Ratel
Open-source context engineering layer for AI agents that reduces token usage by ~80% and improves accuracy by selectively providing relevant tools and skills.
Ratel
What is Ratel?
Ratel vs Similar AI Tools
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How to use Ratel?
- 1Install the SDK for your language (TypeScript, Python, Rust).
- 2Create a ToolCatalog and SkillCatalog, registering your tools and skills with descriptions.
- 3Use the searchCapabilitiesTool, invokeToolTool, and getSkillContentTool in your agent framework to let Ratel dynamically select and invoke relevant capabilities.
Ratel Key Features
- BM25 retrieval engine for fast, deterministic tool and skill selection
- Tool and skill catalogs with schema-aware indexing
- Progressive disclosure: injects only matching capabilities per turn
- No vector database or external infrastructure required
- Multi-language SDKs: TypeScript, Python, Rust
- Reduces token usage by ~80% and recovers accuracy from tool overload
- Semantic and hybrid ranking opt-in per catalog
Ratel Use Cases
- Reducing AI agent token costs
- Improving agent accuracy with large tool sets
- Managing skills and memory for coding agents
- Progressive disclosure of capabilities
Ratel Pricing & Free Credits
Ratel currently operates on a Free model.
This tool is completely free to use
Ratel Pros & Cons
Pros
- Reduces token usage significantly
- Improves accuracy by reducing tool overload
- Open-source and free to use
- No external infrastructure (no vector DB needed)
- Supports multiple programming languages
Cons
- Requires integration effort to set up catalogs and tools
- Retrieval quality depends on quality of tool/skill metadata
What is Ratel best for?
- AI agent developers
- Teams looking to reduce API costs
- Developers working with large tool sets
- Open-source enthusiasts
Ratel FAQ
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