The shortlist
Ask questions over your documents with a visual platform or a code-first retrieval pipeline. Choose by who will maintain the system, where data travels and whether the answer can point to a supporting passage.
These are documentation and catalogue-based suggestions, not hands-on benchmarks. Check the linked product sources, supported languages, regional availability, permissions and current costs. Use the same evaluation worksheet for each candidate.
158k stars · Open source · TypeScript
A visual platform combining workflows, retrieval and agent capabilities, with cloud and self-hosted deployments. Check its actual license and edition boundaries, model charges and administration requirements.
Capability source ↗— You need deep programmatic control beyond what a visual platform allows.
27k stars · Open source · Python
A Python framework for modular retrieval pipelines and agent workflows. Evaluate the components and retrieval controls your documents need; a framework alone does not establish answer quality.
Capability source ↗30k stars · Open source · TypeScript
A knowledge-base and visual workflow platform to evaluate on your own documents. Check deployment requirements, the applicable license and how well it retrieves supporting passages.
Capability source ↗— You want to offer it as a SaaS — the license forbids that without commercial authorization.
RAG quality is a data problem before it's a model problem: bad chunking and stale documents produce confident wrong answers. Build a small eval set of real questions first, and re-index on a schedule — an assistant over last quarter's docs is worse than none.
