This course covers how to build AI agents on top of NousResearch’s Hermes, an open-weight model you run yourself instead of calling a hosted API. Everything runs locally through Ollama on Hermes 3 8B, so the course has no API costs and works offline. The code is plain Python with the standard requests and ollama calls, not a wrapper framework, which means you work directly with the model’s chat endpoint and see the raw ChatML the model actually receives.
You begin with the runtime: installing Ollama, pulling Hermes, and calling both the native /api/chat endpoint and the OpenAI-compatible /v1 route. You’ll look at ChatML directly, including the <|im_start|> and <|im_end|> tokens, the role turns, and how the system prompt steers generation. The course uses apply_chat_template rather than hand-writing tokens, and explains where the two differ.
Function calling is the core of the course. You’ll work with Hermes’s native tool format, the <tools>, <tool_call>, and <tool_response> blocks, and the official function-calling system prompt. You build an agent that parses a <tool_call> out of the model output, executes a real API, and feeds the result back as a <tool_response> for the model to answer from. Then you cover the easier path through Ollama’s tools=[…] parameter and where the JSON parsing tends to break.
