Skip to main content

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.

Your agents deserve fresh context.

Star us ❤️ → Star CocoIndex on GitHub — open-source Python framework for RAG, vector search, and live agent context  ·  cocoindex.io — the CocoIndex homepage: incremental data pipelines for AI agents  ·  CocoIndex documentation — quickstart, connectors, ops, transformations, target stores, RAG and knowledge graph recipes  ·  Join the CocoIndex Discord community — help, showcase, release notes, and live chat with maintainers

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

stars downloads pypi python rust license discord

CI release links

cocoindex-io/cocoindex | Trendshift




Built with CocoIndex ❤️

CocoIndex-code — flagship MCP server for AI coding agents. AST-aware incremental semantic code index that keeps live call graphs, symbols, vectors, and chunks fresh on every commit. 70% fewer tokens per turn, 80-90% cache hits on re-index, sub-second freshness. Supports Python, TypeScript, Rust, and Go. Features: Δ-only incremental processing, semantic search by meaning (not grep), call graphs and blast-radius analysis, global repo view for duplicates and architecture. Build coding agents (generate, refactor) and code-review agents (catch, approve). One install — Claude Code, Cursor, and other MCP-aware agents see your whole repository instantly. Keywords: MCP server, coding agent, code intelligence, AST chunking, semantic code search, call graph, vector embedding, repository context, Claude Code, Cursor, incremental indexing, blast radius.

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="./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.

Full quickstart — open-book icon linking to the CocoIndex documentation quickstart: pip install, declare sources and targets, run the incremental engine    Learn the concept — lightbulb icon linking to the CocoIndex core-concepts guide: sources, targets, flows, incremental engine, and data lineage

Animated GitHub Star button for the cocoindex-io/cocoindex repository: a cursor clicks the star, it fills yellow, confetti bursts, the star count ticks up, and an 'Appreciate a star if you like it!' caption with a beating heart shows below the button



React — for data engineering

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.

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), target dots-matrix on the right. Keywords: incremental indexing, change data capture, delta processing, fine-grained invalidation, code-aware caching, hash-of-code invalidation, memoization, reproducible pipelines, incremental recomputation.

See the React ↔ CocoIndex mental model →



Incremental engine for long-horizon agents

Data transformation for any engineer, designed for AI workloads —
with a smart incremental engine for always-fresh, explainable data.

Learn the concept — purple button with a lightbulb icon linking to the CocoIndex core-concepts guide: sources, targets, flows, incremental engine, and data lineage

CocoIndex's Python-native transformation flows connect 8 source categories (Codebases, Meeting Notes, Web · APIs, File System · Blob Stores, Databases, Message Queues, Images · Video, Voice · Transcripts) through the incremental engine out to 6 target stores (Relational DB, Data Warehouse, Vector DB, Graph DB, Message Queue, Feature Store). A flow.py code block (@coco.fn · def f(src): · chunks = split(src) · target.row(embed(chunks))) shows the shared pipeline; only the Δ is reprocessed — unchanged src hits the cache, changed src re-runs split() and Δ → re-embed. The persistent data-pipeline control plane runs eight always-on subsystems: live caching, pipeline catalog, version tracking, continuously learning, lineage, task scheduling, metrics collection, and failure management. Keywords: data pipeline, ETL, source connectors, vector database, graph database, incremental engine, streaming ingestion, caching, lineage, versioning, scheduling, metrics, retries.



Why incremental?

Your agents are only as good as the data they see.
Batch pipelines drift stale. CocoIndex stays live — and only runs the Δ.

Why incremental? — one illustration combining the four core benefits of CocoIndex's incremental engine. Sub-second fresh (mint): a stopwatch ticking under a second, source changes propagate to the target in under a second so agents see the world as it is, not as it was yesterday. 10× cheaper at scale (yellow): a 10,000-row corpus block where only a thin Δ 0.1% column re-runs and 99.9% stays cached — you skip the other 99.9% of your corpus and pay a fraction of the compute, embedding, and LLM bill. Explainable by default (coral): a lineage thread links a source byte (handbook.md L42) to a target vector — every vector, row, or graph node in the target traces back to its exact source byte for debuggable, auditable, regulator-friendly AI pipelines. Production-grade (purple): a shield stamped with the Rust crab surrounded by retry loops, back-off dots, a DLQ tray, and a no-data-loss check — Rust core with retries, exponential back-off, dead-letter queues, and no-data-loss guarantees, production-ready for long-horizon AI agents. Keywords: incremental indexing, Δ-only reprocessing, sub-second freshness, low-latency RAG, cost-efficient embeddings, data lineage, retrieval-augmented generation, Rust core, retries, back-off, dead letters, no data loss, long-horizon agents.



