get_tools()
    agent = create_react_agent(ChatAnthropic(model="claude-sonnet-4-6"), tools)
    result = await agent.ainvoke({"messages": [{"role": "user", "content": prompt}]})
    return result["messages"][-1].content
```

{% endcode %}

***

## With memory

{% code title="agent.py" overflow="wrap" %}

```python
from langgraph.checkpoint.memory import MemorySaver

client = MultiServerMCPClient({...})
tools = await client.get_tools()
agent = create_react_agent(
    ChatAnthropic(model="claude-sonnet-4-6"),
    tools,
    checkpointer=MemorySaver(),
)

config = {"configurable": {"thread_id": "session-1"}}

# Turn 1
await agent.ainvoke({"messages": [{"role": "user", "content": "Connect Bitrix24"}]}, config)
# Turn 2 — agent remembers the previous turn
result = await agent.ainvoke({"messages": [{"role": "user", "content": "Now list my deals"}]}, config)
```

{% endcode %}

***

## Using OpenAI instead of Anthropic

{% code title="agent.py" overflow="wrap" %}

```python
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o")
agent = create_react_agent(model, tools)
```

{% endcode %}

{% hint style="success" %}
**Any LLM works.** AllMCP tools work with any LangChain-compatible LLM.
{% endhint %}

***

## See also

{% content-ref url="/pages/uBaUeZYQpupBMd5c1BY0" %}
[Overview](/documentation/agent-frameworks/frameworks.md)
{% endcontent-ref %}

{% content-ref url="/pages/QJVxDLqxIz5KMCH5afbE" %}
[All Providers](/documentation/providers/providers.md)
{% endcontent-ref %}


---

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