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for [agent-memory](https://github.com/WHQ25/agent-memory) — long-term, cross-project memory for AI agents.\n\nFor AI agents that don't have shell access (e.g. Claude Desktop, Windsurf, Cline), this package exposes agent-memory as an [MCP](https://modelcontextprotocol.io/) server over stdio.\n\n## Install\n\n```bash\nnpm install -g @agent-memory/mcp\n```\n\n## Setup\n\nAdd to your MCP client config:\n\n**Claude Desktop** (`claude_desktop_config.json`):\n\n```json\n{\n  \"mcpServers\": {\n    \"agent-memory\": {\n      \"command\": \"agmem-mcp\"\n    }\n  }\n}\n```\n\n**Claude Code** (`~/.claude/settings.json`):\n\n```json\n{\n  \"mcpServers\": {\n    \"agent-memory\": {\n      \"command\": \"agmem-mcp\"\n    }\n  }\n}\n```\n\n**Cursor** (`.cursor/mcp.json`):\n\n```json\n{\n  \"mcpServers\": {\n    \"agent-memory\": {\n      \"command\": \"agmem-mcp\"\n    }\n  }\n}\n```\n\n## Tools\n\n14 tools organized in 4 groups:\n\n### Memory CRUD\n\n| Tool | Description |\n|------|-------------|\n| `memory_add` | Store a new memory with optional tags and digest |\n| `memory_get` | Retrieve one or more memories by ID |\n| `memory_update` | Update a memory's content, tags, or digest |\n| `memory_delete` | Permanently delete memories by ID |\n\n### Search & Browse\n\n| Tool | Description |\n|------|-------------|\n| `memory_search` | Hybrid search (full-text + vector + RRF fusion) |\n| `memory_list` | Browse and filter memories with pagination |\n| `memory_tags` | List all tags with usage counts |\n| `memory_stats` | Show memory store statistics |\n\n### Source Management\n\n| Tool | Description |\n|------|-------------|\n| `source_list` | List configured memory sources |\n| `source_run` | Run source commands (embed, rebuild, status) |\n\n### Config\n\n| Tool | Description |\n|------|-------------|\n| `config_set` | Set a configuration value |\n| `config_get` | Get a configuration value |\n| `config_list` | List all configuration values |\n| `config_delete` | Delete a configuration value |\n\n## First Run\n\nOn first use, initialize the embedding model for semantic search:\n\n```\nmemory_add → works immediately (FTS search available)\nsource_run { command: \"init\" } → downloads embedding model (~65MB) for vector search\n```\n\nWithout initialization, search falls back to keyword-only (FTS5). Semantic search activates once the model is downloaded.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}