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Agentset.ai

Open-source local Semantic Search + RAG for your data

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What is Agentset.ai?

Building a Retrieval-Augmented Generation (RAG) system from scratch is a notoriously difficult task. While basic prototypes are easy to spin up, moving from a "demo" to a production-ready application requires solving complex problems like document parsing, chunking strategies, vector indexing, and reranking. Agentset.ai enters the market as an open-source solution designed to simplify this entire pipeline, offering a "RAG-as-a-service" platform that bridges the gap between raw data and actionable AI insights.

At its core, Agentset.ai is a comprehensive platform for building, evaluating, and deploying semantic search and agentic RAG applications. It allows developers to index their private data—ranging from PDFs and spreadsheets to Notion pages and Google Drive files—and then query that data using natural language. Unlike many proprietary search engines, Agentset provides an open-source foundation, giving users the choice between using a managed cloud service or self-hosting the entire stack on their own infrastructure for maximum privacy and control.

The platform is built with a modern developer-first stack, including TypeScript, Next.js, and Supabase. It doesn't just return links; it uses "Agentic RAG" to reason through multiple sources, provide deep research, and generate answers with built-in citations. By handling the heavy lifting of infrastructure and retrieval optimization, Agentset.ai enables teams to ship production-grade AI features in hours rather than months.

Key Features

  • Turnkey RAG Infrastructure: Agentset handles the entire lifecycle of data ingestion, including high-resolution parsing, intelligent chunking, and vector indexing. This eliminates the need for developers to write custom code for every different file format.
  • Model Agnostic Architecture: Users are not locked into a single provider. Agentset works with various Large Language Models (LLMs), embedding models, and vector databases, allowing you to swap components based on your performance or cost requirements.
  • Deep Research Mode: Beyond simple keyword matching, the "Deep Research" feature allows the AI to spend more time reviewing a broader set of sources. It performs multi-query expansion to cover more ground, resulting in highly detailed and contextually rich answers.
  • Automatic Citations: Transparency is critical in AI. Agentset automatically provides citations for its answers, allowing users to click through to the specific source document or "chunk" used to generate the response, effectively eliminating the "black box" problem.
  • Support for 22+ File Formats: The platform supports a wide array of data types, including PDFs, Markdown, HTML, spreadsheets, and even tabular data, ensuring that almost any organizational knowledge base can be indexed.
  • MCP Server Integration: By supporting the Model Context Protocol (MCP), Agentset allows your knowledge base to be easily connected to external AI agents and tools, making your data accessible across different environments.
  • Hybrid Search & Reranking: To ensure high accuracy, Agentset combines semantic search (meaning-based) with traditional keyword search. It then applies a reranking layer to ensure the most relevant information is prioritized before being sent to the LLM.
  • Developer SDKs and API: With robust Python and TypeScript SDKs, Agentset is designed to be integrated directly into existing applications, rather than existing as a standalone silo.

Pricing

Agentset.ai offers a flexible pricing model that caters to individual developers, growing startups, and large enterprises. Because it is open-source, there is also a self-hosting option for those who want to avoid monthly fees entirely.

  • Free Tier: Ideal for personal projects and testing. Includes 1,000 pages (defined as 1,000 characters per page) and 10,000 retrievals per month. No credit card is required to start.
  • Pro Tier ($49/month): Designed for professional use and production apps. It includes 10,000 pages, unlimited retrievals, and email support. Additional pages are billed at $0.01 per page, and external connectors (like Notion or Slack) are available for $100 each.
  • Enterprise Tier: Custom pricing for high-volume users. This tier offers unlimited pages, "Bring Your Own Cloud" (BYOC) or on-premise deployment, SOC 2/HIPAA compliance, and dedicated engineering support.
  • Open Source (Self-Hosted): Users can download the source code from GitHub and host it themselves. This version is free of licensing costs but requires the user to manage their own infrastructure and pay for their own LLM/embedding API usage.

Pros and Cons

Pros

  • Privacy and Control: The open-source nature and self-hosting options make it a top choice for industries with strict data residency requirements.
  • High Accuracy: The combination of hybrid search, reranking, and agentic reasoning helps prevent hallucinations and provides much higher precision than basic RAG implementations.
  • Excellent Developer Experience: The availability of typed SDKs, an OpenAPI spec, and a built-in "Chat Playground" makes debugging and integration seamless.
  • Extensive File Support: Handling over 22 file formats out of the box saves significant development time.

Cons

  • Setup Complexity (Self-Hosted): While the cloud version is "turnkey," self-hosting requires familiarity with Docker, databases, and environment configurations.
  • Connector Costs: On the Pro tier, the $100 per connector fee can become expensive for small teams looking to sync multiple data sources like Slack, Drive, and Jira simultaneously.
  • Hardware Requirements: If running locally with local LLMs, users will need significant GPU resources to maintain the performance levels seen in the cloud version.

Who Should Use Agentset.ai?

Agentset.ai is a versatile tool, but it is particularly well-suited for three specific profiles:

1. Software Developers and Product Teams

If you are building an AI-powered feature—such as an internal knowledge base, a customer support bot, or a research assistant—Agentset provides the infrastructure you need to get to market quickly. Its SDKs make it easy to embed into existing software without becoming a RAG expert.

2. Privacy-Conscious Organizations

For legal, medical, or financial firms that cannot upload sensitive data to third-party SaaS platforms, Agentset’s self-hosting capability is a game-changer. It allows these organizations to leverage the power of LLMs while keeping their data entirely within their own firewall.

3. Researchers and Knowledge Workers

Individuals dealing with massive amounts of documentation (hundreds of PDFs, research papers, or notes) can use Agentset to create a "second brain" that they can talk to. The built-in citations make it reliable enough for academic or professional research where fact-checking is mandatory.

Verdict

Agentset.ai is one of the most robust and developer-friendly RAG platforms currently available. It successfully tackles the "80% problem"—the reality that while building a basic RAG app is easy, making it accurate and scalable is incredibly hard. By providing a production-ready pipeline that includes hybrid search, reranking, and deep research capabilities, Agentset.ai allows developers to focus on building their core product rather than fighting with vector database configurations.

While the cost of connectors in the Pro tier might be a hurdle for some, the ability to self-host the open-source version provides a powerful "escape hatch" for cost-conscious or privacy-focused users. If you need a search engine for your data that is more than just a list of links, Agentset.ai is a top-tier recommendation.

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