Build practical LangChain/LangGraph agents that can survive real implementation constraints.
A practical professional workshop focused on current LangChain agent and LangGraph implementation. Participants incrementally build a bounded, stateful, graph-based agent application; connect real tools; model explicit state and routing; add persistence and human control; test and evaluate behavior; inspect traces; and produce a deployment-minded implementation and handoff package. The workshop uses current official reference patterns, normally in Python, while identifying material JavaScript/TypeScript API and naming differences. It complements vendor-agnostic agent and framework-selection courses by concentrating on implementation decisions inside the LangChain, LangGraph, and LangSmith ecosystem. Mastery requires a minimum score of 80%.
Workshop structure: 6 modules · 18 applied topics · 6 Decision Labs · 7 assessments · 80% mastery · approximately 6–8 hours.
Applied outcome: build and defend a working LangChain/LangGraph architecture with tools, orchestration, state/persistence, evaluation, observability, failure handling, and a deployment-minded handoff.
- Define bounded agent goals, system boundaries, authority limits, and criteria for choosing a model call, a LangChain agent, or a custom LangGraph workflow.
- Implement a current LangChain create_agent application with explicit instructions, tools, middleware, runtime context, and structured output.
- Build and explain a LangGraph StateGraph with typed state, reducers, nodes, edges, conditional routing, termination conditions, and component contracts.
- Apply checkpointing, threads, stores, interrupts, resume behavior, and durable-execution practices without confusing graph state, runtime context, short-term memory, and cross-thread memory.
- Control tools and side effects through validation, least-privilege permissions, approval gates, idempotency controls, retries, fallbacks, and explicit error paths.
- Test representative graph paths and outputs, run regression evaluations, inspect traces, and diagnose routing, state, tool, latency, and failure behavior.
- Prepare a deployment-minded implementation package that distinguishes open-source LangChain/LangGraph libraries from LangSmith observability, evaluation, Studio, Deployment, and Agent Server services.
- Defend an integrated stateful agent architecture and achieve at least 80% on implementation, testing, operational-readiness, and architecture-rationale criteria.
Your strongest mastered Decision Lab work is collected in your personal LangChain & LangGraph Agent Build Portfolio.