LangGraph Agents

Course Overview

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Free Course

For engineers preparing for AI engineer or agent developer interviews that cover LangGraph 1.x, from the graph mental model to multi-agent systems. You will be able to answer questions on StateGraph, reducers, routing and loops, checkpointers and threads, interrupts and human approval, tool-calling agents with create_agent, and multi-agent design, with a short spoken answer and a real production example for each.

Instructor: MantraMindAI

Sections: 7

Course Content

Section 1: LangGraph Fundamentals and Mental Model

Prepares you to explain what LangGraph is in 2026, how a graph run, state, nodes and edges work, and when a graph is the right tool instead of a plain chain or a prebuilt agent.

1. What is LangGraph, and when would you choose it over LangChain Chains or Agents?

2. What is a “graph run,” and how does it differ from a single LLM call?

3. What makes LangGraph “stateful,” and why is state central to its design?

4. What are nodes and edges in LangGraph, and what do they represent in execution?

5. What kinds of workflows are hard with DAGs but natural with LangGraph?

6. How do orchestration and reasoning differ in agentic systems, and where does LangGraph fit?

7. What are common real-world use cases where LangGraph works better than a linear pipeline?

Section 2: Graph API and StateGraph Construction

Prepares you to build a `StateGraph` from schema to compiled graph, explain why the schema exists, draw good node boundaries, validate what nodes write, and compose graphs from subgraphs.

1. What is the Graph API in LangGraph, and how do you build a basic graph end-to-end?

2. What is StateGraph, and why does it require a state schema?

3. How do you define nodes, add edges, and compile a graph into an executable object?

4. What is the difference between a simple sequence of nodes and an explicit graph?

5. What are best practices for designing node boundaries?

6. How do you handle node output formats and enforce structured state updates?

7. How do you reuse subgraphs or compose graphs for larger systems?

Section 3: State Design, Reducers, and Message Passing

Prepares you to design a production state schema, explain reducers and how nodes communicate through state, keep long threads from bloating, separate short-term from long-term memory, and make parallel writes safe.

1. How do you design a state schema for a production agent (minimal vs rich state)?

2. What is a reducer in LangGraph, and why is it needed for state updates?

3. How does message passing relate to how nodes update shared state?

4. How do you prevent state from growing unbounded?

5. How do you model short-term vs long-term memory using state and persistence?

6. What’s the difference between storing “messages,” “artifacts,” and “decisions” in state?

7. How do you manage concurrency issues when multiple nodes update overlapping state?

Section 4: Control Flow: Branching, Conditional Routing, and Loops

Prepares you to explain conditional edges, safe loops, classifier-based routing, fallback chains, `Send` fan-out, `Command` routing and layered stopping rules, with code that runs on LangGraph 1.x.

1. How do conditional edges work in LangGraph, and when do you use them?

2. How do you implement a loop (reflection / critique-revise / retry) safely without infinite cycles?

3. How do you implement dynamic routing based on classifier output or tool results?

4. How do you implement fallback paths (tool failure → alternate tool → safe response)?

5. What is the Send API used for (map-reduce / fan-out fan-in patterns)?

6. What is the Command API and when is it useful for “state updates + hop”?

7. How do you design stopping criteria for iterative agent loops (budget, confidence, time)?

Section 5: Runtime, Checkpointing, Persistence, and “Time Travel”

Prepares you to explain what a checkpoint holds, how threads scope memory, which checkpointer to pick, how resume, replay and forking work, how to build human approval with `interrupt()` and `Command`, and what persistence cannot fix.

1. What is checkpointing in LangGraph, and what gets saved at checkpoints?

2. What is a thread or session in LangGraph execution, and why does it matter for persistence?

3. What checkpointers exist (in-memory vs sqlite vs postgres), and how do you choose?

4. How do you resume an interrupted graph run from a checkpoint?

5. What is “time travel” or replay in LangGraph terms, and how does it help debugging?

6. How do you implement human-in-the-loop approvals using persistence and resumption?

7. What are common failure modes, and how does persistence help?

Section 6: Agents, Tool Use, and Action Selection Patterns

Prepares you to explain when a graph beats a hand-written agent loop, how `create_agent` and `ToolNode` model tool calls, and how to limit, recover from, validate and secure tool use, including an evidence-first retrieval policy.

1. How does a LangGraph-based agent differ from an “agent loop” implemented in plain code?

2. How do you represent tool calls in the graph (tool node, router node, validation node)?

3. How do you constrain tool usage (allowlist, rate limits, max tool calls, safe tools only)?

4. How do you handle tool errors and recover (retry, alternate tool, partial results)?

5. How do you implement structured tool outputs and robust parsing in a graph workflow?

6. How do you prevent prompt/tool injection when tools can access external systems?

7. How do you design an “evidence-first” tool policy (retrieve → cite → answer)?

Section 7: Multi-Agent Orchestration and Collaboration

Prepares you to decide when several agents beat one, model roles and handoffs in LangGraph, keep collaboration cheap, build voting and fan-out patterns, and evaluate a multi-agent system against a single-agent baseline.

1. When should you use multi-agent systems instead of one strong agent with tools?

2. How do you model roles (planner, researcher, writer, critic) as nodes/agents in LangGraph?

3. How do you implement agent-to-agent handoffs and shared state safely?

4. How do you avoid “agent chatter” and keep collaboration cost-effective?

5. How do you implement consensus (voting, self-consistency, referee agent) in a graph?

6. How do you implement parallel work (fan-out research) and aggregation (fan-in synthesis)?

7. How can multi-agent graphs be evaluated for task success, tool efficiency, and result quality?