Initialize a LitServer for high-performance AI model serving. LitServer transforms AI models into production-ready APIs with automatic scaling,

Enable Model Context Protocol (MCP) integration for LitServe APIs. This enables LitServe APIs to be seamlessly integrated into MCP-compatible AI

Producer class for sending messages to consumers.

_LoggerConnector is responsible for connecting Logger instances with the LitServer and managing their lifecycle. This class handles the following

Outputs from a single step of the loop.

Async consumer class for receiving messages using asyncio.

Message broker that routes messages between producers and consumers.

Connects LitServer to MCP server. It creates HTTP streamable MCP server with Starlette and mounts to the FastAPI app.

Mock FastAPI Request object for testing.

API that crashes after 2 predictions to simulate worker failure.

PDB Subclass for debugging multi-processed code Suggested in: https://stackoverflow.com/questions/4716533/how-to-attach-debugger-to-a-python-