LiveGraph vs LangGraph

LangGraph is the strongest answer to “code already does this.” Its persistence layer is genuinely excellent: interrupt() parks a run, a checkpointer snapshots state at every super-step, and you can edit or fork that state and resume — time travel included. If you're comparing intervention depth, LangGraph is the bar.

The boundary is what's editable. Everything you can change mid-run in LangGraph is state inside a graph whose topology was fixed at compile time — rewiring which node connects to which means editing source and redeploying. LiveGraph's bet is that the wiring itself should be live: drag an edge while the run is executing and the next hop follows it.

Side by side

Dimension	LangGraph	LiveGraph
What you build on	A Python or TypeScript library inside your own codebase — graphs are code, compiled before they run.	A hosted canvas — the graph is the product, not an artifact your code produces.
Mid-run intervention	The deepest in the field — interrupt(), checkpoint state edits, and time-travel forks, all inside a fixed topology.	Stays live — every hop is visible while it executes, and dragging an edge changes where this run goes next.
Changing the wiring	Edit the graph code and redeploy — edges are compiled, not draggable.	Drag an edge; the next hop follows the new route.
Human approval gates	interrupt() plus Command(resume=…) — powerful, and you wire it in code at the points you choose.	Run-level gates that park at the approvals queue, plus per-tool-call gates (e.g. a single http_request endpoint) — the pending payload is always visible to the approver.
Watching a run live	Stream events via the SDK; the standard observability surface is LangSmith, a separate trace product.	The canvas itself — or an embeddable live run view you can drop into your own app.
Model roster	Whatever you wire — it's your code calling the providers.	Per-node provider and model — Anthropic, OpenAI, Google, Groq, OpenRouter, or an OpenAI-compatible endpoint; your call per node.
Hosting	Your infrastructure, or LangGraph Platform — self-hosting the platform is Enterprise-gated.	Hosted web app at livegraph.ai.
Pricing	The library is open source; LangSmith and LangGraph Platform are separately metered on top.	Flat team price — runs are never metered. Model usage on hosted keys is metered on top; your own keys are never marked up.

Competitor rows are limited to claims verified in our own research — "Not evaluated" means we didn't check, not that the capability is missing. On the LiveGraph side, one nuance: runs started from chat or the canvas Run button are steerable; unattended runs (API, schedules, webhooks) run pinned to the graph they started on — steering is a watched-run feature, by design.

When LangGraph is the better pick

You want a library inside your own codebase, not a service. If the agent graph should live in your repo — versioned with the code, tested with it, deployed with it — LangGraph is the right shape. Wrapping it in a hosted product would be a compromise, not a feature.
You need surgical state control. Checkpoint forking and time travel let you replay and edit a run's state at any super-step — deeper state surgery than a canvas gives you, and for research-grade iteration it's genuinely better.
LangSmith is already your observability stack. If the team's traces, evals, and datasets already live in LangSmith, LangGraph's integration is first-party and deep. We'd rather concede that than pretend a canvas replaces a trace platform.

The honest framing: LangGraph hands you a scalpel for run state; LiveGraph hands you the wiring. If your incidents are “the state was wrong,” LangGraph's checkpoints are the deeper tool. If they're “it routed to the wrong place and we had to restart the whole run,” that's the run LiveGraph was built for.

See it yourself

The live demo runs a real multi-agent graph in your browser — no signup, no API key — including the mid-run edge drag. If the canvas earns it, an account takes a minute.

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LiveGraph vs LangGraph

Side by side

Dimension	LangGraph	LiveGraph
What you build on	A Python or TypeScript library inside your own codebase — graphs are code, compiled before they run.	A hosted canvas — the graph is the product, not an artifact your code produces.
Mid-run intervention	The deepest in the field — interrupt(), checkpoint state edits, and time-travel forks, all inside a fixed topology.	Stays live — every hop is visible while it executes, and dragging an edge changes where this run goes next.
Changing the wiring	Edit the graph code and redeploy — edges are compiled, not draggable.	Drag an edge; the next hop follows the new route.
Human approval gates	interrupt() plus Command(resume=…) — powerful, and you wire it in code at the points you choose.	Run-level gates that park at the approvals queue, plus per-tool-call gates (e.g. a single http_request endpoint) — the pending payload is always visible to the approver.
Watching a run live	Stream events via the SDK; the standard observability surface is LangSmith, a separate trace product.	The canvas itself — or an embeddable live run view you can drop into your own app.
Model roster	Whatever you wire — it's your code calling the providers.	Per-node provider and model — Anthropic, OpenAI, Google, Groq, OpenRouter, or an OpenAI-compatible endpoint; your call per node.
Hosting	Your infrastructure, or LangGraph Platform — self-hosting the platform is Enterprise-gated.	Hosted web app at livegraph.ai.
Pricing	The library is open source; LangSmith and LangGraph Platform are separately metered on top.	Flat team price — runs are never metered. Model usage on hosted keys is metered on top; your own keys are never marked up.

When LangGraph is the better pick

You want a library inside your own codebase, not a service. If the agent graph should live in your repo — versioned with the code, tested with it, deployed with it — LangGraph is the right shape. Wrapping it in a hosted product would be a compromise, not a feature.

You need surgical state control. Checkpoint forking and time travel let you replay and edit a run's state at any super-step — deeper state surgery than a canvas gives you, and for research-grade iteration it's genuinely better.

LangSmith is already your observability stack. If the team's traces, evals, and datasets already live in LangSmith, LangGraph's integration is first-party and deep. We'd rather concede that than pretend a canvas replaces a trace platform.