Technical Track Introduction
Master the art of building sophisticated AI agents with ElizaOS
Welcome to the Technical Learning Track for ElizaOS - your comprehensive guide to building production-ready AI agent systems.
What You'll Build
By completing this track, you'll be able to create:
- Autonomous AI Agents that can reason, plan, and execute complex tasks
- Custom Plugins extending ElizaOS with new capabilities
- Multi-Agent Systems where agents collaborate and coordinate
- Production Deployments handling thousands of concurrent users
- Advanced Integrations with external APIs, blockchains, and services
Who This Track Is For
Perfect For
- Full-Stack Developers wanting to add AI capabilities to applications
- Backend Engineers building scalable AI infrastructure
- DevOps Engineers deploying and monitoring agent systems
- AI Researchers experimenting with agent architectures
- Technical Founders building AI-powered products
Prerequisites
Required Knowledge:
- TypeScript/JavaScript: Comfortable with ES6+, async/await, modules
- Node.js: Understanding of event loop, npm/bun, package management
- Git: Version control, branching, pull requests
- APIs: REST, WebSockets, authentication patterns
Helpful but Not Required:
- Docker and containerization
- Database design (PostgreSQL)
- AI/ML fundamentals
- Cloud deployment (AWS/GCP/Azure)
Technical Architecture Overview
Core Technical Concepts
1. Agent Runtime
The AgentRuntime
is the heart of ElizaOS - an orchestration system that:
// Core runtime responsibilities
class AgentRuntime {
// Manages agent lifecycle
async initialize(): Promise<void>
// Processes incoming messages
async processMessage(message: Memory): Promise<Response>
// Manages state and memory
async composeState(message: Memory): Promise<State>
// Coordinates actions
async evaluateActions(state: State): Promise<Action[]>
// Handles service registration
registerService(service: Service): void
}
Key Concepts:
- Event-driven architecture for scalability
- Plugin-based extensibility for modularity
- State composition for context awareness
- Action evaluation for decision making
2. Memory System
ElizaOS implements a sophisticated memory architecture:
interface MemorySystem {
// Short-term memory (conversation context)
conversationMemory: Memory[]
// Long-term memory (persistent storage)
longTermMemory: MemoryStore
// Semantic memory (knowledge base)
knowledgeBase: KnowledgeStore
// Working memory (current state)
workingMemory: State
}
Memory Types:
- Conversational: Recent messages and context
- Episodic: Specific events and interactions
- Semantic: Facts and knowledge
- Procedural: How to perform tasks
3. Plugin Architecture
Plugins extend ElizaOS capabilities:
interface Plugin {
name: string
description: string
// Optional components
actions?: Action[]
evaluators?: Evaluator[]
providers?: Provider[]
services?: Service[]
// Lifecycle hooks
onInitialize?: (runtime: IAgentRuntime) => Promise<void>
onMessage?: (message: Memory) => Promise<void>
}
Plugin Categories:
- Model Providers: AI model integrations (OpenAI, Claude, Ollama)
- Platform Clients: Social platforms (Discord, Twitter, Telegram)
- Service Integrations: External APIs and services
- Custom Logic: Domain-specific functionality
4. Action System
Actions are the "muscles" of your agent:
interface Action {
name: string
description: string
// Validation logic
validate: (runtime: IAgentRuntime, message: Memory) => Promise<boolean>
// Execution logic
handler: (runtime: IAgentRuntime, message: Memory) => Promise<Response>
// Optional examples for better AI understanding
examples?: Example[]
}
Action Types:
- Response Actions: Generate replies
- Tool Actions: Use external tools
- State Actions: Modify agent state
- Integration Actions: Interact with services
Track Curriculum
Phase 1: Foundation (Week 1)
Day 1-2: Architecture Deep Dive
- Understanding the runtime lifecycle
- Plugin system architecture
- Memory and state management
- Event-driven patterns
Day 3-4: Development Environment
- Setting up TypeScript development
- Debugging ElizaOS agents
- Testing strategies
- Performance profiling
Day 5-7: First Custom Plugin
- Plugin structure and lifecycle
- Creating custom actions
- Adding evaluators and providers
- Testing and deployment
Phase 2: Advanced Development (Week 2)
Day 8-9: Memory Systems
- Implementing custom memory stores
- Vector databases for semantic search
- Knowledge graph integration
- Memory optimization techniques
Day 10-11: Multi-Agent Systems
- Agent communication protocols
- Shared state management
- Coordination patterns
- Consensus mechanisms
Day 12-14: Production Systems
- Scaling strategies
- Error handling and recovery
- Monitoring and observability
- Security best practices
Phase 3: Specialization (Week 3)
Choose your specialization path:
Path A: Platform Integration
- Building custom clients
- WebSocket management
- API rate limiting
- Platform-specific features
Path B: AI/ML Enhancement
- Custom model providers
- Fine-tuning strategies
- Prompt engineering
- RAG implementation
Path C: Infrastructure
