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Give Claude, Cursor, or any MCP client long-term memory.","maintainers":[{"name":"jghiglia","email":"jghiglia@gmail.com"}],"readme":"# @0latency/mcp-server\n\n[![npm version](https://img.shields.io/npm/v/@0latency/mcp-server.svg)](https://www.npmjs.com/package/@0latency/mcp-server)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n\n**Persistent memory for AI agents.** Give Claude, Cursor, Windsurf, or any MCP-compatible tool long-term memory in one config change.\n\n[0Latency](https://0latency.ai) automatically extracts, stores, and recalls memories across conversations — so your AI assistant actually remembers you.\n\n## Features\n\n- **Automatic memory extraction** — Detects facts, preferences, and relationships from conversations\n- **Semantic recall** — Retrieves the right memories for the current context\n- **Knowledge graph** — Tracks entities and relationships across all conversations\n- **Document & conversation import** — Bulk-load context from existing docs or chat exports\n- **Token-budgeted recall** — Control exactly how much context gets injected\n\n## Quick Start\n\n### 1. Get an API Key\n\nSign up at [0latency.ai](https://0latency.ai) and grab your API key from the dashboard.\n\n### 2. Configure Your AI Tool\n\n#### Claude Desktop\n\nAdd to `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\\Claude\\claude_desktop_config.json` (Windows):\n\n```json\n{\n  \"mcpServers\": {\n    \"0latency\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@0latency/mcp-server\"],\n      \"env\": {\n        \"ZERO_LATENCY_API_KEY\": \"your-api-key-here\"\n      }\n    }\n  }\n}\n```\n\n#### Claude Code\n\nAdd to your project `.mcp.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"0latency\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@0latency/mcp-server\"],\n      \"env\": {\n        \"ZERO_LATENCY_API_KEY\": \"your-api-key-here\"\n      }\n    }\n  }\n}\n```\n\n#### Cursor\n\nAdd to `.cursor/mcp.json` in your project root:\n\n```json\n{\n  \"mcpServers\": {\n    \"0latency\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@0latency/mcp-server\"],\n      \"env\": {\n        \"ZERO_LATENCY_API_KEY\": \"your-api-key-here\"\n      }\n    }\n  }\n}\n```\n\n#### Windsurf / Other MCP Clients\n\nSame pattern — just point your client's MCP config at:\n\n```\ncommand: npx -y @0latency/mcp-server\nenv: ZERO_LATENCY_API_KEY=your-api-key-here\n```\n\n### 3. Alternative: Install Globally\n\n```bash\nnpm install -g @0latency/mcp-server\n```\n\nThen use `0latency-mcp` as the command instead of `npx`:\n\n```json\n{\n  \"command\": \"0latency-mcp\",\n  \"env\": {\n    \"ZERO_LATENCY_API_KEY\": \"your-api-key-here\"\n  }\n}\n```\n\n## Available Tools\n\n| Tool | Description |\n|------|-------------|\n| `memory_add` | Extract and store memories from a conversation turn |\n| `remember` | Simple interface — \"remember this\" for quick facts |\n| `seed_memories` | Bulk-load known facts directly (bypass extraction) |\n| `memory_recall` | Recall relevant memories for current conversation context |\n| `memory_search` | Full-text search across stored memories |\n| `memory_list` | List memories with type/pagination filters |\n| `memory_delete` | Delete a specific memory by ID |\n| `import_document` | Import a document and extract memories from it |\n| `import_conversation` | Import a chat export and extract memories from each turn |\n| `memory_graph` | Query the knowledge graph (entities, relationships, paths) |\n\n## Environment Variables\n\n| Variable | Required | Default | Description |\n|----------|----------|---------|-------------|\n| `ZERO_LATENCY_API_KEY` | ✅ | — | Your 0Latency API key |\n| `ZERO_LATENCY_API_URL` | — | `https://api.0latency.ai` | API base URL (for self-hosted) |\n\n## How It Works\n\n1. **Your AI tool connects** to the 0Latency MCP server via stdio\n2. **During conversations**, the AI calls `memory_add` or `remember` to store important facts\n3. **In future conversations**, the AI calls `memory_recall` to retrieve relevant context\n4. **Memories compound** — the more you use it, the better your AI knows you\n\nNo prompt engineering required. The AI tools are designed to be self-describing — your AI assistant will naturally use them when appropriate.\n\n## Usage Examples\n\n**Store a memory:**\n> \"Remember that I prefer TypeScript over JavaScript and use Vim keybindings.\"\n\n**Recall memories:**\n> \"What do you know about my coding setup?\"\n\n**Import existing context:**\n> \"Import this project README as background knowledge.\"\n\n**Explore the knowledge graph:**\n> \"What entities are connected to the auth-service project?\"\n\n## Development\n\n```bash\ngit clone https://github.com/0latency-ai/mcp-server.git\ncd mcp-server\nnpm install\nnpm run build\nnpm start\n```\n\n## Links\n\n- 🌐 [0latency.ai](https://0latency.ai) — Product & dashboard\n- 📖 [API Documentation](https://docs.0latency.ai) — Full API reference\n- 🐛 [Issues](https://github.com/0latency-ai/mcp-server/issues) — Bug reports & feature requests\n\n## License\n\nMIT © [Justin Ghiglia](https://github.com/0latency-ai)\n","readmeFilename":"README.md"}