How to integrate CompanyCam MCP with Autogen

This guide walks you through connecting CompanyCam to AutoGen using the Composio tool router. By the end, you'll have a working CompanyCam agent that can create your projects, add project photos, tag your photos, create project tasks, and add comments through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a CompanyCam account through Composio's CompanyCam MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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CompanyCam is a photo-first jobsite management platform for project teams. It helps crews document work, coordinate jobs, and share visual updates fast.

24 Tools

Introduction

This guide walks you through connecting CompanyCam to AutoGen using the Composio tool router. By the end, you'll have a working CompanyCam agent that can create your projects, add project photos, tag your photos, create project tasks, and add comments through natural language commands.

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

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

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

The CompanyCam MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your CompanyCam account. It provides structured and secure access to your projects, customers, jobsite photos, tasks, comments, labels, tags, and users, so your agent can create projects, document jobsites, coordinate tasks, organize photos, and share project updates on your behalf.

  • Project and customer management: Have your agent create, search, review, and update projects and customer records, including addresses, contact details, and notes.
  • Jobsite photo documentation: Direct your agent to add photos from public web links, review project photos, update photo descriptions, and organize images with tags.
  • Task coordination: Let the agent create, review, and update project tasks, track completion status, and assign available team members.
  • Project communication: Instruct your agent to add comments to projects or photos and review existing discussions before sharing an update.
  • Project organization: Have your agent apply project labels, create reusable photo tags, and find projects using lifecycle, assignee, or text-based searches.

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

Supported Tools

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

Add Comment

Add a comment to either a CompanyCam project or photo.

Add Photo Tags

Attach media tags by display name.

Add Project Labels

Attach project labels by display name.

Add Project Photo From URL

Add a photo to a CompanyCam project by having CompanyCam fetch a publicly reachable HTTPS image URL.

Create Customer

Create a CompanyCam customer with optional contact details, company status, notes, address, and initial contacts.

Create Project

Create a CompanyCam project with its required address and optional contact, customer, location, and template details.

Create Project Task

Create a task on a CompanyCam project with task details and optional assignees.

Create Photo Tag

Create a reusable CompanyCam media tag for organizing photos.

Get Account Context

Return the connected CompanyCam user and company so an agent can identify the account, role, and company before acting.

Get Photo

Get one CompanyCam photo by ID.

Get Project

Get one CompanyCam project by ID.

List Comments

List comments attached to either a CompanyCam project or photo.

List Customers

List or search CompanyCam customers by name or contact text.

List Project Labels

List reusable CompanyCam project labels that can be attached to projects.

List Project Photos

List and filter photos in a CompanyCam project by tags, users, or groups, one cursor page at a time.

List Projects

List projects with optional lifecycle and assigned-user filters, one cursor page at a time.

List Project Tasks

List tasks for a CompanyCam project, including status, completion, assignees, and attached media.

List Photo Tags

List reusable CompanyCam media tags and return an opaque cursor for the next page.

List Users

List CompanyCam users available for task assignment and project workflows.

Search Projects

Search CompanyCam projects by a free-text query when project list filters are insufficient.

Set Photo Description

Create or replace the description attached to a CompanyCam photo.

Update Customer

Update the name, notes, city, or postal code of an existing CompanyCam customer.

Update Project

Update editable project identity, address, or primary-contact fields without changing archive state.

Update Project Task

Update a CompanyCam project task's details or add and retain assignees.

FRAMEWORKS

How to build CompanyCam MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use CompanyCam MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use CompanyCam MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use CompanyCam MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use CompanyCam MCP with Claude Cowork

Codex logo

Codex

Use CompanyCam MCP with Codex

OpenClaw logo

OpenClaw

Use CompanyCam MCP with OpenClaw

Hermes logo

Hermes

Use CompanyCam MCP with Hermes

CLI logo

CLI

Use CompanyCam MCP with CLI

Google ADK logo

Google ADK

Use CompanyCam MCP with Google ADK

LangChain logo

LangChain

Use CompanyCam MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use CompanyCam MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use CompanyCam MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use CompanyCam MCP with LlamaIndex

CrewAI logo

CrewAI

Use CompanyCam MCP with CrewAI

FAQ

Frequently asked questions

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

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

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