# How to integrate Astica ai MCP with Pydantic AI

```json
{
  "title": "How to integrate Astica ai MCP with Pydantic AI",
  "toolkit": "Astica ai",
  "toolkit_slug": "astica_ai",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/astica_ai/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/astica_ai/framework/pydantic-ai.md",
  "updated_at": "2026-05-12T10:02:05.768Z"
}
```

## Introduction

This guide walks you through connecting Astica ai to Pydantic AI using the Composio tool router. By the end, you'll have a working Astica ai agent that can transcribe this meeting recording to text, extract all text from uploaded receipt image, convert podcast audio link to transcript through natural language commands.
This guide will help you understand how to give your Pydantic AI agent real control over a Astica ai account through Composio's Astica ai MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Astica ai with

- [OpenAI Agents SDK](https://composio.dev/toolkits/astica_ai/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/astica_ai/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/astica_ai/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/astica_ai/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/astica_ai/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/astica_ai/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/astica_ai/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/astica_ai/framework/cli)
- [Google ADK](https://composio.dev/toolkits/astica_ai/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/astica_ai/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/astica_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/astica_ai/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/astica_ai/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/astica_ai/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 Astica ai
- 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 Astica ai 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 Astica ai MCP server, and what's possible with it?

The Astica ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Astica ai account. It provides structured and secure access to advanced cognitive intelligence APIs, so your agent can perform actions like analyzing audio, transcribing speech, extracting text from images, and automating media understanding tasks on your behalf.
- Audio transcription automation: Instantly convert audio files or streams to accurate text using the Analyze Audio tool, perfect for meeting notes, podcasts, or voice memos.
- OCR text extraction from images: Quickly read and extract raw text from images (like photos, scanned documents, or screenshots) with the ASTICA_READ_TEXT tool.
- Seamless media processing: Let your agent process audio and visual inputs end-to-end, enabling smarter workflows that bridge speech and text content.
- Hands-off document digitization: Automate the tedious process of turning printed or handwritten materials into searchable, editable text for downstream applications.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ASTICA_AI_ANALYZE_AUDIO` | Analyze Audio | Tool to analyze audio input for transcription. Use when you need to convert an audio URL or Base64 string to text. |
| `ASTICA_AI_ASTICA_READ_TEXT` | ASTICA_READ_TEXT | Perform OCR (Optical Character Recognition) on an image to extract text. Supports HTTPS image URLs and Base64-encoded images. Returns detected text blocks with bounding box coordinates. Works with printed text, handwritten notes, and various image qualities. Note: SVG and WebP formats are not supported. |

## Supported Triggers

None listed.

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

The Astica ai MCP server is an implementation of the Model Context Protocol that connects your AI agent to Astica ai. It provides structured and secure access so your agent can perform Astica ai 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 Astica ai
- 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 Astica ai
- MCPServerStreamableHTTP connects to the Astica ai 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 Astica ai 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 Astica ai
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["astica_ai"],
    )
    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 Astica ai endpoint
- The agent uses GPT-5 to interpret user commands and perform Astica ai operations
- The instructions field defines the agent's role and behavior
```python
# Attach the MCP server to a Pydantic AI Agent
astica_ai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[astica_ai_mcp],
    instructions=(
        "You are a Astica ai assistant. Use Astica ai 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
- Astica ai 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 Astica ai.\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 Astica ai
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["astica_ai"],
    )
    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
    astica_ai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[astica_ai_mcp],
        instructions=(
            "You are a Astica ai assistant. Use Astica ai 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 Astica ai.\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 Astica ai through Composio's Tool Router. With this setup, your agent can perform real Astica ai 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 + Astica ai 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 Astica ai MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/astica_ai/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/astica_ai/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/astica_ai/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/astica_ai/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/astica_ai/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/astica_ai/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/astica_ai/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/astica_ai/framework/cli)
- [Google ADK](https://composio.dev/toolkits/astica_ai/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/astica_ai/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/astica_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/astica_ai/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/astica_ai/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/astica_ai/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.
- [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.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Astica ai MCP?

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

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

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

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