Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agent Patterns: A Practical Primer

• Graphs versus linear chains: understanding when and why to choose each
• Exploring agents, tools, and planner-executor loops
• Creating a minimal agentic graph: a "Hello workflow" example

State, Memory, and Context Passing

• Structuring graph state and defining node interfaces
• Distinguishing between short-term memory and persisted memory
• Managing context windows, summarization, and state rehydration

Branching Logic and Control Flow

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

Tool Use and External Integrations

• Executing function and tool calls from graph nodes and agents
• Interacting with REST APIs and databases directly from the graph
• 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 inspecting node interactions
• Creating golden sets, running evaluations, and conducting regression tests
• Monitoring quality, safety, cost, and latency metrics

Packaging and Delivery

• Serving applications via FastAPI and managing dependencies
• Versioning graphs and establishing rollback strategies
• Developing operational playbooks and incident response procedures

Summary and Next Steps