Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: An Applied Introduction

• Comparing graphs and linear chains: scenarios and rationale
• Understanding agents, tools, and planner-executor cycles
• Hello workflow: creating a basic agentic graph

Managing State, Memory, and Context

• Structuring graph state and node interfaces
• Distinguishing between short-term and persistent memory
• Handling context windows, summarization, and state restoration

Branching Logic and Control Mechanisms

• Implementing conditional routing and multi-path decision making
• Configuring retries, timeouts, and circuit breakers
• Defining fallbacks, handling dead-ends, and recovery nodes

Utilizing Tools and External Integrations

• Executing function and tool calls from nodes and agents
• Accessing REST APIs and databases within the graph structure
• Parsing and validating structured outputs

Retrieval-Augmented Agent Processes

• Strategies for document ingestion and chunking
• Using embeddings and vector stores with ChromaDB
• Generating grounded responses with citations and safety measures

Evaluation, Debugging, and Visibility

• Tracking paths and analyzing node interactions
• Utilizing golden sets, evaluations, and regression testing
• Monitoring quality, safety, and cost/latency metrics

Deployment and Packaging

• Serving via FastAPI and managing dependencies
• Version control for graphs and rollback tactics
• Operational guides and incident management protocols

Recap and Future Directions