Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs versus linear chains: Identifying the right approach
• Understanding agents, tools, and planner-executor loops
• Building a basic agentic graph: A starting point

State, Memory, and Context Management

• Defining graph state and node interfaces
• Distinguishing between short-term and persistent memory
• Managing context windows, summarization, and data rehydration

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 Usage and External Integrations

• Executing function and tool calls from nodes and agents
• Interacting with REST APIs and databases via 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 measures

Evaluation, Debugging, and Observability

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

Packaging and Deployment

• Serving applications with FastAPI and managing dependencies
• Version control for graphs and rollback strategies
• Developing operational playbooks and incident response plans

Conclusion and Future Directions