# 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.

---

## 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 Agentsets knowledge base to external applications, allowing data from Agentset to power third-party tools.
- **Who uses it:** Developers integrating company knowledge bases into chatbots, workflow tools, or AI assistants.
- **Key features:**
  - External knowledge delivery via server endpoints
  - Flexible integration with various AI frameworks
  - Model-agnostic compatibility

### AI SDK Integration
- **What it does:** Provides an integration layer between Agentset and the AI SDK ecosystem for streamlined connectivity with leading AI models.
- **Who uses it:** Teams leveraging existing AI systems such as OpenAI, Anthropic, or Azure OpenAI Service.
- **Key features:**
  - Support for OpenAI, Anthropic Claude, Google AI, Azure, Cohere, Mistral, DeepSeek, Pinecone, Qdrant, and Qwen
  - Model-agnostic design for flexible implementation
  - Unified framework for managing AI inference and retrieval

---

## Use Cases & Applications

### AI-Powered Document Intelligence
- **Their needs:** Organizations require accurate summarization, search, and question-answering systems based on internal content while avoiding hallucinations.
- **How they use it:** Agentset processes diverse data such as PDFs, spreadsheets, and slides, embeds them into a vector store, and provides precise retrieval with citation-backed responses.
- **Results:** Rapid deployment of intelligent assistants capable of verifying answers, improving decision-making accuracy, and reducing manual data lookup.

### Developer Productivity Tools
- **Their needs:** Developers seek to build RAG functionality quickly without manually coding ingestion pipelines or search algorithms.
- **How they use it:** Using the Agentset SDKs, developers upload data, implement semantic and hybrid search, and integrate context-aware chat interfaces.
- **Results:** Production-ready RAG applications built within hours instead of months of manual optimization.

### Enterprise Knowledge & Compliance
- **Their needs:** Enterprises require secure, compliant AI systems that handle sensitive data while maintaining control.
- **How they use it:** Agentset enables self-hosted or EU-only deployments under strict encryption policies, with options to bring private storage, vector databases, and custom LLMs.
- **Results:** Full data sovereignty, compliance with SOC 2, HIPAA, and GDPR standards, and auditability through source citation features.

---

## Pricing & Plans

### Free
- **$0 (Forever Free)**
  - 1,000 pages included
  - 10,000 retrievals
  - Community support
  - RAG, citations, and semantic search included

### Pro
- **$49 per month**
  - 10,000 included pages
  - $0.01 per additional page
  - Unlimited retrievals
  - Deep research capabilities
  - Email support
  - Optional connectors at $100 each

### Enterprise
- **Custom pricing**
  - Unlimited pages and retrievals
  - On-premise or bring-your-own-cloud deployment
  - SOC 2, HIPAA, and GDPR reports
  - Single sign-on (SSO)
  - Dedicated engineering and account support

Enterprise plans cater to large organizations seeking customized workflows, private hosting, and advanced compliance requirements.

---

## Company Information

### Metrics & Traction
Agentset is a growing open-source project with an active community and over 1,500 stars on GitHub. It was founded by Abdellatif Abdelfattah, who previously led the development of one of the largest RAG systems in existence, processing more than 6 billion tokens. The platform was built from the lessons learned during that experienceaiming to drastically reduce the development time and complexity of creating scalable RAG systems.

### Customers & Case Studies
Agentsets technology is used by companies including **Arcadia**, **Brius**, **Digna**, **Farol**, **Jupid**, and **Usul**. These customers span industries such as healthcare, technology, and professional services, employing Agentset to build AI assistants and business intelligence systems powered by accurate document retrieval and contextual analysis.

### Integrations & Compatibility
Agentset integrates with a wide range of machine learning and AI tools, including OpenAI, Anthropic, Azure, Cohere, Google AI, Mistral, and DeepSeek. It supports multiple vector databases such as Pinecone and Qdrant and offers seamless file ingestion from 22+ formats. The platform provides end-to-end encryption, EU data residency options, and full self-hosting capabilities to satisfy enterprise compliance standards.

