ratel
Reduces AI agent token consumption by 80% while eliminating tool overload through intelligent context retrieval, lowering inference costs and improving respo…
Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.
- Reduce token costs by optimizing AI agent context retrieval without external databases.
- Organize agent skills and memory using built-in semantic search for faster access.
- Fix tool overload by progressively disclosing only relevant functions to AI agents.
Reduces AI agent token consumption by 80% while eliminating tool overload through intelligent context retrieval, lowering inference costs and improving response latency without external dependencies.
Engineering teams deploying AI agents who need cost-efficient context management and reduced hallucination from irrelevant tool suggestions.
https://github.com/ratel-ai/ratel
By ratel-ai
How to Get It
claude plugins install ratel-ai/ratel
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After installing, paste this into Claude:
Help me reduce token costs by optimizing AI agent context retrieval without external databases
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Reviewer notes
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Scored from trust signals (evidence-eval-v1): 419 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
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Evaluation
Scored from trust signals (evidence-eval-v1): 419 GitHub stars; contributors unknown; last commit 0d ago; license MIT.