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 🥥

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.0.20)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for cocoindex from gravatar.com cocoindex Avatar for georgehe from gravatar.com georgehe

Credits

Author: CocoIndex

License

Apache Software License

Requires

Python >=3.11

Tags

indexing real-time incremental pipeline search ai etl rag dataflow context-engineering

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cocoindex-1.0.17.tar.gz (730.4 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

cocoindex-1.0.17-cp314-cp314t-win_amd64.whl (9.6 MB view details)

Uploaded CPython 3.14tWindows x86-64

cocoindex-1.0.17-cp314-cp314t-manylinux_2_28_x86_64.whl (9.4 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.28+ x86-64

cocoindex-1.0.17-cp314-cp314t-manylinux_2_28_aarch64.whl (9.2 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.28+ ARM64

cocoindex-1.0.17-cp314-cp314t-macosx_11_0_arm64.whl (9.3 MB view details)

Uploaded CPython 3.14tmacOS 11.0+ ARM64

cocoindex-1.0.17-cp311-abi3-win_amd64.whl (9.6 MB view details)

Uploaded CPython 3.11+Windows x86-64

cocoindex-1.0.17-cp311-abi3-manylinux_2_28_x86_64.whl (9.4 MB view details)

Uploaded CPython 3.11+manylinux: glibc 2.28+ x86-64

cocoindex-1.0.17-cp311-abi3-manylinux_2_28_aarch64.whl (9.2 MB view details)

Uploaded CPython 3.11+manylinux: glibc 2.28+ ARM64

cocoindex-1.0.17-cp311-abi3-macosx_11_0_arm64.whl (9.3 MB view details)

Uploaded CPython 3.11+macOS 11.0+ ARM64

cocoindex-1.0.17-cp311-abi3-macosx_10_12_x86_64.whl (9.2 MB view details)

Uploaded CPython 3.11+macOS 10.12+ x86-64

File details

Details for the file cocoindex-1.0.17.tar.gz.

File metadata

  • Download URL: cocoindex-1.0.17.tar.gz
  • Upload date:
  • Size: 730.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: maturin/1.14.1

File hashes

Hashes for cocoindex-1.0.17.tar.gz
Algorithm Hash digest
SHA256 d508fbaa3b5b81ee6b74121477a60c23c812965779bc5160145a469dd5df6aad
MD5 fb50275540e3c381795fae378908e9fc
BLAKE2b-256 7520b9a47f44aef67ceadb1e9475960d3dabd627f4c5dd6b0e55b75444d77f8d

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp314-cp314t-win_amd64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp314-cp314t-win_amd64.whl
Algorithm Hash digest
SHA256 105ad7d976792047a3b23a556dd8b22866a5296f8c1f184c6c979131a2ca78f2
MD5 d59d80d8194718adc32fde1f05329f69
BLAKE2b-256 f350fa1e6da58b55309b57f2da051a2c5b428ea41c1ef67c2bdc5038b6faa0bf

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp314-cp314t-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp314-cp314t-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6634ec3e3bd1bf81fb97e66c31de6ddcdb625ea3e533dd1065607b930b051a18
MD5 40215e52475a31b7259b06c9f2992824
BLAKE2b-256 d8a0123746a3c516a1335f2bf1b4dcfd061a34240337fc15152811c4a6478123

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp314-cp314t-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp314-cp314t-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 fc348e8850dfff079b14df3a43933ba4cc1f552a90ebd35f6c1ae044e33434f7
MD5 2394f6f01bd7d65ec51b0ab53337033d
BLAKE2b-256 a8afcf3efc21558a37fe384720336c8510320575c4325955ce273c255f825ca0

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp314-cp314t-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp314-cp314t-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e512c56528e801ac7ed7060d425e332e7d58484611cd43a9e76779d7d6ba9cc5
MD5 55910c617c22d52e8187875e07c0765b
BLAKE2b-256 06325d5073610ad1e8f7afbb5b6314b0acdd7290599b6688553c12c84dce0c39

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp311-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp311-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 7829128b83cc1dd578b9ba3ca9298b74fc0737f9053c2efe7cdfe39dd62f29d1
MD5 837557db712310a8b286c21466f89875
BLAKE2b-256 240b6e0b1a90dd7e5ba58b74753c5f37c23138aa2fe3899541985d02788cc0f4

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp311-abi3-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp311-abi3-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 da11d944493f480ac463104ac2e829610eff6896e23e578baa7aad5555be132e
MD5 5e9430b4b16572bdda2028e9f5284f19
BLAKE2b-256 f6817184521cabe7ddf84449d3dca490ef06de7954e52d60a56ca3bc7529adf2

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp311-abi3-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp311-abi3-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 afcfb15d438927fb1e9f506053d402c5729c8503211639b86182e951acc0b421
MD5 9d163b05d7a645ce54d7c97521e4cb23
BLAKE2b-256 75f7414b8c9662c33cb5975143c6b477c7214f865b4077a2881a769accf8810c

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp311-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp311-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 74e37c91fcdb09b692fe1ebe176ecef8149eba413c20bbf5de91948db2ea2288
MD5 309a4c75ad1aa653cb17fc187e837951
BLAKE2b-256 a929bf8ff83ab07a4303227dc80421a2daacbd66774347182e5f2a34837c1f2e

See more details on using hashes here.

