{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/20"},{"label":"Indexing","url":"https://skillfed.io/packages/category/text-processing-indexing"}],"enrichment":{"capability":"CocoIndex maintains a live, incrementally-updated index of codebases, documents, and other sources for AI agents and LLM applications, recomputing only the changed portions rather than re-processing everything.","skillfed_tags":["incremental-indexing","rag-pipeline","ai-agents"],"use_cases":["Keep a RAG vector store continuously fresh as source documents change, re-embedding only modified or new chunks.","Maintain a live codebase index for AI coding agents, updating only files that have changed since the last run.","Build an incremental ETL pipeline that transforms and loads data into a data warehouse, processing only deltas.","Index meeting notes and Slack messages in real-time for agent context without re-processing the entire conversation history.","Reconcile changes across joined or looked-up records in a data pipeline without touching unchanged rows."],"what_it_does":"CocoIndex is a Python library that builds and maintains live indexes of source data\u2014codebases, documents, PDFs, Slack messages, and videos\u2014for AI agents and LLM applications. Rather than re-processing entire datasets on each update, it uses an incremental engine to identify and recompute only the changed portions (the delta), keeping derived indexes fresh without the overhead of full re-embedding or re-indexing cycles. The library is built on a Rust core for production-grade performance and includes connectors for common sources (local filesystems, databases) and targets (PostgreSQL, vector stores).\n\nYou declare your transformation logic in Python using decorators and async functions, then CocoIndex handles the incremental scheduling, change propagation, and state management. It's designed for data engineering workflows in AI\u2014particularly RAG pipelines and agent context management\u2014where stale or incomplete data undermines agent reasoning. The package supports modern Python (3.11\u20133.14), runs on macOS, Linux, and Windows, and carries no known security vulnerabilities.","worth_installing":"Yes, if you are building AI agents or LLM applications that need fresh context and want to avoid the cost and latency of full re-indexing on every update. The incremental engine, Rust-backed performance, and Apache 2.0 license make it production-ready. Install friction is moderate due to dependencies, but wheels are widely available. No security vulnerabilities are known. Not necessary if your data is small, infrequently updated, or you don't need real-time freshness."},"id":"cocoindex","links":{"html":"https://skillfed.io/packages/cocoindex","md":"https://skillfed.io/packages/cocoindex.md","pypi":"https://pypi.org/project/cocoindex/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":null,"license_treatment":"permissive","name":"cocoindex","python_support":"supports_current","summary":"With CocoIndex, users declare the transformation, CocoIndex creates & maintains an index, and keeps the derived index up to date based on source update, with minimal computation and changes."},"popularity":{"monthly_downloads":102206,"position":12884,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.20"}