DifyOdprtokodna platforma za ustvarjanje in usklajevanje LLM aplikacij z vgrajenimi RAG in agentnimi delovnimi tokovi.

5.0 (5)

Pregledal Daniel Nikulshyn·Posodobljeno maj 2026

Open Source LLM Platform RAG Agents Workflow Builder Self-Hosted Developer Tools Observability

Pregled

Dify je odprtokodna razvojna platforma, zasnovana za poenostavitev načina, kako ekipe gradijo, uvajajo in upravljajo aplikacije, pogojene z velikimi jezikovnimi modeli. Združuje vizualni graditelj delovnih tokov, orodja za prompt engineering in pipeline za retrieval‑augmented generation (RAG), tako da lahko razvijalci preidejo iz prototipa v produkcijo, ne da bi morali spajati več storitev. Platforma podpira širok nabor ponudnikov modelov, vključuje agentni okvir za uporabo orodij in večstopenjsko razmišljanje ter ponuja funkcije opazljivosti za spremljanje uporabe, stroškov in kakovosti. Ker je mogoče platformo samostojno gostovati, Dify privlači organizacije, ki potrebujejo nadzor nad podatki, infrastrukturo in skladnostjo, hkrati pa želijo izkoristiti sodoben LLMOps toolchain. Tipični primeri uporabe vključujejo interne asistente za znanje, bote za podporo strankam, pipeline za ustvarjanje vsebin in prilagojene AI izdelke, ki morajo kombinirati zasebne podatke s komercialnimi ali odprtokodnimi modeli.

Ključne funkcije

Vizualni urejevalnik LLM delovnih tokov
Cevovod za retrieval-augmented generation
Agentni okvir z integracijami orodij
Upravljanje promptov in različic
Podpora večmodelnim ponudnikom
Analitika uporabe in opazovanje

Cene

Model: Free
Kategorija: Platforma za umetne inteligence agenti
Ocena: 5.0 / 5 (5)

Primeri uporabe

Ustvarjanje asistentov znanja z RAG

Uporabite vgrajeni pipeline za retrieval-augmented generation in orodja za zbirko znanja, da ustvarite klepetalne bote, ki odgovarjajo na vprašanja, utemeljena v notranjih dokumentih.

Vizualno prototipiranje in uvajanje LLM aplikacij

Oblikujte prompte in večstopenjske LLM delovne tokove v vizualnem urejevalniku, nato pa preidite od prototipa do produkcije brez integracije več ločenih storitev.

Usklajevanje večstopenjskih AI agentov

Izkoristite agentni okvir z integracijami orodij za gradnjo asistentov, ki razmišljajo po korakih in kličo zunanja orodja za kompleksne naloge.

Samostojno gostovanje LLM aplikacij za skladnost

Namestite Dify na lastno infrastrukturo, da ohranite nadzor nad podatki in izpolnite zahteve skladnosti, hkrati pa še vedno uporabljate širok nabor LLM ponudnikov.

Prednosti in slabosti

Prednosti

Odprtokodno z možnostjo samostojnega gostovanja
Vizualni delovni tok in orkestracija promptov
Vgrajena orodja za RAG in zbirko znanja
Podpira številne LLM ponudnike in modele
Aktivna skupnost in pogoste posodobitve

Slabosti

Samostojno gostovanje zahteva tehnično nastavljanje in vzdrževanje
Napredne funkcije imajo krivuljo učenja
Nekatere podjetniške zmožnosti so zaklenjene za plačane nivoje

Ocene

5.0

Povprečje iz 5 ocen.

Prijavi se za oddajo ocene.

Camille Laurent

May 3, 2026

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on agent framework with tool integrations, and visual workflow and prompt orchestration caught me off guard. Self-hosting requires technical setup and maintenance is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Esther Adeyemi

Mar 14, 2026

Solid for our team

We rolled this out across the team last quarter and open-source with self-hosting options. Usage analytics and observability fits neatly into how we already work, and usage analytics and observability removed a step we used to do by hand. Self-hosting requires technical setup and maintenance, which is the main caveat, but it has held up under daily use.

Pierre Dubois

Dec 9, 2025

Does the job

Pretty happy overall. Multi-model provider support just works and active community and frequent updates. Self-hosting requires technical setup and maintenance can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Nadia Petrova

Jul 24, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on prompt management and versioning, and built-in RAG and knowledge base tools caught me off guard. Advanced features have a learning curve is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Liam O’Connor

Jun 13, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on usage analytics and observability, and supports many LLM providers and models caught me off guard. Advanced features have a learning curve is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Vprašanja

What are common use cases for Dify, and how steep is the learning curve?

Typical use cases include internal knowledge assistants and customer-facing applications built on RAG and agent workflows. Basic prototyping is approachable via the visual builder, but advanced features like agent tool use, prompt versioning, and observability have a learning curve.

Asked by Diego Fernández · Sep 18, 2025

Which LLM providers and models does Dify support?

Dify offers multi-model provider support, allowing you to connect a wide range of LLM providers and switch between models within the same workflows. This flexibility is useful for comparing outputs, optimizing costs, or meeting provider-specific compliance requirements.

Asked by Carlos Mendoza · Aug 21, 2025

Can I self-host Dify, and what trade-offs come with that?

Yes, Dify is open-source and supports self-hosting, which gives you control over data, infrastructure, and compliance. The trade-off is that self-hosting requires technical setup and ongoing maintenance, so teams without DevOps capacity may prefer a managed deployment.

Asked by Camille Laurent · Jul 19, 2025