What is Agentset?
Agentset is an open-source retrieval-augmented generation (RAG) platform that helps developers build, evaluate, and deploy production-ready chat and search experiences on top of their own data. It provides end-to-end infrastructure for ingesting and partitioning documents, indexing them in your preferred vector database, and retrieving cited answers through an API, cloud service, or self-hosted deployment. Designed for engineers, it focuses on high-quality retrieval, strong developer ergonomics, and flexible integration with existing AI stacks and tooling.
Key Features End-to-End RAG Workflow
Offers a turnkey pipeline covering ingestion, chunking, embeddings, retrieval, and evaluation so teams can move from prototype to production without stitching together multiple systems.
Multi-Format Document Ingestion
Supports parsing and partitioning 22+ file types, extracting content, metadata, and structure to build a robust knowledge base from heterogeneous sources.
Citation-Aware Answers
Returns answers with automatic source citations, enabling users to inspect underlying passages and improving trust and auditability for critical use cases.
Model- and Infra-Agnostic Design
Works with your choice of LLMs, embedding models, and vector databases, giving teams freedom to adopt existing providers and infrastructure rather than being locked into a single stack.
Developer-First Tooling & SDKs
Provides TypeScript and Python SDKs, chat and search playgrounds, AI SDK integration, and an MCP server so developers can prototype, debug, and integrate RAG flows quickly into their own apps.
Cloud and Self-Hosted Options
Offers Agentset Cloud with a managed environment as well as an open-source codebase for teams that prefer to self-host and customize their deployment.
Use Cases Knowledge-Base Chatbots : Build chat assistants that answer user questions from product docs, wikis, and manuals with cited passages, reducing support load and improving self-service search. Internal Enterprise Search : Expose secure, organization-wide search over internal files and knowledge repositories so employees can quickly find accurate, reference-backed information. Document-Centric QA Systems : Create question-answering interfaces for policy documents, legal contracts, research papers, and technical specs where traceable, citation-aware responses are essential. Research & Analysis Assistants : Prototype research agents that aggregate, filter, and summarize findings across large document corpora while keeping links to original sources for verification. Embedded Semantic Search in Products : Integrate semantic search and retrieval into SaaS applications or internal tools, leveraging Agentset’s APIs and SDKs to provide natural-language query capabilities over custom data. FAQs
- What is Agentset?
- Who is Agentset designed for?
- Is Agentset open-source or paid?
- Which models and vector databases can I use with Agentset?
- What types of data can Agentset ingest?
- How does Agentset improve answer reliability?
- How do I integrate Agentset into my application?
- Can I start quickly without managing my own infrastructure?
