The Pi Agent Loop is a core component in many LLM agent frameworks, orchestrating the interaction between the model, tools, and user. This analysis examines its key mechanisms: how context is managed across turns, how streaming responses are handled, how tool calls are integrated, and how steering and stop conditions control the loop's behavior. Understanding these patterns is crucial for developers building custom agents or debugging existing ones. The post highlights design trade-offs and offers practical insights for implementing robust agent loops.
A deep dive into the Pi Agent Loop's architecture, covering context handling, streaming, tool calling, steering, and stop conditions.