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LangflowVizualni niskokodirani framework za gradnju i uvoz LLM-pokretanih aplikacija i agenta

4.2 (6)
Daniel NikulshynRecenzirao Daniel Nikulshyn·Ažurirano svibanj 2026.

Pregled

Langflow je otvoreni izvor za vizualno razvojni okoliš projektiranja aplikacija koje baziraju se na velikim jezičnim modelima. Putem drag-and-drop korisnici mogu povezati prompte, modele, vektorske skladište, memoriju, alate i kustodne logike kako bi kreirali bavečnike, tokove RAG-a i samodatovljive agente bez pisnja izrazito dugih predloška koda. Što se tiče svakog protoka, može se direktno testirati u uređivaču i izvoziti kao API endpoint, čime se omogućuje kako brzo prototipiranje, tako i uvođenje u proizvodnju. Langflow podržava širok niz providera i integracija, uključujući najveće modeli za LLM, ugrađene modele i baze podataka, te omogućuje razvojiteljima da nadograduju funkcionalnost kroz priložene komponente iz Pythona kad je potreban više kontrola.

Ključne značajke

  • Gradio-zapisovnik sliječnjika
  • Integrirano podrška za glavne LLM pružatelje
  • Povezane baze podataka RAG i vektora
  • Orkestracija agenta i alata
  • Izvoz API za uposljednju
  • Kreiranje prilagođenih komponenti u Pythonu

Cijene

Model
Freemium
Ocjena
4.2 / 5 (6)

Slučajevi uporabe

Provizirajte vizualno LLM čat bote

Brzo dizajnirajte i testirajte čat botove članke prigušivanjem pokretačkih tema, modela i memorije komponente u vizualnom kanvasu bez pisanja detaljnog koda.

Sagradite RAG tokove

Povežite vektorske baze podataka, komponente za embedding modele i LLM-e za stvaranje tokova za uzimanje koji uzimaju odgovore s prilagođenim baza znanja.

Uvozite toke kao proizvodne API-e

Izvažite potpune protokole kao API krajnjice, omogućavajući timove integrirati LLM opremljenu funkcionalnost s postojećim aplikacijama i proizvodnim sustavima.

Orkestriraji avtonomne agente

Povežite alate, modele i prilagođene Python komponente za građenje agenata koji mogu razmišljati, pozivati vanjske usluge i izvršavati višekraka zadatke.

Prednosti i nedostaci

Prednosti

  • Otvoren kôd s aktivan zajedom
  • Intuitivni vizualni interfejs ubrzava prototipiranje
  • Široka integracija s LLM-ovima, vektorskim skladištima i alatima
  • Tokovi mogu biti izloženi kao API za proizvodne upotrebe
  • Prilagođenje s prilagođenim komponentama u Pythonu

Nedostaci

  • Kompleksni tokovi mogu postati teški za upravljivanje vizualno
  • Krivčna linija za korisnike koji su novi u konceptu LLM
  • Samohranjenje zahtijeva neke tehnosetape

Recenzije

4.2

Prosjek iz 6 ocjena.

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Prijavi se za ostavljanje recenzije.

Leila Hassan

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

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.

IB

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.

TA

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.

GO

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

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.

Pitanja

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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