Graphs, not chunks.
Most AI tools flatten a codebase into vector chunks and lose the structure that matters. Gristle parses a repo into a graph — function calls, imports, inheritance, data flow as first-class edges — so an agent can query code the way you actually think about it.
The problem
Point an AI agent at your codebase and it usually reads it as a pile of text chunks in a vector database. “Function A calls B, which inherits from C” becomes three unrelated snippets. The relationships — the part that tells you what breaks when you change something — are exactly what gets thrown away.
The solution
Gristle keeps the relationships as edges, not afterthoughts. It runs as an MCP server, so an agent can ask real structural questions — what breaks if I change this, how data flows from handler to database, what conventions a project follows — and get answers grounded in the actual structure.
How it works
Read code as a graph.
Gristle ingests Python, TypeScript, and JavaScript into a graph database — functions, classes, imports, routes, and tests.
Calls, imports, inheritance, and data flow are first-class, not flattened into text. The structure survives.
Exposed over MCP: impact analysis, call tracing, convention and security checks — 28 tools an agent can call directly.
Under the hood
The build story
"Everyone was chunking code into a vector DB and losing the structure. We kept it."
Gristle came out of a simple frustration: AI agents are great at reading code and bad at understanding how it connects. Vectors throw away the graph that was right there. We built the graph instead and open-sourced it — because the idea is worth more spread than owned, and the same structure-first thinking (tenet 02) runs under our bigger bets. See Mentat.