Dify

Open-source platform for building and orchestrating LLM apps with built-in RAG and agent workflows.

5.0 (5)

Geprüft von Daniel Nikulshyn·Aktualisiert Mai 2026

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

Übersicht

Dify is an open-source development platform designed to simplify how teams build, deploy, and manage applications powered by large language models. It combines a visual workflow builder, prompt engineering tools, and a retrieval-augmented generation (RAG) pipeline so developers can move from prototype to production without stitching together multiple services. The platform supports a wide range of model providers, includes an agent framework for tool use and multi-step reasoning, and offers observability features to monitor usage, costs, and quality. Because it can be self-hosted, Dify appeals to organizations that need control over data, infrastructure, and compliance while still benefiting from a modern LLMOps toolchain. Typical use cases include internal knowledge assistants, customer support bots, content generation pipelines, and custom AI products that need to combine private data with commercial or open-source models.

Hauptfunktionen

Visual LLM workflow builder
Retrieval-augmented generation pipeline
Agent framework with tool integrations
Prompt management and versioning
Multi-model provider support
Usage analytics and observability

Anwendungsfälle

Build RAG-powered knowledge assistants

Use the built-in retrieval-augmented generation pipeline and knowledge base tools to create chatbots that answer questions grounded in internal documents.

Prototype and deploy LLM apps visually

Design prompts and multi-step LLM workflows in the visual builder, then move from prototype to production without integrating multiple separate services.

Orchestrate multi-step AI agents

Leverage the agent framework with tool integrations to build assistants that reason across steps and call external tools for complex tasks.

Self-host LLM apps for compliance

Deploy Dify on your own infrastructure to retain control over data and meet compliance needs while still using a wide range of LLM providers.

Pro & Contra

Pro

Open-source with self-hosting options
Visual workflow and prompt orchestration
Built-in RAG and knowledge base tools
Supports many LLM providers and models
Active community and frequent updates

Contra

Self-hosting requires technical setup and maintenance
Advanced features have a learning curve
Some enterprise capabilities are gated behind paid tiers

Bewertungen

5.0

Durchschnitt aus 5 Bewertungen.

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Camille Laurent

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

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

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

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

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.

Q&A

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.

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.

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.