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Course Outline
LangGraph and Agent Patterns: A Practical Primer
- Graphs versus linear chains: timing and rationale.
- Agents, tools, and planner-executor loops.
- Hello workflow: a minimal agentic graph.
State, Memory, and Context Passing
- Designing graph state and node interfaces.
- Short-term memory 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 from the graph.
- Structured output parsing and validation.
Retrieval-Augmented Agent Workflows
- Document ingestion and chunking strategies.
- Embeddings and vector stores with ChromaDB.
- Grounded responses with citations and safeguards.
Evaluation, Debugging, and Observability
- Tracing paths and inspecting node interactions.
- Golden sets, evaluations, and regression tests.
- Quality, safety, and cost/latency monitoring.
Packaging and Delivery
- FastAPI serving and dependency management.
- Versioning graphs and rollback strategies.
- Operational playbooks and incident response.
Summary and Next Steps
Requirements
- Practical knowledge of Python.
- Experience in developing LLM applications or prompt chains.
- Familiarity with REST APIs and JSON.
Audience
- AI engineers.
- Product managers.
- Developers creating interactive LLM-driven systems.
14 Hours
