# How to integrate Algodocs MCP with Autogen ```json { "title": "How to integrate Algodocs MCP with Autogen", "toolkit": "Algodocs", "toolkit_slug": "algodocs", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/algodocs/framework/autogen", "markdown_url": "https://composio.dev/toolkits/algodocs/framework/autogen.md", "updated_at": "2026-05-12T10:00:47.517Z" } ``` ## Introduction This guide walks you through connecting Algodocs to AutoGen using the Composio tool router. By the end, you'll have a working Algodocs agent that can extract invoice totals from uploaded pdfs, pull key data fields from bank statements, summarize contract terms from scanned documents through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Algodocs account through Composio's Algodocs MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Algodocs with - [OpenAI Agents SDK](https://composio.dev/toolkits/algodocs/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/algodocs/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/algodocs/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/algodocs/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/algodocs/framework/codex) - [OpenClaw](https://composio.dev/toolkits/algodocs/framework/openclaw) - [Hermes](https://composio.dev/toolkits/algodocs/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/algodocs/framework/cli) - [Google ADK](https://composio.dev/toolkits/algodocs/framework/google-adk) - [LangChain](https://composio.dev/toolkits/algodocs/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/algodocs/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/algodocs/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/algodocs/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/algodocs/framework/crew-ai) ## 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 Algodocs - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Algodocs tools - Run a live chat loop where you ask the agent to perform Algodocs 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 Algodocs MCP server, and what's possible with it? The Algodocs MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Algodocs account. It provides structured and secure access to your document processing workflows, so your agent can perform actions like extracting key data, processing business documents, and automating document-driven tasks on your behalf. - Automated data extraction from documents: Seamlessly have your agent pull structured data from invoices, receipts, contracts, and other business files—no manual setup required. - Document upload and processing: Let your agent upload PDF, image, or scanned documents for instant AI-powered extraction and analysis. - Batch processing for bulk workflows: Direct your agent to process multiple documents at once, saving time on high-volume operations and back-office work. - Secure results delivery and export: Have your agent retrieve extracted data in structured formats (like CSV or JSON) and send it where you need it—databases, spreadsheets, or apps. - End-to-end automation for business paperwork: Empower your agent to trigger full document-processing pipelines, turning unstructured files into actionable insights without human intervention. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ALGODOCS_GET_ME` | Get Current User | Tool to retrieve current user information. Use after authenticating with your API key to verify identity. | | `ALGODOCS_LIST_EXTRACTORS` | List document data extractors | Tool to list document data extractors. Use when you need identifiers and names of all extractors in your AlgoDocs account. | | `ALGODOCS_LIST_FOLDERS` | List folders | Tool to list folders. Use when you need to retrieve all folder IDs and their hierarchy. Must be called after obtaining an API key. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Algodocs MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Algodocs. Instead of manually wiring Algodocs APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Algodocs account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Algodocs via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables 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 Algodocs connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Algodocs tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```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 Algodocs session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["algodocs"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen 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 ```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")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Algodocs tools from the workbench ```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 Algodocs assistant agent with MCP tools agent = AssistantAgent( name="algodocs_assistant", description="An AI assistant that helps with Algodocs operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Algodocs 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 ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Algodocs 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") ``` ## Complete Code ```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 Algodocs session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["algodocs"] ) 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 Algodocs assistant agent with MCP tools agent = AssistantAgent( name="algodocs_assistant", description="An AI assistant that helps with Algodocs 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 Algodocs 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 Algodocs 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 Algodocs, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Algodocs MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/algodocs/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/algodocs/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/algodocs/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/algodocs/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/algodocs/framework/codex) - [OpenClaw](https://composio.dev/toolkits/algodocs/framework/openclaw) - [Hermes](https://composio.dev/toolkits/algodocs/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/algodocs/framework/cli) - [Google ADK](https://composio.dev/toolkits/algodocs/framework/google-adk) - [LangChain](https://composio.dev/toolkits/algodocs/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/algodocs/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/algodocs/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/algodocs/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/algodocs/framework/crew-ai) ## Related Toolkits - [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices. - [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze. - [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization. - [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data. - [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease. - [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses. - [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files. - [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently. - [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price. - [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere. - [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code. - [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more. - [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place. - [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work. - [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems. - [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project. - [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks. - [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API. - [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale. - [Docmosis](https://composio.dev/toolkits/docmosis) - Docmosis generates PDF and Word documents from user-defined templates. It's perfect for merging data fields to quickly produce reports, invoices, and business letters. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Algodocs MCP? With a standalone Algodocs MCP server, the agents and LLMs can only access a fixed set of Algodocs tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Algodocs and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Autogen? 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 Algodocs tools. ### Can I manage the permissions and scopes for Algodocs while using Tool Router? Yes, absolutely. You can configure which Algodocs 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. ### How safe is my data with Composio Tool Router? 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 Algodocs data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)