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Agentset

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

Freemium· Free 1K pages/10K retrievals; Pro $49/mo + $0.01/page; Enterprise customRAGMulti-model (Claude, OpenAI, Google, xAI, Cohere, Mistral, DeepSeek)7.3 / 10

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Best for

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

Skip if

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.

Agentset is a managed retrieval-augmented generation platform that handles the unglamorous parts of building reliable AI search and Q&A: ingestion of 22+ file formats, intelligent chunking, multimodal parsing of images/tables/graphs, metadata filtering, and an agentic retrieval loop that returns answers with inline citations. It ships JavaScript and Python SDKs, an AI SDK integration, and an MCP server, so you can wire it into existing apps without re-implementing the RAG stack from scratch.

It is aimed at product teams who need accurate document Q&A in production but don't want to babysit vector databases, embedding pipelines, or eval rigs. Agentset is model-agnostic, brokering between Claude, OpenAI, Google, xAI, Cohere, Mistral and DeepSeek on the LLM side and Pinecone or Qdrant on the vector side, so you're not locked into a single vendor. Pricing is genuinely usage-friendly: a forever-free tier covers 1,000 pages and 10K retrievals, the Pro plan is $49/month with $0.01 per additional page, and Enterprise adds on-prem/BYOC, SOC 2/HIPAA/GDPR reports, SSO, and dedicated support.

The GitHub repo (~2k stars) plus MCP server mean it slots cleanly into agent stacks, and connectors ($100 each on Pro) let you pull from common SaaS sources. The trade-off is that connector pricing and per-page overage can add up for document-heavy workloads, and serious compliance/deployment flexibility is gated to Enterprise.

Editor's take

Agentset is one of the more credible managed-RAG plays we've seen: model-agnostic, citation-first, and priced so a small team can actually ship on it. The MCP server and SDKs make it agent-ready, but heavy document workloads will want to price-check the per-page math before committing.

— The AI Tool Bible editorial team

Pros

✅ 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

Cons

⚠️ 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

Use cases

document-qaagentic-searchknowledge-basecitationsmultimodal-rag

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