memories-mcp
memories-mcp
Connect AI agents to the memories.sh memory layer via MCP (Model Context Protocol).
The CLI is the primary interface for memories.sh — use
memories generateto create native config files for each tool. The MCP server is a fallback for real-time access when static configs aren't enough. It's also the best choice for browser-based agents (v0, bolt.new, Lovable) where the CLI can't run.
Quick Start
# Local stdio transport (most reliable)
memories serve
# HTTP/SSE transport (for web clients like v0)
memories serve --sse --port 3030
# Cloud-hosted (no local install needed)
# Endpoint: https://memories.sh/api/mcp
# Header: Authorization: Bearer YOUR_KEY
More from webrenew/memories
memories-dev
Developer guide for contributing to and extending the memories.sh codebase. Use when: (1) Understanding the memories.sh architecture and lifecycle model, (2) Adding new CLI commands or MCP tools, (3) Modifying the memory storage layer (SQLite/libSQL), (4) Working on the web dashboard (Next.js/Supabase), (5) Adding new generation targets for AI tools, (6) Extending cloud sync, session compaction, or embeddings functionality, (7) Debugging build, test, or deployment issues in the monorepo.
30memories-cli
CLI reference and workflows for memories.sh — the persistent memory layer for AI agents. Use when: (1) Running memories CLI commands to add, search, edit, or manage memories, (2) Managing lifecycle workflows (session/checkpoint/compaction/consolidation/OpenClaw memory files), (3) Setting up memories.sh in a new project (memories init), (4) Generating AI tool config files (CLAUDE.md, .cursor/rules, etc.), (5) Importing existing rules from AI tools (memories ingest), (6) Managing cloud sync, embeddings, git hooks, or reminders, (7) Troubleshooting with memories doctor, (8) Working with memory templates, links, history, tags, or reminder schedules.
29memories-sdk
Build against the memories.sh SDK packages in application code. Use when working with `@memories.sh/core` or `@memories.sh/ai-sdk`, including: (1) Initializing `MemoriesClient`, (2) Reading, writing, searching, or editing memories from backend code, route handlers, workers, or scripts, (3) Integrating memories with the Vercel AI SDK via `memoriesMiddleware`, `memoriesTools`, `preloadContext`, or `createMemoriesOnFinish`, (4) Choosing and applying `tenantId` / `userId` / `projectId` scoping, (5) Managing SDK skill files or management APIs, or (6) Debugging memories SDK usage in TypeScript or JavaScript applications. Use `memories-cli` for CLI workflows, `memories-mcp` for MCP setup, and `memories-dev` for monorepo internals.
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