How to integrate Facebook MCP with LangChain

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Introduction

This guide walks you through connecting Facebook to LangChain using the Composio tool router. By the end, you'll have a working Facebook agent that can post new product launch on our page, upload latest event photos to album, reply to comments on latest post, delete outdated promotional post through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Facebook account through Composio's Facebook MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Facebook project to Composio
  • Create a Tool Router MCP session for Facebook
  • Initialize an MCP client and retrieve Facebook tools
  • Build a LangChain agent that can interact with Facebook
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Facebook MCP server, and what's possible with it?

The Facebook MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Facebook Page account. It provides structured and secure access to your Facebook Pages, so your agent can perform actions like publishing posts, managing comments, uploading media, and handling page roles on your behalf.

  • Automated content publishing: Have your agent create new posts, photo posts, or video posts directly to your Facebook Page, keeping your audience engaged without manual effort.
  • Media management: Effortlessly upload photos to existing albums or create new albums for organized visual storytelling on your Page.
  • Interactive engagement: Let your agent add reactions, post comments, or reply to comments, fostering genuine interaction with your followers.
  • Page moderation and cleanup: Ask your agent to delete unwanted comments or posts, helping you keep your Facebook Page professional and on-brand.
  • Page team management: Assign tasks or roles to users for your Facebook Page, streamlining collaboration and access control.

Supported Tools & Triggers

Tools
Add Photos to AlbumAdds photos to an existing facebook album.
Add ReactionAdds a specific reaction (like, love, wow, etc.
Assign Page TaskAssigns tasks/roles to a user for a specific facebook page.
Create CommentCreates a comment on a facebook post or replies to an existing comment.
Create Photo AlbumCreates a new photo album on a facebook page.
Create Photo PostCreates a photo post on a facebook page.
Create PostCreates a new post on a facebook page.
Create Video PostCreates a video post on a facebook page.
Delete CommentDeletes a facebook comment.
Delete PostDeletes a facebook page post.
Get CommentRetrieves details of a specific facebook comment.
Get CommentsRetrieves comments from a facebook post or comment (for replies).
Get Conversation MessagesRetrieves messages from a specific conversation.
Get Message DetailsRetrieves details of a specific message sent or received by the page.
Get Page ConversationsRetrieves a list of conversations between users and the page.
Get Page DetailsFetches details about a specific facebook page.
Get Page InsightsRetrieves analytics and insights for a facebook page.
Get Page PhotosRetrieves photos from a facebook page.
Get Page PostsRetrieves posts from a facebook page.
Get Page RolesRetrieves a list of people and their tasks/roles on a facebook page.
Get Page VideosRetrieves videos from a facebook page.
Get PostRetrieves details of a specific facebook post.
Get Post InsightsRetrieves analytics and insights for a specific facebook post.
Get Post ReactionsRetrieves reactions (like, love, wow, etc.
Get Scheduled PostsRetrieves scheduled and unpublished posts for a facebook page.
Get User PagesRetrieves a list of pages the user manages, including tasks and access tokens.
Like Post or CommentLikes a facebook post or comment.
Mark Message SeenMarks a user's message as seen by the page.
Publish Scheduled PostPublishes a previously scheduled or unpublished facebook post immediately.
Remove Page TaskRemoves a user's tasks/access from a specific facebook page.
Reschedule PostChanges the scheduled publish time of an unpublished facebook post.
Send Media MessageSends a media message (image, video, audio, or file) from the page to a user.
Send MessageSends a text message from the page to a user via messenger.
Toggle Typing IndicatorShows or hides the typing indicator for a user in messenger.
Unlike Post or CommentRemoves a like from a facebook post or comment.
Update CommentUpdates an existing facebook comment.
Update Page SettingsUpdates settings for a specific facebook page.
Update PostUpdates an existing facebook page post.
Upload PhotoUploads a photo file directly to a facebook page.
Upload Photos BatchUploads multiple photo files in batch to a facebook page or album.
Upload VideoUploads a video file directly to a facebook page.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Facebook functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Facebook tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Facebook
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['facebook']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Facebook tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Facebook tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "facebook-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Facebook MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Facebook tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Facebook related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Facebook and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['facebook']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "facebook-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Facebook related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Facebook through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Facebook MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Facebook MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Facebook MCP with Claude Agent SDK

Google ADK

Google ADK

Use Facebook MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Facebook MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Facebook MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Facebook MCP with LlamaIndex

CrewAI

CrewAI

Use Facebook MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Facebook MCP?

With a standalone Facebook MCP server, the agents and LLMs can only access a fixed set of Facebook tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Facebook and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Facebook tools.

Can I manage the permissions and scopes for Facebook while using Tool Router?

Yes, absolutely. You can configure which Facebook scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Facebook data and credentials are handled as safely as possible.

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