cocoindex vs swiftide
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 — Rust framework for LLM apps: an agent harness, compile-time-typed task graphs, and streaming RAG pipelines — MCP toolboxes, human-in-the-loop approval, tracing with Langfuse support.
Two Rust-core indexing engines: CocoIndex is declarative — Target = F(Source), incrementally synced, driven from Python; Swiftide is compositional — streaming pipelines you assemble in Rust, with agents and task graphs living in the same framework.
| cocoindex | swiftide | |
|---|---|---|
| Stars | 11k | 748 |
| Forks | 859 | 65 |
| Language | Rust | Rust |
| License | Apache-2.0 | MIT |
| Last activity | today | today |
| Topics | rag | rag, agents |
| Curated connections | 4 | 2 |
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.
swiftide — the curator's take
The Rust-native full stack — harness, task graphs, streaming indexing — for teams who want LLM logic inside a Rust codebase instead of bolting on a Python sidecar. Typed task graphs are the real differentiator: hand-offs and fan-out/join results checked at compile time, a correctness class Python graph frameworks structurally can't offer. Read ★748 correctly — it measures the size of Rust's LLM niche, not quality; the project is mature, actively maintained and properly documented. When NOT: if your retrieval stack leans on the long tail of Python-ecosystem loaders, rerankers and eval tooling, Rust's integration surface is thinner and you will write the glue yourself.