Last month, the SambaNova team, in partnership with Stanford and UC Berkeley, introduced the viral paper Agentic Context Engineering (ACE), a framework for building evolving contexts that enable self-improving language models and agents. Today, the team has released the full ACE implementation, available on GitHub, including the complete system architecture, modular components (Generator, Reflector, Curator), and ready-to-run scripts for both Finance and AppWorld benchmarks. The repository provides everything needed to reproduce results, extend to new domains, and experiment with evolving playbooks in your own applications. Feel free to try it out... we’d love your feedback and contributions!
ACE changes how AI learns — instead of updating weights, it grows its memory, storing lessons from every win and mistake. Like an AI that journals after each task, it reflects, notes, and reasons better next time, turning static models into experience-driven, self-improving systems. ACE consistently outperforms strong baselines including +10.6% on agentic benchmarks and +8.6% on domain specific benchmarks, while significantly reducing latency and rollout cost.
1. Why Context Engineering
Context engineering has rapidly become a central theme in building capable, reliable, and self-improving AI systems. It has gained attention across both research and industry, from Anthropic’s guidebook on context engineering [1], to mem0’s and Letta’s development of persistent memory layers for AI agents [2,3], to Databricks’ enterprise agent that integrates prompt optimization [4].
