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How to integrate Bitquery MCP with Pydantic AI

Connect Pydantic AI to Bitquery MCP. Show real-time ethereum mempool transactions, count unique wallet addresses for solana, and more using natural language, with authentication handled for you.

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Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.

10 Tools

Introduction

This guide walks you through connecting Bitquery to Pydantic AI using the Composio tool router. By the end, you'll have a working Bitquery agent that can query real-time and archived blockchain data, combine database queries, filter results by metric, and track price asymmetry through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Bitquery with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAIAutoGen

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

The Bitquery MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bitquery account. It provides structured and secure access to blockchain datasets and real-time analytics, so your agent can perform actions like querying historical transactions, streaming mempool activity, selecting blockchain networks, and aggregating metrics across 40+ supported chains.

  • Seamless blockchain data querying: Let your agent run powerful queries on historical or real-time blockchain data across multiple networks using Bitquery's combined or archive databases.
  • Live mempool monitoring: Subscribe and stream pending transactions from EVM-compatible chains in real time, enabling instant insights into network activity as it happens.
  • On-demand network and database selection: Have your agent dynamically select blockchain networks and datasets—like Ethereum, BNB Chain, or others—to tailor queries for your specific use case.
  • Metric aggregation and analysis: Automate the aggregation of transaction counts, unique values, or conditional metrics, empowering your agent to analyze blockchain trends without manual intervention.
  • Advanced GraphQL customization: Use aliases and conditional snippets to refine data responses, ensuring clarity and precise control in complex blockchain analytics workflows.

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

Archive Database Query

Query the Bitquery V1 GraphQL API for historical blockchain data using blockchain-specific root types such as bitcoin and ethereum.

Combined Database Query

Query Bitquery's Combined Database (v2 API) for blockchain data across 40+ networks.

Conditional Metrics Snippet

Generate a Bitquery GraphQL metric snippet with an if condition for count, sum, avg, min, or max, for embedding in a query.

Database Selection

Generate a GraphQL dataset selection fragment for the archive, realtime, or combined dataset.

Early Access Program Query

Submit a GraphQL query to Bitquery's Early Access Program endpoint for real-time blockchain data, including data from Solana and EVM networks.

Network Selection

Generate a GraphQL network selection fragment for a supported blockchain network.

Options Query

Fetch root-level GraphQL query fields and their arguments through schema introspection for the selected Bitquery environment.

Price Asymmetry Metric

Tool to generate GraphQL PriceAsymmetry filter snippet.

Realtime Database Query

Query the Bitquery Streaming (V2) API for realtime blockchain data.

Select By Metric

Tool to generate a GraphQL metric snippet filtering by its value using selectWhere.

FRAMEWORKS

How to build Bitquery MCP Agent with another framework

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Claude Code

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OpenAI Agents SDK logo

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Codex logo

Codex

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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Atomic Agent logo

Atomic Agent

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Google ADK logo

Google ADK

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LangChain logo

LangChain

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

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LlamaIndex logo

LlamaIndex

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CrewAI logo

CrewAI

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AutoGen logo

AutoGen

Use Bitquery MCP with AutoGen

FAQ

Frequently asked questions

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