Agentset vs Quivr

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

Tagline

Agentset

Production-ready RAG infrastructure with agentic search, citations, and model-agnostic plumbing.

Quivr

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

Pricing

Agentset

Freemium· Free: $0 · Pro: $49 · Enterprise: Custom

Quivr

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

Lowest paid tier

Agentset

$49 · Pro

captured 2026-08-06

Quivr

—

Free trial

Agentset

Yes

Quivr

Yes

API

Agentset

Yes

Quivr

Yes

Platforms

Agentset

api

Quivr

api

Open source

Agentset

Not listed

Quivr

Yes · Apache-2.0

GitHub stars

Agentset

—

Quivr

7,414

checked 2026-09-29

Last GitHub push

Agentset

—

Quivr

2025-02-21

First commit

Agentset

—

Quivr

2024-05

Model used

Agentset

Multi-model (Claude, OpenAI, Google, xAI, Cohere, Mistral, DeepSeek)

Quivr

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

Best for

Agentset

Pick Agentset if you want production RAG with citations and multimodal ingestion without building the pipeline, embeddings, and eval loop yourself.

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

Agentset

Skip it if you already run your own vector DB and chunking stack, or if your corpus is millions of pages where per-page pricing breaks down.

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

Agentset

7.3 / 10

Quivr

8.4 / 10

Use cases

Agentset

document-qaagentic-searchknowledge-basecitationsmultimodal-rag

Quivr

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

Pros

Agentset

Forever-free tier covers real prototyping (1K pages, 10K retrievals)
Model- and vector-DB-agnostic; avoids LLM vendor lock-in
Agentic retrieval with automatic citations out of the box
Ships SDKs plus an MCP server for agent stacks
SOC 2, HIPAA, and GDPR posture available on Enterprise

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

Agentset

Connectors are $100 each on top of the Pro plan
Per-page overage adds up fast for document-heavy corpora
On-prem/BYOC and compliance reports are Enterprise-only
License terms not clearly surfaced despite GitHub presence

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

Agentset

agentset.ai

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 Agentset if

✅ Forever-free tier covers real prototyping (1K pages, 10K retrievals)
✅ Model- and vector-DB-agnostic; avoids LLM vendor lock-in
✅ Agentic retrieval with automatic citations out of the box
✅ Ships SDKs plus an MCP server for agent stacks

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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