---

## Feature Deep Dive

### Hybrid Search and Reranking
- **How it works:** Agentset combines semantic embeddings with keyword-based retrieval to optimize result relevance. Retrieved results are automatically reranked for precision.
- **Benefits:** Delivers accurate answers by balancing semantic meaning and exact keyword matches.
- **Requirements:** Works with any supported vector store or embedding model.

### Deep Research Mode
- **How it works:** Extends typical RAG retrieval by reviewing a broader context across multiple sources before synthesizing answers.
- **Benefits:** Produces richer, more context-aware answers for complex queries.
- **Requirements:** Configurable via ingestion and query API parameters.

### Citations and Source Tracking
- **How it works:** Each retrieved answer includes cited sources, allowing direct traceability back to the originating document chunk.
- **Benefits:** Builds user trust and enables verification for compliance-sensitive use cases.
- **Requirements:** Enabled by default on managed and self-hosted deployments.

### Security and Compliance
- **How it works:** Agentset employs bank-grade AES-256 encryption and TLS for data transmission. It offers full BYOC and on-prem deployment options for data control.
- **Benefits:** Protects sensitive information and meets enterprise security requirements.
- **Requirements:** Configurable during setup via self-hosted or enterprise plans.

---

## Getting Started

To begin using Agentset, users can create a free account and start building applications from the dashboard at [app.agentset.ai](https://app.agentset.ai/login). Developers can integrate Agentset within minutes using official JavaScript or Python SDKs. Typical implementation from ingestion to production occurs within hours.

Agentset provides community support through its [Discord channel](https://discord.gg/AqMkKAYZCu), email support at [contact@agentset.com](mailto:contact@agentset.com), and dedicated engineering assistance for enterprise customers. Demos and enterprise consultations can be scheduled via [agentset.ai/schedule-demo](https://agentset.ai/schedule-demo).

Documentation, blog insights, and developer guides are available at [docs.agentset.ai](https://docs.agentset.ai) to help users build and optimize frontier-grade RAG applications.

## Site Map

Pages crawled from agentset.ai (20 total):

- [Agentset: Build Frontier RAG Apps](https://agentset.ai/)
- [Graphlit vs Agentset](https://agentset.ai/compare/graphlit-vs-agentset)
- [Blog](https://agentset.ai/blog)
- [Pricing](https://agentset.ai/pricing)
- [Schedule a demo](https://agentset.ai/schedule-demo)
- [Privacy Policy](https://agentset.ai/privacy)
- [Terms of Service](https://agentset.ai/terms)
- [Ragie vs Agentset](https://agentset.ai/compare/ragie-vs-agentset)
- [Vectara vs Agentset](https://agentset.ai/compare/vectara-vs-agentset)
- [Credal vs Agentset](https://agentset.ai/compare/credal-vs-agentset)
- [Building Effective RAG Pipelines: A Practical Guide](https://agentset.ai/blog/building-effective-rag-pipelines-practical-guide)
- [Cohere vs ZeRank: Which Reranker Actually Performs Better?](https://agentset.ai/blog/cohere-vs-zerank-comparison)
- [Is RAG Dead?](https://agentset.ai/blog/is-rag-dead)
- [Building a Proof-of-Concept RAG System in an Afternoon](https://agentset.ai/blog/building-a-proof-of-concept-rag-system-in-an-afternoon)
- [Citation Tracking in AI Systems: Ensuring Accuracy and Trust](https://agentset.ai/blog/citation-tracking-in-ai-systems-ensuring-accuracy-and-trust)
- [How to Implement Semantic Search Without a PhD](https://agentset.ai/blog/how-to-implement-semantic-search-without-a-phd)
- [The Art of Document Chunking for LLM Applications](https://agentset.ai/blog/the-art-of-document-chunking-for-llm-applications)
- [Parsing PDF Documents at Scale](https://agentset.ai/blog/parsing-pdf-documents-at-scale)
- [AI-Powered Document Analysis: Beyond Simple RAG](https://agentset.ai/blog/ai-powered-document-analysis-beyond-simple-rag)
- [Embeddings 101: Representing Text as Vectors](https://agentset.ai/blog/embeddings-101-representing-text-as-vectors)

---
Generated by trylapis.com on November 05 2025