AnythingLLM
Open-source, self-hosted AI chat that turns your own files into a private, searchable assistant
- Category
- Chatbots & Assistants
- Pricing
- Free forever if self-hosted (MIT license, desktop app or Docker, no token/document limits). AnythingLLM Cloud (hosted for you) starts at $50/mo for the Basic plan (you supply your own LLM API key), $99/mo for Pro (higher usage tier, 72-hour support SLA), and custom-quoted Enterprise pricing for on-prem/SSO/RBAC deployments. No free trial was listed for the cloud plans as of this check.
- Best for
- Best suited to developers, IT-savvy small teams, researchers, and privacy-focused individuals who want a document-grounded chat assistant they fully control, rather than a plug-and-play hosted chatbot.
- Official site
- anythingllm.com
- Last updated
- August 2026
AnythingLLM occupies a different corner of the "AI chatbot" space than most tools in this category. It isn't primarily a place to talk to a model — it's infrastructure for building a private knowledge assistant out of your own files, your own choice of LLM, and (optionally) your own server. The free desktop app and the self-hosted Docker version are the real product: MIT-licensed, no account required, and genuinely capable of running fully offline if you pair it with a local model via Ollama or llama.cpp. The paid AnythingLLM Cloud tier ($50–99/mo) is a convenience layer for people who want the same software without managing a server themselves, not the core value proposition.
That makes it a poor match for Poe, which solves a completely different problem: Poe's whole pitch is paying once to access many different hosted frontier models (GPT, Claude, Gemini, and others) through one subscription and one chat interface, with no setup and no document infrastructure to manage. AnythingLLM doesn't give you access to more models in that sense — you still need your own API keys or your own compute — but it gives you something Poe fundamentally doesn't: a persistent, citable knowledge base built from your own documents that any connected model can reason over, plus the option to own the entire stack and keep data off anyone else's servers.
The comparison to HARPA AI is even more orthogonal. HARPA lives inside your browser and acts on live web pages — filling forms, scraping sites, automating repetitive browser tasks. AnythingLLM has no browser-automation layer at all; its "agents" operate inside chat threads against your documents and configured tools, not against arbitrary websites in real time. If what you actually need is something to click buttons and extract data from pages you're viewing, AnythingLLM won't do that job. If what you need is a durable, searchable memory built from PDFs, internal docs, or meeting notes that you can query conversationally and keep under your own control, that's exactly its lane.
The honest verdict: AnythingLLM is worth using if you're comfortable with (or want to learn) light self-hosting, you care about data staying local, and you want one interface to route between local and API-based models while chatting with your own document corpus. It's a poor fit if you want a zero-setup, mobile-friendly chat experience, want access to many polished hosted models without touching infrastructure (that's Poe's job), or need an agent that acts on live web pages instead of static documents (that's HARPA's job). Teams without any technical owner willing to run Docker or manage API keys will likely find the cloud tier's $50–99/mo, on top of a separate LLM API bill, a harder sell than a simpler all-in-one hosted chatbot subscription.
Best suited to developers, IT-savvy small teams, researchers, and privacy-focused individuals who want a document-grounded chat assistant they fully control, rather than a plug-and-play hosted chatbot.
Key features
Document RAG with citations
Upload PDFs, Word docs, text files, or scrape websites into a workspace, and the assistant answers questions against that content with source citations rather than relying purely on the base model's training data.
No-code AI agents
Build custom agents inside chat threads that can browse the web, call tools, and run background jobs, without writing agent orchestration code yourself.
Broad LLM provider support
Connects to 40+ providers including OpenAI, Anthropic, Groq, and DeepSeek, as well as fully local models via Ollama, letting you swap models per workspace.
Flexible vector database backend
Ships with LanceDB by default but can be pointed at Pinecone, Chroma, Weaviate, Qdrant, Milvus, or PGVector for larger or production-grade deployments.
Multi-user, permissioned workspaces
Self-hosted Docker deployments support multiple users and instance-level access control, useful for small team or internal-tool use cases (the desktop app itself is single-user).
