AnythingLLM

AnythingLLM is the all-in-one open-source AI application from Mintplex Labs that turns any laptop or self-hosted server into a private ChatGPT — chat with your documents, run no-code AI agents, plug in any LLM (cloud or local), and ship a multi-user knowledge assistant in minutes. We rate it 88/100 — the right pick for privacy-focused teams, solo developers and self-hosters who want a polished RAG + agents stack under MIT, and the wrong pick only if you want a fully managed, no-API-key SaaS with consumer-grade polish on day one.

What is AnythingLLM?

AnythingLLM was created by Tim Carambat and the Mintplex Labs team in June 2023. The first commit landed on June 4, 2023, and the project has since become one of the most-starred open-source AI apps on GitHub: as of April 27, 2026, the repo at Mintplex-Labs/anything-llm sits at 59,074 stars, 6,381 forks and 191 contributors, with the latest release v1.12.1 shipped on April 22, 2026.

The pitch is simple: install AnythingLLM Desktop on Mac, Windows or Linux — or run the Docker image on a homelab, NAS or VPS — point it at your documents, pick any LLM provider (OpenAI, Anthropic, Ollama, LM Studio, OpenRouter, Groq, Gemini, Bedrock, Azure, Mistral, DeepSeek, and 25+ others), and you have a private chatbot with RAG, agents and a built-in API. Unlike ChatGPT or Claude, your documents never leave your machine unless you explicitly choose a cloud LLM provider, and the entire codebase is verifiably MIT-licensed.

Key Features of AnythingLLM

• Bring your own LLM — any of them: Native support for OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Azure OpenAI, Groq, Mistral, DeepSeek, Cohere, OpenRouter, Ollama, LM Studio, LocalAI, llama.cpp, Together AI, Fireworks, Perplexity, Hugging Face, NVIDIA NIM, KoboldCPP, LiteLLM and more — over 25 chat providers in one app.
• RAG out of the box: Built-in document ingestion for PDF, DOCX, TXT, EPUB, HTML, MD, audio (Whisper) and websites; vector storage via the embedded LanceDB by default, with first-class support for Pinecone, Chroma, Qdrant, Weaviate, Milvus, Astra and pgvector. Source citations are returned in every chat response.
• No-code AI Agent builder + Agent Flows: Drag-and-drop workflow builder with web browsing, web scraping, SQL queries, document summarisation, and custom skills. As of v1.12 it ships full MCP (Model Context Protocol) compatibility, so any MCP server — GitHub, Slack, Notion, Filesystem, Postgres — plugs in as an agent tool.
• Multi-user, permissioned workspaces (Docker only): Role-based access control with admin, manager and default-user roles, per-workspace document scoping, and per-user usage tracking — no SaaS account required.
• Embeddable chat widget: Drop a one-line script onto any website to expose a workspace as a public chatbot, with theming, suggested prompts and rate limits configurable from the admin UI.
• Built-in developer API: Every workspace exposes REST endpoints (chat, document upload, agent invocation) authenticated with API keys — the same surface used by the official Chrome and mobile clients.
• Local-first by default: The Desktop app bundles llama.cpp + LanceDB + a local Whisper transcriber so it works fully offline with zero account signup, zero telemetry by default, and zero outbound network calls if you pick a local LLM.
• v1.12 highlights (April 2026): Intelligent Skill Selection (claims up to 80% token reduction by routing tools dynamically), expanded MCP support, better multi-modal handling for images and audio, and meaningfully faster vector search on large libraries.

What Users Say About AnythingLLM

Sentiment across r/LocalLLaMA, r/selfhosted and r/Ollama is broadly positive — the most upvoted threads describe AnythingLLM as the easiest way to get a usable RAG chat over personal documents without writing code, and frequently rank it ahead of LM Studio's built-in chat for document-heavy use cases. On Hacker News, commenters consistently praise the breadth of LLM provider integrations and the MIT license. Recurring complaints are real and worth knowing: large PDFs occasionally fail to chunk cleanly on the Desktop build (improving with each release), the Desktop app does not support multi-user (you must run the Docker server for that), the default LanceDB index can grow large on heavy libraries and benefits from periodic re-embedding, and the documentation occasionally lags new features by a release or two. Several users also note that hosted Cloud pricing is steeper than self-hosting — which is by design, since the Docker image is free.

AnythingLLM Pricing

AnythingLLM has three deployment modes: Desktop (free), Docker self-hosted (free), and managed Hosted Cloud. The MIT license applies to both Desktop and Docker editions equally, with no feature paywalls and no commercial-revenue threshold.

Plan	Price	Includes
Desktop	$0 forever	Mac/Windows/Linux app, all LLM providers, RAG, agents, built-in API, local-only by default. Single-user.
Docker (self-hosted)	$0 forever	Everything in Desktop plus multi-user permissions, embeddable chat widget, admin controls, API keys.
Cloud Basic	$50 / month	Private hosted instance, custom subdomain, <5 users, <100 documents, RAG & agents, BYO LLM API key.
Cloud Pro	$99 / month	Larger private instance, more users and documents, 72-hour support SLA, faster compute.
Enterprise	Contact	On-premise install, custom domain, custom SLA, white-glove integration.

