How to integrate Pinecone MCP with Autogen

This guide walks you through connecting Pinecone to AutoGen using the Composio tool router. By the end, you'll have a working Pinecone agent that can query all vectors similar to user question, upsert document embeddings into a namespace, delete vectors from the archive index through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Pinecone account through Composio's Pinecone MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Introduction

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

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

Also integrate Pinecone with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

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

The Pinecone MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pinecone account. It provides structured and secure access to your Pinecone indexes, namespaces, vectors, assistants, models, backups, and imports, so your agent can create indexes, generate embeddings, search records, manage vectors, and retrieve assistant context on your behalf.

Index and namespace management: Have your agent create, configure, inspect, list, or delete indexes and namespaces based on your application needs.
Semantic search and retrieval: Direct your agent to query vectors, search namespace records, fetch stored vectors, and inspect index statistics.
Embeddings and relevance ranking: Let the agent generate text embeddings with hosted models and rerank documents based on their relevance to a query.
Assistant and RAG workflows: Instruct your agent to create and update Pinecone assistants, retrieve supporting context, chat with assistants, and manage their files.
Backups and bulk data operations: Have your agent create or restore backups, monitor restore jobs, and start, inspect, list, or cancel bulk imports from supported object storage.

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:

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

Step-by-step Guide

Step by step08 STEPS

Prerequisites

You will need:

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

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

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 Pinecone via MCP
autogen-agentchat provides the AssistantAgent class
autogen-ext-openai provides the OpenAI model client
autogen-ext-tools provides MCP workbench support

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 Pinecone connections to use

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 Pinecone session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["pinecone"]
    )
    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 Pinecone tools
session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Pinecone assistant agent with MCP tools
    agent = AssistantAgent(
        name="pinecone_assistant",
        description="An AI assistant that helps with Pinecone 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 Pinecone tools from the workbench

Run the interactive chat loop

python

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Pinecone 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 Pinecone 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 Pinecone 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 Pinecone session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["pinecone"]
    )
    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 Pinecone assistant agent with MCP tools
        agent = AssistantAgent(
            name="pinecone_assistant",
            description="An AI assistant that helps with Pinecone 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 Pinecone 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 Pinecone 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 Pinecone, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

TOOLS

Supported Tools

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

Cancel Bulk Import

Tool to cancel a bulk import operation in Pinecone.

Configure Index

Tool to configure an existing Pinecone index, including pod type, replicas, deletion protection, and tags.

Create Backup

Tool to create a backup of a Pinecone index for disaster recovery and version control.

Create Index

Tool to create a Pinecone index with specified configuration.

Create Index with Embedding Model

Tool to create a Pinecone index with integrated embedding model for automatic vectorization.

Create Index from Backup

Tool to create an index from a backup.

Create Namespace

Tool to create a namespace within a serverless Pinecone index.

Delete Index

Tool to permanently delete a Pinecone index.

Delete Namespace

Tool to permanently delete a namespace from a serverless index.

Describe Backup

Tool to retrieve detailed information about a specific backup.

Describe Bulk Import

Tool to describe a specific bulk import operation in Pinecone.

Describe Index Stats

Tool to get index statistics including vector count per namespace, dimensions, and fullness.

Describe Restore Job

Tool to get detailed information about a specific restore job in Pinecone.

Generate Embeddings

Tool to generate vector embeddings for input text using Pinecone's hosted embedding models.

Get Model Information

Tool to retrieve detailed information about a specific model hosted by Pinecone.

List Bulk Imports

Tool to list all recent and ongoing bulk import operations in Pinecone.

List Collections

Tool to list all collections in a Pinecone project (pod-based indexes only).

List Index Backups

Tool to list all backups for a specific Pinecone index.

List Indexes

Tool to list all indexes in a Pinecone project.

List Available Models

Tool to list all available embedding and reranking models hosted by Pinecone.

List Namespaces

Tool to list all namespaces in a serverless Pinecone index.

List Project Backups

Tool to list all backups for indexes in a Pinecone project.

List Restore Jobs

Tool to list all restore jobs for a project with pagination support.

List Vectors

Tool to list vector IDs in a Pinecone serverless index.

Query Vectors

Tool to perform semantic search within a Pinecone index using a query vector.

Rerank Documents

Tool to rerank documents by semantic relevance to a query.

Search Records in Namespace

Tool to search records within a Pinecone namespace using text, vector, or ID query.

Start Bulk Import

Tool to start an asynchronous bulk import of vectors from object storage (S3, GCS, or Azure Blob Storage) into a Pinecone index.

Update Vector

Tool to update a vector in Pinecone by ID.

Upsert Records to Namespace

Tool to upsert text records into a Pinecone namespace.

FRAMEWORKS

How to build Pinecone MCP Agent with another framework

ChatGPT

Use Pinecone MCP with ChatGPT

OpenAI Agents SDK

Use Pinecone MCP with OpenAI Agents SDK

Claude Agent SDK

Use Pinecone MCP with Claude Agent SDK

Claude Code

Use Pinecone MCP with Claude Code

Claude Cowork

Use Pinecone MCP with Claude Cowork

Codex

Use Pinecone MCP with Codex

Cursor

Use Pinecone MCP with Cursor

OpenClaw

Use Pinecone MCP with OpenClaw

Hermes

Use Pinecone MCP with Hermes

CLI

Use Pinecone MCP with CLI

Google ADK

Use Pinecone MCP with Google ADK

LangChain

Use Pinecone MCP with LangChain

Vercel AI SDK

Use Pinecone MCP with Vercel AI SDK

Mastra AI

Use Pinecone MCP with Mastra AI

LlamaIndex

Use Pinecone MCP with LlamaIndex

CrewAI

Use Pinecone MCP with CrewAI

OpenCode

Use Pinecone MCP with OpenCode

VS Code

Use Pinecone MCP with VS Code

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FAQ

Frequently asked questions

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

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

Start with Pinecone.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Pinecone tool your agent needs.Free to start.

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Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

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.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Pinecone MCP with Autogen

Connect Pinecone without auth hassles

Introduction

Also integrate Pinecone with

TL;DR

What is AutoGen?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

Supported Tools

How to build Pinecone MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

OpenCode

VS Code

Explore Other Toolkits

Excel

1password

Adafruit IO

Agenty

Frequently asked questions

What are the differences in Tool Router MCP and Pinecone MCP?+

Can I use Tool Router MCP with Autogen?+

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

How safe is my data with Composio Tool Router?+

Start with Pinecone.It takes 30 seconds.

Teams ship real agents on Composio