APIs for building AI chat and search
Open-source RAG infrastructure that survives production workloads. Upload documents, query via API, get answers with sources. Hybrid search, multimodal, complex reasoning - all included. Model-agnostic. Used by 1,500+ teams in medical AI, legal tech, enterprise search.
Agentset automatically cites sources for every answer, allowing users to inspect and verify the grounding. This is critical for legal, medical, and financial use cases where accuracy and traceability are non-negotiable.
Agentset's pricing scales from free (1,000 pages) to Pro ($49/month for 10,000 pages) to Enterprise (unlimited). Users can upgrade or downgrade anytime with prorated billing. The platform handles scaling complexity automatically, eliminating the need to rewrite infrastructure as data volume and usage grow.
Agentset handles 22+ file formats including PDFs, images, tables, and graphs natively. It supports metadata filtering, automatic chunking, and hybrid search with reranking to deliver accurate answers across large knowledge bases. Benchmarks show industry-leading performance on MultiHopQA and FinanceBench.
Agentset offers JavaScript and Python SDKs, AI SDK integration, MCP server support, and model-agnostic architecture. Developers can select their own LLM, embedding model, and vector database while Agentset handles retrieval, ranking, and citation generation automatically.
Agentset provides RAG-as-a-service with production-ready accuracy, automatic citations, semantic search, and agentic reasoning built-in. Developers can ship reliable AI features in minutes using TypeScript/Python SDKs instead of spending months building and tuning RAG pipelines from scratch.