Hiveflow
Visual workflow orchestration for AI agents and automation
Dify
Open-source platform for building production-ready LLM applications
Dify combines visual AI workflows, RAG pipelines, agent capabilities, and model management into an intuitive platform for developing and deploying LLM applications from prototype to production.
Overview
What is Dify?
Dify is a comprehensive platform designed for teams and developers building LLM-powered applications. It bridges the gap between experimentation and production deployment through a visual interface that requires minimal coding.
Core Capabilities
The platform offers a visual workflow canvas for designing agentic AI systems, extensive RAG pipelines with native document processing (PDFs, PPTs), and seamless integration with hundreds of LLMs from providers like OpenAI, Mistral, and Llama3. Built-in agent capabilities support both Function Calling and ReAct patterns, with over 50 pre-built tools including Google Search, DALL·E, and WolframAlpha.
Deployment & Operations
Dify provides flexible deployment options: a managed cloud service with 200 free GPT-4 calls, self-hosted Community Edition via Docker Compose, and enterprise editions with custom branding. LLMOps features enable continuous monitoring, prompt refinement, and performance analysis based on production data. Every feature is API-accessible, functioning as a backend-as-a-service for seamless integration into existing business logic.
Ideal for teams moving beyond proof-of-concept stages who need observability, model flexibility, and production-grade infrastructure without building from scratch.
Highlights
Visual workflow builder with agentic AI and RAG pipeline support
Integration with hundreds of LLMs from dozens of providers
50+ built-in tools for agents plus custom tool support
LLMOps monitoring with prompt IDE and performance analytics
Pros
- Comprehensive feature set covering workflows, RAG, agents, and observability in one platform
- Flexible deployment: managed cloud, self-hosted Docker, or enterprise options
- Extensive model support with provider-agnostic architecture
- Backend-as-a-Service APIs enable integration into existing applications
Considerations
- Minimum 2-core CPU and 4GB RAM requirements may limit resource-constrained deployments
- Learning curve for teams unfamiliar with RAG pipelines or agentic workflows
- Self-hosted setup requires Docker and Docker Compose knowledge
- Enterprise features and custom branding require paid plans
Managed products teams compare with
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Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Teams transitioning LLM prototypes to production environments
- Organizations needing multi-model flexibility and vendor independence
- Developers building RAG applications with document processing requirements
- Enterprises requiring observability, monitoring, and continuous improvement workflows
Not ideal when
- Simple single-prompt applications without workflow complexity
- Environments unable to meet 4GB RAM minimum requirements
- Teams seeking fully managed solutions without any infrastructure decisions
- Projects requiring specialized LLM frameworks beyond standard integrations
How teams use it
Enterprise Knowledge Base with RAG
Process internal PDFs and documents to build a searchable AI assistant that answers employee questions using company-specific information with full audit trails
Multi-Step Research Agent
Create workflows combining web search, data analysis, and report generation tools to automate competitive intelligence gathering and synthesis
Customer Support Automation
Deploy chatbots with RAG-powered knowledge retrieval and agent tools for ticket creation, monitoring performance through LLMOps dashboards
Content Generation Pipeline
Build visual workflows that orchestrate multiple LLMs for drafting, editing, and optimizing marketing content with A/B testing capabilities
Tech snapshot
TypeScript51%
Python41%
JavaScript5%
MDX2%
CSS1%
HTML1%
Frequently asked questions
What deployment options does Dify offer?
Dify provides three deployment paths: Dify Cloud (managed service with 200 free GPT-4 calls), self-hosted Community Edition via Docker Compose, and enterprise editions with custom branding available on AWS Marketplace and other cloud platforms.
Which LLM providers does Dify support?
Dify integrates with hundreds of models from dozens of providers including OpenAI, Mistral, Llama3, and any OpenAI API-compatible models. It supports both proprietary and open-source LLMs with self-hosted inference options.
What are the minimum system requirements for self-hosting?
Self-hosted Dify requires at least 2 CPU cores and 4GB RAM. You'll also need Docker and Docker Compose installed to run the platform using the provided docker-compose configuration.
Can I integrate Dify into my existing application?
Yes, Dify functions as a backend-as-a-service with comprehensive APIs for all features. You can integrate workflows, RAG pipelines, and agent capabilities directly into your business logic without using the dashboard.
What tools are available for AI agents in Dify?
Dify includes 50+ built-in tools such as Google Search, DALL·E, Stable Diffusion, and WolframAlpha. You can also create custom tools and define agents using LLM Function Calling or ReAct patterns.
Project at a glance
Active- Stars
- 126,631
- Watchers
- 126,631
- Forks
- 19,723
Repo age2 years old
Last commit23 hours ago
Self-hostingSupported
Primary languageTypeScript
Last synced 23 hours ago