IVQ 1-50

1. What is LangGraph and how does it differ from traditional workflow engines?

LangGraph is a graph-based orchestration framework specifically designed for building LLM-powered agent workflows. Unlike traditional workflow engines (like Airflow, Prefect, or Step Functions), LangGraph treats workflows as stateful graphs, where each node can run LLM calls, tool invocations, or custom logic with dynamic context.

Key difference:

• Traditional engines = DAGs (directed acyclic graphs) → static dependencies → no context-driven branching at runtime.

• LangGraph = Stateful, feedback-capable graphs → supports dynamic edges, conditionals, loops, retries, and multi-agent chat flows, with LLMs participating in the graph logic.

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2. What is a StateGraph in LangGraph?

A StateGraph is the core structure in LangGraph. It defines:

• Nodes: Units of work (e.g., call an LLM, run a tool, query data).

• Edges: Valid transitions between nodes based on state changes.

• State schema: Typed data that flows through the graph and gets updated.

The StateGraph enforces that all transitions respect the state structure, allowing LLM-powered flows to maintain reliable, safe context between steps.

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3. What is a Node in LangGraph and how is it defined?

A Node is a single unit of computation in a LangGraph. A node:

• Receives the current state.

• Performs an operation (e.g., an LLM prompt, a tool call).

• Returns an updated state or triggers next edges.

In code, nodes are just Python callables or LangGraph helper components decorated and added to the graph with add_node().

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4. What are edges in LangGraph and how do they define flow?

Edges define the possible transitions between nodes.

• They specify which next node(s) can run after a node finishes.

• The decision can be static (always run next) or dynamic (use state to decide).

• This allows conditional flows, branching, loops, and agentic back-and-forth.

Edges make LangGraph more flexible than a rigid DAG by supporting dynamic runtime transitions.

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5. What is the difference between StateGraph and SingleNode?

• StateGraph = A full multi-node workflow, with multiple nodes and edges describing complex flows.

• SingleNode = A simple wrapper for a standalone operation, useful for debugging or isolated steps that don’t need transitions or state passing to other nodes.

In practice, you use StateGraph for real workflows; SingleNode is mostly for testing or simple tool calls.

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6. What is a workflow in LangGraph?

A workflow is the complete execution of a StateGraph:

• Starts with an initial state.

• Runs nodes according to the edges and state changes.

• Continues until a terminal node is reached or a stopping condition is met.

The workflow combines LLM calls, tool integrations, memory updates, and conditional logic — all running in a single orchestrated loop.

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7. What role does State play in LangGraph?

State is the single source of truth passed between nodes. It:

• Holds inputs, intermediate results, chat history, tool outputs, and control flags.

• Gets validated and typed, so each node knows the expected input/output shape.

• Drives dynamic decisions (e.g., which edge to follow).

Without state, the graph wouldn’t know how to adapt at runtime.

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8. How does LangGraph enforce type-safety for state transitions?

LangGraph uses Pydantic or typed Python classes to define the state schema.

• Each node must declare its input/output parts of the state.

• The framework checks that any state returned by a node matches the declared schema.

• This prevents runtime bugs due to missing or wrongly shaped data.

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9. What are the key components of a LangGraph-based application?

A typical LangGraph app includes:

1. State schema → Typed class that defines what the workflow knows.

2. Nodes → Callables that update the state.

3. Edges → Transition logic (which node runs next).

4. StateGraph → The full graph containing nodes + edges + type rules.

5. Executor → Runs the workflow with an initial state and manages the loop until done.

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10. What is the difference between static and dynamic execution in LangGraph?

• Static execution: The graph runs in a fixed path, like a DAG → the same nodes in the same order every time.

• Dynamic execution: The path is decided at runtime, depending on state.

  • Example: An LLM node decides whether to run NodeA or NodeB next.

  • Loops, retries, agent feedback are possible.

This dynamic execution is what makes LangGraph suitable for agentic, conversational, or uncertain flows where context changes the path.

