How to integrate Zep MCP with LangChain

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Introduction

This guide walks you through connecting Zep to LangChain using the Composio tool router. By the end, you'll have a working Zep agent that can store a memory about today's meeting, retrieve all memories tagged urgent, summarize knowledge about client preferences through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Zep account through Composio's Zep 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 Zep project to Composio
  • Create a Tool Router MCP session for Zep
  • Initialize an MCP client and retrieve Zep tools
  • Build a LangChain agent that can interact with Zep
  • 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 Zep MCP server, and what's possible with it?

The Zep MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zep account. It provides structured and secure access so your agent can perform Zep operations on your behalf.

Supported Tools & Triggers

Tools
Add Fact TripleTool to add a manually specified fact triple (subject-predicate-object) to the Zep knowledge graph.
Add Session MemoryTool to add memory messages to a specified Zep session.
Add Thread MessagesTool to add chat messages to a thread in Zep and ingest them into the user knowledge graph.
Clone GraphTool to clone a user or group graph with new identifiers in Zep.
Create GraphTool to create a new graph by adding data to Zep.
Create GroupTool to create a new group in Zep for multi-user graph management.
Create SessionTool to create a new session in Zep for storing conversation memory.
Create ThreadTool to create a new thread in Zep for a specific user.
Create UserTool to create a new user in Zep with properties like user_id, email, and metadata.
Delete GraphTool to delete a graph from Zep.
Delete GroupTool to delete a group from Zep.
Delete Session MemoryTool to delete a session and its memory from Zep.
Delete ThreadTool to delete a thread and its messages from Zep.
Delete UserTool to delete a user and all associated threads and artifacts from Zep.
Get Edge by UUIDTool to retrieve a specific edge by its UUID from the Zep knowledge graph.
Get Graph by IDTool to retrieve a graph by its unique identifier from Zep.
Get Group by IDTool to retrieve a group by ID from Zep.
Get Node Entity EdgesTool to retrieve all entity edges for a specific node in the Zep knowledge graph.
Get Project InfoTool to retrieve project information based on the provided API key.
Get Session by IDTool to retrieve a session by its unique identifier from Zep.
Get Session MemoryTool to retrieve memory for a given session including relevant facts and entities.
Get Session Message by UUIDTool to retrieve a specific message by UUID from a Zep session.
Get Session MessagesTool to retrieve messages for a given session from Zep.
Get Task StatusTool to check the status of asynchronous operations in Zep.
Get Thread MessagesTool to retrieve conversation history for a specific thread from Zep.
Get Thread User ContextTool to retrieve the most relevant user context from the user graph based on thread messages.
Get User by IDTool to retrieve a user by their user ID from Zep.
Get User NodeTool to retrieve a user's graph node and summary from Zep.
Get User NodesTool to retrieve all nodes for a specific user from their graph in Zep.
Get User SessionsTool to retrieve all sessions for a user from Zep.
Get User ThreadsTool to retrieve all threads for a specific user from Zep.
Graph SearchTool to perform hybrid graph search combining semantic similarity and BM25 full-text search across the Zep knowledge graph.
List GraphsTool to retrieve all graphs from Zep with pagination support.
List Groups OrderedTool to retrieve all groups from Zep with pagination support.
List Sessions OrderedTool to retrieve all sessions from Zep with pagination and ordering support.
List ThreadsTool to retrieve all threads from Zep with pagination support.
List Users OrderedTool to retrieve all users from Zep with pagination support.
List All ThreadsTool to list all threads with pagination and ordering support.
Update GraphTool to update graph information in Zep including name and description.
Update GroupTool to update group information in Zep including name, description, and fact rating instructions.
Update MessageTool to update a message in a Zep thread.
Update Session MetadataTool to update session metadata in Zep.
Update UserTool to update an existing user's information in Zep including email, metadata, and ontology settings.

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 Zep 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 Zep tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Zep 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 Zep tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "zep-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 Zep MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Zep 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 Zep 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 Zep 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=['zep']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "zep-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 Zep 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 Zep 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 Zep MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Zep MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Zep MCP with Claude Agent SDK

Google ADK

Google ADK

Use Zep MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Zep MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Zep MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Zep MCP with LlamaIndex

CrewAI

CrewAI

Use Zep MCP with CrewAI

Claude Code

Claude Code

Use Zep MCP with Claude Code

Codex

Codex

Use Zep MCP with Codex

FAQ

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

With a standalone Zep MCP server, the agents and LLMs can only access a fixed set of Zep tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zep 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 Zep tools.

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

Yes, absolutely. You can configure which Zep 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 Zep data and credentials are handled as safely as possible.

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