How to integrate VLM Run MCP with Pydantic AI

This guide walks you through connecting VLM Run to Pydantic AI using the Composio tool router. By the end, you'll have a working VLM Run agent that can upload files, extract structured data, run agents, find skills, and review run artifacts through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a VLM Run account through Composio's VLM Run MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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VLM Run is a multimodal AI platform for structured extraction, predictions, files, skills, feedback, and evaluations. It helps teams build reliable vision-language workflows without stitching together model, file, and eval infrastructure by hand.

11 Tools

Introduction

This guide walks you through connecting VLM Run to Pydantic AI using the Composio tool router. By the end, you'll have a working VLM Run agent that can upload files, extract structured data, run agents, find skills, and review run artifacts through natural language commands.

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

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

Also integrate VLM Run with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 VLM Run
  • 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 VLM Run 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 VLM Run MCP server, and what's possible with it?

The VLM Run MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your VLM Run account. It provides structured and secure access to your multimodal agents, files, skills, extraction workflows, and runs, so your agent can upload files, extract structured data, execute agents, create reusable skills, and track results on your behalf.

  • Structured data extraction: Have your agent extract structured JSON from documents, images, videos, or audio using a supported domain, custom schema, or existing skill.
  • Multimodal agent execution: Direct your agent to choose an available VLM Run agent or provide an inline configuration, then run it against multimodal inputs.
  • File and skill management: Let the agent upload local files, find previously uploaded files, browse existing skills, or create a reusable skill from an uploaded zip, prompt, or chat session.
  • Run tracking and result retrieval: Instruct your agent to list extraction and agent runs, check work in progress, and retrieve completed results.
  • Schema and artifact discovery: Have your agent explore supported extraction domains and schemas, then list artifacts created during a chat session or agent execution.

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 VLM Run
  • 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 VLM Run
  • MCPServerStreamableHTTP connects to the VLM Run 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 VLM Run
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["vlm_run"],
    )
    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 VLM Run 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
vlm_run_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[vlm_run_mcp],
    instructions=(
        "You are a VLM Run assistant. Use VLM Run tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the VLM Run endpoint
  • The agent uses GPT-5 to interpret user commands and perform VLM Run 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 VLM Run.\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
  • VLM Run 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 VLM Run 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 VLM Run
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["vlm_run"],
    )
    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
    vlm_run_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[vlm_run_mcp],
        instructions=(
            "You are a VLM Run assistant. Use VLM Run 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 VLM Run.\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 VLM Run through Composio's Tool Router. With this setup, your agent can perform real VLM Run 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 + VLM Run 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 VLM Run action and event your agent gets out of the box.

Create Skill

Create a reusable skill from exactly one uploaded zip, prompt, or chat session.

Discover Extraction Schemas

List supported structured-extraction domains, or return the full JSON schema for one domain when domain is provided.

Execute Agent

Start a VLM Run agent execution from an existing agent name or inline configuration over multimodal inputs.

Extract Structured JSON

Start structured JSON extraction from images, a document, a video, or audio using a domain, custom schema, or skill.

Find Files

List uploaded files or find one by file ID or MD5 hash.

Find Skills

List VLM Run skills or find one exact skill by ID, name, and optional version.

Get Run

Get the current status and result of one structured-extraction prediction or agent execution; call repeatedly to poll asynchronous work.

List Agents

Return agents available to the connected account for selection before execution.

List Artifacts

List artifact metadata belonging to exactly one chat session or agent execution.

List Runs

List structured-extraction predictions or agent executions for the connected account.

Upload File

Upload a local file to VLM Run for extraction, agent input, or skill creation.

FRAMEWORKS

How to build VLM Run MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use VLM Run MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use VLM Run MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use VLM Run MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use VLM Run MCP with Claude Cowork

Codex logo

Codex

Use VLM Run MCP with Codex

OpenClaw logo

OpenClaw

Use VLM Run MCP with OpenClaw

Hermes logo

Hermes

Use VLM Run MCP with Hermes

CLI logo

CLI

Use VLM Run MCP with CLI

Google ADK logo

Google ADK

Use VLM Run MCP with Google ADK

LangChain logo

LangChain

Use VLM Run MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use VLM Run MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use VLM Run MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use VLM Run MCP with LlamaIndex

CrewAI logo

CrewAI

Use VLM Run MCP with CrewAI

FAQ

Frequently asked questions

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

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

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