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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

Built with CocoIndex ❤️

See all 20+ examples · updated every week →

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://raw.githubusercontent.com/cocoindex-io/cocoindex/HEAD/./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.}