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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.
This repository page is useful for visitors, but Git-Stars keeps it out of search indexing until more original evidence and comparison context are available.
75
review score
Problem solved
Dify solves the complexity of integrating multiple AI components (LLMs, vector stores, tools) into a cohesive agent workflow. Unlike building from scratch, it offers a visual drag-and-drop interface and pre-built templates, reducing development time from weeks to hours while maintaining production scalability.
Deployment reality
The available setup signal starts with: docker compose up -d. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is NOASSERTION. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build customer support chatbots, automated research assistants, document analysis pipelines, and multi-step reasoning agents. The platform supports custom tool integration, RAG with various vector databases, and deployment to cloud or self-hosted environments, enabling enterprise-grade AI applications with monitoring and logging.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
langgenius/dify is tracked as a TypeScript project in the Automation, LLM Tool, AI Agent, MCP Server, Developer Tool area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 153k total stars, with +106 today, +748 this week, and +3.4k this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Moderate; the latest push was 60 days ago, and the open issue queue is 976, about 0.64% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 24k forks and 153k watchers suggest how often the project is reused or followed. License signal: NOASSERTION. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 1 explicit source reference(s) for this report and a recent growth signal of 3.4k. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
LimitedNeeds clearer source references.
License
LimitedLicense is unknown and should be checked before commercial use.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
153k Stars
Reuse
24k Forks
Attention
153k Watchers
Maintenance
moderate
License
NOASSERTION
Open issues
976
Dify is an open-source LLM app development platform with an intuitive interface that combines AI workflow, RAG pipeline, agent capabilities, model management, and observability features, enabling rapid prototyping to production.
Key Features
- Visual workflow builder for AI applications - Integrated RAG pipeline and agent capabilities - Model management and observability features (Opik, Langfuse, Arize Phoenix)
Automation
Workflow automation, integration glue, and orchestration
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
MCP Server
Model Context Protocol servers, clients, and integrations
Developer Tool
Tools that improve coding, testing, build, and local workflow
docker compose up -d80
Health Score
Moderate
Commit Activity
Apr 12, 2023
Created
Aug 15, 2026
Last push
Star history will appear after daily syncs accumulate data.
Forks
Open
Watchers
langgenius
GitHub profilecodecrafters-io/build-your-own-x
Master programming by recreating your favorite technologies from scratch.
nilbuild/developer-roadmap
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
practical-tutorials/project-based-learning
Curated list of project-based tutorials
obra/superpowers
An agentic skills framework & software development methodology that works.
react/react
The library for web and native user interfaces.
✓
License
✓
Forked
~ Moderate
Maintained
Problem Solved
Dify solves the complexity of integrating multiple AI components (LLMs, vector stores, tools) into a cohesive agent workflow. Unlike building from scratch, it offers a visual drag-and-drop interface and pre-built templates, reducing development time from weeks to hours while maintaining production scalability.
Capabilities
Developers can build customer support chatbots, automated research assistants, document analysis pipelines, and multi-step reasoning agents. The platform supports custom tool integration, RAG with various vector databases, and deployment to cloud or self-hosted environments, enabling enterprise-grade AI applications with monitoring and logging.
Bottom Line
Dify is ideal for teams wanting to quickly build and deploy AI agents without extensive coding, especially for customer service and internal tools. Developers needing fine-grained control or handling massive scale may find it limiting. The key trade-off is ease-of-use versus deep customization.