riponcm/projectmem
by Various
Local-first memory layer for AI coding agents. Captures issues, attempts, decisions, and cross-project library gotchas — your AI starts experienced, not amnesiac. Native MCP server
MCP
riponcm/projectmem
Added 19 June 2026
Overview
A local-first memory layer for AI coding agents that captures issues, attempts, decisions, and cross-project library gotchas. It runs as a native MCP server verified across Claude Desktop, Cursor, Antigravity, and Codex, keeping all data 100% local with no cloud or telemetry.
Best for
Best for
Developers who want persistent, private memory for AI coding agents across sessions.
Use cases
- Persisting AI agent context across coding sessions to avoid repeating past discoveries
- Recording cross-project library pitfalls so agents learn from related work
- Storing decision logs for complex debugging or refactoring tasks
How to use
Install
pip install projectmem\ncd your-project\npjm init Tools exposed
pipgit
Notes
A local-first memory layer for AI coding agents that captures issues, attempts, decisions, and cross-project library gotchas. It runs as a native MCP server verified across Claude Desktop, Cursor, Antigravity, and Codex, keeping all data 100% local with no cloud or telemetry.
19 stars on GitHub. Last updated 2026-06-18. Licensed MIT.
Use cases
- Persisting AI agent context across coding sessions to avoid repeating past discoveries
- Recording cross-project library pitfalls so agents learn from related work
- Storing decision logs for complex debugging or refactoring tasks
Pros
- No cloud dependency or telemetry — fully offline and private
- Works with multiple agent frontends via standard MCP protocol
- Low overhead: Python-based and easy to integrate into existing workflows
Cons
- Small community (19 stars) — limited long-term support or roadmap
- Requires manual setup and configuration of the MCP server
- No built-in sharing or syncing between multiple machines
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- No cloud dependency or telemetry — fully offline and private
- Works with multiple agent frontends via standard MCP protocol
- Low overhead: Python-based and easy to integrate into existing workflows
Cons
- Small community (19 stars) — limited long-term support or roadmap
- Requires manual setup and configuration of the MCP server
- No built-in sharing or syncing between multiple machines
Pairs with
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