Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Introduction

• Graphs versus linear chains: when and why to use them
• Agents, tools, and planner-executor loops
• Hello workflow: a minimal agentic graph

State, Memory, and Context Passing

• Designing graph state and node interfaces
• Short-term memory versus persisted memory
• Context windows, summarisation, and rehydration

Branching Logic and Control Flow

• Conditional routing and multi-path decision-making
• Retries, timeouts, and circuit breakers
• Fallbacks, dead-ends, and recovery nodes

Tool Use and External Integrations

• Function and tool calling from nodes and agents
• Consuming REST APIs and databases from the graph
• Structured output parsing and validation

Retrieval-Augmented Agent Workflows

• Document ingestion and chunking strategies
• Embeddings and vector stores using ChromaDB
• Grounded responses with citations and safeguards

Evaluation, Debugging, and Observability

• Tracing paths and inspecting node interactions
• Golden sets, evaluations, and regression testing
• Quality, safety, and cost and latency monitoring

Packaging and Delivery

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

Summary and Next Steps