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Course Outline
LangGraph and Agent Patterns: A Practical Introduction
- Graphs versus linear chains: contexts and advantages
- Agents, tools, and planner-executor loops
- Hello workflow: a minimal agentic graph example
State, Memory, and Context Passing
- Designing graph state and node interfaces
- Short-term versus persisted memory
- Context windows, summarization, and rehydration
Branching Logic and Control Flow
- Conditional routing and multi-path decisions
- Retries, timeouts, and circuit breakers
- Fallbacks, dead-ends, and recovery nodes
Tool Use and External Integrations
- Function/tool calling from nodes and agents
- Consuming REST APIs and databases via the graph
- Structured output parsing and validation
Retrieval-Augmented Agent Workflows
- Document ingestion and chunking strategies
- Embeddings and vector stores utilizing ChromaDB
- Grounded responses with citations and safeguards
Evaluation, Debugging, and Observability
- Tracing paths and inspecting node interactions
- Golden sets, evaluations, and regression tests
- Monitoring quality, safety, and cost/latency
Packaging and Delivery
- FastAPI serving and dependency management
- Graph versioning and rollback strategies
- Operational playbooks and incident response
Summary and Next Steps
Requirements
- Proficiency in Python
- Experience developing LLM applications or prompt chains
- Understanding of REST APIs and JSON
Target Audience
- AI engineers
- Product managers
- Developers creating interactive LLM-driven systems
14 Hours
