Agentset is an open-source RAG platform positioned as an end-to-end tool for building, evaluating, and shipping production-grade RAG and agent applications.
Purpose Agentset is aimed at developers and teams that need to put retrieval-augmented generation (RAG) or agent applications into production. It covers the complete process from data ingestion to final managed deployment, helping users reduce the repetitive setup work involved in retrieval, indexing, evaluation, and deployment.
Main capabilities
- Out-of-the-box RAG: includes data ingestion, chunking, embedding, and retrieval.
- Model-agnostic: can be used with the user's choice of LLM, embedding model, and vector database.
- Chat playground: supports message editing and citation display.
- Production hosting: provides preview links and custom domains.
- API and typed SDKs, with an OpenAPI specification.
- Built-in multi-tenancy support.
- Built-in capabilities such as citations, deep research, 22+ file formats, partitioning, and MCP servers.
- Evaluation and benchmarking tools.
How to use Officially, two starting paths are provided:
- Managed use: visit https://app.agentset.ai/login to sign up. The official description offers generous free credits, including 1,000 pages and 10,000 retrievals, with no credit card required.
- Self-hosting: refer to the complete guide https://docs.agentset.ai/open-source/prerequisites。
Useful scripts mentioned in the local development quick start include:
- bun db:studio – open Prisma Studio
- bun dev:web – run only the web app
Tech stack Agentset is built with TypeScript, Next.js, AI SDK, Prisma, Supabase, and Trigger.dev.
Use cases Suitable for development teams that need to quickly build and ship RAG or agent applications, especially projects that want to choose their own models and vector databases and need multi-tenancy, citation display, evaluation benchmarks, or production hosting capabilities.
Limitations and additions Specific pricing, platform support scope, performance metrics, and similar details are not provided in the materials. The project welcomes contributions: you can submit issues to report bugs and feature ideas, submit focused PRs, or improve the documentation and examples.
