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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

T

MCP

TheStack-ai/waypath

Added 1 June 2026

#ai-agent #anthropic #claude-code #cli #codex #coding-agents #developer-tools #external-brain

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
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