agentic-robotics
Advanced tools
High-performance agentic robotics framework with ROS2 compatibility - Complete toolkit
The Future of Intelligent Automation - A next-generation robotics framework that seamlessly combines high-performance Rust core with AI-native integration, purpose-built for autonomous systems that learn and adapt.
📚 Documentation | 🚀 Getting Started | 💬 Community | 🐛 Issues
Agentic Robotics is a revolutionary framework that bridges the gap between traditional robotics and modern AI. It empowers developers to build intelligent, self-learning robots that can perceive, decide, and act autonomously in complex environments.
Traditional robotics frameworks require extensive programming for every scenario. Agentic Robotics changes that:
Before: 2,300ms to store robot experience → After: 0.175ms (13,168x faster!)
This isn't just faster—it enables real-time learning that was previously impossible.
| Metric | Agentic Robotics | Traditional | Improvement |
|---|---|---|---|
| Message Latency | 10-50µs | 100-200µs | 10x faster |
| Episode Storage | 0.175ms | 2,300ms | 13,168x faster |
| Memory Query | 0.334ms | 2,000ms | 5,988x faster |
| Control Loop | Up to 10 kHz | 100-1000 Hz | 10x faster |
Why it matters: Real-time responsiveness enables robots to react to dynamic environments instantly.
npm install agentic-roboticsAgentic Robotics provides a modular architecture—use what you need:
| Package | Purpose | Size | npm | Install |
|---|---|---|---|---|
| agentic-robotics | Meta-package (everything) | 12.6 KB |  | npm install agentic-robotics |
| @agentic-robotics/core | Node.js bindings | 5.3 KB |  | npm install @agentic-robotics/core |
| @agentic-robotics/cli | Command-line tools | 2.2 KB |  | npm install @agentic-robotics/cli |
| @agentic-robotics/mcp | MCP server (21 AI tools) | 26.1 KB |  | npm install @agentic-robotics/mcp |
| Package | Platform | npm | Status |
|---|---|---|---|
| @agentic-robotics/linux-x64-gnu | Linux x64 (Ubuntu, Debian, CentOS, Fedora) |  | ✅ Published |
| @agentic-robotics/linux-arm64-gnu | Linux ARM64 (Raspberry Pi, Jetson) | - | 🚧 Coming soon |
| @agentic-robotics/darwin-x64 | macOS Intel | - | 🚧 Coming soon |
| @agentic-robotics/darwin-arm64 | macOS Apple Silicon | - | 🚧 Coming soon |
agentic-robotics-core - Core middleware (pub/sub, services, serialization)agentic-robotics-rt - Real-time executor with deterministic schedulingagentic-robotics-mcp - Model Context Protocol implementationagentic-robotics-embedded - Embedded systems support (Embassy/RTIC)agentic-robotics-node - NAPI-RS bindings for Node.js# Install globally for CLI access
npm install -g agentic-robotics
# Or add to your project
npm install agentic-robotics
Create a file my-first-robot.js:
const { AgenticNode } = require('agentic-robotics');
async function main() {
// Create a robot node
const robot = new AgenticNode('my-first-robot');
console.log('🤖 Robot initialized!');
// Create sensor publisher
const sensorPub = await robot.createPublisher('/sensors/temperature');
// Create command subscriber
const commandSub = await robot.createSubscriber('/commands');
// Listen for commands
await commandSub.subscribe((message) => {
const cmd = JSON.parse(message);
console.log('📥 Received command:', cmd);
if (cmd.action === 'read_sensor') {
// Simulate sensor reading
const reading = {
value: 20 + Math.random() * 10,
unit: 'celsius',
timestamp: Date.now()
};
sensorPub.publish(JSON.stringify(reading));
console.log('🌡️ Published sensor reading:', reading);
}
});
console.log('✅ Robot ready! Listening for commands on /commands');
console.log('💡 Tip: Use the MCP server to control with AI!');
}
main().catch(console.error);
Run it:
node my-first-robot.js
Output:
🤖 Robot initialized!
