RAG & Knowledge
ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
★ 91.6k starsApache-2.0
View guide →Incremental engine that keeps RAG indexes and agent context fresh by processing only the delta.
cocoindex is an open-source rag & knowledge project by cocoindex-io (cocoindex-io/cocoindex on GitHub). It has 11,653 GitHub stars and 910 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 7 October 2026.
Pick a source (a docs folder, a codebase, Slack, meeting transcripts) and the target it should feed, such as a pgvector table for RAG.
Add the Python package to your project with pip.
Write a small Python flow: walk the source, split and embed content, and declare rows and a vector index in the target - the quickstart example is about twenty lines.
Run the app once to backfill; run it again any time (or continuously) and only changed files re-embed, keeping the index fresh at a fraction of the cost.
Only the change - in source data or in your transformation code - is reprocessed; unchanged content hits the cache, so freshness costs a fraction of full recomputation.
You declare the desired target state in plain Python functions rather than hand-managing DAGs, state and invalidation.
Every vector, row or graph node traces back to its exact source byte, making pipelines debuggable and auditable.
Connectors span codebases, file systems and blob stores, databases, message queues, images, video and transcripts; targets include vector, graph and relational stores, warehouses, queues and feature stores.
The core engine is Rust, with retries, exponential back-off, dead-letter queues and failure isolation so one bad record does not stall the flow.
20+ working starters: real-time code indexing with AST chunking, PDF-to-RAG, conversation-to-knowledge-graph, structured extraction, podcast transcription graphs and CSV-to-Kafka streaming.
Clone CocoIndex for the examples tree; most users install the package from PyPI instead.
git clone https://github.com/cocoindex-io/cocoindex.git
cd cocoindexInstall CocoIndex into your Python environment.
pip install -U cocoindexDefine an App that walks a docs folder, chunks and embeds it into Postgres, then run its update - re-runs only process the delta.
python main.py # your flow file - see the README quickstart for the exact codeCocoIndex is developed by cocoindex-io and open-source contributors, with an additional Enterprise offering for very large corpora.
Yes. The open-source engine is Apache-2.0 licensed.
Only for the models you choose in your transformations: local embedding models need no key, while hosted LLM or embedding APIs use your own credentials.
Those move or schedule batch jobs; CocoIndex is a stateful incremental engine that tracks per-record provenance and recomputes only the delta when sources or code change.
Run pip install -U cocoindex and copy the quickstart flow: declare a source, a splitter, an embedding target, and run the app.
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