What can you build?

See all 20+ examples · updated every week →

Working starters from the examples tree — clone, plug your source, ship.

Real-time code index — walk a git repo, AST-chunk source files, embed with sentence-transformers, upsert to pgvector / LanceDB, incremental on every commit. Keywords: code search, code embedding, semantic code retrieval, Python.

PDF → RAG index — ingest PDFs from local, S3, or GDrive, extract + chunk text, embed chunks, upsert to pgvector / LanceDB. Classic retrieval-augmented-generation stack, incremental. Keywords: RAG, document Q&A, PDF search, vector database.

HN trending topics — pull Hacker News threads via Algolia, recursively parse comments, LLM-extract topics with Gemini 2.5 Flash, rank by weighted hit count (thread=5, comment=1), store in Postgres. Incremental. Keywords: Hacker News, trending topics, LLM extraction, Gemini, Postgres, news intelligence, topic ranking.

Conversation → knowledge graph — LLM extracts people, topics, decisions, action items from transcripts and upserts into Neo4j / Kuzu. Live graph, incremental. Keywords: knowledge graph, entity extraction, meeting intelligence, agent memory.

Multi-repo summarization — walk N git repos, extract structure, LLM-summarize per-repo + a rolled-up org summary, refresh on every push. Keywords: internal platform, developer experience, monorepo, SDK docs.

Structured extraction — BAML / DSPy typed schema extraction from forms, PDFs, intakes, invoices into Postgres / warehouse. Incremental. Keywords: ETL, LLM extraction, schema-first, patient intake, invoice processing, KYC, contracts.

Podcast → knowledge graph — transcribe YouTube / Spotify audio with speaker diarization, LLM-extract speakers and statements, resolve entities across episodes, store in SurrealDB / Neo4j. Keywords: podcast, diarization, YouTube, Whisper, SurrealDB, knowledge graph, entity resolution.

CSV → Kafka live — watch a folder of CSV files, publish each row as a JSON message to a Kafka topic via CocoIndex's Kafka target connector. Incremental, sub-second, no producer loop. Keywords: Kafka, CDC, streaming, StreamNative, Confluent, CSV ingestion, event streaming.


Share what you build — a banner with a trail of tiny hearts rising from the bottom behind the text, inviting the CocoIndex community to share projects built with the framework

Building something with CocoIndex? We want to see it.
Tag @cocoindex_io on X or drop a link in #showcase on Discord. We'll boost it. 🥥



Community

Join the CocoIndex Discord community — live chat with maintainers and users, showcase your projects, get help building RAG pipelines and knowledge graphs Subscribe to the CocoIndex YouTube channel — video tutorials, live demos, architecture deep dives, and AI agent recipes Read the CocoIndex blog — engineering deep dives, release notes, RAG and knowledge graph tutorials, and case studies Follow @cocoindex_io on X (formerly Twitter) for release notes, demos, launches, and AI data pipeline updates



We love Contributors — section title banner with a pulsing coral heart badge and cream twinkle sparkles. Every typo fix, new connector, and doc tweak makes CocoIndex better. Keywords: open-source contribution, pull request, typo fix, new connector, good first issue, Hacktoberfest, community, coconut heart.

We are so excited to meet you.
Every typo fix, new connector, doc tweak, or full-on rewrite makes CocoIndex better.
Come hang out — big PRs and small ones, both welcome.

📝 Read the contributing guide  ·  🐛 good first issues  ·  💬 Say hi on Discord



CocoIndex Enterprise

CocoIndex Enterprise — built for enterprise scale. Four headline stats for PB-scale incremental indexing: PB corpus scale incrementally indexed (coral), 10× fewer LLM embedding calls vs. full recompute (yellow), 100% lineage coverage with every byte traceable (mint), Δ only the delta always (sky). Below, a wide 50×8 corpus matrix of 400 dim tiles represents a petabyte-scale store where a single coral Δ slice of 8 tiles re-runs while the other 99.9% stays cached. Keywords: enterprise RAG, petabyte-scale indexing, incremental compute, delta-only, lineage, parallel chunking, zero-copy, failure isolation.

Large corpus — built for enterprise scale.

Incremental compute is the only way to keep large corpora fresh without re-embedding them every cycle.
CocoIndex scales from a single repo to petabyte-scale stores — parallel by default, delta-only by design.


Process once. Reconcile forever.

When a source changes, CocoIndex identifies the affected records, propagates the change
across joins and lookups, updates the target, and retires stale rows —
without touching anything that didn't change.


