# How to integrate Ragie MCP with Pydantic AI

```json
{
  "title": "How to integrate Ragie MCP with Pydantic AI",
  "toolkit": "Ragie",
  "toolkit_slug": "ragie",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/ragie/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/ragie/framework/pydantic-ai.md",
  "updated_at": "2026-03-29T06:47:09.580Z"
}
```

## Introduction

This guide walks you through connecting Ragie to Pydantic AI using the Composio tool router. By the end, you'll have a working Ragie agent that can ingest new product documentation into ragie, run a semantic search for project roadmap, summarize key findings from all q2 reports through natural language commands.
This guide will help you understand how to give your Pydantic AI agent real control over a Ragie account through Composio's Ragie MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Ragie with

- [OpenAI Agents SDK](https://composio.dev/toolkits/ragie/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/ragie/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/ragie/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/ragie/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/ragie/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/ragie/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/ragie/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/ragie/framework/cli)
- [Google ADK](https://composio.dev/toolkits/ragie/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/ragie/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/ragie/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/ragie/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/ragie/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/ragie/framework/crew-ai)

## 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 Ragie
- 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 Ragie 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 Ragie MCP server, and what's possible with it?

The Ragie MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ragie account. It provides structured and secure access so your agent can perform Ragie operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `RAGIE_CREATE_DOCUMENT` | Create Document | Tool to upload and process a document file in Ragie. Use when you need to create a new document with support for various formats including text, images, and documents. The endpoint accepts multipart/form-data and returns a Document object with processing status and metadata. |
| `RAGIE_CREATE_DOCUMENT_FROM_URL` | Create Document From URL | Tool to ingest a document from a publicly accessible URL. Use when you need to add documents to Ragie from external sources. The document undergoes processing steps (pending, partitioning, indexed, ready) before becoming available for retrieval. |
| `RAGIE_CREATE_DOCUMENT_RAW` | Create Document Raw | Tool to ingest a document as raw text or JSON. Use when creating a new document from text or JSON data. The document goes through processing steps and becomes available for retrieval once in the ready state. |
| `RAGIE_CREATE_INSTRUCTION` | Create Instruction | Tool to create a new instruction that applies natural language directives to documents as they're ingested or updated. Use when you need to define structured data extraction or analysis rules for documents in Ragie. |
| `RAGIE_CREATE_OAUTH_REDIRECT_URL` | Create OAuth Redirect URL | Tool to create an OAuth redirect URL for initializing embedded connector OAuth flows. Use when you need to set up OAuth authentication for connectors like Google Drive, Notion, or HubSpot. |
| `RAGIE_CREATE_PARTITION` | Create Partition | Tool to create a new partition for scoping documents and connections in Ragie. Use when you need to organize documents and set resource limits for different workspaces or tenants. |
| `RAGIE_DELETE_DOCUMENT` | Delete Document | Tool to delete a document from Ragie. Use when you need to remove a document permanently from the system. Supports both synchronous and asynchronous deletion modes. |
| `RAGIE_DELETE_INSTRUCTION` | Delete Instruction | Tool to delete an instruction and all associated entities. Use when you need to permanently remove an instruction (irreversible operation). Requires the instruction ID (UUID format). |
| `RAGIE_DELETE_PARTITION` | Delete Partition | Tool to delete a partition and all associated data irreversibly. Use when you need to permanently remove a partition. Returns status 200 for synchronous deletion or 202 for asynchronous deletion. |
| `RAGIE_GET_DOCUMENT` | Get Document | Tool to retrieve a specific document by its unique identifier. Use when you need to get document details, metadata, processing status, or check for errors. Returns comprehensive document information including chunk count, page count, and any processing errors. |
| `RAGIE_GET_DOCUMENT_CHUNK` | Get Document Chunk | Tool to retrieve a specific document chunk by its document and chunk ID. Use when you need detailed information about a specific chunk within a document, including its content, metadata, position index, and optional modality data for audio/video chunks. |
| `RAGIE_GET_DOCUMENT_CHUNK_CONTENT` | Get Document Chunk Content | Tool to retrieve document chunk content in requested format with streaming support for media. Use when you need to get the actual content of a specific chunk from a document. |
| `RAGIE_GET_DOCUMENT_CHUNKS` | Get Document Chunks | Tool to retrieve document chunks with pagination support. Lists all document chunks sorted by index in ascending order (max 100 items per page). Documents created prior to 9/18/2024 that have not been updated since have chunks sorted by ID instead of index. |
| `RAGIE_GET_DOCUMENT_CONTENT` | Get Document Content | Tool to retrieve the content of a document by its ID. Use when you need to access the full content of a specific document. The media_type parameter can be used to request content in different formats. |
| `RAGIE_GET_DOCUMENT_SUMMARY` | Get Document Summary | Tool to retrieve an LLM-generated summary of a document by its ID. Use when you need to get a concise summary of a document's content. |
| `RAGIE_GET_PARTITION` | Get Partition | Tool to retrieve a partition by ID with usage statistics and resource limits. Use when you need to get detailed information about a specific partition. |
| `RAGIE_GET_RESPONSE` | Get Response | Tool to retrieve a response by its unique identifier. Use when you need to check the status or details of a previously created response. |
| `RAGIE_LIST_CONNECTIONS` | List Connections | Tool to list all connections sorted by creation date descending with pagination support. Use when you need to retrieve connections, optionally filtered by metadata. |
