Assembly Line

AnythingLLM

Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience

AnythingLLM is an open-source local AI project on GitHub under the MIT license (permissive), with 68k stars. It is built with V8. Its last push was today.

Stars
68k
License
MIT
Activity
Active
Latest release
v1.19.0

Code on GitHub Website

Judgement

What it is
Complete system
What it does
A desktop and self-hosted app for chatting with your documents and running agents, connecting to local or cloud LLMs and vector databases.
Adopt it when
You want private document chat for a small team without building a RAG stack yourself.
Watch out
Answer quality depends on the model and embedding choices you make, and it covers many features shallowly.
Ready for
Usable
Runs as
Desktop app and self-hosted server
Built with
JavaScript, Node.js and React
How established
Established: started 3 years ago, latest release v1.19.0.
What the license allows
You can build it into a product you sell, as long as you keep its copyright notice.

Judged on 2026-11-03 from the project's README and GitHub data. The license and age lines are worked out directly.

Compare AnythingLLM

Built from

Follow it down: AnythingLLM → V8 → LLVM project → runs on a microchip

How compilers were built, back to punched paper tape

Open-source alternatives to AnythingLLM

  1. 1
    Ollama ollama

    Get up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

    Developer tool Production-ready You want to run local LLMs for development, privacy or offline use with minimal setup.

    ★ 186k MIT Go Active, pushed today
  2. 2
    Llama.cpp ggml-org

    LLM inference in C/C++

    Building block Production-ready You need efficient local or edge LLM inference and want the engine many other tools are built on.

    ★ 133k MIT C++ Active, pushed today
  3. 3
    vLLM vllm-project

    A high-throughput and memory-efficient inference and serving engine for LLMs

    Building block Production-ready You serve open-weight models to many users on your own GPUs and need throughput.

    ★ 96k Apache-2.0 Python Active, pushed today
  4. 4
    LocalAI mudler

    LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.

    Complete system Usable You want a drop-in local replacement for OpenAI's API covering more than just text.

    ★ 50k MIT Go Active, pushed today
  5. 5
    OpenLLM bentoml

    Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.

    Developer tool Usable You want a quick way to stand up an open model behind an OpenAI-style API.

    ★ 13k Apache-2.0 Python Active, pushed today
  6. 6
    BentoML bentoml

    The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

    Framework or platform Production-ready An ML team needs to serve custom models as production APIs without writing serving infrastructure.

    ★ 8.9k Apache-2.0 Python Active, pushed today
  7. 7
    RikkaHub rikkahub

    RikkaHub is an Android APP that supports for multiple LLM providers.

    Complete system Usable You want one Android app for chatting with several AI providers using your own API keys.

    ★ 8.6k AGPL-3.0 Kotlin Active, pushed today
  8. 8
    GPUStack gpustack

    A GPU cluster manager for high-performance AI model serving (vLLM, SGLang) and on-demand SSH-accessible GPU instances.

    Complete system Usable An organisation pools its own GPUs and wants a control panel for serving models to internal teams.

    ★ 6k Apache-2.0 Python Active, pushed today

Commercial tools for running AI models locally

  1. 1
    LM Studio Element Labs

    Bionic is LM Studio's agent for work and code. Create documents, slides, PDFs, and software with local or frontier open models.

    Complete system Usable You want to run open models privately on your own machine without command-line setup.

    Commercial

Stars, license and activity come from GitHub and refresh on a rolling hourly schedule. The project's own description is quoted as written.