LangflowVizualni low-code okvir za gradnjo in uvajanje aplikacij ter agentov, pogonanih z LLM-ji.

4.2 (6)

Pregledal Daniel Nikulshyn·Posodobljeno maj 2026

Open Source Low-Code Visual Builder LLM RAG Agents API Python

Pregled

Langflow je open-source vizualno razvojno okolje za oblikovanje aplikacij, zgrajenih na LLM. Z drag-and-drop vmesnikom lahko uporabniki povežejo prompts, models, vector stores, memory, tools, in custom logic, da ustvarijo chatbots, RAG pipelines in autonomous agents brez pisanja obsežnega boilerplate kode. Vsak tok lahko neposredno preizkusite v urejevalniku in ga izvozite kot API endpoint, s čimer je primeren tako za hitro prototipiranje kot za produkcijsko uporabo. Langflow podpira širok spekter ponudnikov in integracij, vključno z glavnimi LLM-ji, embedding modelji in bazami podatkov, ter omogoča razvijalcem razširitev funkcionalnosti z lastnimi Python komponentami, kadar je potreben več nadzora.

Ključne funkcije

Sistem za ustvarjanje tokov z vlečenjem in spustom
Vgrajena podpora za glavne ponudnike LLM-jev
Integrirani povezovalci za RAG in vektorsko bazo podatkov
Orkestracija agentov in orodij
Izvoz API za uvajanje
Ustvarjanje po meri komponent v Pythonu

Cene

Model: Freemium
Kategorija: Agnosti AI
Ocena: 4.2 / 5 (6)

Primeri uporabe

Vizualno prototipiranje LLM klepetalnih robotov

Hitro oblikujte in preizkusite tokove klepetalnih robotov z vlečenjem povpraševanj, modelov in komponent pomnilnika na vizualno platno brez pisanja obsežnega boilerplate kode.

Ustvarjanje RAG cevovodov

Povežite vektorske podatkovne baze, modele vgradb in LLM-je, da ustvarite delovne tokove za pridobivanje-obožene generacije, ki odgovarjajo na vprašanja nad lastnimi bazami znanja.

Vdražanje tokov kot produkcijskih API-jev

Izvozite dokončane tokove kot API končne točke, kar ekipam omogoča integracijo funkcionalnosti z LLM v obstoječe aplikacije in produkcijska sistemi.

Orkestriranje avtonomnih agentov

Povežite orodja, modele in komponenti po meri v Pythonu, da ustvarite agente, ki lahko razmišljajo, kličejo zunanje storitve in izvajajo večkorakove naloge.

Prednosti in slabosti

Prednosti

Odprtokodni z aktivno skupnostjo
Intuitiven vizualni vmesnik pospešuje prototipiranje
Splošne integracije z LLM-ji, vektorskimi shrambi in orodji
Tokovi lahko izpostavite kot API-je za produkcijsko uporabo
Razširljivo z komponentami po meri v Pythonu

Slabosti

Skomplikirani tokovi se lahko težko upravljajo vizualno
Nivo učenja za uporabnike, ki niso seznanjeni z LLM koncepti
Lastna gostovanje zahteva nekaj tehničnega nastavitev

Ocene

4.2

Povprečje iz 6 ocen.

Prijavi se za oddajo ocene.

Leila Hassan

Mar 8, 2026

Solid for our team

We rolled this out across the team last quarter and open-source with active community. Built-in support for major LLM providers fits neatly into how we already work, and aPI export for deployment removed a step we used to do by hand. Learning curve for users new to LLM concepts, which is the main caveat, but it has held up under daily use.

Pierre Dubois

Jan 10, 2026

Does the job

Pretty happy overall. API export for deployment just works and extensible with custom Python components. Learning curve for users new to LLM concepts can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Ingrid Bauer

Dec 29, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: custom component creation in Python and broad integrations with LLMs, vector stores, and tools. On balance the feature set — especially integrated RAG and vector database connectors — justifies the 5 stars for our use case.

Tariq Aziz

Dec 22, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: agent and tool orchestration and flows can be exposed as APIs for production use. Where it lags: self-hosting requires some technical setup. On balance the feature set — especially built-in support for major LLM providers — justifies the 4 stars for our use case.

Grace Okafor

Nov 20, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: drag-and-drop flow builder and open-source with active community. Where it lags: complex flows can become difficult to manage visually. On balance the feature set — especially agent and tool orchestration — justifies the 4 stars for our use case.

Liam O’Connor

Jul 15, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: built-in support for major LLM providers and open-source with active community. Where it lags: complex flows can become difficult to manage visually. On balance the feature set — especially custom component creation in Python — justifies the 4 stars for our use case.

Vprašanja

Do I need to know how to code to use Langflow?

While Langflow has a visual interface that speeds up prototyping, users new to LLM concepts may still face a learning curve, but prior coding knowledge is not necessarily required.

Asked by Ola Eriksen · Oct 24, 2025

Can I customize Langflow with my own code?

Yes, Langflow allows developers to extend its functionality with custom Python components when more control is needed.

Asked by Fatima Zahra · Oct 22, 2025

What integrations does Langflow support?

Langflow supports a wide range of providers and integrations, including major LLMs, embedding models, databases, and hundreds of data sources, models, or vector stores.

Asked by Linda Petersen · Sep 27, 2025

Is Langflow free?

Langflow is open-source, which means it is free to use. Additionally, it offers a free enterprise-grade cloud to deploy apps.

Asked by Sanjay Gupta · Sep 21, 2025

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