What is Agentset?

Agentset is a developer toolkit designed to simplify the creation of production-ready AI applications, specifically for chat and search functionalities using Retrieval-Augmented Generation (RAG). It addresses the common challenge where RAG systems perform well in demos but fail under real-world conditions with large datasets and actual users. The platform provides a complete, optimized infrastructure out-of-the-box, allowing developers to implement intelligent question-answering and search features without needing deep expertise in RAG pipeline construction, document processing, or maintenance. Its core value is delivering consistent, accurate, and cited answers from diverse data sources, enabling teams to ship reliable AI features confidently and quickly.

Main Features

1. Accurate Answers: Delivers high-precision responses on proprietary data, achieving industry benchmarks for complex queries.

2. Multimodal Support: Natively processes images, graphs, tables, and text within documents for comprehensive knowledge retrieval.

3. Automatic Citations: Generates source citations for all answers, allowing users to verify information origins.

4. Metadata Filtering: Enables answers based on specific data subsets using customizable filters.

5. Broad File Format Support: Handles over 22 file types including PDF, DOCX, images, and presentations for easy data ingestion.

6. Developer-Friendly SDKs: Offers JavaScript and Python SDKs for straightforward integration and data upload.

7. Model Agnostic: Compatible with various LLMs, embedding models, and vector databases like OpenAI, Anthropic, Pinecone, and Qdrant.

8. Preview Links: Provides customizable chat interfaces for capturing external feedback rapidly.

9. MCP Server Integration: Facilitates connecting knowledge bases to external applications via the Model Context Protocol.

Use Cases

1. Building intelligent customer support chatbots that answer queries based on company documentation.

2. Creating internal knowledge search tools for employees to find information across manuals, reports, and presentations.

3. Developing educational platforms where students can ask questions and get answers from course materials.

4. Implementing research assistants that analyze and cite information from academic papers and datasets.

5. Powering search functionality in SaaS products that require querying user-uploaded documents, images, and tables.

6. Enhancing healthcare or legal applications where accurate, sourced answers from complex documents are critical.

Supported Languages

1. The platform interface and primary documentation are in English.

2. It supports processing and querying documents in multiple languages through its compatible LLMs and embedding models, though specific language listings are not detailed on the site.

Pricing Plans

1. Free Plan: $0 USD forever. Includes 1,000 pages, 10,000 retrievals, and community support. Excludes connectors and custom integrations.

2. Pro Plan: $49 USD per month. Includes 10,000 pages, $0.01 per additional page, $100 per connector, unlimited retrievals, and email support.

3. Enterprise Plan: Custom pricing. Includes unlimited pages and retrievals, on-premise or BYOC deployment, SOC 2/HIPAA/GDPR reports, single-sign-on, and dedicated engineering support.

Frequently Asked Questions

1. Q: What is Agentset?

A: Agentset is the infrastructure for developers building production-ready RAG applications, powering search and Q&A inside their products.

2. Q: Who is Agentset for?

A: It is for developers and teams building AI chat and search features who want reliable, scalable answers without building RAG pipelines from scratch.

3. Q: Can large enterprises use Agentset?

A: Yes, the Enterprise plan offers custom solutions, compliance reports, and dedicated support for large organizations.

4. Q: Is Agentset a framework like LangChain or LlamaIndex?

A: No, it is a complete, managed infrastructure service rather than a framework, designed for production deployment.

5. Q: Can Agentset work with my existing stack and infrastructure?

A: Yes, it is model-agnostic and integrates with various LLMs, vector databases, and through SDKs for flexible compatibility.

6. Q: Why not build this myself or in-house?

A: Building and maintaining a production-grade RAG system requires significant time and expertise; Agentset provides an optimized, ready-to-use alternative.

Pros and Cons

Pros:

1. Offers a powerful, production-ready RAG solution without requiring deep expertise.

2. Provides high accuracy and automatic citation features for reliable answers.

3. Supports a wide range of file formats and multimodal content natively.

4. Features flexible integration with popular models and databases.

5. Includes a generous free plan for testing and small projects.

Cons:

1. The Pro plan's connector fee ($100 each) could be costly for applications needing multiple integrations.

2. Enterprise pricing is not transparent, requiring contact with sales.

3. May have a learnin