5 Open-Source No-Code Platforms for LLMs, RAG, and AI Agents

5 open-source no-code platforms for building LLMs, RAG, and AI agents

1. AutoAgent — a fully automated framework with a zero code entry barrier. It’s enough to describe a high-level goal in natural language, and the system will independently handle planning, task decomposition, and execution. It turns a prompt into a working agent system.

GitHub repository: HKUDS/AutoAgentgithub.com/HKUDS/AutoAgent

2. AnythingLLM. One of the best all-in-one solutions for building internal tools. It combines RAG, agent workflows, and document management in a single self-hosted space. The platform is privacy-focused and designed for both technical and non-technical teams that need to build tools based on their own data.

GitHub repository: Mintplex-Labs/anything-llmgithub.com/Mintplex-Labs/anything-llm

3. LangChain Open Agent Platform. A specialized UI built on top of LangGraph. Instead of hiding the internal logic, it makes the agent’s execution flow visual through nodes and connections. This enables fine-grained control over routing, loops, and coordination of multiple agents without needing to write the graph code itself.

GitHub repository: langchain-ai/open-agent-platformgithub.com/langchain-ai/open-agent-platform

4. Sim. A visual workflow builder with a built-in AI Copilot. You design agent pipelines as executable graphs, while the built-in AI can generate or modify these workflows for you. The platform also provides detailed execution tracing, which greatly simplifies debugging complex chains.

GitHub repository: simstudioai/simgithub.com/simstudioai/sim

5. Dify. A production-ready platform with an emphasis on observability. It supports prompt management, complex RAG pipelines, and agent logic, and also provides runtime monitoring. If you’re deploying a solution for real users, this is one of the standard options.

GitHub repository: langgenius/difygithub.com/langgenius/dify