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Course Outline
LangGraph and Agent Patterns: A Practical Introduction
- Comparing graphs versus linear chains: appropriate use cases and rationale.
- Understanding agents, tools, and planner-executor loops.
- Hello Workflow: establishing a minimal agentic graph.
State, Memory, and Context Passing
- Designing graph state structures and node interfaces.
- Differentiating between short-term and persisted memory.
- Managing context windows, summarization, and rehydration techniques.
Branching Logic and Control Flow
- Implementing conditional routing and multi-path decision-making.
- Handling retries, timeouts, and circuit breakers.
- Managing fallbacks, dead-ends, and recovery nodes.
Tool Use and External Integrations
- Utilizing 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
- 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.
- Utilizing golden sets, evaluations, and regression tests.
- Monitoring quality, safety, and cost/latency metrics.
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
- Functional knowledge of Python.
- Hands-on experience in building LLM applications or prompt chains.
- Familiarity with REST APIs and JSON.
Target Audience
- AI engineers.
- Product managers.
- Developers creating interactive LLM-driven systems.
14 Hours