One-file desktop installer
Mac, Windows, and Linux builds that install with a single download and no account, terminal, or configuration step, keeping models, documents, and chat history entirely on-device.
Local meeting assistant and dictation
The desktop app can transcribe and summarize meetings on-device and offers voice dictation and text autocomplete without sending audio to a cloud service.
Pricing breakdown
Self-Hosted / Desktop (Free)
- Desktop app for Mac, Windows, Linux, or Docker self-host
- No account, no token limits, no document caps
- Bring your own LLM (local via Ollama or your own API keys)
- Data stays on your machine or your own server
Cloud Basic
- Managed private hosted instance with custom subdomain
- RAG and agents included
- You supply your own LLM API key
Cloud Pro
- Managed private instance sized for larger teams
- RAG and agents included
- 72-hour support SLA
Enterprise
- On-premise deployment option
- Custom SLA and integrations
- SSO and role-based access control (RBAC)
Pros and cons
Pros
- The self-hosted core is not a crippled free tier — it's the same MIT-licensed software the paid cloud plans run on, so you can get the full document-RAG and agent feature set for $0 as long as you're willing to run Docker or the desktop app yourself.
- Grounding chat in your own documents with citations is a meaningfully different capability from generic chatbots — it's closer to an internal search-and-answer tool than a conversational novelty, which makes it genuinely useful for research, internal documentation, or personal knowledge management.
- Supporting 40+ model providers plus fully local inference means you're never locked into one vendor's pricing or availability, and you can go fully offline if data residency or cost is a concern.
- Docker-based multi-user support with permissioning gives small teams a real shared knowledge assistant without needing to build custom infrastructure from scratch.
Cons
- The desktop app's ease of use masks the fact that anything beyond solo, single-machine use requires standing up and maintaining a Docker deployment, picking a vector database, and managing model API keys — a real barrier for non-technical buyers.
- AnythingLLM Cloud is not cheap relative to what you get: $50–99/mo buys hosting and RAG/agent features, but you're still paying separately for whatever LLM API you connect, unlike aggregator subscriptions that bundle model access into one price.
- There's no browser-automation or live-web-action capability, so tasks like filling out forms, monitoring pages, or automating a workflow across websites are simply out of scope — you'd need a separate tool for that.
- Because the desktop app is single-user by design, a team evaluating it purely as a "free chatbot" will hit a wall the moment they want shared access, at which point the real cost is either self-hosting effort or the cloud subscription.
Alternatives to AnythingLLM
Frequently asked questions
Is AnythingLLM actually free?
Yes, for self-hosting. The software is MIT-licensed and the desktop app or Docker deployment costs nothing and has no document or token limits. The $50–99/mo Cloud plans are optional and only needed if you want AnythingLLM to host and manage the server for you.
Do I need a GPU to run it?
Not necessarily. You can connect it to cloud LLM APIs (OpenAI, Anthropic, Groq, etc.) and run with no local GPU at all. Running local models through Ollama for privacy or offline use benefits from a decent GPU, but is not required for lighter models.
Can multiple people share one AnythingLLM instance?
Yes, but only through the self-hosted Docker deployment, which supports multi-user accounts and permissioning, or through a Cloud plan. The single-file desktop app is single-user by design.
What LLMs does it work with?
AnythingLLM connects to 40+ providers including OpenAI, Anthropic, Groq, and DeepSeek, plus fully local models via Ollama or similar runtimes, and you can switch providers per workspace.
How is this different from using ChatGPT or Poe directly?
ChatGPT and Poe give you access to hosted models to chat with, but not a persistent, citable knowledge base built from your own files, and you don't control where your data lives. AnythingLLM is built around ingesting and querying your own documents, with the option to keep everything self-hosted.
Is my data private?
If you self-host via the desktop app or your own Docker server, your documents, chats, and models stay on your machine or infrastructure. If you use AnythingLLM Cloud, your data is hosted on Mintplex Labs' infrastructure, so you should review their data-handling terms before putting sensitive material there.
Ready to try AnythingLLM?
Head to the official site to explore pricing and start a free trial where available.
Visit AnythingLLM →