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Course Outline
LangGraph and Agent Patterns: A Practical Overview
- Understanding the difference between graphs and linear chains: when and why to use each
- Exploring agents, tools, and planner-executor loops
- Getting started with a minimal agentic graph
State, Memory, and Context Passing
- Designing graph state and node interfaces
- Managing short-term versus persisted memory
- Handling context windows, summarization, and rehydration
Branching Logic and Control Flow
- Implementing conditional routing and multi-path decisions
- Managing retries, timeouts, and circuit breakers
- Utilizing fallbacks, handling dead-ends, and recovery nodes
Tool Use and External Integrations
- Executing function/tool calls from nodes and agents
- Connecting to REST APIs and databases within 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 execution paths and inspecting node interactions
- Conducting evaluations and regression tests using golden sets
- Monitoring quality, safety, cost, and latency
Packaging and Delivery
- Managing dependencies and serving via FastAPI
- Versioning graphs and implementing rollback strategies
- Creating operational playbooks and incident response plans
Summary and Next Steps
Requirements
- Proficiency in Python
- Experience in developing 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
