Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and s...
Copy the install, test the workflow, then decide if it earns a permanent slot.
Still active enough to matter. Good candidate for a fast stack test instead of a long evaluation loop.
Copy the install, test the workflow, then decide if it earns a permanent slot.
You can test this quickly and remove it cleanly if it misses.
GitHub health 75/100. no security policy. Fresh enough repo health and manageable issue load keep the risk controlled.
AI Agent
Universal
Model
Claude
Fastest way to find out if ratel belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
claude mcp add ratel -- npx ratelRun this first. You will know quickly if the workflow earns a permanent slot.
claude mcp remove ratelNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude.json └─ mcp_servers/ └─ ratel ← registers here
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.
Source: GitHub repository
Source check: July 18, 2026
Upstream commit: July 18, 2026
Repository state: Not marked archived
Honeystax upvotes are community interest signals, not star ratings. GitHub stars and repository health are source measurements; editorial risk and trial-cost notes are Honeystax analysis.