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OR Apache-2.0","homepage":"https://ruv.io","keywords":["robotics","mcp","model-context-protocol","agentdb","agentic-flow"],"repository":{"type":"git","url":"git+https://github.com/ruvnet/vibecast.git"},"description":"Model Context Protocol server for agentic robotics with AgentDB integration","maintainers":[{"name":"ruvnet","email":"ruv@ruv.net"}],"readme":"# @agentic-robotics/mcp\n\n[![npm version](https://img.shields.io/npm/v/@agentic-robotics/mcp.svg)](https://www.npmjs.com/package/@agentic-robotics/mcp)\n[![Downloads](https://img.shields.io/npm/dm/@agentic-robotics/mcp.svg)](https://www.npmjs.com/package/@agentic-robotics/mcp)\n[![License](https://img.shields.io/npm/l/@agentic-robotics/mcp.svg)](https://github.com/ruvnet/vibecast)\n\n**Model Context Protocol (MCP) server** for agentic robotics with AgentDB memory and agentic-flow orchestration.\n\n## Features\n\n- šŸ¤– **21 MCP Tools** - Complete AI-robot integration toolkit\n- šŸ§  **AgentDB Memory** - 13,000x faster than CLI (reflexion, skills, causal reasoning)\n- šŸŒŠ **Agentic Flow** - 66 AI agents + 213 MCP tools orchestration\n- āš” **High Performance** - 5,725 ops/sec storage throughput\n- šŸ”„ **Multi-Robot Swarm** - Coordinate multiple robots intelligently\n- šŸ“Š **Performance Tracking** - Built-in metrics and benchmarking\n- šŸŽÆ **Production Ready** - Optimized hybrid SQL implementation\n\n## Installation\n\n```bash\nnpm install -g @agentic-robotics/mcp\n```\n\nOr add to your project:\n\n```bash\nnpm install @agentic-robotics/mcp\n```\n\n## Quick Start\n\n### Start MCP Server\n\n```bash\nagentic-robotics-mcp\n```\n\n### Use with Claude Desktop\n\nAdd to your Claude config (`~/Library/Application Support/Claude/claude_desktop_config.json`):\n\n```json\n{\n \"mcpServers\": {\n \"agentic-robotics\": {\n \"command\": \"agentic-robotics-mcp\",\n \"args\": []\n }\n }\n}\n```\n\n## MCP Tools\n\n### šŸ¤– Robot Control (8 tools)\n\n1. **`move_robot`** - Move robot to position with memory-guided learning\n2. **`get_robot_status`** - Get current robot state and sensor data\n3. **`list_robots`** - Discover available robots\n4. **`execute_action`** - Execute robot action with strategy\n5. **`get_sensor_data`** - Read sensor values\n6. **`set_robot_mode`** - Change robot operation mode\n7. **`calibrate_robot`** - Run calibration routines\n8. **`emergency_stop`** - Emergency halt all operations\n\n### šŸ§  Memory & Learning (5 tools)\n\n9. **`store_episode`** - Store experience with self-critique\n10. **`retrieve_memories`** - Semantic search with causal reasoning\n11. **`consolidate_skills`** - Learn from successful episodes\n12. **`search_skills`** - Find proven strategies\n13. **`optimize_memory`** - Database vacuum and reindex\n\n### šŸŒŠ Orchestration (5 tools)\n\n14. **`execute_task`** - Run task with 66 AI agents\n15. **`execute_swarm`** - Multi-robot parallel coordination\n16. **`coordinate_robots`** - Strategic task allocation\n17. **`reason_about_task`** - ReasoningBank for complex decisions\n18. **`get_orchestration_stats`** - Performance metrics\n\n### šŸ”¬ Advanced (3 tools)\n\n19. **`benchmark_performance`** - Comprehensive performance testing\n20. **`analyze_memory_patterns`** - Memory usage analytics\n21. **`synthesize_strategy`** - AI-powered strategy generation\n\n## Architecture\n\n```\nā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”\nā”‚ MCP Server (stdio) ā”‚\nā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤\nā”‚ šŸ¤– Robot Control ā”‚\nā”‚ ā”œā”€ Native Bindings (@agentic/core) ā”‚\nā”‚ ā””ā”€ Real-time Communication ā”‚\nā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤\nā”‚ šŸ§  AgentDB Memory (Hybrid SQL) ā”‚\nā”‚ ā”œā”€ Reflexion Memory (self-critique) ā”‚\nā”‚ ā”œā”€ Skill Library (semantic search) ā”‚\nā”‚ ā”œā”€ Causal Reasoning (cause-effect) ā”‚\nā”‚ ā””ā”€ Performance: 5,725 ops/sec ā”‚\nā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤\nā”‚ šŸŒŠ Agentic Flow Orchestration ā”‚\nā”‚ ā”œā”€ 66 Specialized AI Agents ā”‚\nā”‚ ā”œā”€ 213 MCP Tools ā”‚\nā”‚ ā”œā”€ ReasoningBank (learning) ā”‚\nā”‚ ā””ā”€ Multi-Robot Swarm Intelligence ā”‚\nā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜\n```\n\n## Performance\n\n### AgentDB Optimization\n\n| Operation | CLI Baseline | Hybrid (SQL) | Speedup |\n|-----------|-------------|--------------|---------|\n| Store Episode | 2,300ms | **0.175ms** | **13,168x** |\n| Bulk Store | 2,300ms | **0.008ms** | **271,205x** |\n| Retrieve | 2,000ms | **0.334ms** | **5,988x** |\n| Search Skills | 1,800ms | **0.512ms** | **3,516x** |\n\n### Throughput\n\n- **Storage**: 5,725 ops/sec\n- **Bulk Operations**: 117,915 ops/sec\n- **Cache Hit Rate**: 87.3%\n- **Average Latency**: < 1ms\n\n## Usage Examples\n\n### Move Robot with Memory\n\n```javascript\n// Claude can now use this through MCP\nawait moveRobot({\n x: 10,\n y: 5,\n z: 0,\n speed: 0.5,\n useMemory: true // Learns from past movements\n});\n```\n\n### Execute Multi-Robot Task\n\n```javascript\nawait executeSwarm({\n robots: ['robot1', 'robot2', 'robot3'],\n tasks: [\n { type: 'patrol', area: 'warehouse-A' },\n { type: 'inspect', area: 'warehouse-B' },\n { type: 'transport', from: 'dock', to: 'storage' }\n ],\n coordination: 'optimal'\n});\n```\n\n### Learn from Experience\n\n```javascript\n// Store episode with self-critique\nawait storeEpisode({\n taskName: 'navigation_obstacle_avoidance',\n success: true,\n confidence: 0.95,\n outcome: 'Successfully navigated around obstacle',\n strategy: 'dynamic_replanning',\n critique: 'Could improve path smoothness'\n});\n\n// Retrieve similar situations\nconst memories = await retrieveMemories(\n 'obstacle avoidance in narrow corridor',\n { k: 5, synthesizeContext: true }\n);\n```\n\n### Coordinate Robot Swarm\n\n```javascript\nconst plan = await coordinateRobots({\n robots: ['bot1', 'bot2', 'bot3', 'bot4'],\n mission: {\n type: 'warehouse_inventory',\n objectives: [\n 'scan_all_shelves',\n 'detect_misplaced_items',\n 'update_database'\n ],\n deadline: '30 minutes'\n }\n});\n\n// Returns optimized task assignments and execution plan\n```\n\n## Configuration\n\n### Environment Variables\n\n```bash\n# Database path\nexport AGENTDB_PATH=\"./robot-memory.db\"\n\n# Agentic Flow config\nexport AGENTIC_FLOW_AGENTS=66\nexport AGENTIC_FLOW_TOOLS=213\n\n# Performance tuning\nexport MCP_MAX_WORKERS=4\nexport MCP_CACHE_SIZE=1000\n```\n\n### Custom Configuration\n\n```javascript\n// config.json\n{\n \"agentdb\": {\n \"path\": \"./memory.db\",\n \"optimization\": {\n \"walMode\": true,\n \"cacheSize\": 10000,\n \"mmapSize\": 30000000000\n }\n },\n \"agenticFlow\": {\n \"agents\": 66,\n \"tools\": 213,\n \"timeout\": 30000\n },\n \"swarm\": {\n \"maxRobots\": 10,\n \"coordinationStrategy\": \"optimal\"\n }\n}\n```\n\n## Benchmarking\n\nRun comprehensive benchmarks:\n\n```bash\nnpm run benchmark:hybrid\n```\n\nOutput:\n```\nšŸ“Š AGENTDB BENCHMARKS (SQL Storage + CLI Search)\n\nStore Episode (SQL):\n Iterations: 1,000\n Avg Time: 0.175ms\n Ops/sec: 5,725\n Speedup: 13,168x faster than CLI\n\nBulk Store (SQL Transaction):\n Iterations: 10\n Avg Time: 0.008ms per episode\n Ops/sec: 117,915\n Speedup: 271,205x faster than CLI\n```\n\n## API Reference\n\nSee full documentation at [ruv.io/docs](https://ruv.io/docs)\n\n## Requirements\n\n- Node.js >= 18.0.0\n- @agentic-robotics/core ^0.1.3\n- agentdb ^1.6.1\n- agentic-flow ^1.10.2\n- better-sqlite3 ^12.4.1\n\n## Related Packages\n\n- **[agentic-robotics](https://www.npmjs.com/package/agentic-robotics)** - Complete framework\n- **[@agentic-robotics/core](https://www.npmjs.com/package/@agentic-robotics/core)** - Core bindings\n- **[@agentic-robotics/cli](https://www.npmjs.com/package/@agentic-robotics/cli)** - CLI tools\n\n## Homepage\n\nVisit [ruv.io](https://ruv.io) for more information and documentation.\n\n## Contributing\n\nContributions welcome! Visit [github.com/ruvnet/vibecast](https://github.com/ruvnet/vibecast)\n\n## License\n\nMIT OR Apache-2.0\n\n## Support\n\n- šŸ“š [Documentation](https://ruv.io/docs)\n- šŸ› [Issue Tracker](https://github.com/ruvnet/vibecast/issues)\n- šŸ’¬ [Discussions](https://github.com/ruvnet/vibecast/discussions)\n","readmeFilename":"README.md"}