marm-memory
Reduces context switching overhead and eliminates cloud dependency for AI-assisted development by maintaining persistent, indexed memory of codebase patterns…
Local-first 3-in-1 AI memory layer & MCP server for Claude Code, Codex, Grok, Gemini, VS Code and Cursor. Fuses session history, codebase indexing & concept graphs in SQLite. Enables zero-cloud, privacy-first context & instant recall, supports multi-agent swarms.
- Ask Claude to recall previous code decisions and architectural patterns from your local project history.
- Generate automated summaries of codebase changes and dependencies without uploading files to cloud.
- Automate multi-agent workflows by sharing indexed code concepts across Claude, Cursor, and VS Code.
Reduces context switching overhead and eliminates cloud dependency for AI-assisted development by maintaining persistent, indexed memory of codebase patterns and session history. Enables faster problem-solving and reduces token costs in multi-agent workflows.
Engineering teams managing large codebases who need local AI context without external dependencies or privacy constraints.
https://github.com/Lyellr88/marm-memory
By Lyellr88
How to Get It
claude plugins install Lyellr88/marm-memory
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Recall previous code decisions and architectural patterns from my local project history
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 321 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 321 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.