How to integrate Ragie MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Ragie to the OpenAI Agents SDK 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 OpenAI Agents SDK 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.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Ragie
  • Configure an AI agent that can use Ragie as a tool
  • Run a live chat session where you can ask the agent to perform Ragie operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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 & Triggers

Tools
Create DocumentTool to upload and process a document file in Ragie.
Create Document From URLTool to ingest a document from a publicly accessible URL.
Create Document RawTool to ingest a document as raw text or JSON.
Create InstructionTool to create a new instruction that applies natural language directives to documents as they're ingested or updated.
Create OAuth Redirect URLTool to create an OAuth redirect URL for initializing embedded connector OAuth flows.
Create PartitionTool to create a new partition for scoping documents and connections in Ragie.
Delete DocumentTool to delete a document from Ragie.
Delete InstructionTool to delete an instruction and all associated entities.
Delete PartitionTool to delete a partition and all associated data irreversibly.
Get DocumentTool to retrieve a specific document by its unique identifier.
Get Document ChunkTool to retrieve a specific document chunk by its document and chunk ID.
Get Document Chunk ContentTool to retrieve document chunk content in requested format with streaming support for media.
Get Document ChunksTool to retrieve document chunks with pagination support.
Get Document ContentTool to retrieve the content of a document by its ID.
Get Document SummaryTool to retrieve an LLM-generated summary of a document by its ID.
Get PartitionTool to retrieve a partition by ID with usage statistics and resource limits.
Get ResponseTool to retrieve a response by its unique identifier.
List ConnectionsTool to list all connections sorted by creation date descending with pagination support.
List Connection Source TypesTool to list available connection source types like 'google_drive' and 'notion' along with their metadata.
List DocumentsTool to list all documents sorted by creation date (descending) with pagination support.
List Entities By DocumentTool to retrieve all extracted entities from a specific document with pagination support.
List Entities by InstructionTool to retrieve entities generated by a specific instruction.
List InstructionsTool to retrieve all instruction records from the Ragie system.
List PartitionsTool to retrieve a paginated list of all partitions sorted by name in ascending order.
Patch Document MetadataTool to update metadata for a specific document with partial update support.
Retrieve Document ChunksTool to retrieve relevant document chunks based on a query.
Set Partition LimitsTool to set usage limits on partition pages and media.
Update Document From URLTool to update an existing document by fetching content from a publicly accessible URL.
Update Document RawTool to update a document's content from raw text or JSON data.
Update InstructionTool to update an instruction's active status.
Update PartitionTool to update a partition's configuration including description, context-aware settings, and metadata schema.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Ragie project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Ragie.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Ragie Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["ragie"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only ragie.
  • The router checks the user's Ragie connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Ragie.
  • This approach keeps things lightweight and lets the agent request Ragie tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Ragie. "
        "Help users perform Ragie operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Ragie and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Ragie operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Ragie.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Ragie and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["ragie"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Ragie. "
        "Help users perform Ragie operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Ragie MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Ragie.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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FAQ

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 OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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.

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