Knowledge Graphs
Build, reason over and serve knowledge graphs — ontologies, triple stores and graph context for agents.

Neo4j Labs' graph-native agent memory: conversations, a POLE+O entity knowledge graph and reasoning traces in one store, with a 16-tool MCP server and hosted or self-hosted backends.

Turns buildings, streets, GTFS/GBFS feeds and origin-destination flows into spatial heterogeneous graphs, round-tripping GeoDataFrames, NetworkX and PyTorch Geometric for GNNs.
AWS's ontology-based context layer: scan your sources, induce ontologies, then serve validated context to agents over MCP — SPARQL federation, a virtual knowledge graph and OWL reasoning.

Documents to validated knowledge graphs: Docling parses, an LLM or VLM fills Pydantic schemas, and you get a directed NetworkX graph with provenance, Cypher/CSV export and HTML views.

ZJU's self-hosted enterprise AgentOS on AgentScope 2.0: domain ontology as a control plane for agents, plus RAG, sub-agents, MCP, skills, sandbox, memory and approval-gated self-evolution.
Object-store-native distributed graph DB in Rust: graph on SlateDB over S3, snapshot-consistent OpenCypher, GraphBLAS traversal, Neo4j Bolt 5.x compatibility, disaggregated data nodes and indexers.
Knowledge-extraction CLI: LLMs turn documents into structured graphs, hypergraphs and spatio-temporal knowledge — with an MCP server for agents and Obsidian vault export.

Notebook recipe that turns any text corpus into a concept graph with a local Mistral 7B via Ollama — chunk, extract concepts and relations, add proximity edges — for Graph RAG and KG QA.
Embedded single-file graph database in Zig: graph traversal, HNSW vector search and BM25 full-text in one query language, plus a durable event log — built for Graph RAG and local agent memory.

Legible persistent agent memory: records observable execution events, consolidates only validated knowledge and explains every relation — MCP server on PostgreSQL 17 with a web explorer.

MCP server that lets a coding agent build and query a typed concept graph of the project — SQLite store, in-process embeddings, merge-conflict resolution and a local web graph explorer.
Self-hosted agent memory stack in Docker: Postgres + Qdrant + Neo4j hybrid retrieval, a temporal knowledge graph, ontology layer and freshness checks behind one MCP-native API.

Neo4j Labs' semantic layer for data agents: ingest warehouse schema, business glossary, metrics and query history into one graph, then serve it over MCP so agents route queries and write grounded SQL.
Git-native agent memory on Google's OKF v0.2: Markdown+YAML concepts in knowledge/, a zero-dep Go CLI/MCP with <300µs BM25 search, provenance and trust tiers.
Lakehouse graph database for agent context — graph, vector and full-text retrieval fused in one runtime on branchable Lance/S3 storage; agent fleets write on isolated branches and merge Git-style.
Turns Databricks Unity Catalog tables into a materialized knowledge graph: OWL ontology design, R2RML mapping, OWL 2 RL/SWRL/SHACL reasoning, auto-generated GraphQL — exposed to agents over MCP.

Agentic ontology-assisted RDF extraction: co-evolves domain ontologies and fact graphs in a map/reduce pipeline with RDF 1.2 provenance, entity disambiguation and SHACL autofix.

Desktop app plus MCP server keeping a codebase's ontology as reviewable Markdown in the repo: domains and capabilities linked to code evidence, compiled to a graph for blast-radius queries.

Rust MCP server + desktop Studio for AI-native ontology engineering: 70+ tools over an in-memory Oxigraph store — OWL2-DL tableaux reasoning, SHACL, SPARQL, versioning. Single binary, no JVM.

Open metadata platform turned AI context layer: 130+ connectors feed a unified knowledge graph of lineage, quality, ownership, glossaries and contracts — served to agents via MCP and APIs.

Browser ontology workbench for OWL and SKOS on Streamlit + rdflib: OWL-RL reasoning, 22 SKOS checks, SPARQL console, bulk edits, gist starters and a vis-network graph — no Java, no Protégé.

PostgreSQL extension adding graph search, traversal and shortest-path over your existing tables — a derived graph index queried from plain SQL, no separate graph DB or query language. Rust.

Graph-native context infrastructure for accountable AI: ingest enterprise data, extract a knowledge/context graph (RDF + LPG), run graph analytics and causal reasoning with decision provenance.

DeepLethe's open 'enterprise world model': one Rust binary plus Postgres running a bitemporal knowledge graph with ontology packs, cited hybrid search, an agent harness and MCP. Air-gap ready.
An open spec for Markdown vaults as linked data: YAML-LD frontmatter plus a shared @context makes notes an RDF graph, with reference scripts that round-trip vault to Turtle and back.