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