You still pay LLM provider costs separately when using cloud models — AnythingLLM does not bundle inference. The cheapest realistic setup is Desktop + Ollama + a local model, which costs $0 indefinitely.

Who Should Use AnythingLLM?

Best for: Privacy-conscious solo professionals (lawyers, researchers, analysts) who need a private "chat with my files" tool without sending documents to OpenAI; small teams that want a self-hosted alternative to ChatGPT Team or Notion AI; developers prototyping RAG and agent applications who want a polished UI before building their own; and anyone running a homelab who already has Ollama or LM Studio and wants a real workspace UI on top.

Not ideal for: Non-technical individuals who want a fully managed SaaS with no setup or API-key juggling — ChatGPT Plus or Claude Pro are smoother out of the box; and large enterprises that need SOC 2 + SSO + audit logs as table stakes — those land in the Cloud Pro / Enterprise tiers, where the pricing premium starts to compete with managed alternatives.

Pros and Cons

Pros:

• MIT-licensed, fully open source — no premium-feature paywall on Desktop or Docker.
• Broadest LLM provider matrix in the open-source RAG space — 25+ chat providers and a dozen vector stores.
• Native MCP support means you inherit the entire MCP server ecosystem (GitHub, Slack, Postgres, Filesystem) without writing glue code.
• Real local-first mode — with Ollama and LanceDB, your documents never touch the network.
• Active development: dozens of contributors, a release roughly every 2–4 weeks, and v1.12 shipped April 22, 2026.

Cons:

• Multi-user only on Docker — the Desktop app is single-user by design.
• Document chunking on very large or complex PDFs is still imperfect; users on Reddit recommend pre-splitting 200+ page books.
• Hosted Cloud pricing ($50/$99) is steeper than self-hosting and you still pay LLM API costs on top.
• The agent builder is powerful but the UI is dense — expect a 30-minute learning curve.

Alternatives to AnythingLLM

The closest open-source alternatives are Open WebUI — a great Ollama-first ChatGPT clone, but with weaker document-RAG ergonomics — and Dify, which is more focused on building production AI apps and APIs than on personal document chat. Jan is a strong Desktop-only competitor for purely local chat but lacks AnythingLLM's multi-user mode, agents and MCP support. On the closed-source side, NotebookLM is friendlier for casual users, while ChatGPT Team and Claude Projects offer cloud convenience at the cost of privacy.

Verdict: Is AnythingLLM Worth It?

Yes — if you want a private, all-in-one ChatGPT replacement that you control end-to-end, AnythingLLM is the strongest open-source pick on the market in 2026. The combination of MIT licensing, 25+ LLM providers, native MCP, agents, multi-user Docker mode and a polished Desktop client is genuinely unmatched in the self-hosted RAG category. Stick with Desktop or Docker for the best ROI; only consider Cloud Basic/Pro if you cannot run a server and need a managed instance with a custom subdomain. Our 88/100 reflects an excellent product with minor rough edges in PDF handling and a steeper-than-average learning curve on agents.

Frequently Asked Questions

Is AnythingLLM free?

Yes. AnythingLLM Desktop (Mac/Windows/Linux) and the self-hosted Docker image are 100% free under an MIT license with no feature paywalls. Hosted Cloud plans start at $50/month for the Basic tier and $99/month for Pro; Enterprise is contact-based. You also pay separately for any cloud LLM API you connect (OpenAI, Anthropic, etc.).

Is AnythingLLM open source?

Yes — the entire repo at github.com/Mintplex-Labs/anything-llm is MIT-licensed, with 59,000+ stars and 191+ contributors as of April 2026.

What platforms does AnythingLLM support?

AnythingLLM Desktop runs on macOS (Apple Silicon and Intel), Windows 10/11 and Linux. The Docker image runs anywhere Docker runs — Linux servers, NAS appliances (Synology, TrueNAS, Unraid), Proxmox, and any major cloud. There are also official mobile companion apps and a Chrome extension.

Which LLMs does AnythingLLM work with?

Over 25 providers, including OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Azure OpenAI, Mistral, Cohere, DeepSeek, Groq, OpenRouter, Together, Fireworks, Perplexity, Hugging Face, plus all local options — Ollama, LM Studio, LocalAI, llama.cpp and KoboldCPP.

How does AnythingLLM compare to Open WebUI?

Open WebUI is leaner and Ollama-first — the better choice for a pure ChatGPT-style local chat UI. AnythingLLM is broader: it adds workspaces with isolated document libraries, a no-code agent builder, native MCP support, an embeddable chat widget for websites, and a built-in developer API. If your main use case is "chat with my documents" or "build agents over my data," AnythingLLM wins; for pure local chat, Open WebUI is faster to set up.

Does AnythingLLM support MCP?

Yes. As of v1.12 (April 2026), AnythingLLM is fully MCP-compatible, meaning any Model Context Protocol server — including the official GitHub, Slack, Filesystem and Postgres servers — plugs into AnythingLLM agents as a tool with no custom code.