TheStack-ai/waypath
by Various
Open-source memory CLI for Claude Code, Codex & MCP — local-first SQLite, graph-aware recall, review gate. npm i -g waypath
MCP
TheStack-ai/waypath
Added 1 June 2026
Overview
Waypath is an open-source CLI that provides persistent memory for AI coding assistants including Claude Code, Codex, and MCP-compatible tools. It stores data locally in SQLite, uses graph relationships for context-aware recall, and includes a review gate to filter memory entries. Installation is via npm with the command npm i -g waypath.
Best for
Best for
Developers using Claude Code or Codex who need persistent, local memory across sessions
Use cases
- Persist conversation context across Claude Code sessions
- Share memory between Codex and MCP-based tools
- Review and approve memory entries before they are stored
How to use
Install
npm install -g waypath Tools exposed
Local-first
Tested with
Claude Code, Cursor
Notes
Waypath is an open-source CLI that provides persistent memory for AI coding assistants including Claude Code, Codex, and MCP-compatible tools. It stores data locally in SQLite, uses graph relationships for context-aware recall, and includes a review gate to filter memory entries. Installation is via npm with the command npm i -g waypath.
3 stars on GitHub. Last updated 2026-04-28. Licensed MIT.
Use cases
- Persist conversation context across Claude Code sessions
- Share memory between Codex and MCP-based tools
- Review and approve memory entries before they are stored
Pros
- Local-first design keeps data private and offline-capable
- Graph-aware recall surfaces relevant context automatically
- Open-source and npm installable with no dependency on cloud services
Cons
- Very early-stage project with only 3 GitHub stars and limited community support
- Requires command-line comfort and manual setup with each AI tool
- Graph recall quality depends on how users structure their memory entries
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Local-first design keeps data private and offline-capable
- Graph-aware recall surfaces relevant context automatically
- Open-source and npm installable with no dependency on cloud services
Cons
- Very early-stage project with only 3 GitHub stars and limited community support
- Requires command-line comfort and manual setup with each AI tool
- Graph recall quality depends on how users structure their memory entries
Pairs with
Other entries in the index that connect to this one. Click through to see the chain.
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