[{"data":1,"prerenderedAt":86},["ShallowReactive",2],{"seo-verification":3,"marketplace-app-en-anything-llm":6},{"google":4,"bing":5},"EycwPY2XMyTkVzas3n1ygeNJFGAH513qrMjfDljzsMQ","",{"slug":7,"name":8,"description":9,"phase":10,"docsUrl":11,"logo":12,"github":13,"tagline":14,"longDescription":15,"features":16,"useCases":25,"steps":35,"faq":51,"specs":67,"compatibleOs":77,"relatedApps":78,"relatedPosts":79,"category":80},"anything-llm","AnythingLLM","Open-source AI workspace with built-in RAG, AI agents, and multi-model chat. Connect your own documents, use any LLM — hosted privately on your VPS.",1,"https:\u002F\u002Fservorbit.com\u002Fblog\u002Fself-host-anythingllm-vps","https:\u002F\u002Fcdn.jsdelivr.net\u002Fgh\u002Fselfhst\u002Ficons\u002Fsvg\u002Fanythingllm.svg","https:\u002F\u002Fgithub.com\u002FMintplex-Labs\u002Fanything-llm","Private AI workspace with RAG and agents — your documents, your models, your server.","AnythingLLM is an all-in-one open-source AI application that turns your documents into a private, queryable knowledge base. It ships as a single Docker image that runs on any 2 GB VPS and connects to whichever LLM you already use — Ollama (local), OpenAI, Anthropic, Mistral, LiteLLM, and more.\n\nThe built-in RAG engine ingests PDFs, Word docs, plain text, code repositories, YouTube transcripts, Notion pages, and Confluence spaces, then chunks, embeds, and indexes them into an integrated LanceDB vector store. Each workspace is isolated: different documents, different LLM, different system prompt. Ask a question and AnythingLLM retrieves the relevant chunks, passes them as context to your model, and returns a cited answer.\n\nAnythingLLM also ships a no-code AI agent builder: string together web search, code execution, document retrieval, and external API calls without writing a line of code. User management with roles (admin, manager, default) makes it suitable for team deployments. MIT-licensed and 62k+ GitHub stars as of July 2026.",[17,18,19,20,21,22,23,24],"Built-in RAG: ingest PDFs, Word docs, TXT, code repos, YouTube, Confluence, Notion, and more","Connect any LLM: Ollama, OpenAI, Anthropic, Mistral, Azure OpenAI, LiteLLM, Groq, and local models","No-code AI agent builder with web search, code execution, and API call tools","Multi-workspace isolation: separate documents, LLM, and system prompt per workspace","Multi-user with roles: admin, manager, default user — team-ready out of the box","REST API: embed AnythingLLM into your own products via a documented HTTP API","Pluggable vector store: built-in LanceDB (zero setup) or connect Qdrant, Pinecone, Chroma, Weaviate","Zero telemetry, MIT licence, fully air-gap capable",[26,29,32],{"title":27,"body":28},"Internal knowledge base","Upload your SOPs, contracts, and technical docs. Your team asks questions in natural language and gets accurate, cited answers — without sending any data to OpenAI.",{"title":30,"body":31},"Developer assistant","Ingest your codebase, architecture docs, and API references. Use AnythingLLM as a private coding assistant that knows your stack and answers in context.",{"title":33,"body":34},"Customer-facing AI","Expose AnythingLLM's REST API to power an AI chat widget on your product, trained exclusively on your own documentation.",[36,39,42,45,48],{"title":37,"body":38},"Start the AnythingLLM container","Connect via SSH and run: `docker run -d --restart=always -p 3001:3001 --cap-add SYS_ADMIN -v anythingllm-storage:\u002Fapp\u002Fserver\u002Fstorage -e STORAGE_DIR=\u002Fapp\u002Fserver\u002Fstorage --name anythingllm mintplexlabs\u002Fanythingllm:latest`. The image (~700 MB) downloads once and starts in a few seconds. The named volume `anythingllm-storage` persists all documents, settings, and the SQLite database.",{"title":40,"body":41},"Create your admin account","Open `http:\u002F\u002Fyour-vps-ip:3001` in your browser. AnythingLLM detects a fresh install and walks you through creating the first admin account. This also sets the JWT secret used to sign all user sessions.",{"title":43,"body":44},"Connect an LLM provider","Go to Settings → LLM Provider. Choose Ollama (if running on the same VPS), OpenAI (enter your API key), or any supported provider. AnythingLLM tests the connection before saving. You can also point it at LiteLLM to switch providers without touching your workspace settings.",{"title":46,"body":47},"Create a workspace and upload documents","Click New Workspace, give it a name (e.g. \"Product Docs\"), then drag-and-drop PDFs or paste a URL. AnythingLLM chunks the content, generates embeddings, and stores them in LanceDB. Ask a question — the system retrieves the relevant chunks and passes them to your LLM.",{"title":49,"body":50},"Optional: add HTTPS with Caddy","Create a Caddyfile: `ai.yourdomain.com { reverse_proxy localhost:3001 }`. Run `caddy run`. Caddy automatically issues a Let's Encrypt certificate. AnythingLLM now serves over HTTPS on your custom domain.",[52,55,58,61,64],{"q":53,"a":54},"What is AnythingLLM?","AnythingLLM is a self-hosted AI workspace that combines document RAG, multi-model chat, and AI agents in a single Docker container. You upload your documents, connect any LLM provider, and interact with your data in natural language — everything stays on your server.",{"q":56,"a":57},"What LLMs does AnythingLLM support?","Ollama (local models), OpenAI, Anthropic, Azure OpenAI, Mistral, Google Gemini, LiteLLM, Cohere, TogetherAI, Groq, Llama.cpp, and more. You can switch providers per workspace without losing your documents.",{"q":59,"a":60},"What is the minimum RAM?","AnythingLLM itself needs ~512 MB RAM. For document embeddings and inference, you need an LLM: if you use Ollama locally with a 7B model, plan for 8 GB total. If you use an external API (OpenAI, Anthropic), 2 GB RAM is sufficient.",{"q":62,"a":63},"Can it handle large document collections?","Yes. AnythingLLM uses LanceDB for its built-in vector store, which scales to millions of vectors on a single VPS. For very large collections you can swap the vector store to Qdrant, Pinecone, or Weaviate in Settings → Vector DB.",{"q":65,"a":66},"Is AnythingLLM free?","The self-hosted version is MIT-licensed and completely free. Mintplex Labs also offers a cloud-hosted plan and a desktop app, but everything you need for a private deployment is in the open-source Docker image.",{"ram":68,"cpu":69,"stack":70,"port":76},"512 MB (2 GB+ with local Ollama)","1 vCPU",[71,72,73,74,75],"Docker","Node.js","Python","SQLite","LanceDB","3001",[],[],[],{"key":81,"slug":81,"name":82,"objective":83,"icon":84,"color":85},"ia","Artificial Intelligence","Build, host and run AI solutions.","ai","text-purple-400 bg-purple-500\u002F10",1785714045960]