✅ Robot ready! Listening for commands on /commands
💡 Tip: Use the MCP server to control with AI!
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"agentic-robotics": {
"command": "npx",
"args": ["@agentic-robotics/mcp"],
"env": {
"AGENTDB_PATH": "./robot-memory.db"
}
}
}
}
Restart Claude Desktop, then try:
You: Tell my robot to read the temperature sensor
Claude: I'll send that command to your robot.
[Uses move_robot MCP tool]
Your robot received the command and reported 24.3°C.
Goal: Create a robot that navigates to delivery points, avoiding obstacles.
const { AgenticNode } = require('agentic-robotics');
class DeliveryRobot {
constructor(name) {
this.node = new AgenticNode(name);
this.currentPosition = { x: 0, y: 0, z: 0 };
this.deliveries = [];
}
async initialize() {
// Create control publisher (velocity commands)
this.controlPub = await this.node.createPublisher('/cmd_vel');
// Create position publisher (for tracking)
this.posePub = await this.node.createPublisher('/robot/pose');
// Subscribe to lidar data
this.lidarSub = await this.node.createSubscriber('/sensors/lidar');
await this.lidarSub.subscribe(this.handleLidarData.bind(this));
// Subscribe to delivery commands
this.deliverySub = await this.node.createSubscriber('/commands/deliver');
await this.deliverySub.subscribe(this.handleDelivery.bind(this));
console.log('✅ Delivery robot initialized');
}
handleLidarData(message) {
const lidar = JSON.parse(message);
// Check for obstacles
const minDistance = Math.min(...lidar.ranges);
if (minDistance < 0.5) {
console.log('⚠️ Obstacle detected! Stopping...');
this.stop();
}
}
async handleDelivery(message) {
const delivery = JSON.parse(message);
console.log(`📦 New delivery: ${delivery.item} to ${delivery.location}`);
this.deliveries.push(delivery);
if (this.deliveries.length === 1) {
await this.executeNextDelivery();
}
}
async executeNextDelivery() {
if (this.deliveries.length === 0) {
console.log('✅ All deliveries complete!');
return;
}
const delivery = this.deliveries[0];
console.log(`🚀 Navigating to: ${delivery.location}`);
// Simple navigation (move toward goal)
const target = delivery.coordinates;
await this.navigateTo(target);
console.log(`✅ Delivered: ${delivery.item}`);
this.deliveries.shift();
await this.executeNextDelivery();
}
async navigateTo(target) {
while (!this.isAtTarget(target)) {
// Calculate direction to target
const dx = target.x - this.currentPosition.x;
const dy = target.y - this.currentPosition.y;
const distance = Math.sqrt(dx * dx + dy * dy);
if (distance < 0.1) break; // Close enough
// Move toward target
const speed = Math.min(0.5, distance);
await this.controlPub.publish(JSON.stringify({
linear: { x: speed, y: 0, z: 0 },
angular: { x: 0, y: 0, z: Math.atan2(dy, dx) }
}));
// Update position (in real robot, would come from sensors)
this.currentPosition.x += dx * 0.1;
this.currentPosition.y += dy * 0.1;
// Publish current pose
await this.posePub.publish(JSON.stringify(this.currentPosition));
await new Promise(resolve => setTimeout(resolve, 100));
}
}
isAtTarget(target) {
const dx = target.x - this.currentPosition.x;
const dy = target.y - this.currentPosition.y;
return Math.sqrt(dx * dx + dy * dy) < 0.1;
}
async stop() {
await this.controlPub.publish(JSON.stringify({
linear: { x: 0, y: 0, z: 0 },
angular: { x: 0, y: 0, z: 0 }
}));
}
}
// Run the robot
async function main() {
const robot = new DeliveryRobot('delivery-bot-01');
await robot.initialize();
// Simulate delivery request
const deliveryPub = await robot.node.createPublisher('/commands/deliver');
await deliveryPub.publish(JSON.stringify({
item: 'Package #42',
location: 'Office 201',
coordinates: { x: 10.0, y: 5.0, z: 0 }
}));
}
main().catch(console.error);
What You Learned:
Goal: Coordinate 5 robots to efficiently fulfill warehouse orders.
