Dify

Dify is an open-source platform for building LLM applications. It packages the parts of an AI app — prompt orchestration, retrieval over a knowledge base, workflows, model switching, logs and evaluation — into one visual platform. You can use it to build chat assistants, knowledge-base Q&A systems and multi-step automations, then plug them into your own product via API.

Its key advantage is being open source: it can run on your own servers so data never leaves your network. There is also a hosted cloud version if you want to try it without setting anything up.

It is well suited to internal enterprise deployments and works with hosted APIs or local models. The learning curve is steeper than consumer builders like Coze, and deployment needs some technical skill — in return you get far more control and room to extend.

Key features

• Visual workflows: Orchestrate multi-step logic with nodes, including conditional branches, loops, code execution and HTTP requests.
• Knowledge base and RAG: Uploaded documents are chunked and indexed automatically, with adjustable retrieval settings, so apps answer from internal material.
• Multi-model support: Connect major commercial model APIs or locally hosted open-source models and switch between them.
• Agents and tool use: Let the model decide which tools to call, and extend capabilities through plugins.
• Logs and annotation: Records each conversation and call, making it easier to debug and refine prompts.
• API publishing: Once built, an app can be served as an API or a web page.

How to use

1. Sign up for the cloud version on the Dify website, or grab the source from GitHub and deploy it on your own server with Docker following the docs.
2. Add the models you want in settings, entering API keys or local model endpoints.
3. Create an app, choose a type such as chat assistant or workflow, and set up prompts and flow.
4. If it needs to answer from your material, create a knowledge base, upload documents and link it to the app.
5. Publish after testing, share it via API or web link, and review logs regularly to improve results.

Best for

• Internal knowledge Q&A: Self-hosting plus a knowledge base meets requirements that data stay in-house.
• Rapid prototyping of AI features: Product teams can validate a flow on the platform before deciding whether to build it themselves.
• Business process automation: Chain document processing and information extraction into workflows that run in bulk.
• Comparing models: Swap models within the same flow to compare quality and cost.

Strengths and limitations

Strengths

• Open source and self-hostable, with full control over data and model choice.
• Covers the full path from prompt to deployment, with solid engineering depth.
• Active community, with plugins and documentation steadily growing.

Limitations

• Self-hosting needs servers and ops skills, and upgrades are your responsibility.
• Many concepts and settings, which is a hurdle for non-technical users.
• The open-source licence carries some commercial restrictions; read the terms before building a multi-tenant service on it.