Dify
Build and orchestrate LLM applications and agentic workflows with visual workflow design, RAG, and knowledge-base management, source-available under a modified Apache 2.0 license with 139,000+ GitHub stars.
The verdict on Dify: Teams building production chatbots or agent pipelines who want a visual editor instead of raw LangChain code Dify's real pitch is speed to production for teams who would otherwise write LangChain code from scratch. Pricing: Free Sandbox / $59/mo Professional / $159/mo Team.
Best For
Teams building production chatbots or agent pipelines who want a visual editor instead of raw LangChain code
Standout Feature
A visual canvas with built-in RAG tooling and observability, reducing development time versus building from primitives
TL;DR
Dramatically faster to build with than coding from scratch, complex workflows can still get hard to debug once wired up.
Alternatives
Fit Score: use-case reference
How this is scoredDify scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
Building knowledge-base chatbots with visual RAG
4.0/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 3/5
- Ease
- 4/5
- Compatibility
- 4/5
Build a knowledge-base chatbot with visual document ingestion, retrieval controls and observability instead of assembling those pieces from raw framework code. Active development supports the platform choice, but retrieved answers and complex flows still need testing. Managed cloud plans step up materially from the sandbox, so verify the required capacity and model costs rather than assuming free production operation.
Self-hosting single-tenant tool-calling agent pipelines
4.0/5- Capability
- 5/5
- Reliability
- 3/5
- Value
- 4/5
- Ease
- 3/5
- Compatibility
- 5/5
Build a single-tenant agent pipeline with tool calls, branches, loops and human checkpoints, then run it on your own infrastructure. The visual editor exposes the flow, but complicated pipelines can still be difficult to debug. Standard single-tenant self-hosting is free under the stated modified license; budget infrastructure and models separately and do not extend that assumption to multi-tenant resale or branding changes.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
Dify is an open-source LLM application development platform that provides a visual workflow editor, built-in RAG pipeline tools, and an observability layer, enabling teams to build production chatbots, knowledge bases, and multi-step agent pipelines without the code complexity of building from LangChain or LlamaIndex primitives directly. The visual canvas wires together models, retrievers, tools, and logic through a drag-and-drop interface that makes the application architecture visible and modifiable by team members without deep LLM framework expertise. The RAG module handles document ingestion, chunking, embedding, and retrieval with configurable parameters accessible through the interface rather than requiring code changes for each tuning iteration. Agent workflows support multi-step execution with tool calling, conditional branching, loop handling, and human-in-the-loop checkpoint nodes.
The annotation system enables human review and correction of model outputs during development, feeding those corrections back into evaluation datasets. Self-hosted Dify runs on Docker and is free for standard single-tenant use under a modified Apache License 2.0; operating it as a multi-tenant service, or removing/modifying the frontend logo and copyright notice, requires a separate commercial license from LangGenius, the producer. Dify Cloud provides a managed version with a generous free tier. With over 90,000 GitHub stars, it is among a widely adopted open-source LLM platforms in production globally, particularly in teams that want the visual workflow development experience with the ability to self-host and customize.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Visual editor dramatically reduces development time
- • Strong open-source community and active development
- • Works with any major LLM provider
- • Complex workflows can become difficult to debug
- • Cloud pricing jumps significantly beyond free tier
Key Features
- Visual workflow editor
- RAG pipeline builder
- Multi-model support
- Built-in observability
- API and webhook integration
- Self-hostable
Trust & Data
Verified 2026-09
- Training on your data
- Not publishedThe cloud terms of service grant a license to use customer content to improve and update the service but make no explicit AI-training statement; the enterprise tier separately markets a 'no model-training leakage' guarantee not published for cloud-tier users, and self-hosted deployments process data entirely on the customer's own infrastructure.
- Compliance
- Not published
- Retention
- Not published
- Export
- Not published
Sources: dify.ai/privacy, dify.ai/terms
As published by the vendor. Verify independently before purchase decisions.
Our Take
The visual workflow canvas with built-in RAG tooling and observability compresses what would be weeks of integration work. At 139,000+ GitHub stars it has genuine community momentum. The honest tradeoffs are debugging complexity and pricing: complex workflows become hard to trace visually once wired up, and cloud pricing jumps significantly beyond the free tier. The right fit is a team building production chatbots or agent pipelines who want to move faster than raw code allows and have the budget for the professional tier.