Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs versus linear chains: determining the right approach
• Agents, tools, and planner-executor loops
• Getting started: a minimal agentic graph

State, Memory, and Context Management

• Designing graph state and node interfaces
• Comparing short-term memory with persisted memory
• Context windows, summarisation, and rehydration techniques

Branching Logic and Control Flow

• Conditional routing and multi-path decision making
• Implementing retries, timeouts, and circuit breakers
• Handling fallbacks, dead-ends, and recovery nodes

Tool Use and External Integrations

• Function and tool calling from nodes and agents
• Accessing REST APIs and databases from 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 safeguards

Evaluation, Debugging, and Observability

• Tracing execution paths and inspecting node interactions
• Utilising golden sets, evaluations, and regression testing
• Monitoring quality, safety, and cost/latency metrics

Packaging and Deployment

• FastAPI serving and dependency management
• Versioning graphs and implementing rollback strategies
• Operational playbooks and incident response procedures

Summary and Future Directions