How to integrate Gist MCP with Autogen

This guide walks you through connecting Gist to AutoGen using the Composio tool router. By the end, you'll have a working Gist agent that can list all your public and private gists, show comments for gist with id abc123, check if i starred gist aa5a315d61ae9438b18d through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Gist account through Composio's Gist MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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GitHub Gist is a service for sharing code snippets and notes. It makes it easy to collaborate and organize your code or text, publicly or privately.

20 Tools

Introduction

This guide walks you through connecting Gist to AutoGen using the Composio tool router. By the end, you'll have a working Gist agent that can list all your public and private gists, show comments for gist with id abc123, check if i starred gist aa5a315d61ae9438b18d through natural language commands.

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

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

Also integrate Gist with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Gist
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Gist tools
  • Run a live chat loop where you ask the agent to perform Gist operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

The Gist MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your GitHub Gist account. It provides structured and secure access to your gists, so your agent can create, manage, comment on, and organize code snippets and notes on your behalf.

  • Gist creation and management: Quickly ask your agent to create new public or private gists, update existing ones, or fetch details for any gist you own or have access to.
  • Commenting and collaboration: Let your agent add, list, retrieve, or delete comments on your gists to streamline discussion and feedback without manual effort.
  • Revision history and tracking: Effortlessly review a gist's revision history, retrieve specific past versions, and understand changes over time.
  • Star and fork management: Direct your agent to list your starred gists, check if you've starred a gist, or see all forks of a particular gist to keep track of popularity and collaboration.
  • Personalized gist organization: Have your agent list all your gists or your starred gists, making it easy to find, organize, and revisit important code snippets and notes.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Gist account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

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.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Gist via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Gist connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Gist session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["gist"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Gist tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Gist assistant agent with MCP tools
    agent = AssistantAgent(
        name="gist_assistant",
        description="An AI assistant that helps with Gist operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Gist tools from the workbench
8

Run the interactive chat loop

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

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Gist tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Gist and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Gist session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["gist"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Gist assistant agent with MCP tools
        agent = AssistantAgent(
            name="gist_assistant",
            description="An AI assistant that helps with Gist operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

Conclusion

You now have an Autogen assistant wired into Gist through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Gist, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Gist action and event your agent gets out of the box.

Check Gist Star

Check if the authenticated user has starred a specific gist.

Create Gist

Tool to create a new gist with one or more files.

Create Gist Comment

Tool to create a comment on a gist.

Delete Gist

Tool to delete a gist.

Delete Gist Comment

Tool to delete a comment on a gist.

Fork Gist

Tool to fork a gist.

Get Gist

Tool to retrieve a specific gist by its ID.

Get Gist Comment

Tool to get a specific comment on a gist.

Get Gist Revision

Tool to retrieve a specific gist revision.

List Gist Comments

Tool to list comments on a gist.

List Gist Commits

Tool to list the commit history of a gist.

List Gist Forks

Tool to list forks of a gist.

List Gists

Tool to list the authenticated user's gists or all public gists if called anonymously.

List Public Gists

Tool to list public gists sorted by most recently updated.

List Starred Gists

Tool to list the authenticated user's starred gists.

List User Gists

Tool to list public gists for a specified GitHub user.

Star a Gist

Star a GitHub gist.

Unstar a Gist

Unstar a GitHub gist.

Update Gist

Tool to update a gist's description and files.

Update Gist Comment

Tool to update a Gist comment.

FRAMEWORKS

How to build Gist MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Gist MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Gist MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Gist MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Gist MCP with Claude Cowork

Codex logo

Codex

Use Gist MCP with Codex

OpenClaw logo

OpenClaw

Use Gist MCP with OpenClaw

Hermes logo

Hermes

Use Gist MCP with Hermes

CLI logo

CLI

Use Gist MCP with CLI

Google ADK logo

Google ADK

Use Gist MCP with Google ADK

LangChain logo

LangChain

Use Gist MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Gist MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Gist MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Gist MCP with LlamaIndex

CrewAI logo

CrewAI

Use Gist MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Autogen 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 Gist tools.

Yes, absolutely. You can configure which Gist 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.

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 Gist data and credentials are handled as safely as possible.

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