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Direct answer: The three leading open-source AI app builders in 2026, ranked by GitHub stars verified 2026-08-07: dify (151,640 ★), Langflow (152,911 ★), and Flowise (55,226 ★). dify is the most complete platform — visual workflows, RAG, agent nodes, model routing, and a full app lifecycle (API, logs, deployment) built in. Langflow matches it on stars and is stronger for graph-based, developer-centric workflows. Flowise is the lightweight option for fast prototypes. All counts verified via the GitHub API on 2026-08-07.
\nWhat these tools are
\nAll three let you build LLM-powered applications — chatbots, RAG assistants, agent workflows, content pipelines — with a visual interface instead of writing code. You connect model providers, prompts, knowledge bases, and tools on a canvas, then deploy the result as an app or API.
\nThe comparison (verified 2026-08-07)
\n\n\n\n\n\n\n| Tool | Stars (GitHub) | License | Best for |
|---|---|---|---|
| dify | 151,640 | Other | Full product lifecycle: workflows → API → deployment |
| Langflow | 152,911 | MIT | Developer-centric graph workflows, custom components |
| Flowise | 55,226 | Other | Fast prototypes, lighter setups |
Which one for which job
\ndify (151,640 ★) — the closest thing to a complete platform. It covers the whole lifecycle: build the workflow visually, test it, expose it as an API, monitor logs, manage versions. Includes RAG, agent nodes, and multi-model routing out of the box. If you're building a product to ship — a customer support bot, an internal assistant — dify has the least \"glue work\" left over.
\nLangflow (152,911 ★, MIT) — matches dify on community size and leads on developer flexibility. Its graph model is closer to how engineers think, and custom components are easier to write. The MIT license is a plus for commercial embedding. Choose it when you want more control over the pipeline internals and don't mind trading some product polish for that control.
\nFlowise (55,226 ★) — the lightweight pick. Faster to spin up, simpler mental model, good for prototypes and internal tools. It covers the same core capabilities but with less product infrastructure around them. Choose it when you want to validate an idea quickly and don't need the full app lifecycle yet.
\nHow to decide
\n- Shipping a product for end users → dify (lifecycle completeness)
- Building custom, developer-heavy pipelines → Langflow (flexibility + MIT)
- Prototyping fast / internal tools → Flowise (simplicity)
- Heaviest RAG needs → pair any of these with RAGflow (87,000 ★, Apache-2.0)
The honest part
\nStars are close between dify and Langflow, and both are genuinely good — this is a \"match the tool to your workflow\" decision, not a \"one is better\" decision. All three have learning curves; the visual canvas takes a day to get comfortable with either way.
\nLicensing note: dify and Flowise licenses are marked \"Other\" on GitHub — review before commercial embedding. Langflow is MIT, the most permissive of the three.
\nFAQ
\nWhich is best for a chatbot? All three handle chatbots. dify adds the most product infrastructure (API, monitoring) around it; Flowise gets you a working prototype fastest.
\nDo these need coding? No — the core is visual. But Langflow rewards users who can write custom components; dify and Flowise have plugin ecosystems that reduce the need.
\nCan they work with local models? Yes — all three support OpenAI-compatible APIs, which covers most local model servers (Ollama, vLLM, etc.).
\nHow were stars verified? GitHub API, 2026-08-07, official repositories. Dify 151,640 ★, Langflow 152,911 ★, Flowise 55,226 ★.
\nSummary
\ndify (151,640 ★) for complete product lifecycle, Langflow (152,911 ★, MIT) for developer flexibility, Flowise (55,226 ★) for fast prototypes — all verified 2026-08-07. Pick by workflow, not by stars: the two leaders are within 1% of each other. Browse the full 461-tool catalog at ylyvip.net/tools.
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