We open-sourced Stanford's "Agentic Context Engineering" implementation - agents that learn from execution

We shipped an implementation of Stanford's "Agentic Context Engineering" paper: agents that improve by learning from their own execution.

How does it work? A three-agent system (Generator, Reflector, Curator) builds a "playbook" of strategies autonomously:

Execute task → Reflect on what worked/failed → Curate learned strategies into the playbook
+10.6% performance improvement on complex agent tasks (according to the papers benchmarks)
No training data needed

My open-source implementation works with any LLM, has LangChain/LlamaIndex/CrewAI integrations, and can be plugged into existing agents in ~10 lines of code.

GitHub: https://github.com/kayba-ai/agentic-context-engine

Paper: https://arxiv.org/abs/2510.04618

Would love feedback!