Automation
Flowise
Flowise — build AI agents visually: an open-source drag-and-drop UI over LangChain and LlamaIndex, with chatflows, agentflows and a marketplace of templates
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View guide →Langflow — the visual builder for AI agents and workflows: drag-and-drop flows over LangChain components, deployed as APIs or MCP tools, MIT-licensed
Langflow is an open-source automation project by langflow-ai (langflow-ai/langflow on GitHub). It has 155,769 GitHub stars and 10,241 forks, is released under the MIT license, and was last updated on GitHub on 17 October 2026.
Install it into a fresh environment with uv, per the README: uv pip install langflow -U (Python 3.10–3.13).
Run uv run langflow run and open http://127.0.0.1:7860 — the visual editor, playground and component library are all in the browser.
Drag components into a flow, test it in the playground, then call it through the generated API endpoint or publish it as an MCP server.
Agents and RAG pipelines composed on a canvas from prebuilt components — prompts, models, tools, vector stores — each editable down to the Python.
A finished flow becomes a REST endpoint, or an MCP server that other agents and tools can invoke directly.
Flows can coordinate several specialised agents, with a playground for tracing each step before anything ships.
The component ecosystem is LangChain-based, so mainstream models, embeddings and retrievers are available without glue code.
The README install path: uv is the recommended installer.
uv pip install langflow -U
uv run langflow runThe official image, with an admin password set at first run.
read -rsp "Langflow administrator password: " LANGFLOW_SUPERUSER_PASSWORD; echo
export LANGFLOW_SUPERUSER_PASSWORD
docker run -p 7860:7860 -e LANGFLOW_SUPERUSER_PASSWORD langflowai/langflow:latestOnce it starts, the whole builder runs in the browser.
http://127.0.0.1:7860The Langflow open-source project (langflow-ai on GitHub); it grew out of the Logspace team and is now stewarded with backing from DataStax/IBM.
Yes — MIT licensed and fully self-hostable. You pay only for your own infrastructure and any model API keys you plug in.
No for standard flows — the builder is visual. Custom components are Python, and every built-in component can be opened and edited when you outgrow the defaults.
Against n8n, Langflow is AI-first: its canvas is built around models, prompts and agents rather than app integrations. Against code frameworks like LangChain itself, it trades some flexibility for a visual builder and one-click API/MCP deployment.
uv pip install langflow -U, then uv run langflow run and open http://127.0.0.1:7860.
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