Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs versus linear chains: determining when and why to use each
• Exploring agents, tools, and planner-executor loops
• Creating a minimal agentic graph as a starting point

State, Memory, and Context Management

• Structuring graph state and 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 multi-path decision making
• Configuring 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
• 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 with ChromaDB
• Generating grounded responses with citations and safety safeguards

Evaluation, Debugging, and Observability

• Tracing execution paths and analyzing node interactions
• Utilizing golden sets, evaluations, and regression testing
• Monitoring quality, safety, and cost/latency metrics

Packaging and Deployment

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

Summary and Future Directions