LangChain – Open Source Integrated AI and Semantic Tech

Flexible GraphRAG v0.8.0 released, with an optional CocoIndex integration

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

Repost of my LinkedIn post from August 20, 2026 (original LinkedIn post)

Flexible GraphRAG v0.8.0 released, with an optional CocoIndex integration. CocoIndex is a Rust based incremental sync and processing engine for context, agents, and data pipelines.
You can now use a CocoIndex pipeline working inside the Flexible GraphRAG app instead of the default pipeline.

Mixing components: CocoIndex components (source/target connectors, functions, splitting) and Flexible GraphRAG “Flexible” components: data sources, LlamaIndex / LangChain targets, document processing (LiteParse, Docling, LlamaParse), splitting, chunking, ontologies, KG extraction. These are separate from the Langflow “Flexible” components. In .env you pick a backend per piece (source, chunker, property graph, vector, search, KG extractor): llamaindex, langchain, or cocoindex; the database is configured separately. RDF is LangChain only, and KG extraction is LlamaIndex / LangChain, although the new custom KG extractors can use CocoIndex’s entity resolver.

Incremental updates: CocoIndex deals with incremental handling — step-level memoization, automatic delete reconciliation — instead of the Python based incremental update system. Postgres still keeps track of the data sources configured in the UI (datasource_config table), but the document_state table is not used. The event change detectors from the default system are also used with the CocoIndex pipeline for “Flexible” data sources: Alfresco ActiveMQ, Nuxeo Kafka, S3 SQS, Azure Blob change feed, GCS Pub/Sub, Google Drive Changes API, OneDrive/SharePoint MS Graph delta query, Box Events API, and Local Filesystem watchdog.

Outside the app too: this CocoIndex pipeline “app file” can be used outside the Flexible GraphRAG app for custom mixed apps. Also, CocoIndex provides CLI support.

Flexible GraphRAG components can be used : 14 data sources (10 auto syncing) and all the DBs — 15 property graphs, 4 RDF stores, 10 vector stores, 3 search engines. CocoIndex native components can be used too: vector DBs, property graph DBs, relational and analytical DBs, and message streams: https://cocoindex.io/docs/connectors/

A Flexible GraphRAG CocoIndex Integration Meeting Notes Knowledge Graph example is provided: any of 10 data sources, to any of 15 property graph databases.

Flexible GraphRAG is an Apache 2.0 open source AI context platform: document processing, knowledge graph auto-building, ontologies, many LLM providers, GraphRAG and RAG, hybrid semantic search, AI query, and AI chat. Python backend (LlamaIndex + LangChain), Angular / React / Vue frontends, an MCP server, Hyland Alfresco , Hyland Nuxeo support, and Docker Compose any database, dashboard.

GitHub: https://github.com/stevereiner/flexible-graphrag
Docs: https://stevereiner.github.io/flexible-graphrag/
PyPI: https://pypi.org/project/flexible-graphrag/
MCP PyPI: https://pypi.org/project/flexible-graphrag-mcp/
Docker: https://hub.docker.com/u/integratedsemantics
Blog: https://integratedsemantics.org/
CocoIndex: https://github.com/cocoindex-io/cocoindex

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 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

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