Can I use this with OpenAI or other cloud APIs?

Yes. While it can run fully locally, you can configure it to use APIs from OpenAI, Anthropic, Google, and many others for a hybrid approach.

How is data stored and managed?

All data, including chat logs and vector embeddings, is stored in local SQLite databases and file directories you specify. You have full ownership.

What's the difference between the desktop app and Docker deployment?

The desktop app is a single-user client. Docker deployment enables a multi-user, network-accessible instance with admin controls, suitable for teams.

How does the RAG pipeline work?

Documents are chunked, embedded into vectors, and stored in a configured vector database. During chat, your query is embedded, relevant chunks are retrieved, and fed as context to the LLM.

Can I extend its capabilities?

Yes, via custom agent skills (plugins). You can write JavaScript handlers to add new tools or integrate with external APIs, then import them into the system.

Directory/Chatbots/AI Assistants/AnythingLLM

AnythingLLM

AnythingLLM is a self-hosted, open-source AI platform that lets you run large language models (LLMs) and build AI-powered applications entirely on your own hardware. It provides a full suite for private AI, including document RAG, agent workflows, meeting transcription, and a chat interface, all without sending data to the cloud. Key capabilities include local document knowledge bases, web scraping, dynamic model selection (supporting models from OpenAI, Anthropic, Ollama, and local runners), background job automation, and a meeting assistant that transcribes and summarizes calls locally. It supports Docker deployment for self-hosting and has a cloud version for teams. Target users are developers, founders, and technical teams who need a customizable, privacy-focused AI infrastructure. It's ideal for building internal tools, automating workflows with AI agents, and experimenting with LLMs without cloud dependency or cost surprises.

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Sep 22, 2026

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On-device AIOpen SourceSelf-hostableRAG AgentsMeeting AssistantMagic DictationDocker DeploymentMulti-Model Support

Product information

Everything worth knowing about AnythingLLM

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

On-Device AI Processing

Runs entirely locally on your computer. No accounts, API keys, or token limits. Your data never leaves your device.

Document Knowledge Base (RAG)

Turns every document into part of the agent's knowledge base with no data leaving your device.

AI Meeting Assistant

Automatically transcribes and summarizes meetings entirely on your computer. Action items, decisions, and transcripts ready immediately.

Magic Features (Dictation & Autocomplete)

Intelligent dictation that refines speech in real-time and context-aware text autocomplete. Fully on-device.

Technical capabilities

Multi-Model Support & Routing

Swap between local and cloud LLMs (OpenAI, Anthropic, Ollama, etc.), embedding, and vector database providers. Includes a Model Router for automatic selection.

Self-Hostable & White-Labelable

Fully open-source (MIT License). Self-host a multi-user deployment via Docker. Cloud plans offer white-labeling, SSO, and RBAC.

Must watch videos

15:26

Tutorial15:26

This New Frontier Model Is Not An LLM.

*Updates* Interesting developments: https://laya.convaiinnovations.com/ I also got access 1 day after this video - its just a neat general-purpose classifier. I don't see the term "Frontier" they used in marketing being accurate with respect to what people mean by frontier. Thinking of doing a followup video talking about the above because that **is OSS** and does the exact same thing. I wasnt aware of it at the time of this video - check it out! ---- Recently in the news or on X there was an announcement from TypeSafe, which has team members who founded the concept of RLHF and InstructGPT, move toward making a different type of frontier model. I applied for access, but through a friend got some exposure to what this model can do. Its not AGI (and doesn't have to be!) but it will 100% make more reliable AI systems and automations possible. Essentially, this allows you to tap into the intelligence of frontier models, but fully constrain their output to some known set of values. This means zero hallucinations as well as super fast parallel inference - since it functionally works like classifier and ranks options by probability. Its certainly an interesting take and I do wonder what the specs or hardware it takes to run it is since it is priced per *Billion* tokens and output is literally free, for now at least. *References* : AnythingLLM: https://anythingllm.com/ TypeSafe Website: https://typesafe.ai/ Manifesto: https://typesafe.ai/manifesto System One Model (JEV): https://typesafe.ai/blog/introducing-system-one-models-and-jev *Chapters* : 0:00 Welcome back! 0:57 System One Model (Jev) Launch Video 3:40 Why do these models need to exist? 6:10 How does this differ from LLMs in use and speed? 10:09 Deterministic Outputs 11:32 What are some more use cases? 12:50 This is going to make more AI Automations more reliable 14:00 My opinion on this new class of models

Bonsai 27B Runs Qwen 3.6 27B at 10x less memory.

17:39

Tutorial17:39

Bonsai 27B Runs Qwen 3.6 27B at 10x less memory.