| `RAGIE_LIST_CONNECTION_SOURCE_TYPES` | List Connection Source Types | Tool to list available connection source types like 'google_drive' and 'notion' along with their metadata. Use when you need to discover what connector types are available in Ragie. |
| `RAGIE_LIST_DOCUMENTS` | List Documents | Tool to list all documents sorted by creation date (descending) with pagination support. Use when you need to browse or retrieve document metadata. Supports filtering and pagination up to 100 items per page. |
| `RAGIE_LIST_ENTITIES_BY_DOCUMENT` | List Entities By Document | Tool to retrieve all extracted entities from a specific document with pagination support. Use when you need to access structured data that has been extracted from a document by Ragie's entity extraction instructions. |
| `RAGIE_LIST_ENTITIES_BY_INSTRUCTION` | List Entities by Instruction | Tool to retrieve entities generated by a specific instruction. Use when you need to fetch entities extracted from documents based on a specific instruction's processing. |
| `RAGIE_LIST_INSTRUCTIONS` | List Instructions | Tool to retrieve all instruction records from the Ragie system. Use when you need to view all available instructions that define natural language prompts and entity schemas applied to documents. |
| `RAGIE_LIST_PARTITIONS` | List Partitions | Tool to retrieve a paginated list of all partitions sorted by name in ascending order. Use when you need to list available partitions with their configurations and limits. |
| `RAGIE_PATCH_DOCUMENT_METADATA` | Patch Document Metadata | Tool to update metadata for a specific document with partial update support. Use when you need to modify document metadata fields without replacing the entire metadata object. Supports both synchronous and asynchronous updates. |
| `RAGIE_RETRIEVE_DOCUMENT_CHUNKS` | Retrieve Document Chunks | Tool to retrieve relevant document chunks based on a query. Use when you need to search and retrieve document content that matches a specific query, with optional filtering and reranking capabilities. |
| `RAGIE_SET_PARTITION_LIMITS` | Set Partition Limits | Tool to set usage limits on partition pages and media. Use when you need to configure monthly or maximum limits for pages processed/hosted, video/audio processing, or media streaming/hosting for a specific partition. |
| `RAGIE_UPDATE_DOCUMENT_FROM_URL` | Update Document From URL | Tool to update an existing document by fetching content from a publicly accessible URL. Use when you need to refresh or replace a document's content with data from a web URL. The document goes through processing steps before it is ready for retrieval. |
| `RAGIE_UPDATE_DOCUMENT_RAW` | Update Document Raw | Tool to update a document's content from raw text or JSON data. Use when modifying existing document content. The document undergoes processing and becomes available for retrieval once it reaches the ready state. |
| `RAGIE_UPDATE_INSTRUCTION` | Update Instruction | Tool to update an instruction's active status. Use when you need to activate or deactivate an existing instruction. |
| `RAGIE_UPDATE_PARTITION` | Update Partition | Tool to update a partition's configuration including description, context-aware settings, and metadata schema. Use when you need to modify an existing partition's settings. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Ragie MCP server is an implementation of the Model Context Protocol that connects your AI agent to Ragie. It provides structured and secure access so your agent can perform Ragie operations on your behalf through a secure, permission-based interface.
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

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

### 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 the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like Ragie
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

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

### 5. Create a Tool Router Session

What's happening:
- We're creating a Tool Router session that gives your agent access to Ragie 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
```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 Ragie
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["ragie"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

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

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- Ragie API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```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 Ragie.\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()
```

### 8. Run the application

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

## Complete Code

```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 Ragie
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["ragie"],
    )
    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
    ragie_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[ragie_mcp],
        instructions=(
            "You are a Ragie assistant. Use Ragie 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 Ragie.\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 Ragie through Composio's Tool Router. With this setup, your agent can perform real Ragie 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 + Ragie for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

## How to build Ragie MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/ragie/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/ragie/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/ragie/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/ragie/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/ragie/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/ragie/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/ragie/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/ragie/framework/cli)
- [Google ADK](https://composio.dev/toolkits/ragie/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/ragie/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/ragie/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/ragie/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/ragie/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/ragie/framework/crew-ai)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Ragie MCP?

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

### Can I use Tool Router MCP with Pydantic AI?

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 Ragie tools.

### Can I manage the permissions and scopes for Ragie while using Tool Router?

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)