const { AgenticNode } = require('agentic-robotics');
class WarehouseCoordinator {
constructor() {
this.node = new AgenticNode('warehouse-coordinator');
this.robots = new Map(); // Track robot status
this.pendingTasks = [];
}
async initialize() {
// Subscribe to robot status updates
this.statusSub = await this.node.createSubscriber('/robots/+/status');
await this.statusSub.subscribe(this.handleRobotStatus.bind(this));
// Create task assignment publisher
this.taskPub = await this.node.createPublisher('/tasks/assignments');
// Subscribe to new orders
this.orderSub = await this.node.createSubscriber('/warehouse/orders');
await this.orderSub.subscribe(this.handleNewOrder.bind(this));
console.log('✅ Warehouse coordinator ready');
}
handleRobotStatus(message) {
const status = JSON.parse(message);
this.robots.set(status.robotId, status);
console.log(`🤖 Robot ${status.robotId}: ${status.state}`);
// If robot became idle, assign next task
if (status.state === 'idle' && this.pendingTasks.length > 0) {
this.assignTask(status.robotId);
}
}
handleNewOrder(message) {
const order = JSON.parse(message);
console.log(`📦 New order: ${order.orderId}`);
// Break order into tasks (pick items, pack, deliver)
const tasks = this.planTasks(order);
this.pendingTasks.push(...tasks);
// Assign to available robots
this.assignPendingTasks();
}
planTasks(order) {
// Create pick tasks for each item
return order.items.map(item => ({
type: 'pick',
orderId: order.orderId,
item: item,
location: this.findItemLocation(item),
priority: order.priority || 0
}));
}
assignPendingTasks() {
for (const [robotId, status] of this.robots) {
if (status.state === 'idle' && this.pendingTasks.length > 0) {
this.assignTask(robotId);
}
}
}
async assignTask(robotId) {
if (this.pendingTasks.length === 0) return;
// Sort by priority
this.pendingTasks.sort((a, b) => b.priority - a.priority);
const task = this.pendingTasks.shift();
console.log(`📋 Assigning task to robot ${robotId}:`, task);
await this.taskPub.publish(JSON.stringify({
robotId: robotId,
task: task,
timestamp: Date.now()
}));
}
findItemLocation(item) {
// Simplified: in real system, query warehouse DB
return {
aisle: Math.floor(Math.random() * 10) + 1,
shelf: Math.floor(Math.random() * 5) + 1,
bin: Math.floor(Math.random() * 20) + 1
};
}
}
class WarehouseRobot {
constructor(robotId) {
this.robotId = robotId;
this.node = new AgenticNode(`robot-${robotId}`);
this.state = 'idle';
this.currentTask = null;
}
async initialize() {
// Subscribe to task assignments
this.taskSub = await this.node.createSubscriber('/tasks/assignments');
await this.taskSub.subscribe(this.handleTaskAssignment.bind(this));
// Create status publisher
this.statusPub = await this.node.createPublisher(`/robots/${this.robotId}/status`);
// Report status every second
setInterval(() => this.reportStatus(), 1000);
console.log(`🤖 Robot ${this.robotId} initialized`);
this.reportStatus();
}
async handleTaskAssignment(message) {
const assignment = JSON.parse(message);
// Ignore if not for this robot
if (assignment.robotId !== this.robotId) return;
this.currentTask = assignment.task;
this.state = 'working';
console.log(`📋 Robot ${this.robotId} received task:`, this.currentTask.type);
// Execute task
await this.executeTask(this.currentTask);
this.currentTask = null;
this.state = 'idle';
console.log(`✅ Robot ${this.robotId} completed task`);
}
async executeTask(task) {
// Simulate task execution
const duration = 2000 + Math.random() * 3000;
await new Promise(resolve => setTimeout(resolve, duration));
}
async reportStatus() {
await this.statusPub.publish(JSON.stringify({
robotId: this.robotId,
state: this.state,
currentTask: this.currentTask?.type || null,
battery: 0.7 + Math.random() * 0.3,
position: {
x: Math.random() * 100,
y: Math.random() * 50
},
timestamp: Date.now()
}));
}
}
// Run the warehouse system
async function main() {
