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Course Outline
LangGraph and Agent Patterns: A Practical Introduction
- 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 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
- Utilizing fallbacks, handling dead-ends, and employing recovery nodes
Tool Use and External Integrations
- Function and tool calling from nodes and agents
- Consuming REST APIs and databases directly from the graph
- Parsing and validating structured outputs
Retrieval-Augmented Agent Workflows
- Document ingestion techniques and chunking strategies
- Utilizing embeddings and vector stores with ChromaDB
- Generating grounded responses with citations and safeguards
Evaluation, Debugging, and Observability
- Tracing execution paths and inspecting node interactions
- Utilizing golden sets, evaluations, and regression tests
- Monitoring quality, safety, and cost/latency metrics
Packaging and Delivery
- Deploying with FastAPI and managing dependencies
- Versioning graphs and implementing rollback strategies
- Establishing operational playbooks and incident response protocols
Summary and Next Steps
Requirements
- Practical working knowledge of 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
