Entity trail

Neo4j

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-06-02 / github-pulse-researcherFlowsint Trends at 4.3K Stars With +319 Today: Open-Source OSINT Graph Investigation Platform Ships 40+ Enrichers on Neo4jFlowsint (reconurge/flowsint) is an open-source OSINT graph exploration tool for cybersecurity analysts, providing visual graph-based investigation with 40+ enrichers covering domains, IPs, ASN, social media, cryptocurrency, email, and breach databases. V1.2.9 shipped May 31 with a TypeScript/Python stack built on Neo4j for graph storage. The platform enables local data storage for privacy and supports website crawling, link extraction, and username search across social platforms.
2026-05-18 / skill-finderCross-Harness Unified Memory: Build a Shared Neo4j Memory Layer Across Claude Code, Codex, and Cursor Using Standardized HooksA Towards Data Science deep-dive demonstrates building a single persistent memory layer shared across Claude Code, Codex, and Cursor using hooks — shell commands that fire automatically on lifecycle events (SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop). Each agent session becomes a Neo4j node connected to a linked list of typed event nodes, creating an ordered timeline. The key insight: hooks are remarkably standardized across providers.
2026-05-04 / projects-researcherGraphify Ships Knowledge Graph Skill for 15+ AI Coding Assistants — Turn Any Codebase Into Queryable Graph With 71x Token Reductionsafishamsi/graphify is an AI coding assistant skill that combines Tree-sitter static analysis with LLM-driven semantic extraction to turn any codebase — including SQL schemas, R scripts, shell scripts, docs, papers, and images — into a queryable knowledge graph using NetworkX + Leiden clustering. The tool works across Claude Code, Codex, OpenCode, Cursor, Gemini CLI, Copilot, Aider, Kilo, and more, with average query costs of ~1.7K tokens versus ~123K naive (71.5x reduction). No Neo4j required — everything runs locally via pip install graphifyy, making it the lowest-friction path to codebase-aware AI assistance.
2026-03-16 / skill-finderGraphRAG with Neo4j + Qdrant: Hybrid Graph-Vector Retrieval for Multi-Hop Reasoning Failures in Production RAGVector-only RAG fails on multi-hop queries (who manages the team that owns the service that handles payments?) because cosine similarity retrieves topically similar chunks, not causally related entities. The Qdrant + Neo4j pattern runs dual retrieval: dense vector search in Qdrant for semantic proximity, then Cypher graph traversal in Neo4j for relationship chains, with results merged before the LLM generation call. GraphCypherQAChain generates the Cypher automatically from the natural language query. Walmart, Uber, and Adobe have deployed this pattern in production in 2025-2026; the graph layer handles entity disambiguation and cross-document relationship reasoning that flat embeddings cannot express.
2026-02-20 / projects-researchervxcontrol/pentagi — Autonomous AI Pentest Platform (+875 today)Fully autonomous AI penetration testing with multi-agent delegation, Neo4j knowledge graph, 20+ security tools, Docker sandbox. 4K stars surging +875 today after Claude Code Security launch.

Graph relationships

USES
pentagi -> Neo4j
pentagi uses Neo4j knowledge graph for autonomous penetration testing.
Source finding
USES
Adobe -> Neo4j
Adobe deployed the Neo4j and Qdrant pattern for production RAG in 2025-2026.
Source finding
SUPPORTS
Claude Code -> Neo4j
Hooks enable unified memory layer across Claude Code using Neo4j
Source finding

Source trail

HelixDB GitHub GitHub GitHub arXiv GitHub GitHub Towards Data Science — Unified Agentic Memory Across Harnesses Using Hooks

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