LangGraph
An open-source framework built on LangChain for creating stateful, multi-step AI workflows and agent systems, using a graph structure where nodes represent processing steps and edges define the flow of data and control between them.
What It Is
LangGraph represents AI workflows as directed graphs. Each node in the graph is a processing step: an LLM call, a tool invocation, a data transformation, a human review checkpoint, or a conditional router. Edges connect nodes and define how data and control flow between them, including conditional edges that route to different next steps depending on what a node returns.
The graph structure allows LangGraph to support something that simple sequential pipelines cannot: cycles. A node can route back to an earlier node when the work is not done, such as when a retrieval step returns insufficient data and needs to try again with a refined query, or when a draft requires revision before it meets quality criteria. This loop capability is what makes LangGraph suitable for agentic workflows.
State is a first-class concept in LangGraph. A shared state object passes through the entire graph, accumulating information as each node processes it. When a graph execution spans multiple turns of a conversation or runs asynchronously over minutes or hours, the state preserves everything the agent needs to continue from where it left off.
In B2B Commerce Context
B2B commerce workflows are rarely linear. A quote generation workflow might need to: parse an incoming RFQ, check whether the customer is approved, retrieve catalog pricing, check inventory availability, calculate shipping estimates, draft the quote, run it through a pricing approval check, and route it to a human if any value exceeds a threshold. Each of these is a conditional step: the path through the workflow depends on what each step returns.
LangGraph handles this naturally. The graph for that quote workflow has nodes for each step and edges that route based on conditions: if the customer is not approved, route to the credit check branch; if a line item is out of stock, route to the substitution node; if the total exceeds the auto-approval threshold, route to the human review node. The graph is the workflow, made explicit and executable.
For OroCommerce deployments, LangGraph typically operates as the orchestration layer above the commerce platform’s API. The platform’s APIs are tools that LangGraph nodes call. The state object carries the order data, account data, and pricing data that accumulate as the workflow progresses. LangGraph manages the sequencing and branching; OroCommerce provides the commerce data and business logic.
When You Need It
- Your AI workflow has branching logic, with different steps depending on what the AI finds, that a simple sequential pipeline cannot express.
- You need cycles: the ability to revisit an earlier step when results are insufficient or require revision.
- Your workflow spans multiple agent calls, tool calls, and human checkpoints that need to share state reliably.
- You need to build, test, and debug complex agentic workflows with visibility into each step's inputs and outputs.
A simple sequential AI pipeline, such as call retrieval, then call model, then return response, does not need LangGraph. When your workflow branches, loops, or requires stateful coordination across many steps, LangGraph provides the structure to manage that complexity reliably.
What It Is Not
- LangGraph is not an AI model. It is an orchestration and workflow framework. It uses models as the intelligence inside nodes, but the framework itself is engineering infrastructure for managing execution flow, not a source of intelligence.
- It is not the simplest option for simple workflows. LangGraph has real complexity: defining the graph schema, managing state types, debugging execution paths. For straightforward linear AI workflows, a simpler orchestration approach has lower overhead. Reach for LangGraph when the workflow genuinely needs its features.
- It is not a substitute for good AI design. LangGraph can orchestrate a complex workflow, but it cannot compensate for poorly designed prompts, inadequate retrieval, or missing guardrails. The framework manages execution; the quality of AI output depends on everything that runs inside the nodes.
Comparison
| LangGraph concept | What it means | B2B commerce example |
|---|---|---|
| Node | A single processing step | Pricing lookup, inventory check, quote draft |
| Edge | Connection between steps | After pricing lookup, go to inventory check |
| Conditional edge | Branch based on output | If out of stock, go to substitution node |
| Cycle | Loop back to an earlier node | If draft fails review, revise and re-evaluate |
| State | Shared data across all nodes | Order data, account info, pricing results accumulated through workflow |
See also
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