dify
Dify is an open-source platform for building and deploying AI applications with visual workflows, RAG pipelines, and agent capabilities. It supports hundreds of LLM providers and includes monitoring, APIs, and both cloud and self-hosted deployment options.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | langgenius/dify |
| Owner | langgenius |
| Primary language | TypeScript |
| License | Other — Requires review (not clearly OSI) |
| Stars | 148.1k |
| Forks | 23.3k |
| Open issues | 851 |
| Latest release | 1.15.0 (2026-06-25) |
| Last updated | 2026-07-07 |
| Source | https://github.com/langgenius/dify |
What dify is
TypeScript/Python-based platform providing workflow orchestration, multi-model LLM integration, document ingestion/retrieval, agent frameworks (Function Calling/ReAct), and observability integrations. Deployable via Docker Compose or from source; exposes comprehensive REST APIs for integration.
Get the dify source
Clone the repository and explore it locally.
git clone https://github.com/langgenius/dify.gitcd dify# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Verify license compatibility with your intended use (commercial, proprietary, or open-source); 'Other' designation requires legal review.
- Self-hosting requires minimum 2 CPU cores and 4 GB RAM; plan infrastructure accordingly or use managed Dify Cloud (sandbox includes 200 free GPT-4 calls).
- API-first architecture enables embedded use; design integration points with existing business logic before deployment.
- Observability plugins (Opik, Langfuse, Arize Phoenix) must be configured separately; evaluate which aligns with your monitoring stack.
- Document ingestion supports common formats (PDF, PPT); test with your actual document types and sizes to validate performance.
When to avoid it — and what to weigh
- Proprietary AI Model Lock-in Required — If your business model depends on restricting customer choice to specific LLM providers, Dify's multi-provider approach and open architecture conflict with that strategy.
- Minimal DevOps Capability — Self-hosting requires Docker/Compose proficiency and infrastructure management; cloud option available but data residency or cost constraints may make it unsuitable.
- Real-Time Streaming-Only Workloads — Unknown degree of optimization for extreme low-latency or continuous streaming scenarios; platform targets typical batch/conversational LLM patterns.
- Strict Commercial License Requirements — License marked as 'Other' (not an OSI-approved permissive license); commercial use terms require explicit review before committing to production deployments.
License & commercial use
Licensed under 'Other' (not an OSI-approved open-source license). Exact terms unknown from provided data. Self-hosting and community use are indicated, but commercial or proprietary use terms are not clearly stated.
Requires review. License is marked 'Other' and does not appear to be a permissive OSI license (e.g., MIT, Apache 2.0). Dify Cloud is offered as a commercial service, but terms for deploying the self-hosted version in production or commercial contexts are unclear. Consult the repository's LICENSE file and Dify's legal terms before committing to commercial use.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit, penetration test, or security posture data provided. Self-hosting deployments require careful API key/credential management for model provider access. Observability integrations may log sensitive application data; configure appropriately. No known critical vulnerabilities disclosed in provided data, but requires independent security assessment before production use, especially for sensitive data or regulated workloads.
Alternatives to consider
LangChain
Pure Python/JS framework for LLM orchestration; lighter weight and more developer-centric, but lacks the visual workflow builder and managed observability features Dify provides.
LlamaIndex (formerly GPT Index)
Specialized in RAG and data indexing; narrower scope than Dify but deeply optimized for document retrieval; often paired with LangChain rather than used standalone for full app development.
Hugging Face Spaces / Gradio
Simpler, lower-code option for rapid model experimentation and UI generation; not designed for production orchestration or multi-model workflows at Dify's scale.
Build on dify with DEV.co software developers
Start with Dify Cloud (sandbox included) or deploy self-hosted via Docker Compose. Review the license for your use case.
Talk to DEV.coRelated on DEV.co
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dify FAQ
Can I use Dify for commercial applications?
What are the minimum infrastructure requirements?
Which LLM providers are supported?
Does Dify offer a managed cloud version?
Software development & web development with DEV.co
Need help beyond evaluating dify? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and ai frameworks integrations — and maintain them long-term.
Ready to Build AI Applications?
Start with Dify Cloud (sandbox included) or deploy self-hosted via Docker Compose. Review the license for your use case.