File details

Details for the file cocoindex-1.0.17-cp311-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for cocoindex-1.0.17-cp311-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 154091d11f2deaf15f5bd184e9a88097ecc502625074c282a5fadeb33a129aed
MD5 2fa1019a6e699016d4d7233dc0c5aefb
BLAKE2b-256 cbbd03f5e734821fb72f31ef6bdbdefb2405ecbefec233b395e403f9e49b8326

See more details on using hashes here.

Release history Release notifications | RSS feed

1.0.20

1.0.19

1.0.18

This release

1.0.17

1.0.16

1.0.15

1.0.14

1.0.13

1.0.11

1.0.10

1.0.9

1.0.8 yanked

1.0.7

1.0.6

1.0.5

1.0.4

1.0.3

1.0.2

1.0.1

1.0.0

1.0.0a50 pre-release

1.0.0a49 pre-release

1.0.0a48 pre-release

1.0.0a47 pre-release

1.0.0a46 pre-release

1.0.0a45 pre-release

1.0.0a44 pre-release

1.0.0a43 pre-release

1.0.0a42 pre-release

1.0.0a41 pre-release

1.0.0a40 pre-release

1.0.0a39 pre-release

1.0.0a38 pre-release

1.0.0a37 pre-release

1.0.0a36 pre-release

1.0.0a35 pre-release

1.0.0a34 pre-release

1.0.0a33 pre-release

1.0.0a32 pre-release

1.0.0a31 pre-release

1.0.0a30 pre-release

1.0.0a29 pre-release

1.0.0a28 pre-release

1.0.0a27 pre-release

1.0.0a26 pre-release

1.0.0a25 pre-release

1.0.0a24 pre-release

1.0.0a23 pre-release

1.0.0a22 pre-release

1.0.0a21 pre-release

1.0.0a20 pre-release

1.0.0a19 pre-release

1.0.0a18 pre-release

1.0.0a17 pre-release

1.0.0a16 pre-release

1.0.0a15 pre-release

1.0.0a14 pre-release

1.0.0a13 pre-release

1.0.0a12 pre-release

1.0.0a11 pre-release

1.0.0a10 pre-release

1.0.0a9 pre-release

1.0.0a8 pre-release

1.0.0a7 pre-release

1.0.0a6 pre-release

1.0.0a5 pre-release

1.0.0a4 pre-release

1.0.0a2 pre-release

1.0.0a1 pre-release

0.3.39

0.3.38

0.3.37

0.3.36

0.3.35

0.3.34

0.3.33

0.3.32

0.3.31

0.3.30

0.3.29

0.3.28

0.3.27

0.3.26

0.3.25

0.3.24

0.3.23

0.3.22

0.3.21

0.3.20

0.3.19

0.3.18 yanked

Reason this release was yanked:

bug

0.3.17

0.3.16

0.3.15

0.3.14

0.3.13

0.3.12

0.3.11

0.3.10

0.3.9

0.3.8

0.3.7

0.3.6

0.3.5

0.3.4

0.3.3

0.3.2 yanked

Reason this release was yanked:

0.3.3 makes batching more stable on Gemini

0.3.1 yanked

Reason this release was yanked:

0.3.3 makes batching more stable on Gemini

0.3.0

0.2.23

0.2.22

0.2.21

0.2.20

0.2.19

0.2.18

0.2.17

0.2.16

0.2.15

0.2.14

0.2.13

0.2.12

0.2.11

0.2.10

0.2.9

0.2.8

0.2.7

0.2.6

0.2.5

0.2.4

0.2.3

0.2.2

0.2.1

0.2.0

0.1.83

0.1.82

0.1.81

0.1.80

0.1.79

0.1.78

0.1.77

0.1.76

0.1.75

0.1.74

0.1.73

0.1.72

0.1.71

0.1.70

0.1.69

0.1.68

0.1.67

0.1.66

0.1.65

0.1.64

0.1.63

0.1.62

0.1.61

0.1.60

0.1.59

0.1.58

0.1.57

0.1.56

0.1.55

0.1.54

0.1.53

0.1.52

0.1.51

0.1.50

0.1.49

0.1.48

0.1.47

0.1.46

0.1.45

0.1.44

0.1.43 yanked

Reason this release was yanked:

bug

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page