LiveGraph vs Langflow

Langflow earned its stars honestly: a genuinely pleasant drag-and-drop builder, components you can drop down to Python on, a playground for testing, and every flow deployable as an API endpoint or MCP server. If you want to prototype an LLM pipeline visually, it's a good tool.

The canvas is a build surface, though. Runs execute the flow as built — the Playground is for sending inputs and watching outputs, not for reaching in and moving the wiring while a run is in flight. LiveGraph starts from the assumption that the run itself is when you most need the edit.

Side by side

Dimension	Langflow	LiveGraph
The canvas's job	Where you build and test the flow — components, wires, and a Playground for trying inputs.	The product itself — a visible, editable graph of agents, routers, and approval gates, before and during the run.
Canvas during a run	A test and inspection surface — watch outputs and tool calls, but not a steering wheel.	Stays live — every hop is visible while it executes, and dragging an edge changes where this run goes next.
Mid-run rerouting	No documented way to redirect a run mid-flight — inferred from absent features, not a verified limitation.	Drag an edge; the next hop follows the new route.
Custom logic	Every component's Python source is editable in-app — real code under the drag-and-drop.	MCP servers, scoped http_request endpoints, and file tools — narrower than arbitrary Python, by design.
Deploy a flow as	An API endpoint or MCP server per flow; exportable as JSON.	API keys per graph, schedules, webhooks — plus an embeddable live run view.
Model roster	All the major LLM providers, per component.	Per-node provider and model — Anthropic, OpenAI, Google, Groq, OpenRouter, or an OpenAI-compatible endpoint; your call per node.
Observability	Via integrations — LangSmith, Langfuse, and others.	Every hop lands as an ordered run event — input, output, the routing explanation, and its cost — and approval decisions are stamped with who and when.
Hosting & pricing	Free and open source; you run it — the managed cloud shut down in April 2026.	Hosted web app at livegraph.ai. 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 Langflow is the better pick

You're prototyping LLM pipelines. If the job is visually composing prompts, models, vector stores, and tools into a flow your app calls over an API, Langflow's component library is broader — and it costs nothing to run.
You want the source anyway. Langflow is open source you operate yourself. For teams whose requirement is code they can read, fork, and modify — not a hosted service — that settles it.
Your components need real Python. Every node's source is editable inside the app. When you need a step that does something nobody prebuilt, writing Python beats configuring around the gap.

Langflow and LiveGraph agree the graph should be visible — they disagree about when it should be alive. A flow you can only edit between runs is a blueprint; a canvas you can edit during one is a control surface. If your runs are watched and occasionally wrong, that difference is the product.

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 Langflow

Side by side

Dimension	Langflow	LiveGraph
The canvas's job	Where you build and test the flow — components, wires, and a Playground for trying inputs.	The product itself — a visible, editable graph of agents, routers, and approval gates, before and during the run.
Canvas during a run	A test and inspection surface — watch outputs and tool calls, but not a steering wheel.	Stays live — every hop is visible while it executes, and dragging an edge changes where this run goes next.
Mid-run rerouting	No documented way to redirect a run mid-flight — inferred from absent features, not a verified limitation.	Drag an edge; the next hop follows the new route.
Custom logic	Every component's Python source is editable in-app — real code under the drag-and-drop.	MCP servers, scoped http_request endpoints, and file tools — narrower than arbitrary Python, by design.
Deploy a flow as	An API endpoint or MCP server per flow; exportable as JSON.	API keys per graph, schedules, webhooks — plus an embeddable live run view.
Model roster	All the major LLM providers, per component.	Per-node provider and model — Anthropic, OpenAI, Google, Groq, OpenRouter, or an OpenAI-compatible endpoint; your call per node.
Observability	Via integrations — LangSmith, Langfuse, and others.	Every hop lands as an ordered run event — input, output, the routing explanation, and its cost — and approval decisions are stamped with who and when.
Hosting & pricing	Free and open source; you run it — the managed cloud shut down in April 2026.	Hosted web app at livegraph.ai. Flat team price — runs are never metered. Model usage on hosted keys is metered on top; your own keys are never marked up.

When Langflow is the better pick

You're prototyping LLM pipelines. If the job is visually composing prompts, models, vector stores, and tools into a flow your app calls over an API, Langflow's component library is broader — and it costs nothing to run.

You want the source anyway. Langflow is open source you operate yourself. For teams whose requirement is code they can read, fork, and modify — not a hosted service — that settles it.

Your components need real Python. Every node's source is editable inside the app. When you need a step that does something nobody prebuilt, writing Python beats configuring around the gap.