- Kubernetes deployment
- Auto-scaling agents
- Distributed systems
- High availability
Development Best Practices
Code Organization
// Recommended project structure
my-elizaos-project/
├── src/
│ ├── plugins/ // Custom plugins
│ │ ├── my-plugin/
│ │ │ ├── index.ts
│ │ │ ├── actions/
│ │ │ ├── providers/
│ │ │ └── tests/
│ ├── agents/ // Agent configurations
│ ├── lib/ // Shared utilities
│ └── types/ // TypeScript definitions
├── tests/ // Test suites
├── scripts/ // Build and deploy scripts
└── docs/ // Documentation
TypeScript Patterns
// Use strict typing for safety
import { IAgentRuntime, Memory, State } from '@elizaos/core';
// Define clear interfaces
interface MyCustomAction extends Action {
customProperty: string;
// Use generics for flexibility
process<T extends Memory>(input: T): Promise<ProcessedResult>;
}
// Leverage type guards
function isValidMemory(obj: unknown): obj is Memory {
return obj !== null &&
typeof obj === 'object' &&
'content' in obj;
}
Testing Strategy
// Unit tests for individual components
describe('CustomAction', () => {
it('should validate correct messages', async () => {
const result = await action.validate(runtime, message);
expect(result).toBe(true);
});
});
// Integration tests for plugins
describe('MyPlugin', () => {
it('should register with runtime', async () => {
const runtime = new AgentRuntime(config);
await runtime.registerPlugin(myPlugin);
expect(runtime.plugins).toContain(myPlugin);
});
});
// E2E tests for complete flows
describe('Agent Conversation', () => {
it('should handle multi-turn dialogue', async () => {
const agent = await createAgent(config);
const response = await agent.chat('Hello');
expect(response).toMatchSnapshot();
});
});
Performance Optimization
Key Metrics
- Response Time: < 500ms for simple queries
- Throughput: 100+ concurrent conversations
- Memory Usage: < 500MB per agent instance
- Token Efficiency: Optimize context windows
Optimization Techniques
// 1. Efficient state composition
async function composeState(message: Memory): Promise<State> {
// Parallel data fetching
const [memories, knowledge, context] = await Promise.all([
this.searchMemories(message),
this.searchKnowledge(message),
this.getRecentContext()
]);
// Selective inclusion based on relevance
return {
relevantMemories: memories.slice(0, 5),
knowledge: knowledge.slice(0, 3),
recentMessages: context.slice(0, 10)
};
}
// 2. Caching strategies
const cache = new LRUCache<string, EmbeddingVector>({
max: 1000,
ttl: 1000 * 60 * 60 // 1 hour
});
// 3. Connection pooling
const dbPool = new Pool({
max: 20,
idleTimeoutMillis: 30000,
connectionTimeoutMillis: 2000,
});
Security Considerations
Authentication & Authorization
// Implement API key validation
middleware.authenticate = async (req, res, next) => {
const apiKey = req.headers['x-api-key'];
if (!apiKey || !await validateApiKey(apiKey)) {
return res.status(401).json({ error: 'Unauthorized' });
}
next();
};
// Role-based access control
const checkPermission = (action: string) => {
return async (runtime: IAgentRuntime, message: Memory) => {
const user = await runtime.getUser(message.userId);
return user.permissions.includes(action);
};
};
Data Protection
- Encryption at rest for sensitive data
- Input sanitization to prevent injection
- Rate limiting to prevent abuse
- Audit logging for compliance
Contributing to ElizaOS
Getting Involved
- Start Small: Fix documentation, add tests
- Build Plugins: Share your creations
- Report Issues: Help improve the framework
- Join Discussions: Share your expertise
Contribution Workflow
# Fork and clone
git clone https://github.com/yourusername/eliza.git
cd eliza
# Create feature branch
git checkout -b feature/my-awesome-feature
# Make changes and test
bun test
bun run lint
# Submit PR with clear description
Resources & Support
Documentation
- API Reference: Detailed method documentation
- Architecture Guides: System design patterns
- Example Code: Working implementations
- Video Tutorials: Visual learning resources
Community
- Discord: #technical-discussion channel
- GitHub Discussions: Architecture decisions
- Weekly Office Hours: Live Q&A sessions
- Code Reviews: Learn from experts
Your Learning Journey
Week 1 Goals
- Set up development environment
- Understand core architecture
- Build your first plugin
- Deploy a basic agent
Week 2 Goals
- Implement custom memory system
- Create multi-agent communication
- Add comprehensive tests
- Optimize performance
Week 3 Goals
- Choose specialization path
- Build production system
- Contribute to open source
- Share your learnings
Ready to Begin?
Start your journey with the Architecture Overview to understand how ElizaOS works under the hood. Then move to Development Setup to prepare your environment.
Remember: The best way to learn is by building. Start with simple plugins and gradually increase complexity as you understand the system better.
Estimated completion time: 40-60 hours of focused learning
Difficulty: Advanced
Outcome: Production-ready AI agent development skills
Welcome to the future of AI development. Let's build something amazing together!