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LLM app platform🏆Editor's Choice
D

Dify

Dify is an open-source LLM app development platform that combines a visual workflow builder, RAG pipelines, agent tools, and an LLMOps backbone.

Starting atFree
Visit Dify →
💡

In Plain English

Dify is an open-source LLM app development platform that combines a visual workflow builder, RAG pipelines, agent tools, and an LLMOps backbone.

OverviewFeaturesPricingGetting StartedUse CasesIntegrationsLimitationsFAQSecurityAlternatives

Overview

Dify is an open-source LLM application platform built around the idea that most production AI apps need more than a chat completion call. A typical Dify app is a visual workflow: input variables → retrieval over a knowledge base → tool calls → LLM step → output. The platform supports OpenAI, Anthropic, Google, AWS Bedrock, Azure, Mistral, local Ollama, and dozens of other model providers behind a single abstraction, so teams can swap models without rewriting the pipeline. Dify ships an agent runtime with function calling, scheduled jobs, a marketplace of plugins and connectors, an embedded RAG engine with parent-child chunking and rerankers, prompt versioning, observability dashboards, and team collaboration. The Cloud version offers Sandbox (Free), Professional at $59/month, and Team at $159/month, with Premium at $590/month for higher-volume orgs and a custom Enterprise tier. The self-hosted Community Edition remains free under the Dify Open Source License, which is the path most teams take when data residency or per-message costs matter.

🦞

Using with OpenClaw

▼

Integrate Dify with OpenClaw through available APIs or create custom skills for specific workflows and automation tasks.

Use Case Example:

Extend OpenClaw's capabilities by connecting to Dify for specialized functionality and data processing.

Learn about OpenClaw →
🎨

Vibe Coding Friendly?

▼
Difficulty:beginner
No-Code Friendly ✨

Standard web service with documented APIs suitable for vibe coding approaches.

Learn about Vibe Coding →

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Editorial Review

Strong category fit for teams building real LLM products rather than one-off prompts. The main caution: Pricing could not be verified from the fetched pricing HTML, so buyers should confirm current plan costs before budgeting.

Key Features

Visual Workflow Studio: drag-and-drop canvas with nodes for LLM calls, knowledge retrieval, conditional branching, code execution, HTTP requests, and tool use, with live debugging and step-level inspection+
Multi-Model Gateway: unified abstraction over hundreds of proprietary and open-source models including OpenAI, Anthropic, Gemini, Mistral, DeepSeek, Qwen, Llama, plus local runtimes (Ollama, vLLM, Xinference)+
Built-in RAG Engine: end-to-end document ingestion, automatic and manual chunking, multiple embedding models, hybrid (vector + keyword) retrieval, and reranking with attachable knowledge bases+
Agent Framework with Tool Use: native function calling, ReAct-style agents, and a plugin marketplace for connecting to external APIs, databases, code interpreters, and SaaS platforms+
Prompt IDE and Versioning: side-by-side prompt comparison, A/B testing, version history, and template management to iterate on prompts as first-class artifacts+
LLMOps Observability: full request and response logging, token usage analytics, latency tracking, user feedback capture, and annotation queues for continuous quality improvement+
Application API and Embeds: every Dify app is auto-exposed as a REST API with SDKs and embeddable web chat widgets, plus iframe and JS snippet integration for product surfaces+
Deployment Flexibility: managed cloud, single-node Docker Compose, and production-grade Kubernetes Helm charts for self-hosted environments with full data sovereignty+

Pricing Plans

Sandbox

Free

    Professional

    $59/month

      Team

      $159/month

        Premium

        $590/month

          Enterprise / Self-hosted

          Custom (or free OSS)

            See Full Pricing →Free vs Paid →Is it worth it? →

            Ready to get started with Dify?

            View Pricing Options →

            Getting Started with Dify

            1. 1Define your first Dify use case and success metric.
            2. 2Connect a foundation model and configure credentials.
            3. 3Attach retrieval/tools and set guardrails for execution.
            4. 4Run evaluation datasets to benchmark quality and latency.
            5. 5Deploy with monitoring, alerts, and iterative improvement loops.
            Ready to start? Try Dify →

            Best Use Cases

            🎯

            Internal knowledge assistants over Confluence, Notion, SharePoint, or PDFs

            ⚡

            Customer support automations that combine RAG with structured tool calls

            🔧

            Content workflows: extraction, summarization, classification, multi-step rewriting pipelines

            🚀

            Prototyping multi-model apps where the team wants to compare providers without rewriting code

            Integration Ecosystem

            25 integrations

            Dify works with these platforms and services:

            🧠 LLM Providers
            OpenAIAnthropicGoogleCohereMistralOllama
            📊 Vector Databases
            PineconeWeaviateQdrantChromaMilvuspgvector
            ☁️ Cloud Platforms
            AWSGCPAzure
            💬 Communication
            SlackEmail
            🗄️ Databases
            PostgreSQLMySQL
            📈 Monitoring
            Langfuse
            💾 Storage
            S3
            ⚡ Code Execution
            Docker
            🔗 Other
            GitHubNotionZapier
            View full Integration Matrix →

            Limitations & What It Can't Do

            We believe in transparent reviews. Here's what Dify doesn't handle well:

            • ⚠Dify's visual builder, while powerful, can struggle to express the most complex stateful agent loops cleanly—teams pushing the frontier of multi-agent orchestration often hit ceilings that pure-code frameworks like LangGraph or AutoGen handle more naturally. Self-hosting demands real DevOps capability: running Postgres, Redis, a vector store, and the Dify services in production requires Kubernetes or similar expertise. The cloud tier's request and document quotas can become limiting for high-volume RAG workloads, and certain enterprise-grade features (SSO, granular RBAC, audit trails, dedicated support) are reserved for paid or enterprise plans. Finally, because Dify evolves rapidly, breaking changes between minor versions occasionally surface, so production deployments should pin versions and test upgrades carefully.

