#knowledge-graph

Local-first 3-in-1 AI memory layer & MCP server for Claude Code, Codex, Grok, Gemini, VS Code and Cursor. Fuses session history, codebase indexing & concept graphs in SQLite. Enables zero-cloud, privacy-first context & instant recall, supports multi-agent swarms.

🔗 Semantic MediaWiki turns MediaWiki into a knowledge management platform with query and export capabilities

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

可私有部署的多租户知识智能体平台：统一 RAG、知识图谱、多智能体、MCP/Skills、沙盒与权限管理。Self-hosted knowledge agent platform for RAG, knowledge graphs and multi-agent workflows.

The context orchestration layer powered by hypergraphs. Build a unified semantic context layer where agentic outcomes are deterministic and agent behavior is not just traceable, but cryptographically verifiable.

Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command.

PurRDF — one RDF 1.2 engine, one behavior, in Rust, Python, WebAssembly and C. Deterministic BM25 full-text search, exact GeoSPARQL 1.1 and embedding kNN answered from SPARQL, in-process, over one dataset: the three jobs a triple store usually kept PostgreSQL beside it for. Plus native codecs, RDFC-1.0, SHACL, ShEx, entailment and GTS transport.

Stop feeding broken Markdown to your AI. A deterministic Logseq parser that preserves parent-child context for RAG, plus a 60FPS visualizer 👓 , plus writer ✍️ append only

Zero-config entity resolution feeding a durable identity layer: messy records from any source become stable golden entities, a Customer 360 with provenance, merge/split and audit. Fellegi-Sunter beats hand-tuned Splink. Arrow-native/Rust, 250M rows in 11.2 min. Python + edge TypeScript (WASM), SQL-native in Postgres & DuckDB, 97 MCP tools + REST.

EdegQuake 🌋 High-performance GraphRAG inspired from LightRag written in Rust; Transform documents into intelligent knowledge graphs for superior retrieval and generation

Graphs that teach > graphs that impress. Turn any code into an interactive knowledge graph you can explore, search, and ask questions about. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.

Grafeo is a pure-Rust, high-performance graph database that can be embedded as a library or run as a standalone database, with optional in-memory or persistent storage. Grafeo supports both LPG and RDF and all major query languages.

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.

OriginTrail Decentralized Knowledge Graph (DKG) is a decentralized knowledge infrastructure for multi-agent AI memory — enabling agents to publish, verify, and query shared knowledge as cryptographically verifiable graph assets across a peer-to-peer network.

IDE-style navigation for structured data — code, JSON, YAML. Jump to definitions, find callers, follow references. Available as an MCP server or a mounted folder.

The explainable, local-first memory engine for AI agents. One ~9 MB binary fuses vector + graph + columnar under VelesQL; why() returns the evidence path behind every recall. No cloud, no glue code — runs on server, browser, mobile and desktop.

Your coding agent starts every session with amnesia — memo fixes that, 100% on your machine. Persistent memory for Claude Code, Codex, Cursor & any MCP client: Markdown source of truth, hybrid search (MLX/CPU + sqlite-vec), time-machine, contradiction radar, nightly self-optimization. No cloud, no keys.

Describe a labeled property graph once in a YAML manifest; GraFlo builds the schema and ingests records from CSV, JSON, Parquet, SQL, RDF/SPARQL, REST APIs or Kafka into ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, NebulaGraph, PostgreSQL or a directory on disk.

Open-source governed, local-first memory control plane for AI agents and teams. arXiv:2608.08253