RDF Triple Store Databases – Open Source Integrated AI and Semantic Tech

Flexible GraphRAG v0.9.0 and KG Spaces v0.9.0 released, with support for Hyland OpenArch

X.com Steve Reiner @stevereiner LinkedIn Steve Reiner LinkedIn Posts

Repost of my LinkedIn post from October 27, 2026 (original LinkedIn post)

Flexible GraphRAG v0.9.0 and KG Spaces v0.9.0 are released, with support for Hyland OpenArch.

Hyland OpenArch federates unstructured, AI-ready data from all sources across an organization and makes it available to AI services and agents while always respecting permissions. GitHub: https://github.com/Hyland/OpenArch

In OpenArch mode, OpenArch is the search, vector, and document state store. Flexible GraphRAG sends it parsed text from its doc processors (Docling, LlamaParse, LiteParse) plus embedding chunks, and keeps building knowledge graphs into any of 15 property graph databases and/or 4 RDF stores. Search and AI query fuse graph and RDF results with OpenArch search and vector results.

All 14 data sources work with OpenArch, 10 with incremental auto-sync:
Auto-sync: Alfresco, Nuxeo, SharePoint, OneDrive, Box, Google Drive, Amazon S3, Google Cloud Storage, Azure Blob Storage, Filesystem
Without: CMIS, Web Pages, Wikipedia, YouTube

Builds on the 0.8.x permission work: per-source sign-in/sign-out, results filtered to what each user can see (UI and MCP), and filter pills to pick which sources feed hybrid search and AI chat. v0.9.0 adds separate Graph and RDF columns to the Processing table for per-file/folder options.

The projects

🔹 Flexible GraphRAG: Python backend, doc processing, KG building, ontologies, RAG/GraphRAG, Angular/React/Vue UIs, MCP server
GitHub: https://github.com/stevereiner/flexible-graphrag

🔹 python-openarch-api: Python client for OpenArch’s REST API
GitHub: https://github.com/stevereiner/python-openarch-api

🔹 KG Spaces for Alfresco ACA: “Add to KG Spaces” (ingest, or manage search+vector, graph, RDF, auto-sync) and “Ask KG Spaces about this folder or document”
GitHub: https://github.com/stevereiner/kg-spaces-aca

🔹 KG Spaces for Nuxeo (new): runs in its own tab; the Add/Ask menus in Nuxeo Web UI switch to it, and view links switch back
GitHub: https://github.com/stevereiner/kg-spaces-nuxeo

🔹 KG Spaces Deployment: one Docker Compose with Alfresco, Nuxeo, Hyland OpenArch, Flexible GraphRAG and both KG Spaces UIs; pick your graph DB and/or RDF store
GitHub: https://github.com/stevereiner/kg-spaces-deployment

📸 The image: KG Spaces landing page, Nuxeo Web UI with Add to KG Spaces, KG Spaces for Nuxeo AI query, and KG Spaces for ACA AI chat, with search filter pills from ingesting all 14 sources.

Flexible GraphRAG v0.7.2 is out: Hyland Nuxeo data source, OAuth2 and ticket auth for Alfresco, OAuth2 for the MCP server

Flexible GraphRAG on GitHub

X.com Steve Reiner @stevereiner LinkedIn Steve Reiner LinkedIn Posts

Repost of my LinkedIn post from August 10, 2026 (original LinkedIn post)

Flexible GraphRAG v0.7.2 is out 🚀
🆕 Hyland Nuxeo is now a supported data source, with OAuth2, token, and basic authentication.
🔐 Hyland Alfresco data source gains OAuth2 and ticket authentication in addition to basic authentication.
🔑 The Flexible GraphRAG MCP Server optionally supports OAuth2 on the MCP transport itself.

📡 ActiveMQ carries Alfresco add, delete, and modify events, and Kafka carries Nuxeo document change events off the audit stream.

Alfresco and Nuxeo authentication works from the UI or through MCP, with or without the auto-sync incremental update system enabled, and on either the default pipeline or the Langflow visual ingest pipeline.

The python-alfresco-mcp-server was also updated, adding OAuth2 and ticket auth plus optional MCP transport OAuth2. Both projects build on the updated python-alfresco-api, which provides the enhanced OAuth2, ticket, and basic authentication.

Flexible GraphRAG is an Apache 2.0 open source AI context platform: a document processing pipeline (Docling, LlamaParse, or LiteParse), knowledge graph auto-building, ontologies and schemas, many LLM providers, GraphRAG and RAG, hybrid semantic search (fulltext, vector, property graph, RDF/SPARQL), AI query, and AI chat.

The backend is Python, with both LlamaIndex and LangChain fully supported. Angular, React, and Vue TypeScript frontends and an MCP server are included.

The stack supports 14 data sources (10 with incremental auto-sync), 15 property graph databases, 4 RDF triple stores, 10 vector databases, and OpenSearch / Elasticsearch / BM25 search, plus Alfresco and Nuxeo. All databases and dashboards can be enabled with the provided Docker Compose layout.

Optionally, the ingest pipeline, hybrid search, and AI query can run through customizable Langflow visual flows (12 custom components).

flexible-graphrag github: https://github.com/stevereiner/flexible-graphrag

python-alfresco-mcp-server github: https://github.com/stevereiner/python-alfresco-mcp-server

python-alfresco-api github: https://github.com/stevereiner/python-alfresco-api

Flexible GraphRAG v0.6.0 released: Full LangChain support in addition to LlamaIndex, 15 graph, 4 RDF, 10 vector, 3 search databases

Flexible GraphRAG on GitHub

X.com Steve Reiner @stevereiner LinkedIn Steve Reiner LinkedIn Posts

Repost of my LinkedIn post from May 15, 2026 (original LinkedIn post)

Flexible GraphRAG v0.6.0 released: Full LangChain support in addition to LlamaIndex, more DBs: 15 graph (8 + Arango, Apache AGE, Microsoft Azure Cosmos for Gremlin, SurrealDB, Google Cloud Spanner, HugeGraph, TigerGraph), 4 RDF (3 + Amazon Neptune RDF), 10 vector, 3 search.

GitHub repo: https://github.com/stevereiner/flexible-graphrag

Flexible GraphRAG is an Apache 2.0 open source AI context platform supporting a document processing pipeline (Docling or LlamaParse), knowledge graph auto-building, ontologies, schemas, many LLM providers, GraphRAG and RAG, hybrid semantic search (fulltext, vector, property graph, RDF/SPARQL), AI query, and AI chat. The backend is Python. Angular, React, and Vue TypeScript frontends and an MCP server are included. The stack supports 13 data sources (9 with incremental auto-sync), 15 property graph databases, 4 RDF triple stores , 10 vector databases, OpenSearch / Elasticsearch / BM25 search, and Alfresco.

Goals for added Flexibility with Frameworks and Pipelines:
1. More Databases
2.a. Framework Independence (with adapter layer)
2.b. support both LangChain and LlamaIndex python application / agent developers (will need python apis not just current REST and MCP).
3. Flexible pipelines: custom python for stages and pipelines
4. Support OpenRAG or OpenRAG like with customizable visual Langflow
flows for the pipeline and retrieval flows.
5. Support the CocoIndex rust pipeline and incremental engine as option to current flexible graphrag incremental update system (python/postgres). Also Flexible GraphRAG data sources and property graph, rdf, vector, search dbs could be used with CocoIndex as custom connectors (in addition native and other custom CocoIndex connectors). Flexible GraphRAG’s React, Vue, and Angular frontends would provide UI.

What's new:
• Full LlamaIndex, LangChain or mixed pipeline: chunker, KG extractor, graph/vector/search backends, retrieval fusion — each independently configurable
• 15 property graph DBs (8 LI stores + 8 LangChain matching versions + 6 LC-only + 1 new LI) All 15 can be used in a LlamaIndex or LangChain focused pipeline
• 4 RDF stores — now includes Amazon Neptune RDF
• 10 LangChain vector backends + 3 LangChain search backends duplicate LlamaIndex support
• LangChain EnsembleRetriever (RRF) + text-to Cypher / GQL / SurrealQL / AQL / SPARQL graph QA
• Docling OCR support (RapidOCR, EasyOCR, Tesseract, and more)
• Incremental add/delete/modify across all graph, vector, search, and RDF backends (for both LlamaIndex and LangChain backend stores)
• Integration testing: run_matrix and run_overnight can test all databases, LI / LC pipeline stages, all LLM providers, and all embedding providers

Update, v0.6.1 (May 16, 2026): Updated to v0.6.1 to fix packaging missing adapter files. The issue happened when installing from PyPI, not when using a GitHub source clone. The latest on PyPI is now 0.6.1 for both the main backend: https://pypi.org/project/flexible-graphrag/ and the MCP server: https://pypi.org/project/flexible-graphrag-mcp/

Flexible GraphRAG now has a Zensical documentation web site

Flexible GraphRAG on GitHub

X.com Steve Reiner @stevereiner LinkedIn Steve Reiner LinkedIn Posts

Repost of my LinkedIn post from April 17, 2026 (original LinkedIn post)

Flexible GraphRAG now has a zensical documentation web site https://stevereiner.github.io/flexible-graphrag/
Flexible GraphRAG can create a Knowledge Context from a combination of Alfresco (Hyland) , SharePoint , Box , S3, GCS, Azure Blob sources. Incremental update auto syncs multiple databases (search, property graph, rdf graph, and vector) used for hybrid search / ai query /ai chat , and agents.

Flexible GraphRAG GitHub repo:
https://github.com/stevereiner/flexible-graphrag

In 0.5.1, Flexible GraphRAG added LadybugDB support. Version 0.5.0 added RDF-based ontologies for both property graph dbs and RDF graph dbs (Graphwise, Ontotext GraphDB, Fuseki, Oxigraph), plus document ingestion with KG extraction, auto incremental change detection, and a search UI (hybrid search, AI query, AI chat) for RDF graph dbs in addition to previous property graph / vector / search db support.

Next version will expand flexible LangChain / LlamaIndex support: using everything in LlamaIndex, or LlamaIndex plus 6 additional LangChain new property graph databases, or everything in LangChain (graph, vector, search, llm).

x.com: https://x.com/stevereiner
integrated Semantics Blog: https://integratedsemantics.org/

Flexible GraphRAG v0.5.1 now supports LadybugDB, hybrid_system modularized

Flexible GraphRAG on GitHub

X.com Steve Reiner @stevereiner LinkedIn Steve Reiner LinkedIn Posts

Repost of my LinkedIn post from April 8, 2026 (original LinkedIn post)

Flexible GraphRAG v0.5.1 now supports LadybugDB and hybrid_system was modularized. In v0.5.0 , support for RDF-based ontologies for both property graph databases and RDF graph databases (Graphwise Ontotext GraphDB, Fuseki, and Oxigraph) was added. Also, document ingestion with KG extraction, auto incremental data source change detection, and search UI (hybrid search, AI query, and AI chat) are all supported with both database types. For RDF search, install with the additional LangChain option. Next version will be able to use everything in LlamaIndex, or LlamaIndex with 6 additional LangChain property graph databases, or everything in LangChain (graph,vector,search).
Note: until GitHub support fixes an automated flag on my account, you need to login to GitHub to see my repos:
flexible-graphrag: https://github.com/stevereiner/flexible-graphrag
llama-index-ladybug: https://github.com/stevereiner/llama-index-ladybug