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11. How does add_node work in LangGraph?

add_node() is how you register a unit of work in a StateGraph. What it does:

• Takes a node name (unique string) and a callable (function, class, or LangChain tool).

• Registers the node so you can connect it with edges.

• The node should accept the current state and return an updated state.

Example:

graph.add_node("analyze_text", analyze_text_fn)

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12. How does add_edge function in LangGraph?

add_edge() connects two nodes in the graph. What it does:

• Defines a direct transition: when from_node finishes, run to_node next.

• The edge respects the state output → input match.

Example:

graph.add_edge("analyze_text", "summarize_text")

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13. How does add_conditional_edges work and when should you use it?

add_conditional_edges() creates dynamic branches based on state conditions. What it does:

• Takes a node name, a condition function that checks the state, and a mapping {condition_value: next_node}.

• When the node finishes, LangGraph calls the condition function to decide which edge to follow.

Use it for:

• If/else logic

• Routing based on LLM output

• Multi-agent handoffs

Example:

graph.add_conditional_edges(
    "check_intent",
    lambda state: state.intent,
    {
        "greet": "say_hello",
        "bye": "say_goodbye"
    }
)

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14. What does the compile() method do in LangGraph?

compile() checks your whole graph for validity. What it does:

• Verifies that all nodes exist.

• Checks that edges connect valid nodes.

• Validates that your state schema is consistent.

• Freezes the graph into an executable object.

Example:

compiled_graph = graph.compile()

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15. How do you use builder.build() in LangGraph?

builder.build() is the final step if you’re using a graph builder pattern (StateGraph is often called builder while setting up). What it does:

• It’s an alias for compile().

• Finalizes the graph so you can run it.

Example:

workflow = builder.build()
result = workflow.invoke(initial_state)

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16. What is the purpose of add_start in LangGraph?

add_start() sets the entry point for your workflow. What it does:

• Defines which node runs first when the workflow starts.

• Required, because the graph needs to know where to begin.

Example:

graph.add_start("get_user_input")

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17. How do you define a function_node in LangGraph?

A function_node is a helper to make defining a node easier: What it does:

• You wrap a plain Python function as a node with type hints → LangGraph validates input/output automatically.

• Can attach input/output validation with Pydantic.

Example:

from langgraph.decorators import function_node

@function_node
def analyze_text(state: MyState) -> MyState:
    # Do work
    return state

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18. How do you integrate LangChain tools as nodes in LangGraph?

LangGraph has built-in integration for LangChain tools (agents, chains). How:

• Wrap the LangChain chain/tool in a function that takes state and updates it with the tool’s output.

• Add it with add_node() like any other node.

Example:

from langchain.tools import SomeTool

tool = SomeTool()

def run_tool(state: MyState) -> MyState:
    result = tool.run(state.input)
    state.output = result
    return state

graph.add_node("run_tool", run_tool)

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19. Can you dynamically add nodes or edges at runtime in LangGraph?

No.

• LangGraph graphs are compiled statically — the structure must be defined before execution.

• Dynamic runtime decisions are handled through conditional edges, loops, and LLM-driven branching, not by literally adding nodes at runtime.

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20. How can you inspect or visualize a compiled LangGraph?

LangGraph has utilities to export the graph to DOT/Graphviz or other formats:

• Use graph.visualize() or graph.to_dot() to get a DOT file.

• Use Graphviz or tools like Mermaid to render it.

Example:

dot_string = graph.to_dot()
with open("workflow.dot", "w") as f:
    f.write(dot_string)

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21. How do conditional branches work in LangGraph?

Conditional branches let you choose the next node(s) dynamically at runtime, based on current state. You use add_conditional_edges to:

• Provide a condition function → it inspects the state.

• Return a value → that value maps to the next node(s).

• LangGraph routes execution based on this.

Example:

graph.add_conditional_edges(
    "classify_intent",
    lambda state: state.intent,
    {
        "help": "provide_help",
        "bye": "say_goodbye"
    }
)

So the same node can send different users down different paths — useful for agent flows, tool routing, error handling.

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22. What is a CallableNode and how is it used?

