Incremental Update – Page 2 – Open Source Integrated AI and Semantic Tech

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

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 v0.5.0 now supports RDF-based ontologies for both property graph databases and RDF triple store databases

Flexible GraphRAG on GitHub

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

Repost of my LinkedIn post from March 25, 2026 (original LinkedIn post)

Flexible GraphRAG v0.5.0 now supports RDF-based ontologies for both property graph databases and RDF triple store databases (Graphwise Ontotext GraphDB, Fuseki, and Oxigraph).
Document ingestion with KG extraction, auto incremental data source change detection, and UI search (hybrid search, AI query, and AI chat) are all supported with both database types.
Also added OpenAI-Like, OpenRouter, LiteLLM, and vLLM LLM providers. For RDF search, install with the additional LangChain option:
uv pip install flexible-graphrag[langchain] Flexible GraphRAG github repo: https://github.com/stevereiner/flexible-graphrag

Flexible GraphRAG now also supports embedded ArcadeDB in addition to remote ArcadeDB

Flexible GraphRAG on GitHub

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

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

Flexible GraphRAG now also supports ArcadeDB embedded in addition remote ArcadeDB. Both work with the optional incremental update auto sync from data sources. All supported databases (10 vector, 3 search, and 8 graph databases) now work with auto sync. Github https://github.com/stevereiner/flexible-graphrag. With a full hybrid setup (vector, search, graph) all get auto synced.
Finally have pypi built releases (0.4.0) of flexible-graphrag https://pypi.org/project/flexible-graphrag/ and flexible-graphrag-mcp https://pypi.org/project/flexible-graphrag-mcp/

Big News: Flexible GraphRAG now supports Incremental Update Auto Sync from Data Sources

Flexible GraphRAG on GitHub

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

Repost of my LinkedIn post from February 6, 2026 (original LinkedIn post)

Big News: Flexible GraphRAG now supports Incremental Update Auto Sync from Data Sources
Huge New Feature: Flexible GraphRAG check-in: Added Incremental Update Auto Sync from 9 data sources to all supported vector, search, and graph databases. Added Postgres state management.
🌟 Github: https://github.com/stevereiner/flexible-graphrag

• All auto syncing data sources working: S3 (SQS events), Hyland Alfresco (ActiveMQ events), Azure Blob, GCS (Pub/Sub), Google Drive, Box, OneDrive, SharePoint, Filesystem (watchdog).

• All 10 vector databases working with this (Qdrant, LanceDB, Elastic, OpenSearch Project, Chroma, Milvus, created by Zilliz, Weaviate, Pinecone, PostgreSQL pgvector, Neo4j)

• All 3 full text search engines working with this (Elasticsearch, OpenSearch, built-in LlamaIndex BM25)

• Graph databases working with this: Neo4j, FalkorDB, ArcadeDB (fix to delete coming), MemGraph. More testing needed with Kuzu, NebulaGraph, Amazon Neptunes.

Flexible GraphRAG or Flexible RAG, an Apache 2.0 open source Python platform, uses LlamaIndex , supports 8 graph databases, 10 vector databases, 3 search engines, and 13 data sources (9 auto-sync) Also supports knowledge graph auto-building, schemas, 9 LlamaIndex LLMs, Docling or LlamaParse doc processing, GraphRAG mode, RAG only mode, Hybrid search, AI query, AI chat. Has React, Vue, and Angular TypeScript frontends, a FastAPI backend, and a REST API. Also has a FastMCP MCP server. Has a convenient docker compose that can include any of the databases (Vector, Graph, Search, Alfresco) and Dashboards / Consoles.
Blog: https://integratedsemantics.org/
X.com: https://x.com/stevereiner