metorial 1.0.12

Python SDK for Metorial - The open source integration platform for agentic AI

Metorial Python SDK

The official Python SDK for Metorial - Connect your AI agents to any MCP server with a single line of code. Deploy tools like Slack, GitHub, SAP, and hundreds more without managing infrastructure.

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Complete API Documentation

API Documentation - Complete API reference and guides

Available Providers

Provider	Import	Format	Models (non-exhaustive)
OpenAI	MetorialOpenAI	OpenAI function calling	gpt-4.1, gpt-4o, o1, o3
Anthropic	MetorialAnthropic	Claude tool format	claude-sonnet-4-5, claude-opus-4
Google	MetorialGoogle	Gemini function declarations	gemini-2.5-pro, gemini-2.5-flash
Mistral	MetorialMistral	Mistral function calling	mistral-large-latest, codestral-latest
DeepSeek	MetorialDeepSeek	OpenAI-compatible	deepseek-chat, deepseek-reasoner
TogetherAI	MetorialTogetherAI	OpenAI-compatible	Llama-4, Qwen-3
XAI	MetorialXAI	OpenAI-compatible	grok-3, grok-3-mini
LangChain	MetorialLangChain	LangChain tools	Any model via LangChain
OpenAI-Compatible	MetorialOpenAICompatible	OpenAI-compatible	Any OpenAI-compatible API

Installation

pip install metorial

Quick Start

pip install metorial anthropic

import asyncio
from metorial import Metorial, MetorialAnthropic
from anthropic import AsyncAnthropic

metorial = Metorial(api_key="your-metorial-api-key")
anthropic = AsyncAnthropic(api_key="your-anthropic-api-key")

async def main():
    async def session_handler(session):
        messages = [{"role": "user", "content": "What's the latest news on Hacker News?"}]

        for _ in range(10):
            response = await anthropic.messages.create(
                model="claude-sonnet-4-5",
                max_tokens=1024,
                messages=messages,
                tools=session["tools"]
            )

            tool_calls = [b for b in response.content if b.type == "tool_use"]
            if not tool_calls:
                print(response.content[0].text)
                break

            tool_responses = await session["callTools"](tool_calls)
            messages.append({"role": "assistant", "content": response.content})
            messages.append(tool_responses)

        await session["closeSession"]()

    await metorial.with_provider_session(
        MetorialAnthropic,
        {"serverDeployments": [{"serverDeploymentId": "your-server-deployment-id"}]},
        session_handler
    )

asyncio.run(main())

Session Options

Streaming Mode

When using streaming with tool calls, enable the streaming flag:

await metorial.with_provider_session(
    metorial_provider,
    {
        "serverDeployments": [...],
        "streaming": True,  # Required for streaming with tool calls
    },
    session_handler
)

Closing Sessions

Always close your session when done to free up resources:

async def session_handler(session):
    tools = session["tools"]
    close_session = session["closeSession"]

    # Use tools...

    # When finished, close the session
    await close_session()

Session Object

The session object passed to your callback provides:

async def session_handler(session):
    tools = session["tools"]           # Tool definitions formatted for your provider
    call_tools = session["callTools"]  # Execute tools and get responses
    close_session = session["closeSession"]  # Close the session when done (always call this!)

Error Handling

from metorial import MetorialAPIError

try:
    await metorial.with_provider_session(...)
except MetorialAPIError as e:
    print(f"API Error: {e.message} (Status: {e.status})")
except Exception as e:
    print(f"Unexpected error: {e}")

License

MIT License - see LICENSE file for details.

Support

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