A CallableNode is just a helper wrapper around any callable (function, class) that:

• Accepts the state as input.

• Returns a modified state. LangGraph turns your callable into a valid node when you use add_node.

Purpose: Encapsulates your logic with type-checking, serialization, and edge validation. Most people don’t use CallableNode directly — instead they:

• Use @function_node decorator

• Or pass a function to add_node → LangGraph handles the wrapping.

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23. How do you implement loops or retries in LangGraph?

LangGraph’s graphs allow cycles → loops are edges that point backward to an earlier node. You create them with conditional edges or normal add_edge.

Example loop:

graph.add_edge("retry_check", "retry_operation")
graph.add_edge("retry_operation", "retry_check")

• Inside your retry node, use the state to track attempts.

• The condition function checks whether to loop again or exit.

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24. Can LangGraph handle parallel nodes or concurrent execution paths?

Yes — concurrent branches are possible:

• A node can return multiple next nodes → the engine runs them in parallel.

• Each parallel path works with its own copy of the state slice.

• When the branches complete, you can merge them if needed.

Example:

# Node A splits into B and C in parallel
graph.add_edge("split_node", ["branch_b", "branch_c"])

⚡ Note: True multi-threading is limited by Python concurrency — but you can run async or multi-process tasks inside nodes.

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25. What is the role of EndNode in LangGraph?

An EndNode (or terminal node) is:

• Where the workflow stops executing.

• Once execution reaches an EndNode, the engine halts and returns the final state.

You don’t need to declare EndNode explicitly — any node with no outgoing edge acts as an implicit EndNode.

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26. How do you terminate or exit a workflow early in LangGraph?

To exit early:

• Return a terminal state in a node and ensure there are no edges from that node.

• Or use conditional edges that branch to an exit node if a condition is met.

Example:

if state.should_exit:
    return state  # no next node, so the workflow halts

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27. What are best practices for designing reusable nodes in LangGraph?

✅ Keep nodes pure → input = state → output = updated state. ✅ Use type hints (e.g., @function_node) for clear input/output schemas. ✅ Make nodes small & focused → single responsibility. ✅ Avoid side effects — if you have I/O, wrap it cleanly. ✅ Parameterize tools inside nodes — don’t hardcode secrets or config.

Reusable nodes = easy to test + share across graphs.

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28. How do you pass memory or context between nodes in LangGraph?

All memory/context lives in the state object:

• Nodes read from and write to the state.

• Any chat history, intermediate results, flags, or tool outputs stay inside the state.

This ensures stateless nodes + stateful flow → easier to debug and extend.

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29. How do you log or debug state transitions in LangGraph?

✅ Add logging inside your nodes → log the input state, output, decisions. ✅ Use Python’s logging module or print for dev. ✅ For advanced tracing → integrate with LangSmith, OpenTelemetry, or custom hooks. ✅ Inspect final or intermediate state for post-mortem checks.

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30. How does LangGraph handle state mutation during workflow execution?

• The state is mutable → each node receives a copy and can modify fields.

• Changes are validated against the state schema (if you use Pydantic).

• Transitions pass the updated state forward → so the entire execution trace is state-driven.

This guarantees that all decisions, branches, and outputs flow from one consistent, typed state.

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31. How do you integrate LangGraph with OpenAI function calling?

LangGraph works perfectly with OpenAI’s function calling by:

• Using an LLM node that calls openai.ChatCompletion with a function schema.

• The node inspects the LLM’s response → executes the function → updates the state.

• This can be wrapped in a function_node or custom callable.

Example:

import openai

@function_node
def run_openai_function(state: MyState) -> MyState:
    response = openai.ChatCompletion.create(
        model="gpt-4o",
        messages=state.chat_history,
        functions=[my_function_schema]
    )
    state.llm_response = response
    return state

You can use LangChain’s OpenAI wrapper too — LangGraph just orchestrates the calls.

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32. How do you integrate LangGraph with LangChain tools like agents and retrievers?

