Most MCP servers give AI tools.
OpenTology gives AI understanding — persistent memory, dependency awareness, and impact analysis across every session.
Not just tools — a fundamentally different way for AI to understand your project.
Sessions with activities, todos, and insights persist in an RDF graph. Decisions, issues, and knowledge carry across every conversation.
Before editing, AI checks the dependency graph to understand blast radius. No more accidental breakage of dependent modules.
Symbol-level scanning for TypeScript, Python, Go, Rust, Java, and Swift. Classes, functions, methods, and call graphs.
Oxigraph runs as WebAssembly. No containers, no native builds. Works everywhere Node.js runs.
Automatically injects CLAUDE.md so AI checks impact, searches context, and records decisions without being asked.
Built on RDF, SPARQL, RDFS reasoning, and SHACL validation. Query your graph with any SPARQL-compatible tool.
Real experiment on this codebase. Watch both approaches run.
"What breaks if I change store-adapter.ts?"
Every session is a first-class citizen in the graph. AI picks up exactly where it left off — every time.
Three commands. That's it.
Point your AI assistant to OpenTology.
"command": "npx",
"args": ["-y", "opentology", "mcp"]
Creates graph, hooks, and AI instructions.
opentology context init
Builds the knowledge graph from your code.
opentology context scan