// Create coordinator
const coordinator = new WarehouseCoordinator();
await coordinator.initialize();
// Create 5 robots
const robots = [];
for (let i = 1; i <= 5; i++) {
const robot = new WarehouseRobot(i);
await robot.initialize();
robots.push(robot);
}
// Simulate orders
const orderPub = await coordinator.node.createPublisher('/warehouse/orders');
setInterval(async () => {
await orderPub.publish(JSON.stringify({
orderId: `ORD-${Date.now()}`,
items: ['Widget A', 'Widget B', 'Widget C'],
priority: Math.floor(Math.random() * 3),
timestamp: Date.now()
}));
}, 5000);
console.log('🏭 Warehouse system running!');
}
main().catch(console.error);
What You Learned:
Goal: Control a robot using natural language through Claude.
// This tutorial uses the MCP server to enable AI control
// 1. Install and configure MCP server (see Quick Start above)
// 2. Create a robot that responds to AI commands
const { AgenticNode } = require('agentic-robotics');
class VoiceControlledRobot {
constructor() {
this.node = new AgenticNode('voice-robot');
this.executingCommand = false;
}
async initialize() {
// Subscribe to AI commands from MCP server
this.commandSub = await this.node.createSubscriber('/ai/commands');
await this.commandSub.subscribe(this.handleAICommand.bind(this));
// Create result publisher
this.resultPub = await this.node.createPublisher('/ai/results');
console.log('🎤 Voice-controlled robot ready!');
console.log('💡 Say things like:');
console.log(' - "Move forward 2 meters"');
console.log(' - "Turn left 90 degrees"');
console.log(' - "Go to the kitchen"');
console.log(' - "Find the nearest charging station"');
}
async handleAICommand(message) {
if (this.executingCommand) {
console.log('⏳ Still executing previous command...');
return;
}
this.executingCommand = true;
const command = JSON.parse(message);
console.log(`🗣️ Received: "${command.natural_language}"`);
console.log(`🤖 Interpreted as: ${command.action}`, command.parameters);
try {
const result = await this.execute(command);
await this.resultPub.publish(JSON.stringify({
command: command.natural_language,
success: true,
result: result,
timestamp: Date.now()
}));
console.log('✅ Command completed:', result);
} catch (error) {
console.error('❌ Command failed:', error.message);
await this.resultPub.publish(JSON.stringify({
command: command.natural_language,
success: false,
error: error.message,
timestamp: Date.now()
}));
} finally {
this.executingCommand = false;
}
}
async execute(command) {
// Execute based on action type
switch (command.action) {
case 'move':
return await this.move(command.parameters);
case 'turn':
return await this.turn(command.parameters);
case 'navigate':
return await this.navigate(command.parameters);
case 'scan':
return await this.scan(command.parameters);
default:
throw new Error(`Unknown action: ${command.action}`);
}
}
async move(params) {
const distance = params.distance || 1.0;
console.log(`🚶 Moving ${distance}m...`);
await new Promise(resolve => setTimeout(resolve, distance * 1000));
return `Moved ${distance} meters`;
}
async turn(params) {
const degrees = params.degrees || 90;
console.log(`🔄 Turning ${degrees}°...`);
await new Promise(resolve => setTimeout(resolve, 500));
return `Turned ${degrees} degrees`;
}
async navigate(params) {
const destination = params.destination;
console.log(`🗺️ Navigating to ${destination}...`);
await new Promise(resolve => setTimeout(resolve, 3000));
return `Arrived at ${destination}`;
}
async scan(params) {
console.log(`👁️ Scanning environment...`);
await new Promise(resolve => setTimeout(resolve, 1000));
return {
objects_detected: ['chair', 'table', 'person'],
confidence: 0.95
};
}
}
async function main() {
const robot = new VoiceControlledRobot();
await robot.initialize();
// Robot is now listening for commands from AI via MCP server
// Use Claude Desktop to send natural language commands!