LangGraph can run LangChain agents, retrievers, or chains inside nodes:

• Import the LangChain tool or chain.

• Wrap it in a node function.

• Pass needed input/output via state.

Example:

from langchain.agents import initialize_agent

agent = initialize_agent(...)

def agent_node(state: MyState) -> MyState:
    result = agent.run(state.query)
    state.answer = result
    return state

graph.add_node("agent_node", agent_node)

Same for retrievers: run retriever.get_relevant_documents in a node.

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33. Can LangGraph be used with FastAPI for interactive workflows?

✅ Yes! LangGraph runs server-side, FastAPI provides the API layer:

• A FastAPI route receives user input.

• It creates an initial state.

• Calls graph.invoke() or compiled_graph.invoke().

• Returns the final state as a response.

Example:

@app.post("/chat")
async def chat(input: Input):
    state = MyState(user_input=input.message)
    result = await graph.invoke(state)
    return {"response": result.response}

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34. How do you implement an LLM-powered agent loop using LangGraph?

✅ Use conditional edges + state checks.

• Node runs an LLM → decides if it needs more tools or more clarifications.

• Edges loop back to itself or a tool node.

• Stop when the LLM signals done.

Example:

[ AgentNode ] → (more tools needed?) → [ ToolNode ] → back to [ AgentNode ]

You can track iteration_count in state to limit endless loops.

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35. Can LangGraph be used for RAG (Retrieval-Augmented Generation) pipelines?

✅ Absolutely! A typical RAG flow with LangGraph:

1. Receive query → in state.

2. Retrieve docs → a retriever node.

3. Combine context → update state.

4. Pass to LLM → generate answer.

5. Optional post-processing → final output.

It’s clearer than a single monolithic chain — each step is a node.

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36. How do you build multi-step chatbots using LangGraph?

✅ Design a StateGraph where:

• Each node = single skill (intent classification, retrieval, tool, response).

• Edges connect steps → conditionals branch based on state (intent, user input).

• Chat history lives in state.

Each turn → FastAPI route → graph.invoke() → new or updated state.

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37. What are typical use cases for LangGraph in enterprise applications?

📌 Popular enterprise cases:

• Complex RAG pipelines with secure source control.

• Multi-agent LLM orchestration (chatbots with plug-in tools).

• Call center automation → human escalation → LLM summarization.

• Compliance workflows → conditional checks → human sign-off.

• Autonomous internal tools → fetch data → analyze → report → decide.

LangGraph’s dynamic state, conditional flows, loops = enterprise ready.

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38. How do you handle vector DB operations within LangGraph nodes?

✅ Wrap your vector DB calls (e.g., Weaviate, Pinecone, Qdrant) inside a node:

• Connect with client (e.g., client.query()).

• Use state query as input → update state with results.

Example:

def vector_search_node(state: MyState) -> MyState:
    results = qdrant_client.search(collection="docs", query_vector=state.query_embedding)
    state.relevant_docs = results
    return state

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39. Can LangGraph interact with external APIs in a node?

✅ Yes — any external API call can run inside a node:

• Call REST APIs, GraphQL, third-party services.

• Store results back to the state.

Just make sure your node is async if the API call is async.

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40. How can LangGraph help implement human-in-the-loop workflows?

✅ This is a core strength:

• Your graph runs until it needs human input → pause.

• Store partial state.

• Resume later with updated input (e.g., a manager approval).

• Conditional edges decide next path.

Use case: Contract review → LLM pre-check → legal team review → final sign-off → send email.

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41. How can you serialize or export a LangGraph workflow?

LangGraph supports exporting the graph structure for docs or inspection:

• Use graph.to_dot() → export to DOT format for Graphviz visualization.

• Use graph.visualize() if supported → render inline in notebooks.

• The workflow’s logic (nodes/functions) is Python code — so for full serialization, store your state schema + edges + node registry in versioned code/config.

There’s no out-of-the-box binary export for runtime yet — you redeploy the compiled graph.

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42. What are the limitations of LangGraph’s current execution model?

