Dify

Dify is a development platform for applications built on language models. Its interface gathers AI workflows, retrieval pipelines, agent definitions, model management and observability in one place, which removes the usual scattering of prompt files, vector stores and glue scripts across a codebase.

Work starts on a visual canvas where a workflow is built and tested, with a prompt IDE beside it for drafting instructions and comparing how models respond. Everything built this way is also exposed through an API, so the platform sits behind your own product rather than replacing it.

The platform is organized around a handful of pieces.

• Model coverage: hundreds of proprietary and open models from dozens of inference providers, including any OpenAI-compatible endpoint.
• RAG pipeline: document ingestion through retrieval, with text extraction from PDFs, presentations and other common formats.
• Agents: defined through function calling or ReAct, with more than fifty built-in tools alongside your own.
• LLMOps: logs and performance are tracked over time so prompts, datasets and models improve on production data.
• Observability integrations: Opik, Langfuse and Arize Phoenix connect for tracing.

The quickest path is Docker Compose from the repository, on a machine with at least two CPU cores and four gigabytes of memory, after which the dashboard initializes in a browser. Deploying from source is documented for anyone extending it. A hosted cloud and an enterprise edition exist, but the community edition is the one you run yourself.