How to integrate Jam MCP with Pydantic AI

This guide walks you through connecting Jam to Pydantic AI using the Composio tool router. By the end, you'll have a working Jam agent that can list your bug reports, analyze videos, inspect console logs, trace network requests, and read transcripts through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Jam account through Composio's Jam MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Jam is a browser bug reporting and recording platform for capturing issues, console logs, network data, and repro context. Teams use Jam to inspect, organize, and collaborate on bugs faster without long back-and-forth threads.

16 Tools

Introduction

This guide walks you through connecting Jam to Pydantic AI using the Composio tool router. By the end, you'll have a working Jam agent that can list your bug reports, analyze videos, inspect console logs, trace network requests, and read transcripts through natural language commands.

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

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

Also integrate Jam with

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Jam
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Jam workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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

The Jam MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Jam account. It provides structured and secure access to your browser bug reports, recordings, and debugging context, so your agent can analyze reported issues, inspect technical evidence, trace user interactions, review visual details, and browse workspace content on your behalf.

  • Bug report investigation: Have your agent review a Jam’s core details, reported issue, user goal, feedback, and technical indicators to understand what went wrong.
  • Console and network analysis: Let the agent inspect console messages, stack traces, failed network requests, HTTP traffic, and WebSocket activity captured with a Jam.
  • User journey reconstruction: Direct your agent to trace clicks, form inputs, submissions, navigation, and scrolling in chronological order.
  • Recording and screenshot review: Instruct your agent to examine screenshots, sample video frames, analyze recordings, and read available speech transcripts for visual and spoken context.
  • Workspace discovery: Have your agent search Jams, folders, members, recording links, submitted reports, and connected recording domains across your workspace.

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Jam
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Jam
  • MCPServerStreamableHTTP connects to the Jam MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Jam
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["jam"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Jam 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
jam_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[jam_mcp],
    instructions=(
        "You are a Jam assistant. Use Jam tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Jam endpoint
  • The agent uses GPT-5 to interpret user commands and perform Jam operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Jam.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Jam API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Jam and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Jam
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["jam"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    jam_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[jam_mcp],
        instructions=(
            "You are a Jam assistant. Use Jam tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Jam.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Jam through Composio's Tool Router. With this setup, your agent can perform real Jam actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Jam for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

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

Analyze Jam Video

Extract structured user goals, reported issues, feedback, visual observations, interactions, and technical indicators from a video Jam.

Get Console Logs

Get captured browser console errors, warnings, information, debug output, and stack traces from a Jam.

Get Jam Details

Get the core details and debugging context for a Jam.

Get Jam Metadata

Get custom application debugging context attached through the jam.

Get Network Requests

Get captured HTTP requests and WebSocket frames from a Jam, with filters for failures, hosts, methods, content types, and response bodies.

Get Recording Link

Get one Recording Link's settings and submitted-Jam count by public ID.

Get Jam Screenshots

Get screenshots and image attachments from a screenshot-type Jam for visual inspection.

Get User Events

Get the chronological user interaction timeline from a Jam, including clicks, inputs, submissions, navigation, and scrolling.

Get Jam Video Frames

Get still frames from a video Jam as an overview grid, exact timestamps, or an evenly sampled time window.

Get Jam Video Transcript

Get the timestamped WebVTT speech transcript from a microphone-enabled video Jam when available; otherwise return Jam's explanation that it is pending or unavailable.

List Jam Folders

Browse or search one page of workspace folders and return their IDs, names, Jam counts, and timestamps; pass next_cursor as cursor to continue.

List Jams

Search and browse workspace Jams by text, type, folder, author, source URL, or creation date.

List Jam Members

Browse or search one page of workspace members by name or email for author discovery and attribution; pass next_cursor as cursor to continue.

List Recording Domains

List the workspace's connected recording domains and whether each is verified to capture console and network logs.

List Recording Link Jams

List Jams submitted through one Recording Link, newest first.

List Recording Links

List workspace Recording Links newest first, optionally including revoked links.

FRAMEWORKS

How to build Jam MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Jam MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Jam MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Jam MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Jam MCP with Claude Cowork

Codex logo

Codex

Use Jam MCP with Codex

OpenClaw logo

OpenClaw

Use Jam MCP with OpenClaw

Hermes logo

Hermes

Use Jam MCP with Hermes

CLI logo

CLI

Use Jam MCP with CLI

Google ADK logo

Google ADK

Use Jam MCP with Google ADK

LangChain logo

LangChain

Use Jam MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Jam MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Jam MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Jam MCP with LlamaIndex

CrewAI logo

CrewAI

Use Jam MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Pydantic AI 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 Jam tools.

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

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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.

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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.

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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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