AI Agent Engineering Handbook
What it is
A comprehensive guide documenting the internal architectures, agent loops, and context management strategies of 30+ open-source AI agent frameworks. It extracts implementation details from source code to explain patterns like compaction, memory systems, and tool orchestration.
The problem it solves
Lack of deep, implementation-level documentation on how major AI agent frameworks actually work, forcing developers to reverse-engineer source code or rely on surface-level blog posts.
What you could use it for
- Selecting the appropriate agent framework for a specific use case
- Debugging context rot and tool sprawl in existing agent implementations
- Designing robust agent loops and memory hierarchies
- Understanding how production frameworks handle system prompt assembly
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.