memories-cli
memories-cli
CLI reference for @memories.sh/cli — manage memories, generate configs, and sync across tools.
The CLI is the primary way to interact with memories.sh. Use it to store memories, generate native config files, and manage your memory store. For environments where the CLI isn't available (v0, bolt.new, Lovable, or other browser-based agents), use the MCP server as a fallback.
Install & Init
npm install -g @memories.sh/cli # or: npx @memories.sh/cli
memories init # Initialize in current project
memories init auto-detects AI tools (Claude Code, Cursor, Windsurf, VS Code) and configures MCP + generates instruction files.
Command Quick Reference
More from webrenew/memories
memories-mcp
MCP server integration for memories.sh — the persistent memory layer for AI agents. Use when: (1) Configuring the memories.sh MCP server for any client (Claude Code, Cursor, Windsurf, VS Code, v0, Claude Desktop, OpenCode, Factory), (2) Using MCP tools to store, search, retrieve memories, run lifecycle session workflows, or manage reminders, (3) Understanding get_context vs search_memories vs list_memories, (4) Working with streaming memory tools for SSE content, (5) Troubleshooting MCP connection issues, (6) Choosing between cloud MCP (HTTP) and local MCP (stdio) transports.
32memories-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-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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