Dify

Dify is an open source platform for building, deploying, and managing large language model (LLM) applications. It provides a comprehensive suite of tools including a visual workflow builder, a prompt orchestration studio, retrieval-augmented generation (RAG) pipeline management, an AI agent framework, and a model management interface, all accessible through a single unified application.

The Dify Workflow is a node-based visual builder that allows developers to design complex LLM pipelines with precision. Each node in a workflow can represent an LLM call, a knowledge retrieval step, a code execution block, a conditional branch, a data transformation, or an HTTP request. Developers can chain these nodes together to create multi-step reasoning pipelines, document processing workflows, and agentic systems that interact with external tools and APIs.

Knowledge management is a first-class feature in Dify. Users can create knowledge bases by uploading documents in formats such as PDF, Word, Markdown, and plain text. Dify handles chunking, embedding generation, and indexing automatically, storing the processed data in its built-in vector database or an external one of the user's choice. This makes it straightforward to build RAG-powered applications that answer questions based on proprietary or domain-specific content.

Dify's application types cover a broad range of use cases. Chat applications support multi-turn conversations with memory, tool use, and knowledge retrieval. Completion applications support single-turn text generation workflows. Workflow applications are designed for batch processing and complex pipeline automation. Agent applications enable autonomous, multi-step task completion using a configurable set of tools.

Model management in Dify is highly flexible. The platform supports dozens of LLM providers including OpenAI, Anthropic, Azure OpenAI, Google, Mistral, Cohere, and locally hosted models via Ollama, LMStudio, and other OpenAI-compatible APIs. Teams can configure multiple models and switch between them easily, comparing performance and cost across different providers.

The monitoring and analytics features in Dify provide visibility into application performance. Developers can review conversation logs, trace workflow executions step by step, measure token usage and latency, and collect user feedback. These insights are invaluable for iterating on prompt designs, identifying retrieval failures, and improving overall application quality.

Dify supports a robust plugin and tool ecosystem. Agents can be equipped with built-in tools such as web search, code execution, Wikipedia, and weather lookup, or developers can define custom tools by providing an OpenAPI specification or writing a Python function. This extensibility makes Dify suitable for a wide variety of automation and integration scenarios.

The platform can be self-hosted using Docker Compose or Kubernetes, giving organizations full control over their data and infrastructure. A managed cloud version, Dify Cloud, is also available for teams that prefer a hosted experience with reduced operational overhead. Enterprise features include single sign-on (SSO), role-based access control, audit logging, and priority support.

Dify is an excellent choice for product teams, AI engineers, and enterprises that want to move quickly from LLM experimentation to production deployment, with robust tooling for collaboration, governance, and operational visibility built in from the start.