Dify AiOpen-source platform for building, deploying, and managing generative AI apps and agents.
Overview
Key features
- Visual app and agent builder
- RAG pipeline with dataset management
- Multi-model LLM support
- Prompt engineering and versioning
- Observability and logging tools
- API endpoints for deployed apps
Pricing
- Model
- Freemium
- Category
- Large Language Models (LLMs)
- Rating
- 4.6 / 5 (5)
Use cases
Build Document Q&A Systems
Use the built-in RAG pipeline and dataset management to create chatbots that answer questions from internal documents, manuals, or knowledge bases.
Deploy Internal Copilots
Design AI copilots with the visual builder and expose them as APIs so teams can integrate them into existing tools and workflows.
Prototype and Ship Agent Workflows
Orchestrate multi-step agents using the visual workflow builder, test prompts with versioning, and move from prototype to production on one stack.
Compare and Swap LLM Providers
Leverage multi-model support to test different LLM providers across the same app, optimizing for cost, latency, or quality without rebuilding.
Pros & Cons
Pros
- Open-source with self-hosting option
- Visual workflow and prompt builder
- Supports many LLM providers
- Built-in RAG and dataset tools
- Exposes apps as APIs quickly
Cons
- Self-hosting requires technical setup
- Advanced features have a learning curve
- Performance depends on chosen LLM
Reviews
Average from 5 ratings.
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Use it every day
Honestly didn't expect to like it this much. RAG pipeline with dataset management is exactly what I needed, and exposes apps as APIs quickly. I do wish performance depends on chosen LLM, but I reach for it almost every day now and it just clicks.
Does the job
Pretty happy overall. API endpoints for deployed apps just works and open-source with self-hosting option. Advanced features have a learning curve can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Solid for our team
We rolled this out across the team last quarter and supports many LLM providers. Visual app and agent builder fits neatly into how we already work, and rAG pipeline with dataset management removed a step we used to do by hand. Self-hosting requires technical setup, which is the main caveat, but it has held up under daily use.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on prompt engineering and versioning, and supports many LLM providers caught me off guard. Performance depends on chosen LLM is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Years in this space
I've evaluated a lot of these over the years. What stands out here is multi-model LLM support — handled better than most — and built-in RAG and dataset tools. Worth the time if this is your use case.
Q&A
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