Built on a Rust engine.

The core is Rust — production-grade from day zero.
Parallel chunking, zero-copy transforms where possible, and failure isolation
so one bad record doesn't stall the flow.



Explore CocoIndex Enterprise — bright blue pill button linking to cocoindex.io/enterprise, the PB-scale incremental data pipeline for AI agents



Apache 2.0 · © CocoIndex contributors 🥥

Release files for cocoindex 1.0.24

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for cocoindex 1.0.24
File Size Uploaded
cocoindex-1.0.24.tar.gz 779.9 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for cocoindex 1.0.24
File
cocoindex-1.0.24-cp314-cp314t-win_amd64.whl CPython 3.14 CPython 3.14 free-threading Windows x86-64 Details
cocoindex-1.0.24-cp314-cp314t-manylinux_2_28_x86_64.whl CPython 3.14 CPython 3.14 free-threading Linux glibc 2.28+ x86-64 Details
cocoindex-1.0.24-cp314-cp314t-manylinux_2_28_aarch64.whl CPython 3.14 CPython 3.14 free-threading Linux glibc 2.28+ ARM64 Details
cocoindex-1.0.24-cp314-cp314t-macosx_11_0_arm64.whl CPython 3.14 CPython 3.14 free-threading macOS 11.0+ ARM64 Details
cocoindex-1.0.24-cp311-abi3-win_amd64.whl CPython 3.11 abi3 Windows x86-64 Details
cocoindex-1.0.24-cp311-abi3-manylinux_2_28_x86_64.whl CPython 3.11 abi3 Linux glibc 2.28+ x86-64 Details
cocoindex-1.0.24-cp311-abi3-manylinux_2_28_aarch64.whl CPython 3.11 abi3 Linux glibc 2.28+ ARM64 Details
cocoindex-1.0.24-cp311-abi3-macosx_11_0_arm64.whl CPython 3.11 abi3 macOS 11.0+ ARM64 Details
cocoindex-1.0.24-cp311-abi3-macosx_10_12_x86_64.whl CPython 3.11 abi3 macOS 10.12+ x86-64 Details

Total release size: 87.9 MB

Release files / cocoindex-1.0.24.tar.gz

Download URL cocoindex-1.0.24.tar.gz
Size 779.9 kB
Tags Source
SHA-256 checksum
How to use checksums
4b8a37a8f3395385e459913615effcec2420a7d765713e61dfe7ee6a0aa40a61
BLAKE2b-256 checksum
How to use checksums
60c051e837cc2c3ba6cc8e54adcbaa1d7fa2cc56427f912cdc9b14a190d6a632
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp314-cp314t-win_amd64.whl

Download URL cocoindex-1.0.24-cp314-cp314t-win_amd64.whl
Size 10.0 MB
Tags CPython 3.14 CPython 3.14 free-threading Windows x86-64
SHA-256 checksum
How to use checksums
9c8ccfe09a730e6a747d2a05f0d35d7a0059b7992b86c377356f87468c4a1b1f
BLAKE2b-256 checksum
How to use checksums
bea3ca185657df3a379b2551a47be3809cc10e8bfb8358d4d31ce0b3e0cb25e2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp314-cp314t-manylinux_2_28_x86_64.whl

Download URL cocoindex-1.0.24-cp314-cp314t-manylinux_2_28_x86_64.whl
Size 9.7 MB
Tags CPython 3.14 CPython 3.14 free-threading Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
9196bd67200a5e74357ba83149b3821284a3600374497645bc0b84f024bae4b7
BLAKE2b-256 checksum
How to use checksums
f2061de151c6b1fbb0c7c753c35a8e2ea6e04a926ff7edd8c0a3c676f3846965
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp314-cp314t-manylinux_2_28_aarch64.whl

Download URL cocoindex-1.0.24-cp314-cp314t-manylinux_2_28_aarch64.whl
Size 9.5 MB
Tags CPython 3.14 CPython 3.14 free-threading Linux glibc 2.28+ ARM64
SHA-256 checksum
How to use checksums
c7e2108e226fc0bdcf412778f95e1ff3ea919e1b80a30e63f46b79cace468ed7
BLAKE2b-256 checksum
How to use checksums
4b2289ed6e6eb096348c03a1b24703855005605ed1dba8dff3edbee526bd08a7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp314-cp314t-macosx_11_0_arm64.whl

