Ross ROSS = Recommend OSS · open-source software intelligence for agents

Dify

Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack. observed · 2026-10-20

github.com/langgenius/dify · homepage · Python · NOASSERTION (other) observed · 2026-10-20

Health v2 · maintenance only

92/100

  • Activity 100
  • Release rhythm 82
  • Longevity 91

Flags: no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.

  • gap_med: 7
  • age_days: 1287
  • days_rel: 40
  • days_push: 1
  • n_releases_24m: 66

Full methodology

Adoption not part of the score

158944 stars · 25122 forks observed · 2026-10-20

What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-21, confidence 0.97

Dify is an open‑source platform for building production‑ready AI agents, RAG pipelines and agentic workflows with a visual no‑code studio. It can be run as a SaaS service, in a VPC or self‑hosted via Docker.

Use cases

  • Create a chatbot that answers questions using your own document collection
  • Build an automated RAG pipeline that retrieves and summarizes internal knowledge bases
  • Design multi‑step AI agents that call external tools and APIs
  • Deploy a custom AI assistant for customer support without writing code
  • Integrate proprietary LLMs and third‑party tools through Dify’s plugin marketplace
  • Scale prototype AI workflows to production with built‑in monitoring and versioning

When to choose

  • You need a collaborative, visual environment to design AI agents and RAG flows quickly.
  • Your team prefers a no‑code/low‑code solution but still wants the ability to extend with custom plugins.
  • You want a single platform that can be run on cloud, VPC, or on‑premises with Docker.
  • You require built‑in support for multiple LLM providers and tool integrations.

When to avoid

  • You need fine‑grained control over every component of the stack and prefer building from scratch.
  • Your deployment environment does not support Docker or container orchestration.
  • You require a lightweight library rather than a full‑stack application.

Facets

application · maturity active

rag workflow-automation api-framework cli plugin-system chatbot llm-inference artificial-intelligence large-language-models chatbots cloud self-hosted windows cli no-code visual-builder knowledge-pipeline marketplace enterprise-ready multi-tenant agentic-workflows retrieval-augmented-generation devops web-server docker linux macos

6 sources

Member repositories

For agents

markdown · JSON · MCP: product_card(name="langgenius/dify")

Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem