txtaiAll-in-one AI framework around an embeddings database — dense, sparse, graph and relational fused — with pipelines, workflows, agents and MCP/web APIs. Python, bindings for JS/Java/Rust/Go.
Why switchSame job — the data-to-LLM framework layer. LlamaIndex bets on a vast connector ecosystem; txtai bets on one coherent embeddings database with pipelines and workflows built in.
Full comparison → cocoindexRust-core incremental indexing engine: declare Target = F(Source) in Python and it keeps vector/graph/relational targets fresh forever, reprocessing only the delta — with per-row lineage.
Why switchBoth connect LLMs to your data, at different layers: LlamaIndex is the retrieval framework (loaders, indexes, query engines) typically run as batch ingestion; CocoIndex is the incremental sync engine that keeps whatever store you target continuously fresh, delta-only, with lineage.
Full comparison → PageIndexVectorless, reasoning-based RAG — builds a hierarchical tree index from long documents so an LLM retrieves by relevance instead of similarity. No chunking, no embeddings, no vector DB.
Why switchBoth index documents for LLM retrieval; llamaindex is the general vector/index data framework, PageIndex is a reasoning-tree approach purpose-built for long documents.
Full comparison → searchboxAirgapped closed-corpus QA testbed: a local Qwen agent in a Pi harness explores a .zip dataroom with grep/embeddings/rerankers under a token budget — a bed to study search as test-time compute.
Why switchBoth answer questions over a private corpus, but by opposite paradigms. LlamaIndex is a production RAG framework: build a structured index, then query it. searchbox is an airgapped research harness where an agent explores the raw corpus with grep/embed/rerank tools under a budget — no prebuilt index. Use LlamaIndex to ship RAG, searchbox to study agentic retrieval.
Full comparison →