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Course Outline
LangGraph and Agent Patterns: A Practical Primer
- Understanding graphs versus linear chains: when and why to use them
- Agents, tools, and planner-executor loops
- Constructing a minimal agentic graph: 'Hello workflow'
State, Memory, and Context Passing
- Designing graph state structures and node interfaces
- Differentiating between short-term memory and persisted memory
- Managing context windows, summarization, and rehydration
Branching Logic and Control Flow
- Conditional routing and multi-path decision-making
- Implementing retries, timeouts, and circuit breakers
- Handling fallbacks, dead-ends, and recovery nodes
Tool Use and External Integrations
- Executing function/tool calls from nodes and agents
- Accessing REST APIs and databases via the graph
- Performing structured output parsing and validation
Retrieval-Augmented Agent Workflows
- Strategies for document ingestion and chunking
- Utilizing embeddings and vector stores with ChromaDB
- Ensuring grounded responses with citations and safeguards
Evaluation, Debugging, and Observability
- Tracing execution paths and inspecting node interactions
- Developing golden sets, evaluations, and regression tests
- Monitoring quality, safety, cost, and latency
Packaging and Delivery
- Serving via FastAPI and managing dependencies
- Versioning graphs and implementing rollback strategies
- Creating operational playbooks and incident response plans
Summary and Next Steps
Requirements
- Working proficiency in Python
- Experience in developing LLM applications or prompt chains
- Familiarity with REST APIs and JSON
Target Audience
- AI engineers
- Product managers
- Developers constructing interactive LLM-driven systems
14 Hours
