Langflow vs n8n
Both are catalogued under Workflow & Low-code. The figures come from the GitHub API; the assessments are ours.
LangflowDrag-and-drop flow builder that exports to running codeOfficialMIT
n8nNode-based automation with a growing set of AI agent nodesOfficialSource-availableAt a glance
| At a glance | Langflow | n8n |
|---|---|---|
| License | MIT | Source-available |
| Languages | Python, TypeScript | TypeScript |
| Deployment | Self-hosted / Runs locally / Managed cloud | Self-hosted / Runs locally / Managed cloud |
| Maturity | Established | Established |
| Stars | 153k | 201k |
| Star growth over the last 7 days | +14 ★ | +51 ★ |
| Forks | 9.9k | 60.1k |
| Open issues | 970 | 1.3k |
| Last commit | 16 Aug 2026 | 15 Aug 2026 |
| Activity | Active | Active |
What each one does
Langflow
Useful for sketching an agent visually and for showing non-engineers what a pipeline actually does, then exporting it as an API. Visual editors get unwieldy as branching grows, so treat it as a design and demo surface rather than the final home for complex logic.
Full entry →n8n
Wires hundreds of services together on a visual canvas, and increasingly places LLM agents inside those flows. The licence is the thing to check first: it is source-available under the Sustainable Use License, not OSI-approved open source. Internal use and self-hosting are fine; reselling it as a hosted service is not.
Full entry →What you can do
Langflow
- Build a flow in the browser — uv pip install langflow -U followed by uv run langflow run serves the visual editor at 127.0.0.1:7860, and docker run -p 7860:7860 langflowai/langflow:latest gives the same thing in a container.
- Step through a flow while testing — The interactive playground runs a flow with step-by-step control, which is how you find the node where a branch produces the wrong output.
- Publish a flow as an API or MCP server — A finished flow can be deployed as an API, exported as JSON to embed in a Python app, or served as an MCP server so MCP clients call it as a tool.
- Edit component source in Python — Source code access means a built-in component's Python can be modified directly rather than worked around with adjacent nodes.
- Send traces to existing observability — Built-in integrations forward execution traces to LangSmith, LangFuse and other tools, so flows report into the stack you already run.
n8n
- Run the editor locally —
npx n8nstarts it immediately, or thedocker.n8n.io/n8nio/n8nimage with ann8n_datavolume serves the canvas on port 5678. - Put agents inside workflows — The same canvas that wires 1500+ integrations can hand a step to a multi-step LLM agent with tool use, branching logic and a human approval gate before the flow continues.
- Start from a published workflow — Alongside the 1500+ integrations the README points at 9,000+ workflow templates on n8n.io/workflows, so a flow can begin as an existing one rather than an empty canvas.
- Drop into code inside nodes — JavaScript, Python and npm packages are available inside a node for the parts that visual building cannot express.