Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs vs. linear chains: Understanding when and why to use them
• Exploring 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 and persisted memory
• Managing context windows, summarization, and state 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 Usage and External Integrations

• Executing function/tool calls from nodes and agents
• Accessing REST APIs and databases from 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 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/latency metrics

Packaging and Deployment

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

Summary and Future Steps