Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
LangGraph and Agent Patterns: A Practical Primer
- Graphs vs. linear chains: when and why
- 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 vs. persisted memory
- Context windows, summarization, and rehydration
Branching Logic and Control Flow
- Conditional routing and multi-path decisions
- Retries, timeouts, and circuit breakers
- Fallbacks, dead-ends, and recovery nodes
Tool Use and External Integrations
- Function/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 with ChromaDB
- Grounded responses with citations and safeguards
Evaluation, Debugging, and Observability
- Tracing paths and inspecting node interactions
- Golden sets, evaluations, and regression tests
- Quality, safety, and cost/latency monitoring
Packaging and Delivery
- FastAPI serving and dependency management
- Versioning graphs and rollback strategies
- Operational playbooks and incident response
Summary and Next Steps
Requirements
- Working knowledge of Python
- Experience building LLM applications or prompt chains
- Familiarity with REST APIs and JSON
Audience
- AI engineers
- Product managers
- Developers building interactive LLM-driven systems
14 Hours
