Ollama phi-4:latest

Rules to help creation of autogen components: agents, tools, workflows, etc.

AutoGen is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans.

An agent is a software entity that communicates via messages, maintains its own state, and performs actions in response to received messages or changes in its state. These actions may modify the agent’s state and produce external effects, such as updating message logs, sending new messages, executing code, or making API calls.

A model context supports storage and retrieval of Chat Completion messages. It is always used together with a model client to generate LLM-based responses. For example, BufferedChatCompletionContext is a most-recent-used (MRU) context that stores the most recent buffer_size number of messages. This is useful to avoid context overflow in many LLMs. Let’s see an example that uses BufferedChatCompletionContext.