Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs versus linear chains: identifying appropriate use cases.
• Understanding agents, tools, and planner-executor loops.
• Building a minimal agentic graph: a foundational example.

Managing State, Memory, and Context

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

Branching Logic and Control Flow

• Implementing conditional routing and multi-path decision structures.
• Managing retries, timeouts, and circuit breaker patterns.
• Handling fallbacks, dead-ends, and recovery strategies.

Tool Utilization and External Integrations

• Executing function and tool calls from graph nodes and agents.
• Accessing REST APIs and databases directly from 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 safeguards.

Evaluation, Debugging, and Observability

• Tracing execution paths and analyzing node interactions.
• Establishing golden sets, evaluation metrics, and regression tests.
• Monitoring quality, safety, and operational costs/latency.

Packaging and Deployment

• Serving via FastAPI and managing dependencies.
• Versioning graphs and implementing rollback strategies.
• Developing operational playbooks and incident response protocols.

Summary and Future Directions