Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs versus linear chains: timing and rationale
• Agents, tools, and planner-executor loops
• Hello Workflow: a basic agentic graph

State, Memory, and Context Management

• Structuring graph state and node interfaces
• Comparing short-term and persistent memory
• Context windows, summarization techniques, and rehydration

Branching Logic and Control Flow

• Conditional routing and multi-path decision making
• Retries, timeouts, and circuit breakers
• Fallbacks, dead-ends, and recovery nodes

Tool Usage and External Integrations

• Function and tool calling from nodes and agents
• Accessing REST APIs and databases through the graph
• Parsing and validating structured outputs

Retrieval-Augmented Agent Workflows

• Document ingestion and chunking strategies
• Embeddings and vector stores using ChromaDB
• Generating grounded responses with citations and safety guards

Evaluation, Debugging, and Observability

• Tracing paths and examining node interactions
• Golden sets, evaluations, and regression testing
• Monitoring quality, safety, and cost/latency metrics

Packaging and Deployment

• FastAPI serving and dependency management
• Graph versioning and rollback strategies
• Operational playbooks and incident response protocols

Summary and Future Directions