When to Use LangChain vs LangGraph

Now that we understand the foundations of agent design—state, reasoning, and workflows—we can address the most common question for beginners: "Which framework should I start with?"

The industry often portrays this as a choice between two competing tools. In reality, it is a choice of Architecture. 90% of production agent systems use both. In this lesson, we will define the technical "Triggers" that should push you from a simple LangChain chain into a LangGraph state machine.

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1. The Use Case for "Pure" LangChain

LangChain is best suited for Directed, Non-Cyclic tasks. If your app is a "Pipe" where data flows from left to right without ever going back, LangChain is the faster, simpler choice.

Technical Triggers for LangChain:

• Simple RAG: "Question + Context -> Answer."
• Preprocessing: "HTML -> Clean Text -> Markdown."
• Batch Processing: "CSV row -> Translation -> DB Insert."
• Single-Turn Chat: A chatbot that doesn't need to perform actions.

The Code Complexity

LangChain LECL (LangChain Expression Language) allows you to use a "Pipe" operator (|).

# Simple, Clean, Readable
chain = prompt | model | output_parser

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2. The Use Case for LangGraph

LangGraph is required for Cycles, Persistence, and Human Interaction. As soon as your agent needs to "Think again" or "Wait for input," LangGraph is mandatory.

Technical Triggers for LangGraph:

1. Circular Logic (Loops)

If your logic looks like this: Try Task -> Check Result -> If Fail, Try Task Again, you cannot use LangChain's basic chains. You need a graph node that can loop back to itself.

2. Fine-Grained Human-in-the-loop

If you need to "Pause" the code mid-execution, save the state to a database, and wait for a human to review it before continuing (e.g., an "Approvals" dashboard), LangGraph’s checkpointers are the only production way to do this.

3. State Management

If your agent needs to track more than just chat history (e.g., a "Shopping Cart," "User Permissions," or "Current Search Depth"), managing a custom State object across multiple nodes is built into LangGraph's DNA.

4. Multi-Agent Collaboration

When you have a "Manager" agent and "Worker" agents, you need a shared state that is passed between them. LangGraph's "Sub-graphs" are designed for this.

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3. The Comparison Matrix

Feature	LangChain (Chains)	LangGraph (Graphs)
Structure	Linear / DAG	Cyclic
Logic	Fixed Sequence	Conditional / Dynamic
State	Ephemeral (Lost after run)	Persistent (Checkpoints)
UX	Instant response	Can be Async / Long-running
Debugging	Easy (Trace)	Complex (Requires Graph Viz)
Production Fit	Prototyping / Simple Pipes	Business-critical Agents

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4. The Hybrid Approach: The Standard Production Pattern

The most sophisticated systems use LangChain inside LangGraph nodes.

• LangGraph handles the High-Level Orchestration (The "Who does what next?" logic).
• LangChain handles the Individual Task Code (The "How do I format this prompt and call this model?" code).

graph TD
    subgraph LangGraph
        Node1[Reasoning Node] --> Node2[Execution Node]
        Node2 -->|Retry| Node1
    end
    
    subgraph LangChain_inside_Node
        Node1 --- ChainA[Prompt | LLM | Parser]
    end

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5. Architectural Decision Flowchart

If you are starting a new project, follow this logic flow to decide your stack:

1. Does the task flow only in one direction?
   • Yes → Use LangChain.
   • No (Loops required) → Use LangGraph.
2. Does the task take more than 30 seconds?
   • Yes (Need persistence/resuming) → Use LangGraph.
   • No → Use LangChain.
3. Does a human need to intervene mid-process?
   • Yes → Use LangGraph.
   • No → Either.
4. Is this just a RAG system?
   • Yes → Use LangChain (Check out hub and LCEL).

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6. The Developer Experience (DX) Trade-off

Warning: LangGraph has a steeper learning curve.

• In LangChain, you write functions.
• In LangGraph, you write a System.

You have to think about schemas, edges, nodes, and checkpointers. This is more work upfront, but it prevents what we call the "Spaghetti Code" problem of traditional agent scripts.

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Summary and Mental Model

Think of LangChain as a Script. It starts at line 1 and ends at line 100. It's great for simple tasks.

Think of LangGraph as a Platform. It stays alive, manages users, handles interruptions, and recovers from crashes. It's the infrastructure for an "Agentic Business."

In this course, we will spend Modules 3 and 4 in LangChain to master the "Reasoning" and "Tools," but from Module 5 onwards, we will transition permanently to LangGraph to build production systems.

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Exercise: stack Selection

1. Case Study 1: A tool that summarizes a user-uploaded PDF and translates the summary into French. Stack choice?
2. Case Study 2: A tool that attempts to fix a bug in a codebase. It writes a file, runs pytest, and if the test fails, looks at the error message to try again. Stack choice?
3. Case Study 3: A chatbot that answers questions about a company's internal wiki but needs to wait for a manager's permission if the user asks for "Confidential" info. Stack choice?
4. Case Study 4: An automated email generator that creates a draft based on a customer's LinkedIn profile. Stack choice?