Download URL cocoindex-1.0.24-cp314-cp314t-macosx_11_0_arm64.whl
Size 9.6 MB
Tags CPython 3.14 CPython 3.14 free-threading macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
b669c7031e1c9422ba0b55aa2c75ed77c7ec950171938abb0107a31c052385a0
BLAKE2b-256 checksum
How to use checksums
a56ea5e4002c211c64e1210c47ed7dde5c857177a1906ffcedbba63a7d65a03f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp311-abi3-win_amd64.whl

Download URL cocoindex-1.0.24-cp311-abi3-win_amd64.whl
Size 10.0 MB
Tags CPython 3.11 Windows x86-64 abi3
SHA-256 checksum
How to use checksums
63db183e920b760d852677ddc7ae960bfae339fbc7f55542191f5be1af8b6afb
BLAKE2b-256 checksum
How to use checksums
14e51b9fd2e01845411aafdaf50c5818357526af1fb75205f6102d769e6c047b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp311-abi3-manylinux_2_28_x86_64.whl

Download URL cocoindex-1.0.24-cp311-abi3-manylinux_2_28_x86_64.whl
Size 9.7 MB
Tags CPython 3.11 Linux glibc 2.28+ x86-64 abi3
SHA-256 checksum
How to use checksums
2800b0d9d56fd230b0c7fd56201ed7d5d3471835d0f3d48786a09cb05aeeca34
BLAKE2b-256 checksum
How to use checksums
8a2e8abbc5430bc39782446b142151270f5b9c3dbceb13bf96d81ba9f0ddaa03
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp311-abi3-manylinux_2_28_aarch64.whl

Download URL cocoindex-1.0.24-cp311-abi3-manylinux_2_28_aarch64.whl
Size 9.5 MB
Tags CPython 3.11 Linux glibc 2.28+ ARM64 abi3
SHA-256 checksum
How to use checksums
92fd3cd175dcf10d69a241c64cbd8cb5c592e23c54e18ff741270d07cd88d547
BLAKE2b-256 checksum
How to use checksums
98817d407f2bfb99e12dd401e666a4a07c39f04d68983bf6e65fe607a9e9bc3f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp311-abi3-macosx_11_0_arm64.whl

Download URL cocoindex-1.0.24-cp311-abi3-macosx_11_0_arm64.whl
Size 9.6 MB
Tags CPython 3.11 abi3 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
00f588ee7de50c0fdbd44d619975c833b6f571cf2033ca6dfd8af33a2ac71885
BLAKE2b-256 checksum
How to use checksums
71e98f0c4b2625ab0ad07d0bf2f168744488e54e3008099214f5213334c76ec5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release files / cocoindex-1.0.24-cp311-abi3-macosx_10_12_x86_64.whl

Download URL cocoindex-1.0.24-cp311-abi3-macosx_10_12_x86_64.whl
Size 9.5 MB
Tags CPython 3.11 abi3 macOS 10.12+ x86-64
SHA-256 checksum
How to use checksums
f0ee415663c3c82db2179bca468b2dafd828c65028102b7cccd649db75dc378e
BLAKE2b-256 checksum
How to use checksums
21649213e4c849fda0598bbcbbaeb30c952a40d3fb9ce3b382b17e1e4ff9b9c8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via maturin/1.15.0

Release history Release notifications | RSS feed

This release

1.0.24 This release

10 release files

1.0.9

10 release files

1.0.7

10 release files

1.0.6

10 release files

1.0.5

10 release files

1.0.4

10 release files

1.0.2

10 release files

1.0.1

10 release files

1.0.0

10 release files

0.3.28

6 release files

0.3.27

6 release files

0.3.26

6 release files

0.3.22

6 release files

0.3.21

6 release files

0.3.20

6 release files

0.3.19

6 release files

0.3.18

6 release files

0.3.12

6 release files

0.3.11

5 release files

0.3.10

5 release files

0.3.9

6 release files

0.3.8

6 release files

0.3.7

6 release files

0.3.6

6 release files

0.3.5

6 release files

0.3.4

6 release files

0.3.3

6 release files

0.3.2

6 release files

0.3.1

6 release files

0.3.0

6 release files

0.2.23

6 release files

0.2.22

6 release files

0.2.21

6 release files

0.2.20

6 release files

0.2.19

6 release files

0.2.16

6 release files

0.2.15

6 release files

0.2.14

6 release files

0.2.13

6 release files

0.2.12

6 release files

0.2.11

6 release files

0.2.10

6 release files

0.2.9

6 release files

0.2.8

6 release files

0.2.7

6 release files

0.2.6

6 release files

0.2.5

6 release files

0.2.4

6 release files

0.2.3

6 release files

0.2.2

18 release files

0.2.1

18 release files

0.2.0

18 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page