How to integrate Griptape MCP with LangChain

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Introduction

This guide walks you through connecting Griptape to LangChain using the Composio tool router. By the end, you'll have a working Griptape agent that can create a new assistant named 'dochelper', list all assistants available in my workspace, start a run for assistant 'codegenpro' with input data, fetch logs from the latest assistant run through natural language commands.

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

The Griptape MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Griptape account. It provides structured and secure access to your Griptape platform, so your agent can create assistants, launch and monitor AI runs, retrieve logs, and analyze results automatically on your behalf.

  • Automated assistant creation and management: Easily direct your agent to create new assistants or list existing ones, making it simple to manage your AI resources in Griptape Cloud.
  • Launching and controlling assistant runs: Let your agent start new assistant runs, cancel ongoing executions, or fetch the status of any run for streamlined AI workflow management.
  • Real-time monitoring of assistant activities: Have your agent stream live run events, monitor progress, or retrieve detailed logs to keep tabs on every step of your generative AI processes.
  • Error handling and diagnostics: Ask your agent to fetch detailed error reports for failed runs, helping you troubleshoot and resolve issues quickly and efficiently.
  • Result retrieval and historical analysis: Automatically pull final outputs from completed runs and review historical execution data, empowering you to analyze and improve your generative AI pipelines over time.

Supported Tools & Triggers

Tools
Assistant CreationTool to create a new assistant.
List AssistantsTool to list all assistants.
Cancel Assistant RunTool to cancel an ongoing assistant run.
Assistant Run CreationTool to initiate a new assistant run.
Get Assistant Run Error DetailsTool to fetch detailed error information for a specific assistant run.
Stream Assistant Run EventsTool to stream real-time events for a specific Assistant run.
List Assistant RunsTool to list all runs for a given assistant.
Assistant Run Logs RetrievalTool to retrieve logs generated during the execution of a specific assistant run.
Assistant Run Result RetrievalTool to fetch the final result of a completed assistant run.
Retrieve Assistant RunTool to retrieve an assistant run's status and details.
Retry Assistant RunTool to retry a previously failed assistant run.
Get Ruleset by AliasTool to retrieve a ruleset by its alias.
List Embedding DriversTool to list available embedding drivers.
Ruleset CreationTool to create a new ruleset.
Create ToolTool to create a new tool in Griptape Cloud.
Get Tool Deployment StatusTool to retrieve status of a specific tool deployment.
List ToolsTool to list all tools.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Griptape MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Griptape MCP with Claude Agent SDK

Google ADK

Google ADK

Use Griptape MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Griptape MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Griptape MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Griptape MCP with LlamaIndex

CrewAI

CrewAI

Use Griptape MCP with CrewAI

FAQ

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

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

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

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

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