Dify - Open-Source Agentic Workflow and LLM App Development Platform - Custom Platform (Freemium) Business AI
Dify - Open-Source Agentic Workflow and LLM App Development Platform Access This AI Via Link At Bottom Of Page Dify is an open-source platform for building agentic workflows and AI applications, bringing visual workflow design, RAG knowledge bases, model management, agents, tools, publishing, and observability into one workspace. It is built for teams that need to move from prototype to production without piecing together a fragmented LLMOps stack. For educators, enterprises, publishers, startups, and trust & safety teams, Dify is especially valuable when AI assistants need to connect with internal knowledge, use multiple model providers, expose APIs, and remain observable after launch. Dify supports both cloud use and self-hosting, with a Community Edition that can be deployed through Docker Compose. Key Features & Benefits of Dify Visual Workflow Builder Design agentic workflows and apps on a visual canvas rather than starting from code. Features: ๐น Drag-and-drop workflow and chatflow creation ๐น App types including Chatbot, Agent, Text Generator, Chatflow, and Workflow ๐น User input nodes for forms, chatflows, API calls, and MCP-style workflows ๐น Workflow apps that can be published and reused as tools Benefits: โ Helps non-specialist teams collaborate on AI application logic โ Reduces the time from concept to working prototype โ Makes multi-step AI processes easier to review and maintain โ Supports repeatable workflows for internal or customer-facing use cases Model and Agent Orchestration Connect Dify apps to major hosted and local model providers while giving agents controlled access to tools. Features: ๐น Supported providers include OpenAI, Anthropic, Google, Cohere, Azure OpenAI, Ollama, and local embedding models ๐น System providers for fast prototyping and custom providers for direct API-key control ๐น Agent strategies including Function Calling and ReAct ๐น Tool configuration with credentials, descriptions, parameters, memory, and iteration controls Benefits: โ Lets teams compare, switch, and govern model choices โ Supports both hosted-model and self-managed model strategies โ Enables autonomous workflows without hard-coding every step โ Helps production teams balance capability, cost, and control RAG Knowledge Base Ground AI apps in organization-specific data through Dify knowledge bases and retrieval workflows. Features: ๐น Import data from local files, Notion, web pages, or external knowledge APIs ๐น Configure chunking, indexing, retrieval, reranking, citation, and attribution ๐น Manage knowledge bases, documents, and chunks programmatically via API ๐น Use knowledge bases across Dify application types Benefits: โ Improves relevance by using trusted domain data as context โ Helps reduce unsupported answers in customer support, education, and internal knowledge use cases โ Supports structured maintenance of documents and chunks โ Fits teams that need AI over policies, documentation, research, or product content Publishing, Embeds, and APIs Turn completed Dify applications into web apps, backend APIs, embeds, or reusable workflow services. Features: ๐น Publish apps as shareable web applications ๐น Generate API credentials and app-specific API documentation ๐น Embed published web apps through iframe, chat bubble widget, or JavaScript control ๐น Use Dify apps as backend AI services for custom interfaces Benefits: โ Gives teams multiple deployment paths from the same app โ Supports public demos, internal tools, and embedded website assistants โ Keeps API keys server-side for safer production integration โ Helps product and engineering teams ship AI features without rebuilding orchestration logic Monitoring, Logs, and Enterprise Controls Observe production behavior and manage access as AI applications scale. Features: ๐น Conversation logs for debugging, behavior analysis, and feedback collection ๐น Dashboard metrics including messages, active users, interactions, and token usage ๐น Integrations with observability platforms such as Langfuse, LangSmith, Arize, and Phoenix ๐น Enterprise web app access controls for workspace members, groups, and external SSO users Benefits: โ Helps teams identify regressions and improve prompts over time โ Supports cost and usage visibility for deployed apps โ Gives administrators clearer control over who can create, publish, and access apps โ Fits enterprise teams that need auditability around AI application behavior Category Details Main use Building, deploying, and monitoring agentic AI applications and workflows Best for Educators, enterprises, publishers, startups, and teams building RAG assistants, internal copilots, support bots, or workflow agents Inputs User text, files, knowledge bases, Notion pages, web pages, APIs, model-provider credentials, workflow variables Outputs Web apps, backend APIs, embedded assistants, workflow results, agent responses, logs, and knowledge-backed answers Differentiator Combines visual workflows, RAG, agents, model management, tools, publishing, and observability in one open-source platform Access Dify Cloud; self-hosted Community Edition via Docker Compose Integrations Model providers, plugins, tools, OpenAPI custom tools, MCP tools, knowledge APIs, Langfuse, LangSmith, Arize, Phoenix Security Workspace roles, plugin permissions, API keys, web app access controls, enterprise access levels Plan Free sandbox, paid workspace plans, and an education program for eligible students and educators; taxes excluded Limitation Some file upload, storage, document, rate, access-control, and support limits vary by cloud plan or self-hosted configuration From the Manufacturer: โBuild Production-Ready AI Agent.โ โDify is an open-source platform for building agentic workflows.โ โStart shipping powerful apps in minutes.โ โGive LLMs autonomous control over tools for complex task execution.โ โDify is free for students and educators.โ Visit the provider directly on our Affiliate Link below: https://dify.ai Dead link? 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- Default Title โ 9999.00 GBP โ In stock
AI Readiness
Good foundation, but some important product data is still missing.