Re-understand Dify: the core capabilities of AI agent tools

As a product of LangGenius, Inc., Dify provides the capabilities of "the open source AI workflow platform produced by LangGenius, 143K+ Star, covering Agent, RAG and multi-model orchestration" in the field of AI agents. This article will give a structured review.

Why the team chose Dify

Visual + programmable dual mode: Dify supports the design of workflows on a visual interface, while providing APIs and SDKs for developers to call programmatically. The two modes can be seamlessly switched, lowering the threshold for use by non-technical teams without sacrificing engineering flexibility. Hybrid RAG retrieval: Combining semantic vector retrieval (Dense Retrieval) and keyword full-text retrieval (Sparse Retrieval), it has better recall accuracy than single vector retrieval in long document scenarios, especially suitable for enterprise knowledge base and technical document scenarios. Model agnostic: All workflow nodes support switching the underlying LLM at runtime, allowing enterprises to flexibly trade off cost, performance and compliance (for example, use low-cost small models for routine tasks, and use flagship models for critical steps). Self-hosting and compliance-friendly: The Apache 2.0 open source protocol allows enterprises to deploy completely privately, with data retained in their own infrastructure. It is suitable for industries such as finance and medical care that have strict data compliance requirements.

Function list

• Visual Workflow Orchestration: 20+ node types enable building any workflow, from simple Q&A to multi-step complex automation, without writing Orchestration code.
• RAG Knowledge Base: Upload documents, web pages or structured data, and Dify automatically completes vectorization and indexing, supports semantic retrieval and keyword mixed recall, and provides context enhancement for LLM.
• Multi-Agent Collaboration: Supports the creation of multiple exclusive Agents and task allocation and collaboration, suitable for complex tasks that require division of labor to handle different steps.
• 100+ model support: Unified model management interface, access to mainstream models such as OpenAI, Claude, Gemini, Llama, DeepSeek, Qwen, Mistral, etc., which can be flexibly switched according to workflow nodes.
• LLMOps Monitoring: Built-in conversation logs, performance tracking and cost monitoring to help teams continuously optimize application quality and usage costs.
• API Release: Workflow and Agent applications can generate REST API with one click and can be embedded into any existing system.
• Plug-in Ecosystem: Supports calling external APIs and services through tool nodes to expand the boundaries of workflow capabilities.

Conclusion: Dify provides a clear set of AI agent capability combinations. Whether it is suitable depends on your specific process. It is recommended to refer to official documents and actual measurements.