Ratel

Modern AI agents face a fundamental challenge: every API call includes schemas for every available tool, inflating token usage and degrading response quality. Ratel solves this through "context engineering," a technique that strategically reveals only the most relevant capabilities at each step.

Highlights

• ~80% fewer tokens per API call by selective tool disclosure
• In-process BM25 and semantic retrieval with zero external dependencies
• Progressive skill management to match conversation context
• SDKs for TypeScript, Python, and Rust with integrated examples
• Production-ready with benchmarking suite and Model Context Protocol adapter

Built by developers who ship AI agents, Ratel reduces hallucinations by cutting noise, lowers costs by eliminating redundant schemas, and scales from prototypes to production systems. The framework is open-source (Apache 2.0) and free to use, modify, and integrate into any project.