October 3, 2026
LangGraph comes up in every evaluation we're part of, and the comparison usually gets framed wrong — as if both products were competing answers to the same question. They're answers to different questions. LangGraph asks: how do I express an agent as a state machine in my codebase? LiveGraph asks: how do I operate agents that are already running? The interesting difference isn't features. It's what each one treats as mutable while a run is in flight.
LangGraph is a library — Python or TypeScript — that lives inside your application. You declare a state schema, write nodes as functions, wire edges between them (including conditional ones), and compile the result into a runnable. The word “compile” is doing real work there: the graph's topology — which nodes exist and which routes connect them — is fixed at compile time, the same way a function's control flow is fixed when you deploy it.
What LangGraph does well, it does genuinely well:
Notice what all three mutate: state. The data flowing through the graph is yours to interrupt, inspect, and rewrite. The graph itself isn't. Conditional edges make routing dynamic — a function can choose the next node from the current state — but the menu of possible routes is declared in code at compile time. Adding a route that wasn't written is a code change, a review, and a redeploy; it takes effect on future invocations, and there's no surface for changing where the invocation currently in flight goes next.
LiveGraph inverts the primitive: the topology is data. Nodes and edges are rows, not code — and the engine never plans a run ahead. It dispatches one hop, calls the model, and after the call returns re-reads the graph fresh from the database to resolve where the run goes next. The re-read happening after the model call matters: an earlier version loaded the graph once at hop start, which silently ignored exactly the edits a human makes while watching a hop think.
Concretely, that means while a run executes you can drag an edge to a different specialist and the next hop follows it; add a node and wire it in mid-run; reroute around a specialist that's clearly off-course. A drag during a run you're watching is stored as an overlay on that run — the saved graph changes only if you keep the route. Unattended runs (schedules, webhooks, API launches) default to pinned mode, which snapshots the graph at launch and ignores later edits — LangGraph's frozen-topology semantics, available when you actually want reproducibility over steerability.
The honest costs: a topology-as-data engine can't validate the whole run up front (there is no whole run yet — warnings about suspicious shapes run at edit time instead), needs an explicit cycle guard, and can't tell you a run's remaining path because the route only exists one hop at a time. For agent workloads those costs are cheap — routing is semantic anyway, decided by model output that doesn't exist until the model generates it.
Pick LangGraph when the agent is a feature you're shipping. If the graph is part of your product's codebase — versioned with it, reviewed in pull requests, deployed with it — LangGraph's model is the right one. Topology changes should go through code review when the topology is your feature. Your users never need to see a graph; they need the feature to work. The checkpointer and interrupt primitives are the most mature part of what it offers, and if durable state inside your own runtime is the job, it earns its place.
Pick LiveGraph when you're operating agents, not shipping one. The audience we build for is the founder or small agency already running agents against real systems — a repo, an inbox, a client's stack — who is on the hook when a hop goes to the wrong place at 2 a.m. In that world a topology change isn't a deploy; it's an ops action, as routine as canceling a run or approving a gate. The canvas exists because watching is the first half of steering. Approvals park mid-run instead of aborting it. And when the agent heads somewhere wrong, the answer isn't “kill it and lose the work” or “let it finish and pay for the mistake” — it's drag the edge.
One fair caveat in LangGraph's favor: you could build mutable topology on top of it — indirection through a router node that reads routes from a database is a known pattern. But then you've built LiveGraph's core loop yourself, minus the canvas, the approvals, and the runs surface, and you maintain it forever. That trade only makes sense if the mutable-graph layer is itself your product.
Both can interrupt a run and edit what the run knows. Only one treats where the run goes as data you can change between hops — from a canvas, while the model is still thinking. If that capability is the one you keep reaching for, watch it work — the demo reroutes a live run with a scripted model, no account and no API key.