LangChain helps assemble models and tools quickly. LangGraph gives the workflow explicit state, routing, persistence, and control.

LangChain: the application layer

LangChain provides model integrations, tool definitions, message handling, retrieval components, and prebuilt agent patterns. It is a strong fit when the application has a familiar tool-calling loop and the primary goal is to connect an LLM to useful data and actions quickly.

A typical LangChain flow looks like this:

1. Receive a user request.
2. Give the model a set of well-defined tools.
3. Let the model choose a tool when it needs information or an action.
4. Return the tool result to the model and produce a final answer.

This is often enough for a chat assistant, a retrieval workflow, or a focused automation.

LangGraph: the orchestration layer

LangGraph is a lower-level runtime for long-running, stateful workflows. A graph has nodes that perform work, edges that determine the next step, and shared state that carries information between steps.

Use LangGraph when the workflow needs more control than a linear agent loop:

• Conditional routing - send a request to retrieval, analysis, or escalation based on state.
• Durable execution - checkpoint progress so a run can resume after a failure or a long wait.
• Human-in-the-loop review - pause before an action, gather approval or edits, then resume.
• Parallel or multi-step work - coordinate independent investigations and combine their results.
• Observability - inspect state transitions and diagnose why a workflow reached a decision.

Choosing between them

LangChain and LangGraph are complementary rather than competing. LangChain components can supply models, tools, and retrievers inside a LangGraph workflow. The important design decision is to make side effects explicit: validate inputs, separate planning from execution, and require approval for irreversible actions.

Reference

• LangChain overview
• LangGraph overview
• LangGraph persistence