How to integrate Needle MCP with Autogen

This guide walks you through connecting Needle to AutoGen using the Composio tool router. By the end, you'll have a working Needle agent that can list all document collections i have, show stats for your 'research' collection, find files in collection uploaded this week through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Needle account through Composio's Needle MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Needle is a RAG (retrieval-augmented generation) toolkit that brings semantic search capabilities to your own data. It lets you build smarter AI agents by giving them context-aware, accurate information retrieval.

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Introduction

This guide walks you through connecting Needle to AutoGen using the Composio tool router. By the end, you'll have a working Needle agent that can list all document collections i have, show stats for your 'research' collection, find files in collection uploaded this week through natural language commands.

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

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

Also integrate Needle 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 Needle
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Needle tools
  • Run a live chat loop where you ask the agent to perform Needle 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 Needle MCP server, and what's possible with it?

The Needle MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Needle account. It provides structured and secure access to your semantic search collections, so your agent can perform actions like creating and managing collections, uploading and deleting files, retrieving collection statistics, and listing available data sources on your behalf.

  • Collection management and creation: Easily instruct your agent to create new semantic search collections tailored to your data needs.
  • File listing and retrieval: Have your agent list and retrieve all files within a specific collection, making it simple to view and organize your indexed data.
  • File deletion from collections: Direct your agent to remove unwanted or outdated files from any collection by specifying file IDs for cleanup and maintenance.
  • Collection statistics and insights: Ask your agent to fetch real-time statistics on any collection, including document counts, index size, and timestamps to monitor your data health.
  • Browse all available collections: Let your agent page through and present all your collections, so you can quickly access, search, or manage your data resources.

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 Needle 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 Needle 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 Needle 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 Needle session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["needle"]
    )
    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 Needle 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 Needle assistant agent with MCP tools
    agent = AssistantAgent(
        name="needle_assistant",
        description="An AI assistant that helps with Needle 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 Needle 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 Needle 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 Needle 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 Needle 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 Needle session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["needle"]
    )
    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 Needle assistant agent with MCP tools
        agent = AssistantAgent(
            name="needle_assistant",
            description="An AI assistant that helps with Needle 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 Needle 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 Needle 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 Needle, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

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

Add files to a collection

Tool to add files to a collection by providing file URLs.

Add Files to Local Connector

Tool to add files to a local connector by providing file metadata.

Create Collection

Tool to create a new collection.

Create Local Connector

Tool to create a local connector that monitors specified folders on a device.

Delete files from a collection

Tool to delete files from a specific collection by providing file IDs.

Delete files from local connector

Tool to delete files from a local connector by filename or file IDs.

Get Collection

Tool to retrieve details for a specific collection by its ID.

Get collection stats

Tool to retrieve statistics for a specific collection by its ID.

Get File Download URL

Tool to get a short-lived signed private download URL for a Needle file.

Get File Upload URL

Tool to get signed URLs for uploading local files to Needle.

Get local connector

Tool to retrieve details of a local connector by its ID.

List Collection Files

Tool to list all files within a specific collection by its ID.

List Collections

Tool to list collections.

List Connectors

Tool to list connectors.

List Local Connectors

Tool to list local connectors.

Search Collection

Tool to perform semantic search within a specific Needle collection and return ranked results with source references.

FRAMEWORKS

How to build Needle MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Needle MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Needle MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Needle MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Needle MCP with Claude Cowork

Codex logo

Codex

Use Needle MCP with Codex

OpenClaw logo

OpenClaw

Use Needle MCP with OpenClaw

Hermes logo

Hermes

Use Needle MCP with Hermes

CLI logo

CLI

Use Needle MCP with CLI

Google ADK logo

Google ADK

Use Needle MCP with Google ADK

LangChain logo

LangChain

Use Needle MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Needle MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Needle MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Needle MCP with LlamaIndex

CrewAI logo

CrewAI

Use Needle MCP with CrewAI

FAQ

Frequently asked questions

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

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

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Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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