LangGraph vs LangChain: When to Use What

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We get this question on almost every discovery call: "Should we use LangChain or LangGraph?" The answer isn't "one is better." They solve different problems. Here's how we decide.

The Short Answer

Use LangChain when your workflow is a straight line: input goes in, passes through a few steps, output comes out. Think RAG pipelines, simple chatbots, and linear document processing.

Use LangGraph when your agent needs to loop, branch, retry, or coordinate multiple sub-agents. Think autonomous agents that make decisions, use tools conditionally, and maintain complex state.

When LangChain is the Right Choice

LangChain excels at composing LLM calls into sequential pipelines:

• RAG applications: retrieve documents, stuff them into a prompt, generate a response
• Simple chatbots: conversation with memory and a few tools
• Data extraction: parse a document, extract structured data, validate
• Summarization pipelines: chunk, summarize, merge

If your workflow can be drawn as a straight line from A to B with no loops or conditional branches, LangChain keeps things simple. Don't over-engineer it.

When LangGraph is the Right Choice

LangGraph is built for workflows that require cycles, branching, and stateful orchestration:

• Autonomous agents: agents that decide which tool to call next, then evaluate the result and decide again
• Multi-agent systems: a planner agent delegates to specialist agents, collects results, and synthesizes
• Human-in-the-loop: workflows that pause for human approval at certain decision points
• Error recovery: agents that can detect failures, retry with different strategies, or escalate
• Complex state machines: workflows where the next step depends on accumulated state, not just the previous step

Side-by-Side Comparison

Criteria	LangChain	LangGraph
Workflow type	Linear / sequential	Cyclic / branching
State management	Basic (memory)	Rich (typed state, checkpoints)
Decision loops	Limited	First-class support
Multi-agent	Possible but awkward	Native support
Human-in-the-loop	Manual implementation	Built-in interrupt/resume
Learning curve	Lower	Higher
Best for	RAG, chatbots, pipelines	Autonomous agents, orchestration

A Real-World Example

We recently built a lead qualification agent for a client. Here's why LangGraph was the right choice:

1. The agent receives a new lead from HubSpot
2. It enriches the lead by calling multiple data APIs (company info, funding data, tech stack)
3. Based on the enrichment results, it decides whether to score immediately or gather more data
4. It scores the lead against the client's ICP rubric
5. If the score is high, it drafts a personalized outreach email and sends a Slack notification
6. If the score is ambiguous, it flags the lead for human review

Steps 2-6 involve conditional branching, tool use with error handling, and state that accumulates across steps. A linear LangChain pipeline can't express this cleanly. LangGraph's graph-based approach maps directly to this kind of workflow.

Our Decision Framework

When a new project comes in, we ask three questions:

1. Does the agent need to loop? If the agent evaluates its output and decides to try again, use LangGraph.
2. Are there conditional branches? If the next step depends on a runtime decision (not just the previous step's output), use LangGraph.
3. Does it need to pause and resume? If humans need to approve steps, or the workflow spans hours/days, use LangGraph.

If the answer to all three is "no," LangChain is simpler and faster to ship.

The best architecture is the simplest one that handles your actual requirements, not the one that handles every hypothetical future requirement.