Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs versus linear chains: understanding when and why to choose each.
• Overview of agents, tools, and planner-executor loops.
• Hello Workflow: building a minimal agentic graph.

State, Memory, and Context Management

• Designing graph state and defining node interfaces.
• Distinguishing between short-term memory and persisted memory.
• Managing context windows, summarization, and data rehydration.

Branching Logic and Control Flow

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

Tool Utilization and External Integrations

• Executing function or tool calls from nodes and agents.
• Interacting with 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 safety safeguards.

Evaluation, Debugging, and Observability

• Tracing execution paths and inspecting node interactions.
• Using golden sets, evaluations, and regression tests.
• Monitoring quality, safety, and cost versus latency metrics.

Packaging and Deployment

• Serving applications via FastAPI and managing dependencies.
• Versioning graphs and establishing rollback strategies.
• Developing operational playbooks and incident response protocols.

Summary and Future Directions