Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs vs. linear chains: determining the optimal approach
• Understanding agents, tools, and planner-executor loops
• Getting started: building a minimal agentic graph

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
• Handling retries, timeouts, and circuit breakers
• Managing fallbacks, dead-ends, and recovery pathways

Tool Usage and External Integrations

• Executing function and tool calls from graph nodes and agents
• Interacting with REST APIs and databases via the graph structure
• 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
• Using golden sets, evaluations, and regression testing
• Monitoring quality, safety, and cost/latency metrics

Packaging and Deployment

• Serving via FastAPI and managing dependencies
• Versioning graph structures and implementing rollback strategies
• Developing operational playbooks and incident response procedures

Conclusion and Future Directions