The new Bonsai 27B Model from PrismML is Qwen3.6 27B - a beloved workhorse for many - into something you can run on local computer. 10x less memory and a much more modest file size while still benchmarking 95% of the original FP16 model. Of course, no model is perfect, but if you have been wanting to run Qwen3.6 27B and dont have the computer or headroom - you finally can. The Ternary format is much smarter, but takes a bit more compute. The Binary model is enough to fit into a phone form factor. Imagine 27B, even remotely, in your pocket. That is now a reality. **Links** : Blog: https://prismml.com/news/bonsai-27b Whitepaper: https://github.com/PrismML-Eng/Bonsai-demo/blob/main/bonsai-27b-whitepaper.pdf HuggingFace Collection: https://huggingface.co/collections/prism-ml/bonsai-27b WebGPU Demo: https://huggingface.co/spaces/webml-community/bonsai-webgpu-kernels **As of now you MUST use these forks to run these models** PrismML LLama.cpp repo: https://github.com/PrismML-Eng/llama.cpp MLX Repo: https://github.com/PrismML-Eng/mlx AnythingLLM: https://github.com/Mintplex-Labs/anything-llm OpenComputer: https://github.com/Mintplex-Labs/anything-llm/blob/master/open-computer/README.md **Chapters** : 0:00 The promise of Bonsai 27B 0:57 Lets look at the benchmarks and models 2:52 Why would you choose Bonsai 27B? 5:22 How to run it locally... 6:00 Download the right binary 7:27 Download the model from HuggingFace 7:50 (Shortcut) - Use webGPU! 8:17 Picking the right files from HuggingFace 9:00 Start the llama-server and use the UI 10:52 How does it work with simple agent tasks? 12:12 What about using a whole computer and a longer task? 14:53 My thoughts on Bonsai 27B 15:26 Is this the end of the AI bubble?

Magic Echo Is A Free & Better Way To Control Your Computer With Voice

16:32

Comparison16:32

Magic Echo Is A Free & Better Way To Control Your Computer With Voice

Magic Echo is voice-to-text dictation that works in any application on your computer. Speak naturally and your words are transcribed, cleaned up, and inserted right where your cursor is — no copy-pasting, no switching apps. It even uses on-screen context to improve accuracy. Simple word and phrasing fixes though are not enough. With on-screen awareness you can use a local vision model to even reference multiple windows at once, using more "free form" thought to get what you want out of a voice transcription. *Links* : AnythingLLM: https://anythingllm.com/desktop Magic Echo Docs: https://docs.anythingllm.com/pro/magic-echo AnythingLLM Pro: https://docs.anythingllm.com/pro/overview *Chapters* : 0:00 What is Magic Echo? 1:00 How to enable Magic Echo 2:12 Voice Commands 2:57 Custom Vocabulary 3:20 Advanced Settings, Keybinds, and Dictation Modes 5:25 Demo: Voice Commands 5:57 Echo History 6:33 Demo: Intelligent Dictation 8:17 Demo: On Screen Awareness & Dictation 10:48 Demo: Multi-Window On Screen Awareness and Dictation 14:18 That's Magic Echo 14:33 What is Pro?

Who uses it

Real use cases, no hype

Private Document Analysis

Analyze sensitive contracts, legal docs, or internal reports locally without data leaving your machine.

Personal Research Assistant

Aggregate web sources, scrape content, and synthesize answers with full offline capability.

Meeting Notes Automation

Transcribe and summarize calls locally, extracting action items without cloud bots.

Developer Workflow Automation

Build custom agent skills and scheduled jobs to automate repetitive tasks via a visual flow builder.

Multi-User Team Workspace

Self-host a shared AI workspace for teams with admin controls and tenant isolation.

Custom AI Agent Prototyping

Rapidly prototype agents using built-in tools (web scraper, file system) and custom code.

What's great

Runs entirely on-device, no cloud dependency or API keys needed.
MIT licensed, fully open-source, and self-hostable.
Zero token costs and no rate limits for local inference.

Technical strengths

Supports a wide array of local LLM backends (Ollama, LM Studio, etc.).
Built-in RAG pipeline with multiple vector DB support (LanceDB, Chroma, etc.).
Extensible via custom agent skills and plugin architecture.

Where it falls short

Requires decent local hardware (RAM/GPU) for smooth operation.
Local models can be slower or less capable than cloud-hosted giants.
Advanced features like Meeting Assistant are behind a Pro subscription.

Technical limitations

Debugging complex RAG or agent issues can be opaque without deep logs.
Self-hosted Docker setup may require networking knowledge for remote access.

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

The core LLM inference, RAG processing, and data storage happen on your computer. No audio, documents, or chat logs are sent to external servers by default.