PageIndex

PageIndex takes a fundamentally different approach to document retrieval: no embeddings, no chunking, no vector database. Instead, it builds a tree-structured index and uses an LLM agent to navigate it based on conversation context. This yields answers that are precise and fully traceable to source text — a major win for compliance-sensitive use cases. The recently announced File System layer extends this to millions of documents, making it more scalable. We'd reach for this when we need to query long, complex documents (technical manuals, financial reports) and can't afford information loss from chunking. Where it bites: it's still early-stage (launched 2025), pricing is undisclosed, and latency is likely higher than vector-based alternatives due to the LLM reasoning step. Compared to traditional RAG (e.g., LlamaIndex, Chroma), PageIndex trades simplicity and speed for interpretability and accuracy. It's best for teams that prioritize grounding and auditability over raw speed. The open-source OpenKB (announced April 2026) adds a knowledge base layer, but production readiness at scale is unproven. If you're already deep in the vector DB ecosystem and need low latency, pass. But if chunking headaches and opaque retrieval drive you crazy, PageIndex is worth a test drive.