🔍 Key limitations:

• Single-threaded by default: Python’s GIL applies — true parallel CPU tasks need async/multiprocessing inside nodes.

• No built-in distributed scheduler: Not like Airflow or Prefect → can’t auto-distribute to worker pools.

• No long-lived persistence: You have to persist state externally for long workflows or resumable flows.

• All graph structure must be known upfront: Can’t add nodes/edges dynamically during execution.

• Mostly best for agentic, interactive tasks, not heavy ETL.

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43. How can you simulate or test a LangGraph without real LLMs?

✅ Use mock nodes or stub LLM calls:

• Replace openai.ChatCompletion with dummy functions.

• Use unit tests for each node → test input/output shape.

• Run the entire graph.invoke() with test state → assert the final state.

• Libraries like pytest-mock or unittest.mock are handy.

Mocking LLM calls = fast, cheap, safe for CI.

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44. What are some common anti-patterns when using LangGraph?

⚠️ Top anti-patterns:

• Putting too much logic inside a single node: Makes testing, reuse, and branching hard.

• Mutating state in unpredictable ways: Leads to broken transitions or invalid state shape.

• Skipping type checks: Leads to silent bugs.

• Not using conditionals for dynamic flows: Hard-coding edges kills flexibility.

• Tight-coupling external services directly: Better to isolate with clean node wrappers.

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45. How do you benchmark or optimize a LangGraph pipeline?

✅ Tips for performance + cost:

• Profile node execution time (use logging or tracing).

• Cache repeated LLM calls where possible.

• Keep state lightweight — large chat histories can grow costs.

• Reuse embeddings or vector searches.

• Use async where possible for IO-heavy nodes.

• Measure token usage → break big prompts into minimal calls.

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46. Can LangGraph be used for decision trees or scoring models?

✅ Yes! A StateGraph is a natural fit for:

• Decision trees → each node = check/score → conditional edges = branches.

• Scoring flows → nodes apply scoring rules → update state → final node returns result.

Use simple Python logic inside nodes → more maintainable than a monolithic scoring script.

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47. How does LangGraph differ from Airflow or Prefect?

Aspect	LangGraph	Airflow/Prefect
Core idea	Agentic LLM graph orchestration	Traditional task orchestration
DAG enforced?	Not strictly DAG → supports loops	Strict DAG (Airflow); dynamic DAGs (Prefect)
State management	Explicit typed state object	Implicit task results
Long-running tasks	Not optimized for heavy ETL	Designed for heavy ETL, cron jobs
Agent-specific tools	Native LLM integration, memory	Not built-in
Deployment	Lightweight Python lib	Requires scheduler/server workers

LangGraph = best for dynamic LLM workflows. Airflow/Prefect = best for ETL, pipelines, infra jobs.

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48. How can you ensure fault-tolerance or error recovery in LangGraph?

✅ Patterns to add resilience:

• Add try/except inside nodes → catch API errors.

• Use retries by looping back with an attempt counter in state.

• Persist state externally → so you can restart a stuck run.

• Combine with FastAPI → store intermediate state in a DB.

• For critical tasks → add human-in-loop fallback branches.

LangGraph doesn’t have a built-in worker pool — you design resilience in your graph and infrastructure.

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49. How do you control LLM token usage and cost in LangGraph?

✅ Best practices:

• Keep state.chat_history trimmed → only relevant context.

• Use summarization nodes to shorten history.

• Cache embeddings → don’t embed same text twice.

• Break large LLM calls into smaller steps.

• Track tokens → store usage in state or log them.

• Pick the right model for each node → cheaper models for easy tasks.

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50. What is the roadmap or future direction for LangGraph?

📌 As of now (2024–2025):

• Better integration with LangChain’s evolving ecosystem.

• More built-in tool adapters (retrievers, multi-agent tools).

• Native support for resumable long-running workflows.

• Better visualization & debugging tools.

• Templates for multi-agent loops, RAG patterns, or API backends.

• More robust async support for scalable deployments.

The goal: easier building of robust, agentic, LLM-centric apps without re-implementing orchestration logic.

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