AnythingLLM vs Quivr

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

Tagline

AnythingLLM

Open-source desktop and self-hosted app that turns your documents into a private chat-and-agent workspace.

Quivr

Open-source RAG framework for building custom AI assistants over your own documents in a few lines of Python.

Pricing

AnythingLLM

Freemium· Basic: $50/monthly · Pro: $99/monthly · Enterprise: Contact Us

Quivr

Free· Open source (pip install quivr-core); pay only for LLM/vector-store usage

Lowest paid tier

AnythingLLM

$50 · Basic

captured 2026-08-04

Quivr

—

Free trial

AnythingLLM

Yes

Quivr

Yes

API

AnythingLLM

Yes

Quivr

Yes

Platforms

AnythingLLM

api

Quivr

api

Open source

AnythingLLM

Yes · MIT

Quivr

Yes · Apache-2.0

GitHub stars

AnythingLLM

66,607

checked 2026-09-29

Quivr

7,414

checked 2026-09-29

Last GitHub push

AnythingLLM

2026-09-29

Quivr

2025-02-21

First commit

AnythingLLM

2023-06

Quivr

2024-05

Model used

AnythingLLM

Multi-model

Quivr

Multi-model (OpenAI, Anthropic, Mistral, Gemma)

Best for

AnythingLLM

Pick AnythingLLM if you want a self-hosted, model-agnostic RAG frontend you can deploy in an afternoon and extend via API.

Quivr

Pick Quivr if you are a Python developer who wants a lightweight, model-agnostic RAG library you can extend rather than a hosted chat-your-docs SaaS.

Not for

AnythingLLM

Skip it if you need a polished managed SaaS with SLA-grade retrieval tuning and enterprise SSO baked in by default.

Quivr

Skip it if you want a turnkey no-code product with a polished UI, hosted vector store, and a sales team to call.

Editorial score

AnythingLLM

7.9 / 10

Quivr

8.4 / 10

Use cases

AnythingLLM

document-chatprivate-raglocal-llmai-agentsteam-knowledge-base

Quivr

document-qacustom-knowledge-baserag-pipelineinternal-assistantschat-with-pdf

Pros

AnythingLLM

MIT-licensed and genuinely self-hostable, with a usable desktop build
Pluggable LLMs, embedders, and vector stores — no vendor lock-in
Built-in agents, API, and multi-user workspaces out of the box
Handles PDFs, Office docs, codebases, and websites without extra glue

Quivr

Genuinely open source and pip-installable, no vendor lock-in
Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
Minimal boilerplate to get a working RAG assistant running
Pairs with Megaparse for tougher PDF and document ingestion
Customizable pipeline with tools and web search when you need more

Cons

AnythingLLM

Retrieval quality depends heavily on chosen embedder and chunking
UI and agent tooling lag behind dedicated commercial RAG platforms
Cloud pricing and quotas are less transparent than the OSS story

Quivr

Python library, not a hosted product or UI
You manage infra, vector store, and evals yourself
Documentation site is sparse compared to larger RAG frameworks
LLM and embedding costs are on you

Website

AnythingLLM

anythingllm.com

Quivr

core.quivr.com

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.

Pick AnythingLLM if

✅ MIT-licensed and genuinely self-hostable, with a usable desktop build
✅ Pluggable LLMs, embedders, and vector stores — no vendor lock-in
✅ Built-in agents, API, and multi-user workspaces out of the box
✅ Handles PDFs, Office docs, codebases, and websites without extra glue

Pick Quivr if

✅ Genuinely open source and pip-installable, no vendor lock-in
✅ Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
✅ Minimal boilerplate to get a working RAG assistant running
✅ Pairs with Megaparse for tougher PDF and document ingestion

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