This repository provides a free, open-source course for developers to build multimodal AI agents capable of processing video, images, audio, and text. It focuses on practical, production-ready AI systems, teaching users to design and implement custom agents with advanced capabilities.

How It Works

The course centers around building a "Kubrick AI" agent using the Model Context Protocol (MCP). It leverages Pixeltable for multimodal data processing and stateful agents, FastMCP for creating MCP servers and clients, and Opik for observability and prompt versioning. This approach allows for the creation of complex, observable, and production-ready agentic systems.

Quick Start & Requirements

• Installation: Follow the detailed steps in the GETTING_STARTED.md file.
• Prerequisites: A laptop/PC with any OS. Understanding of Python programming is required. Familiarity with AI/ML concepts, LLMs, MCP, and Agents is beneficial but not mandatory.
• Compute: Primarily uses API-based models (OpenAI, Groq) to minimize local compute requirements. Freemium plans are generally sufficient for the examples.
• Resources: Links to course modules, video lessons, and code examples are provided within the repository.

Highlighted Details

• Builds a multimodal processing pipeline for video, images, text, and audio.
• Develops a video search engine and exposes its functionality via MCP.
• Integrates LLMOps principles, including prompt versioning and tracing with Opik.
• Covers custom MCP client implementation and tool agent creation using Llama 4 Scout and Maverick.

Maintenance & Community

The course is a collaboration between The Neural Maze and Neural Bits. Sponsors include Pixeltable and Opik. Links to their respective publications are provided.

Licensing & Compatibility

The course materials are open-source and free. Specific licensing details for the code components are not explicitly stated in the README, but the overall project is presented as free for use.

Limitations & Caveats

This is described as a comprehensive course, not a simple tutorial, and requires dedicated effort to follow the hands-on implementation steps. The course focuses on API-based models, so performance and cost will be dependent on external service providers.