LangGraph is an orchestration framework, built on top of LangChain, for defining agentic workflows as an explicit graph of states and transitions rather than an implicit chain of prompts. A node in the graph represents a step — often a call to a language model or a tool — and edges represent the possible transitions between steps, which can be conditional on the model's own output.
Why the graph structure matters
For a simple linear tool-call sequence, a graph is unnecessary overhead. Its value shows up once a workflow needs conditional branching, retries with backoff, parallel exploration of multiple paths, or a checkpoint where a human needs to approve a step before the agent continues. Because the control flow is explicit in the graph definition rather than implicit in prompt wording, it's inspectable — a developer can look at the graph and know what states are reachable, rather than having to infer control flow from natural-language instructions.
State persistence
A distinguishing capability is checkpointing agent state across a long-running workflow, so a multi-hour or multi-day process can resume after an interruption instead of restarting. This matters for genuinely long-horizon agentic tasks in a way that simpler request-response tool-calling setups don't need to handle.
Referenced in
Used to build independent agentic AI systems automating client SaaS workflows, reducing manual effort by roughly 50% on the automated tasks.