How to integrate Jam MCP with CrewAI

This guide walks you through connecting Jam to CrewAI 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 CrewAI 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.

Jam logoJam
Api Key

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 CrewAI 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 CrewAI 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

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Jam connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Jam
  • Build a conversational loop where your agent can execute Jam operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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 step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Jam connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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 crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Jam via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Jam MCP URL
6

Create a Composio Tool Router session for Jam

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["jam"])

url = session.mcp.url
What's happening:
  • You create a Jam only session through Composio
  • Composio returns an MCP HTTP URL that exposes Jam tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["jam"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Jam through Composio's Tool Router. The agent can perform Jam operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
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.

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. CrewAI 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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