DATASHEET // ANYTHINGLLM

AnythingLLM

RAG-first local LLM workspace with workspaces and agents.

OPEN SOURCECPU-CAPABLELocal or cloud

Actually FreeNo SignupOpen SourceWatermark-FreeHobbyist-OKAPI

Visit AnythingLLMUPDATED 2026-05-14 · DIRECT LINK

anythingllm.com/

HARDWARE REQUIREMENTS //

Local or cloud · Entry GPU (6–8 GB)

CPU-CAPABLE

Min VRAM

None

Rec. VRAM

12 GB

Min RAM

8 GB

Rec. RAM

16 GB

Disk

10 GB

GPU class

Entry GPU

Apple Silicon ✓CPU-CapableQuant: GGUF

Can run fully local (uses your Ollama / LM Studio) or fully remote.

[ EDITORIAL PICK ]

Why we recommend AnythingLLM

DERIVED FROM METADATA — NOT SPONSORED

Open source
Source is public — you can audit it, fork it, and you'll never lose access to your workflows if AnythingLLM the company changes direction.
CPU-capable
Doesn't require a dedicated GPU — useful on laptops and headless servers.
Apple Silicon
Native Metal / MPS support — runs on M-series Macs without CUDA gymnastics.
Beginner-friendly
You don't need to read a paper before getting your first result — sensible defaults and a quick install.

[ EVIDENCE NOTE ]

Documentation-led datasheet

This page summarizes upstream documentation, release information, and editorially reviewed catalogue fields. It is not presented as a hands-on benchmark. Verify changing requirements at the official project; report stale data through our corrections channel.

Memory guide →

AT-A-GLANCE SIGNALS //

DERIVED FROM THIS PAGE'S DATA

Install difficulty
Easy
Runs CPU-only — no CUDA / driver gymnastics required.
Hardware comfort
Runs anywhere
No dedicated GPU required.
Ecosystem
Strong devkit
Open-source AND ships an API — easy to integrate, possible to host yourself.
Verification
Recent
Catalogue entry last updated 63 days ago — re-verification due soon.

[ COMMUNITY GUIDES & WORKFLOWS ]

Tutorials & deep-dives for AnythingLLM

Hand-picked from YouTube, Reddit, GitHub, and the wider web. Each link goes straight to the source — we don't intercept or rewrite anything.

[ MORE IN THIS NICHE ]

Other local llm runners tools we rate

Three picks across different tradeoffs — so you don't end up with three near-clones of AnythingLLM.

LIGHTEST HARDWARE //

Transformers

The library every LLM ships against first.

OPEN SOURCE2–24 GB VRAM

BEST FREE OPTION //

llama.cpp

The C++ inference engine powering most local LLMs.

OPEN SOURCECPU-CAPABLE

TOP QUALITY //

Ollama

One-command local LLM runtime.

OPEN SOURCECPU-CAPABLE

What is AnythingLLM?

AnythingLLM treats RAG as the primary use case rather than a bolt-on. Documents go into 'workspaces'; each workspace has its own vector store, system prompt, and model. Supports local LLMs (Ollama, LM Studio, GGUF) and remote providers, agentic tool use, and a desktop app or self-hosted Docker deployment.

Pros & cons

✓ PROS

RAG that's genuinely production-quality, not a demo
Workspace model fits team / project use cases naturally
Built-in agents with tool calling

– CONS

Heavier setup than Jan / Msty
Some advanced features behind their paid cloud tier

What's actually free?

MIT (desktop + Docker). Mintplex Labs offers a paid cloud variant.

✓ Actually FreeNo SignupOpen SourceWatermark-Free