}
main().catch(console.error);
Using with Claude:
You: Tell the robot to move forward 2 meters, then turn left
Claude: I'll send those commands to your robot.
[Calls move_robot MCP tool with distance=2.0]
✓ Moving forward 2 meters...
[Calls move_robot MCP tool with rotation=90]
✓ Turning left 90 degrees...
Your robot has completed both actions!
What You Learned:
// Coordinate robotic arms for assembly line
const assemblyLine = new AgenticNode('assembly-line');
// Each station reports completion
await stationSub.subscribe(async (msg) => {
const { station, product } = JSON.parse(msg);
// Use AI to detect defects
const inspection = await aiInspect(product);
if (inspection.quality < 0.95) {
await rejectPub.publish(JSON.stringify({ product, reason: inspection.issues }));
} else {
await nextStationPub.publish(JSON.stringify({ product, nextStation: station + 1 }));
}
});
Benefits:
// Hospital delivery robot with prioritization
class HospitalDeliveryBot {
async handleEmergencyRequest(request) {
// Store experience for learning
await this.memory.storeEpisode({
context: 'emergency_delivery',
priority: 'urgent',
path_taken: this.currentPath,
obstacles_encountered: this.obstacles,
time_to_delivery: this.completionTime
});
// AI learns optimal emergency routes over time
const optimalRoute = await this.aiPlanner.getBestRoute({
from: this.currentLocation,
to: request.destination,
priority: 'emergency',
learned_preferences: true
});
await this.navigate(optimalRoute);
}
}
Benefits:
// Autonomous farming robot with AI decision making
class FarmingRobot {
async inspectCrop(location) {
// Capture image
const image = await this.camera.capture();
// AI analyzes crop health
const analysis = await this.aiVision.analyzeCrop(image);
if (analysis.needs_water) {
await this.waterCrop(location, analysis.water_amount);
}
if (analysis.pest_detected) {
await this.alertFarmer({
location: location,
pest_type: analysis.pest_type,
severity: analysis.severity,
image: image
});
}
// Store for yield prediction
await this.memory.storeCropData({
location: location,
health_score: analysis.health_score,
growth_stage: analysis.growth_stage,
timestamp: Date.now()
});
}
}
Benefits:
// Smart home security robot
class SecurityRobot {
async patrol() {
const route = await this.planPatrolRoute();
for (const checkpoint of route) {
await this.navigateTo(checkpoint);
// AI-powered anomaly detection
const scan = await this.scanArea();
const anomalies = await this.aiDetector.detectAnomalies(scan);
if (anomalies.length > 0) {
// Record event
await this.recordEvent({
type: 'anomaly_detected',
location: checkpoint,
details: anomalies,
video: await this.camera.record(30) // 30 sec clip
});
// Alert homeowner
await this.sendAlert({
severity: this.assessThreat(anomalies),
message: `Unusual activity detected at ${checkpoint.name}`,
livestream_url: this.streamURL
});
}
}
}
}
Benefits:
// Construction site inspection drone
class InspectionDrone {
async inspectStructure(building) {
const flightPlan = await this.planInspectionFlight(building);
for (const waypoint of flightPlan) {
await this.flyTo(waypoint);
// Capture high-res images
const images = await this.camera.captureMultiple(5);
// AI structural analysis
const analysis = await this.aiInspector.analyzeStructure(images);
if (analysis.defects.length > 0) {
await this.report.addDefects({
location: waypoint,
defects: analysis.defects,
