Original Summary

What it is Coding agents relearn your project every session: which files matter, how the tests run, what failed last time. MuscleMemory watches agent sessions (Claude Code, Codex, Cursor, or any MCP client) and remembers how a task was done, but only when a test passed after the last code change. Why I built it I kept watching agents rediscover the same project over and over. Most memory tools store conversations or facts. I wanted memory that only keeps what was proven, so a skill is trusted only after two more separate sessions pass with it, and after you approve it. One failed check pulls it. Failed attempts become short "watch out" notes. How it works / tech stack • Python, FastAPI service with a REST API, an MCP endpoint and a dashboard • SQLite by default, PostgreSQL + pgvector for teams • Retrieval: BM25 + local MiniLM embeddings fused with reciprocal rank fusion; no model calls during recall • Hooks for Claude Code, Codex and Cursor capture each session automatically • Runs fully locally, Apache-2.0 Results so far • Codex, 18 real sessions: input tokens −25.5%, tool calls −30.4%, time −28.2% • Claude Code using skills Codex learned: input tokens −43.5% (small run) Every session passed either way, so this measures cost, not success rate. What I'd love feedback on: is "tests pass after the last change, then two more independent passes" the right bar for trusting agent memory? GitHub: https://github.com/TheWariend/MuscleMemory   submitted by   /u/maa____z [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/14 01:54:57