by ratel-ai · MCP Server · ★ 139
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Ratel Context engineering for AI agents — engineer the context your agent actually needs, on every turn. Docs • Skills • Roadmap • Discord Most agents stuff every tool, skill, and memory into the context window each turn — burning tokens, drifting on the long tail. Ratel sits between the agent and its catalog, and resolves only what matters for this turn. Integrate Ratel in 60 seconds The fastest way to get Ratel into your agent is the Ratel skills suite — five Claude Code / Cursor / Codex skills that integrate Ratel, plan observability, d
| Stars | 139 |
| Forks | 9 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.5058933905015/100 |
| Open Issues | 16 |
| Last Updated | 2026-06-30 |
| Created | 2025-11-12 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~17k |
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ratel is Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 retrieval. No vector DB. No embeddings.. It is categorized as a MCP Server with 139 GitHub stars.
ratel is primarily written in TypeScript. It covers topics such as accuracy, agents, claude-skills.
You can find installation instructions and usage details in the ratel GitHub repository at github.com/ratel-ai/ratel. The project has 139 stars and 9 forks, indicating an active community.
ratel is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ratel on Agent Skills Hub include Sentient, concierge, LLM-Agents-Ecosystem-Handbook. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.