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HKUDS Auto Agent
Repository: github.com/HKUDS/AutoAgent · License: MIT · Paper: arXiv:2502.05957
AutoAgent is a zero-code agent framework developed by the Data Intelligence Institute of the University of Hong Kong. Describe your goals in natural language. The system then builds tools, agents, and multi-agent workflows without manual coding. It comes with an agent editor, workflow editor, and out-of-the-box research assistant mode.
This project is backed by research. The paper claims that the agent framework eliminates non-programmers and reports strong open source results on the GAIA benchmark. AutoAgent also serves as an open alternative to hosted Deep Research products. It works with most major LLMs such as DeepSeek, Grok, and Gemini and is run through a Docker-based CLI.
Perfect for: Researchers and practitioners who want to launch agents and deep research-style assistants from natural language using the papers and benchmarks behind the framework.
Mintplex Labs AnythingLLM
Repository: github.com/Mintplex-Labs/anything-llm · License: MIT · Site: anythingllm.com
AnythingLLM is an all-in-one self-hosted platform for RAGs, agents, and doc chat. Runs as a desktop app or a Docker container. This design targets non-technical users while maintaining a privacy-first, local-first attitude. The no-code Agent Flows builder handles agent logic without scripting.
Features include full MCP compatibility, multimodal input, and embeddable chat widgets. Supports over 30 LLM providers and multiple vector databases. Documents are stored within the environment, making it suitable for teams with strict data rules. The Y Combinator-backed project uses the permissive MIT license, making it easy for commercial and multi-tenant use.
Perfect for: Private documentation Q&A, agents, individuals and small teams who need simple deployment without piecing together components.
LangChain Open Agent Platform (OAP)
Repository: github.com/langchain-ai/open-agent-platform · License: MIT
Open Agent Platform is LangChain’s no-code, web-based interface for building and managing LangGraph agents. It’s aimed at non-developers, but is also extensible for engineers. Each agent is a configuration layered on top of a LangGraph graph, so power users can drop them into their code as needed.
Core features include first-class RAG via LangConnect, tool access via MCP Server, and multi-agent orchestration via Agent Supervisor. It has built-in authentication and access control and uses Supabase as the default provider. The platform ships with pre-built agents such as tool agents and supervisors that can be forked and customized. This is a newer and smaller project than the other entries here.
Perfect for: Teams that require a GUI layer on top of their agents are already investing in the LangChain and LangGraph ecosystem.
Sim (Sim Studio)
Repository: github.com/simstudioai/sim · License: Apache-2.0 · Site: sim.ai
Sim is a visual agent-first workflow builder with a Figma-like canvas. Configure your pipeline by dragging blocks such as Start, Agent, Function, API, Router, and Loop. AI CoPilot can help you assemble your workflows and even do so in plain English. Built-in tracing and live execution make debugging explicit.
This project is Apache-2.0 licensed and supported by YC. Connects to 1,000+ tools and all major LLM providers and supports MCP for custom integrations. You can run a hosted version or self-host using Docker. Recent efforts have expanded toward a broader “AI workspace” with conversational orchestration.
Perfect for: Teams that need a clean visual canvas, an AI co-pilot, and production traction under permissive licenses.
Langgenius Defy
Repository: github.com/langgenius/dify · License: Modified Apache-2.0 (SaaS only) · Site: dify.ai ·
Dify is a production-oriented LLM application platform. It combines visual workflow building, RAG pipelines, agent functionality, and LLMOps monitoring. Prompt IDEs allow you to compare model outputs side by side. Over 50 built-in tools cover search, image generation, and calculations.
Dify focuses on the entire lifecycle, from prototyping to observability. Document import handles formats such as PDF and PPT. The project has a large contributor base and is available on Dify Cloud or as self-hosted. Please pay attention to the license. This is a modified Apache-2.0 that restricts multi-tenant SaaS usage, in which case a commercial license is required. Please check the terms before reselling as a service.
Perfect for: Teams building and operating production LLM apps that need quick management, RAGs, agents, and runtime monitoring in one place.
FlowiseAI Flowise
Repository: github.com/FlowiseAI/Flowise · License: Apache-2.0 Core · Site: flowiseai.com
Flowise is a drag-and-drop builder for LLM apps built on LangChain. Assemble chatbots, RAG pipelines, and multi-agent systems on canvas. Three builder modes support increasing levels of complexity: Assistant, Chatflow, and Agentflow. Ready-made templates shorten the path from idea to prototype.
Flowise is RAG-enabled and integrated with over 100 tools, vector databases, and memory modules. Enterprise features include RBAC, audit logging, observability, and SSO/SAML. You can embed the assistant through the SDK or widgets. The core is Apache-2.0, but the files in that enterprise directory are subject to a separate commercial license, so check the features you need. Deployments are performed locally, on Docker, on major clouds, or through the managed Flowise Cloud.
Perfect for: Developers who want to minimize barriers to working with LLM apps and easily migrate to an embeddable, production-grade assistant.
langflow
Repository: github.com/langflow-ai/langflow · License: MIT · Maintained by DataStax
Langflow is a visual platform for building AI agents and workflows. You can expose all your flows as APIs or MCP servers and integrate them into apps on any framework. A drag-and-drop editor speeds up prototyping, and full access to Python source allows deep customization.
