cocoindex vs LlamaIndex
Rust-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. — versus — Data framework for connecting custom data sources to LLMs — ingestion, indexing, retrieval.
Both 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.
| cocoindex | LlamaIndex | |
|---|---|---|
| Stars | 11k | 51k |
| Forks | 828 | 7.8k |
| Language | Rust | Python |
| License | Apache-2.0 | MIT |
| Last activity | today | today |
| Topics | rag | rag, agents |
| Curated connections | 2 | 8 |
cocoindex — the curator's take
The mental model sells it — 'React for data engineering': you declare what the index should contain, and the engine reconciles it against source changes forever, re-running only affected rows (cached by hash of input AND code, so editing your transform also invalidates precisely). That's the honest answer to stale agent context: sub-second freshness at a fraction of the re-embedding bill, with every vector traceable to its source byte. Sources span code, PDFs, Slack, audio; targets span pgvector, LanceDB, Neo4j, Kafka. The flagship application is cocoindex-code, an AST-aware incremental code-index MCP for coding agents. Use it when your corpus changes constantly and batch re-indexing is bleeding you; overkill for a static document pile — any one-shot RAG ingester handles that.
LlamaIndex — the curator's take
Data framework for connecting custom data sources to LLMs — ingestion, indexing, retrieval.