Chains are simple. Agents are not. Here is the technical breakdown of when linear pipelines break down and why stateful graphs become necessary.
LangChain and LangGraph come from the same team but solve fundamentally different problems. LangChain gives you composable chains: string together prompts, retrievers, and output parsers in a linear sequence. LangGraph gives you stateful, cyclical graphs where nodes can loop back on themselves, branch conditionally, and maintain persistent state across iterations. The choice between them determines whether your AI system can handle real-world complexity or breaks the moment a user asks something unexpected.
LangChain's core abstraction is the chain. A chain is a sequence of steps that execute in order: take input, process it through a prompt template, send it to an LLM, parse the output, and return. You can compose chains together, creating multi-step workflows where the output of one chain feeds into the next.
# Standard LangChain pipeline
chain = prompt | llm | output_parser
result = chain.invoke({"question": "What is our refund policy?"})
This works well for predictable workflows: RAG question-answering, document summarization, translation pipelines, and structured data extraction. The flow is always forward. Step A leads to Step B leads to Step C.
Chains fail when the workflow needs to loop. Consider an AI code reviewer that generates feedback, applies fixes, then checks if the code passes tests. If tests fail, it needs to go back to the "apply fixes" step. A chain cannot represent this. There is no mechanism for a step to say "go back two steps and try again with different input."
Chains also fail when you need conditional branching that depends on intermediate results. "If the retriever returns fewer than 3 relevant chunks, reformulate the query and try again. Otherwise, proceed to generation." This kind of dynamic routing requires state management and decision logic that chains don't natively support.
LangGraph models your workflow as a directed graph with nodes and edges. Each node is a function that receives the current state, performs work, and returns an updated state. Edges define transitions between nodes and can be conditional.
# LangGraph with cycles
graph = StateGraph(AgentState)
graph.add_node("plan", plan_step)
graph.add_node("retrieve", retrieve_step)
graph.add_node("validate", validate_step)
graph.add_node("generate", generate_step)
# The cycle: validate can loop back to retrieve
graph.add_conditional_edges("validate", should_retry,
{"retry": "retrieve", "proceed": "generate"})
The key difference is state. LangGraph maintains a typed state object that persists across nodes. Each node reads from and writes to this shared state. This means your "validate" node can check the retrieval quality, increment a retry counter, and route back to "retrieve" with a modified query. The graph keeps track of where it is, what it has tried, and what still needs to happen.
| Feature | LangChain | LangGraph |
|---|---|---|
| Execution Model | Linear (DAG) | Cyclical (Stateful Graph) |
| State Management | No built-in state | Typed, persistent state |
| Loops / Retries | Not supported natively | First-class support |
| Conditional Routing | Limited (RunnableBranch) | Full conditional edges |
| Human-in-the-Loop | Manual implementation | Built-in interrupt/resume |
| Streaming | Token-level | Token + node-level events |
| Debugging | LangSmith traces | LangSmith + state snapshots |
| Learning Curve | Moderate | Steeper |
| Best For | Simple pipelines, RAG | Agents, multi-step workflows |
Use LangChain when your workflow is predictable and linear. Specific use cases:
Switch to LangGraph the moment your workflow needs any of these:
You don't have to choose one or the other. Most production systems use both. Start with LangChain for your retrieval and generation logic. When you need to wrap that logic in a retry loop, add a planning step, or coordinate multiple chains, move the orchestration layer to LangGraph.
The Common Pattern:
LangChain handles the individual steps (retrieval, generation, extraction). LangGraph handles the orchestration (routing, state management, retries, human-in-the-loop). This layered approach gives you the simplicity of chains where you need it and the power of graphs where you need it.
For the infrastructure side of deploying these frameworks, read about building production-grade AI agents and scaling FastAPI for high-volume AI requests.
Yes. LangGraph is a standalone library. You can use plain Python functions as nodes. However, LangChain's prompt templates, output parsers, and retriever abstractions save time when building the individual steps inside your graph nodes.
The graph orchestration itself adds negligible latency (single-digit milliseconds). The additional LLM calls from retries and validation loops do add latency. Budget for 2-3x the response time of a simple chain if your graph includes reflection loops. See our guide on optimizing LLM latency for mitigation strategies.
Yes, as of 2026 it is stable and used by several large enterprises. It has built-in checkpointing (save state to SQLite, PostgreSQL, or Redis), which makes it resilient to crashes and restarts.
We design and implement agentic systems using LangChain and LangGraph. From architecture to deployment.
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