Features include multi-agent orchestration and integration with observability tools such as LangSmith and LangFuse. It supports all major LLMs, including local models, and offers desktop apps for Windows and macOS. The permissive MIT license simplifies commercial and multi-tenant deployments. Treat this as low code. Visual by default, but code friendly for advanced logic.
Perfect for: Developers who need a flexible, code-extensible visual interface for building agents and workflows with powerful observability options.
Infiniflow RAGFlow
Repository: github.com/infiniflow/ragflow · License: Apache-2.0 · Demo: demo.ragflow.io
RAGFlow is a RAG engine built on a deep understanding of documents. Its DeepDoc layer parses layouts, tables, diagrams, and scanned PDFs before anything reaches the vector store. This depth of analysis is a key differentiator over cumbersome corporate documents. Recent versions have merged RAG and agent functionality to provide a more powerful context layer.
Features include GraphRAG-style knowledge extraction, chunk visualization for human review, and reasoned answers with traceable citations. Supports Word, Slides, Excel, images, and web pages. The MCP server and Python SDK extend it, and deployment is performed through Docker. Web UI handles knowledge bases without code, but requires more infrastructure to set up. Apache-2.0 license is available for commercial use.
Perfect for: Teams whose accuracy depends on correctly parsing complex documents and who emphasize citations and explainable search.
n8n
Repository: github.com/n8n-io/n8n · License: Sustainable Use License (Faircode) · Site: n8n.io
n8n is a workflow automation platform with native AI. Combine visual builder with optional inline code. Bridge Zapier-style automation and agent workflows with 400+ integrations and LangChain-based AI nodes. You can add JavaScript, Python, and npm packages within your visual flow.
Self-hosting allows for complete data control, including air-gapped deployments and SSO. A large template library speeds up common patterns. Please pay attention to the license. n8n uses the “Faircode” sustainable use license. It is not OSI approved open source and is available from source with commercial restrictions. Some enterprise files require separate licenses. Since custom code is a first-class option, it’s best classified as low-code.
Perfect for: Teams automates a wide range of workflows that require AI and agent steps with strong integration coverage and self-hosting.
Love Ring FastGPT
Repository: github.com/labring/FastGPT · License: Apache-2.0 (additional conditions apply) · Site: fastgpt.io
FastGPT is a knowledge base platform built on LLM. It provides out-of-the-box data processing, RAG acquisition, and visual workflow orchestration through flow modules. A useful feature automatically generates question and answer pairs from your documents, improving search over simple chunking. Get up and running in no time with a Docker one-liner.
The project reports over 500,000 users and supports many document formats, as well as URL reading and CSV import. Exposes an API for embedding into applications. Please check the license carefully. Apache-2.0 has additional conditions that allow the use of commercial backends but restrict it from operating as a multi-tenant SaaS without permission. Although popular in the Chinese developer ecosystem, it has not received much coverage in English-language media.
Perfect for: Teams building documentation-based assistants that require powerful knowledge base tools and self-hosted quick startup.
simple comparison
| platform | main job | interface style | License (confirmed) |
|---|---|---|---|
| auto agent | Agent (zero code) | Natural language + editor | Massachusetts Institute of Technology |
| Anything LLM | RAG + Agent + Documentation | Desktop/Docker UI, no-code flow | Massachusetts Institute of Technology |
| open agent platform | Agents on LangGraph | Code-free web UI | Massachusetts Institute of Technology |
| sim | Agent workflow | Visual Canvas + AI Copilot | apache-2.0 |
| Defy | LLM app in production | Visual workflow + LLMOps | Fixed Apache-2.0* |
| Flowwise | LLM App + RAG + Agent | drag and drop canvas | Apache-2.0 core* |
| langflow | Agent + Workflow | Visual, code extensible | Massachusetts Institute of Technology |
| rug flow | RAG (detailed documentation) | Web UI + SDK | apache-2.0 |
| n8n | Automation + AI/Agent | Visual + inline code | Sustainable Use (Fair Code)* |
| Fast GPT | Knowledge Base + RAG | visual flow module | Apache-2.0 + conditions* |
*Commercial or SaaS restrictions apply. Please check license terms before reselling or multi-tenant hosting.
How to choose
To build pure agents from plain English, start with AutoAgent or Open Agent Platform. For an all-in-one private RAG and agent app, AnythingLLM is the fastest path. RAGFlow’s analysis is a differentiator when it comes to documenting complex files with greater accuracy.
When it comes to building visual workflows, Flowise has the lowest barrier, while Langflow and Sim add more power and code extensibility. When it comes to production operations and monitoring, Dify covers the entire lifecycle. For extensive automation that requires an AI step, n8n covers the widest range of integrations. FastGPT provides powerful out-of-the-box tools for knowledge base assistants.
Important points
- The stack has matured, and searches, agents, and workflows are now shipped as visual or plain English tools.
- “No code” is a spectrum. Some platforms reward custom code and are better described as low-code.
- Licenses vary. AutoAgent, AnythingLLM, Open Agent Platform, and Langflow are permissive (MIT). Sim and RAGFlow are Apache-2.0. Dify, Flowise enterprise, n8n, and FastGPT have limitations.
- Choose by job: RAGFlow for hard documentation, Flowise for speed, Dify for production, and n8n for automation.
- Self-hosting is common to all 10 types and maintains control of the data in your environment.
Michal Sutter is a data science expert with a master’s degree in data science from the University of Padova. With a strong foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
