All your benchmarks

If you’re looking to build with large language models, the options can feel overwhelming. To help you navigate this fast-moving landscape, we’ve put Flowise, Dify, and LangChain side by side. Each of these open-source projects offers a different approach to creating AI-powered applications—from drag-and-drop workflows to developer-centric frameworks. Whether you’re a seasoned engineer or just starting out with no-code tools, this benchmark will give you a clear view of how these platforms stack up in terms of features, integrations, community, and more. Dive in and discover which solution might best fit your next AI project.
Feature Flowise Dify LangChain
Category AI development platform AI development platform AI framework
Open Source yes (Apache License 2.0) yes (Dify Open Source License, Apache 2.0 based) yes (Open source)
Core Approach Visual low-code/no-code workflow builder and AI agent orchestration Visual workflow builder, agentic AI, RAG pipelines, model management, prompt IDE Code-first framework for LLM chains, agents, memory, and orchestration
Deployment Options Self-hosted, cloud, Docker, Render, OCI, Replit Cloud, self-hosted (Docker Compose, Kubernetes, AWS, Alibaba Cloud, Helm Charts, Terraform) Python: pip install; JS/TS: npm install; any infrastructure
Integration Ecosystem 100+ data sources, APIs, SDKs (Python, Typescript), vector DBs (Pinecone, Vectara), LangChain, OpenAI, HuggingFace, custom tools Langfuse, Milvus, TiDB, Mem0, Brave Search, Alibaba Cloud, plugin marketplace 600+ integrations: OpenAI, Anthropic, Google, Amazon Bedrock, HuggingFace, Pinecone, Weaviate, pgvector, Chroma, etc.
Main Features Multi-agent orchestration, chatbots, RAG, tool calling, human-in-the-loop, execution tracing, observability, security controls (RBAC, SSO), templates marketplace, embedded chat widgets Visual workflow builder, agentic AI, RAG pipeline, prompt IDE, model management, observability, API integration, knowledge base, plugin marketplace Chains, agents, prompt templates, memory, callbacks, vector store integration, orchestration (LangGraph), observability (LangSmith)
Observability & Monitoring Prometheus, OpenTelemetry, Langfuse integration, execution tracing Built-in observability, Langfuse integration Observability via LangSmith, callbacks, logging
Security & Role Management RBAC, SSO, encrypted credentials, secret managers, rate limiting Role management, multi-tenant, branding customization Not explicit; depends on implementation
Supported Programming Languages Node.js (backend), React (frontend), JS/Python SDK Not specified (platform-based, no-code/low-code focus) Python, JavaScript/TypeScript
LLM Model Support OpenAI, HuggingFace, Pinecone, Vectara, LangChain, custom models GPT, Mistral, Llama3, OpenAI API-compatible models, vLLM, local models OpenAI, Anthropic, Google, Amazon Bedrock, HuggingFace, and many more
No-Code/Low-Code Experience Drag & drop visual builder, low-code/no-code Visual builder, no-code/low-code, prompt IDE Code-first, developer-centric
API & SDK Access REST API, SDKs for Python & Typescript, embeddable widgets API access, plugin marketplace, integrations Python & JS/TS SDKs, API interfaces
Scalability Vertical/horizontal scaling, production-ready, supports cloud/on-prem, message queues & workers Supports cloud scaling, Kubernetes, multi-tenant, production-ready Scalable by design, depends on hosting & orchestration
Community & Adoption Active open-source community, 42,000+ GitHub stars (2025), trending, tutorials, Discord GitHub (70,000+ stars), Discord, Twitter/X, NVIDIA incubator, global developer base Largest GenAI developer community, 100k+ GitHub stars, 1M+ practitioners, global adoption
Documentation & Learning Comprehensive docs, API docs (Swagger UI), guides, community support Yes, documentation, plugin marketplace, community Comprehensive docs, tutorials, how-to guides, API references, community forum
Pricing Free tier, paid plans for teams and enterprise Free for students/educators, cloud/self-hosted plans, AWS Marketplace, free GPT-4 sandbox Free and open source
Main Use Cases Conversational assistants, chatbots, project bots, career coaching, HR/finance/customer support LLM apps Chatbots, document assistants, content generation, agent workflows, enterprise AI apps, data processing Chatbots, semantic search, RAG, question-answering, summarization, multi-agent systems, custom LLM-powered apps
Target Users Developers, teams, enterprises, no-code/low-code users, AI enthusiasts Developers, enterprise teams, students, educators Developers, enterprises, startups, AI engineers
Notable Users / Adoption Used by teams globally, enterprises, trending on GitHub Thousands of developers, enterprise teams, students, educators Klarna, LinkedIn, Uber, GitLab, Trellix, global enterprises and startups
Notable Achievements Acquired by Workday in 2025; powering enterprise AI agent development 2nd most popular LLM tool on GitHub, NVIDIA incubator member Pioneered standardized LLM interfaces, most popular GenAI framework
Website https://flowiseai.com Not specified https://www.langchain.com
GitHub https://github.com/FlowiseAI/Flowise Not specified https://github.com/hwchase17/langchain

Which One Should You Choose?

Still unsure? Here’s a quick guide to help you pick the best fit:

  • Choose Flowise if: You want a visual, low-code platform to rapidly build and deploy AI agents or chatbots, with strong security, team features, and a wide integration ecosystem. Ideal if you’re looking for a blend of no-code usability and developer extensibility—especially for enterprise or production environments.
  • Choose Dify if: You prefer a visual workflow builder focused on agentic AI, RAG pipelines, and model management, with cloud and self-hosting options. Dify stands out for students, educators, and teams who want a streamlined platform with a plugin marketplace and built-in observability.
  • Choose LangChain if: You’re a developer or technical team looking for maximum flexibility, code-first workflows, and the largest selection of integrations. LangChain is the go-to for building custom LLM applications, multi-agent systems, or anything that needs fine-grained control and community support.

Each tool is mature and open source, so your choice mostly comes down to your technical comfort level, preferred workflow (visual vs. code), and your target use case.

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