Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Comparing graphs versus linear chains: identifying when and why to use each
• Understanding agents, tools, and planner-executor loops
• Creating a basic agentic graph: a minimal workflow example

State, Memory, and Context Management

• Structuring graph state and defining node interfaces
• Distinguishing between short-term memory and persistent storage
• Managing context windows, including summarization and rehydration techniques

Branching Logic and Control Flow

• Implementing conditional routing and multi-path decision-making
• Managing retries, timeouts, and circuit breakers
• Handling fallbacks, dead-ends, and recovery nodes

Tool Utilization and External Integrations

• Executing function and tool calls from nodes and agents
• Accessing 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, specifically with ChromaDB
• Generating grounded responses with citations and safety safeguards

Evaluation, Debugging, and Observability

• Tracing execution paths and analyzing node interactions
• Using golden sets, evaluations, and regression tests
• Monitoring quality, safety, cost, and latency

Packaging and Deployment

• Serving applications with FastAPI and managing dependencies
• Versioning graphs and implementing rollback strategies
• Establishing operational playbooks and incident response procedures

Conclusion and Future Directions