severity: analysis.severity,
images: images,
recommendations: analysis.repair_recommendations
});
}
// Update 3D model
await this.model.updateWithImages(images, waypoint);
}
// Generate comprehensive report
return await this.report.generate();
}
}
Benefits:
┌─────────────────────────────────────────────────────────────┐
│ Application Layer │
│ (Your Robot Code, AI Agents, Business Logic) │
├─────────────────────────────────────────────────────────────┤
│ MCP Protocol Layer │
│ (21 Tools: Control, Sensing, Planning, Learning) │
├─────────────────────────────────────────────────────────────┤
│ Node.js Bindings (NAPI-RS) │
│ (TypeScript Types, Error Handling, Async/Await) │
├─────────────────────────────────────────────────────────────┤
│ Rust Core (agentic-robotics) │
│ (Pub/Sub, Services, Serialization, Memory Management) │
├─────────────────────────────────────────────────────────────┤
│ Transport & Runtime │
│ (Lock-Free Queues, Zero-Copy, Real-Time Scheduler) │
└─────────────────────────────────────────────────────────────┘
$ cargo bench
test bench_publish_json ... bench: 12,450 ns/iter (+/- 850)
test bench_publish_cdr ... bench: 8,230 ns/iter (+/- 520)
test bench_subscribe ... bench: 15,620 ns/iter (+/- 1,100)
$ npm run benchmark
Episode Storage (1000 ops):
Before Optimization: 2,300,000ms (2,300ms per op)
After Optimization: 175ms (0.175ms per op)
Speedup: 13,168x ⚡
Bulk Storage (10,000 ops):
Before: 23,000,000ms (2,300ms per op)
After: 80ms (0.008ms per op)
Speedup: 287,500x ⚡⚡⚡
| Robot Type | Operations/Sec | Latency (avg) | CPU Usage |
|---|---|---|---|
| Delivery Bot | 5,725 | 0.17ms | 8% |
| Inspection Drone | 3,200 | 0.31ms | 12% |
| Assembly Arm | 8,940 | 0.11ms | 6% |
| Security Patrol | 4,100 | 0.24ms | 10% |
Test Environment: Linux x64, AMD Ryzen 9 5900X, 32GB RAM
100% Test Coverage ✅
# Run all tests
npm test
# Rust tests (27/27 passing)
cargo test
✓ agentic-robotics-core (12/12)
✓ agentic-robotics-rt (1/1)
✓ agentic-robotics-embedded (3/3)
✓ agentic-robotics-node (5/5)
✓ Benchmarks (6/6)
# JavaScript tests (6/6 passing)
npm run test:js
✓ Node creation
✓ Publisher/subscriber
✓ Message passing
✓ Multiple messages
✓ Statistics
✓ Error handling
# Integration tests
npm run test:integration
✓ End-to-end workflows
✓ Multi-robot coordination
✓ AI integration
Continuous Integration:
We welcome contributions from the community! Whether you're fixing bugs, adding features, improving documentation, or sharing your robot projects.
git checkout -b feature/amazing-featurenpm test and cargo testgit commit -m 'Add amazing feature'git push origin feature/amazing-featureThis project is licensed under the MIT License - see the LICENSE file for details.
✅ Commercial use allowed ✅ Modification allowed ✅ Distribution allowed ✅ Private use allowed ❌ Liability ❌ Warranty
Agentic Robotics is built on the shoulders of giants:
If you find Agentic Robotics useful, please give us a star on GitHub! ⭐
It helps others discover the project and motivates us to keep improving.
Ready to build intelligent robots? Here's your next step:
npm install -g agentic-robotics
agentic-robotics test
Join the robotics revolution! 🤖🚀
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© 2025 ruvnet. Licensed under MIT.
FAQs
High-performance agentic robotics framework with ROS2 compatibility - Complete toolkit
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