<img src="https://cocoindex.io/blobs/github/homepage/enterprise-hero-light.svg" alt="Enterprise corpus — codebase, Slack, meeting notes, and documentation — flowing continuously through the CocoIndex incremental sync engine into a production AI agent with always-fresh context. Only the Δ (delta) is reprocessed on every change. Keywords: RAG pipeline, agent memory, enterprise retrieval, AI agent context, live indexing, retrieval-augmented generation, production LLM apps, streaming ETL, incremental ingestion." width="100%" draggable="false"/>

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CocoIndex turns codebases, meeting notes, inboxes, Slack, PDFs, and videos into live, continuously fresh context for your AI agents and LLM apps to reason over effectively — with minimal incremental processing. Get your production AI agent ready in 10 minutes with reliable, continuously fresh data — no stale batches, no context gap

Incremental · only the delta  ·  Any scale · parallel by default  ·  Declarative · Python, 5 min

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Get started

pip install -U cocoindex

Declare what should be in your target — CocoIndex keeps it in sync forever, recomputing only the Δ.

import cocoindex as coco
from cocoindex.connectors import localfs, postgres
from cocoindex.ops.text import RecursiveSplitter

@coco.fn(memo=True)                          # ← cached by hash(input) + hash(code)
async def index_file(file, table):
    for chunk in RecursiveSplitter().split(await file.read_text()):
        table.declare_row(text=chunk.text, embedding=embed(chunk.text))

@coco.fn
async def main(src):
    table = await postgres.mount_table_target(PG, table_name="docs")
    table.declare_vector_index(column="embedding")
    await coco.mount_each(index_file, localfs.walk_dir(src).items(), table)

coco.App(coco.AppConfig(name="docs"), main, src="https://github.com/cocoindex-io/cocoindex/raw/v1.0.16/docs").update_blocking()

Run once to backfill. Re-run anytime — only the changed files re-embed.

Building with an AI coding agent?

Drop in our CocoIndex skill so your agent writes correct v1 code — concepts, APIs, patterns, all in one file.

_{See Use with AI coding agents for install steps.}

React — for data engineering

<img src="https://cocoindex.io/blobs/github/homepage/react4de-hero-light.svg" alt="React — for data engineering. The CocoIndex mental model: Target = F(Source). A persistent-state-driven dataflow where you declare the desired target state and the engine keeps it in sync with the latest source data and code, forever, at low latency and low cost. Source files (.py, .md, .pdf, .ts) flow through your Python transformation F into a live target dots-matrix index; only the Δ is reprocessed on every change, and every target dot traces back to its exact source byte. Four core properties: Python not a DAG (sky), declare target state (yellow bullseye), lineage end-to-end (coral connected dots), and incremental at any scale (mint Δ+1). Your code is as simple as the one-off version — the engine does the rest. Keywords: React for data engineering, declarative ETL, persistent state, data lineage, dataflow, Δ only, incremental indexing, CocoIndex." width="100%"/>



















<img src="https://cocoindex.io/blobs/github/homepage/either-side-change-light.svg" alt="What happens when either side changes — CocoIndex tracks per-row provenance so the Δ propagates at minimum cost. Two scenarios shown in one illustration: (top) Source change — one file (b.md) is edited and only one target dot re-syncs (coral pulse). (bottom) Code change — the transformation function F is rewritten from v1 to v2 and only the dots whose outputs depend on the changed code re-run (amber/yellow pulses). Source on the left, F in the center (Python code block