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Agentset

Agentset is an open-source platform designed for building production-ready Retrieval-Augmented Generation (RAG) applications. It enables developers and enterprises to integrate large language models (LLMs) with their own data sources to produce more accurate, context-aware, and verifiable AI responses. By combining hybrid search, deep research, and automated citation tracking, Agentset empowers users to minimize hallucinations and improve reliability across AI-powered workflows.

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-25T21:26:37.254Z. Cached copy · Source on agentset.ai

# Agentset

Agentset is an open-source platform designed for building production-ready Retrieval-Augmented Generation (RAG) applications. It enables developers and enterprises to integrate large language models (LLMs) with their own data sources to produce more accurate, context-aware, and verifiable AI responses. By combining hybrid search, deep research, and automated citation tracking, Agentset empowers users to minimize hallucinations and improve reliability across AI-powered workflows.

Built by developers for developers, Agentset provides SDKs, APIs, and flexible deployment options to accelerate RAG adoption. Its platform is compatible with major AI providers, vector databases, and cloud infrastructures, offering both managed and self-hosted environments. As a fully open-source solution, Agentset distinguishes itself by offering transparency, customization freedom, and cost efficiency for organizations seeking to move beyond proprietary RAG systems.

Agentsets focus on document-centric retrieval, agentic reasoning, and security-first architecture positions it as a leading choice for teams building next-generation intelligent assistants, document retrieval systems, and enterprise knowledge tools.

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## Core Products & Services

### Agentset RAG Platform
- **What it does:** Provides end-to-end infrastructure for retrieval-augmented generation, enabling developers to connect large language models to organization-specific data. Agentset handles document processing, embedding, vector storage, and hybrid search to deliver grounded AI responses with full source attribution.
- **Who uses it:** Developers, data teams, and enterprises building intelligent document-based applications and assistants.
- **Key features:**
  - Hybrid search and reranking for high-precision retrieval
  - Deep research mode for in-depth contextual understanding
  - Automatic source citations for transparency
  - Metadata filtering for refined results
  - Seamless ingestion of over 22 document formats
  - Integration via JavaScript and Python SDKs
  - Bring-your-own-cloud and on-premise deployment options
  - AES-256 encryption, TLS transmission, and EU residency compliance
- **Pricing:** Free tier available (1,000 pages, 10,000 retrievals), Pro tier at $49/month, and custom Enterprise plans supporting unlimited pages, self-hosted deployment, and dedicated engineering support.

### Developer SDKs
- **What it does:** The Agentset SDKs for JavaScript and Python enable users to ingest documents, query data, and build AI features directly within their own applications.
- **Who uses it:** Software engineers and data developers integrating Agentset into existing systems.
- **Key features:**
  - Support for 22+ file formats (.PDF, .DOCX, .HTML, .PNG, .XLSX, .CSV, and more)
  - Quick setup and data upload via namespaces
  - Example code snippets for ingestion and querying
  - Compatible with multiple vector stores and LLM endpoints

### MCP Server
- **What it does:** Connects Age
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Machine-readable: GET /v1/resolve?q=agentset.ai · GET /v1/domain/agentset.ai · this page as Accept: application/json or text/markdown · MCP lookup_domain

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Claim my listing on Agent Web Map for agentset.ai:
1. POST https://agentwebmap.com/v1/sites/agentset.ai/claims and keep claim_id and claim_secret private.
2. Publish the verification line "AgentWebMap verification: <token>" (token from step 1) in https://agentset.ai/llms.txt, or use the DNS, meta-tag or well-known method the response describes. This only verifies the listing; your website and domain remain yours.
3. POST https://agentwebmap.com/v1/sites/agentset.ai/verify with {"claim_id": "...", "claim_secret": "..."}.
4. Save the api_key from the answer; it manages the listing (PATCH https://agentwebmap.com/v1/sites/agentset.ai).
MCP equivalent: tools begin_ownership_claim, then verify_domain, at https://agentwebmap.com/mcp. Docs: https://agentwebmap.com/docs#verify

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