All 140 Dependencies
Every package cocoindex depends on, ranked by repo health score.
CocoIndex is an open-source framework that solves one of the most persistent problems in production AI applications: stale context. Traditional RAG pipelines re-index entire datasets on every run, wasting compute and introducing latency. CocoIndex flips this model by tracking exactly which source documents changed and recomputing only the affected delta — so your vector stores, knowledge graphs, and search indexes stay continuously synchronized with their upstream sources.
The framework ships a declarative Python API where you describe the desired state of your index — sources, transformations, and target stores — and CocoIndex's incremental engine maintains that contract automatically. Functions marked with `memo=True` are content-addressed by hash, meaning identical inputs never get re-embedded or re-transformed, regardless of how many times you run the pipeline. This cache-first approach delivers dramatic savings: typical re-indexing runs achieve 80-90% cache hit rates on unchanged content.
Under the hood, CocoIndex's core engine is written in Rust and exposed to Python via PyO3, giving you native performance for the heavy lifting while retaining the expressiveness of Python for pipeline definition. The connector ecosystem spans local filesystems, PostgreSQL, Google Drive, Amazon S3, Kafka, Neo4j, FalkorDB, and a dozen vector stores including pgvector, Qdrant, LanceDB, and SurrealDB. A first-party MCP server called CocoIndex-code extends the framework into AI coding agents, giving tools like Claude Code and Cursor AST-aware, incrementally refreshed views of entire repositories.
CocoIndex targets teams building production AI systems that require reliable, low-latency context retrieval at scale. Whether you are building a semantic code search tool, a meeting-notes knowledge graph, a multi-modal image search pipeline, or a document Q&A system, CocoIndex handles the infrastructure of keeping derived indexes in sync so application logic can focus on reasoning rather than data plumbing.