            Pros & Cons

            ✓ Pros

            • ✓Open-source self-hosted path keeps long-term costs and data residency under your control
            • ✓Model-agnostic gateway lets you swap providers without rewriting workflows
            • ✓Strong built-in RAG with rerankers, metadata filters, and multiple chunking strategies
            • ✓Production-ready observability: traces, prompt versioning, annotations, cost tracking
            • ✓Active plugin marketplace with growing MCP-compatible integrations

            ✗ Cons

            • ✗Complex agent logic with many branches is harder to express than in code-first frameworks
            • ✗Cloud message credits get expensive fast at production volume — most heavy users self-host
            • ✗Plugin ecosystem is smaller than n8n or Zapier; niche integrations often need custom work
            • ✗Visual editor learning curve is real for non-technical users despite the no-code framing
            • ✗Self-hosting requires Docker, Postgres, Redis, and a vector DB — not a zero-ops deployment

            Frequently Asked Questions

            Is Dify free and open source?+

            Yes. Dify is released under an open-source license and can be self-hosted at no cost using Docker Compose or Kubernetes. The team also offers a managed cloud service with paid tiers for users who prefer not to manage infrastructure, plus enterprise plans with SSO, advanced RBAC, and SLA support.

            Which LLMs and model providers does Dify support?+

            Dify is model-agnostic and supports hundreds of providers including OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, Mistral, Cohere, DeepSeek, Qwen, and Llama. It also integrates with locally hosted runtimes such as Ollama, vLLM, LocalAI, and Xinference, allowing fully on-premise deployments.

            How does Dify compare to LangChain or LangGraph?+

            LangChain and LangGraph are code-first Python libraries for building LLM applications, while Dify is a complete platform that wraps similar capabilities behind a visual builder, hosted UI, RAG engine, and observability layer. Teams that want full programmatic control may prefer LangGraph; teams that want a deployable product with less boilerplate typically prefer Dify.

            Can Dify handle Retrieval-Augmented Generation (RAG)?+

            Yes. Dify includes a built-in knowledge base feature that ingests PDFs, Word documents, web pages, and structured data, then handles chunking, embedding, vector storage, hybrid search, and reranking. Knowledge bases can be attached to any chatbot, agent, or workflow without external infrastructure.

            Is Dify suitable for production deployments?+

            Yes. Dify exposes every application as a REST API, supports horizontal scaling on Kubernetes, and includes logging, prompt versioning, and analytics for production monitoring. Many companies run customer-facing chatbots and internal copilots on Dify, though teams with strict compliance needs typically choose self-hosted or enterprise tiers.

            🔒 Security & Compliance

            —
            SOC2
            Unknown
            —
            GDPR
            Unknown
            —
            HIPAA
            Unknown
            ✅
            SSO
            Yes
            ✅
            Self-Hosted
            Yes
            ✅
            On-Prem
            Yes
            ✅
            RBAC
            Yes
            ✅
            Audit Log
            Yes
            ✅
            API Key Auth
            Yes
            ✅
            Open Source
            Yes
            ✅
            Encryption at Rest
            Yes
            ✅
            Encryption in Transit
            Yes
            Data Retention: configurable
            📋 Privacy Policy →
            🦞

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            What's New in 2026

            Through late 2025 and into 2026, Dify has expanded its agent capabilities with deeper multi-agent orchestration, parallel branch execution in workflows, and an enlarged plugin marketplace covering more SaaS connectors and code-execution sandboxes. The platform has added support for the latest reasoning models from major providers (including Claude 4 family, GPT-5-class models, Gemini 2.x, and DeepSeek V3/R1), improved structured output and JSON-mode handling, and introduced richer evaluation and dataset tooling for systematic prompt and agent testing. RAG has been upgraded with stronger hybrid retrieval, parent-child chunking strategies, and broader file-format support. Deployment ergonomics have also improved with cleaner Helm charts and more granular role-based access control on Team and Enterprise tiers.

            Alternatives to Dify

            CrewAI

            AI Agents

            Open-source Python framework for orchestrating role-playing, autonomous AI agents that collaborate as a 'crew' to complete complex tasks.

            Microsoft AutoGen

            Multi-Agent Builders

            Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.

            LangGraph

            AI agent framework

            LangGraph is LangChain's open-source framework for building stateful, durable, multi-agent workflows in Python and JavaScript with graph-based control flow.

            Microsoft Semantic Kernel

            AI Agent Builders

            SDK for building AI agents with planners, memory, and connectors. - Enhanced AI-powered platform providing advanced capabilities for modern development and business workflows. Features comprehensive tooling, integrations, and scalable architecture designed for professional teams and enterprise environments.

            View All Alternatives & Detailed Comparison →

            User Reviews

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            Quick Info

            Category

            LLM app platform

            Website

            dify.ai/
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