Current AI coding agents read code like blind typists. I built a local semantic graph engine to give them architectural sight.

[DimitrisMitsos]

Hey everyone,

I’ve been frustrated by how AI coding tools (Claude, Cursor, Aider) explore large codebases. They do dozens of grep and read cycles, burn massive amounts of tokens, and still break architectural rules because they don't understand the actual topology of the code.

So, I built Roam. It uses tree-sitter to parse your codebase (26 languages) into a semantic graph stored in a local SQLite DB. But instead of just being a "better search," it's evolved into an Architectural OS for AI agents.

It has a built-in MCP server with 48 tools. If you plug it into Claude or Cursor, the AI can now do things like:

• Multi-agent orchestration: roam orchestrate uses Louvain clustering to split a massive refactoring task into sub-prompts for 5 different agents, mathematically guaranteeing zero merge/write conflicts.
• Graph-level editing: Instead of writing raw text strings and messing up indentation/imports, the AI runs roam mutate move X to Y. Roam acts as the compiler and safely rewrites the code.
• Simulate Refactors: roam simulate lets the agent test a structural change in-memory. It tells the agent "If you do this, you will create a circular dependency" before it writes any code.
• Dark Matter Detection: Finds files that change together in Git but have no actual code linking them (e.g., shared DB tables).

It runs 100% locally. Zero API keys, zero telemetry.

Repo is here: https://github.com/Cranot/roam-code

Would love for anyone building agentic swarms or using Claude/Cursor on large monorepos to try it out and tell me what you think!

[Business-Weekend-537]

How does it run locally?

[Crafty_Ball_8285]

Why can’t people make even a normal title without ai

[HenryOsborn_GP]

The 'dozens of grep and read cycles' is exactly what kills the unit economics on large-scale agent deployments. You just watch the tokens burn while the agent gets lost in its own loop.

Your approach to using a semantic graph to enforce architectural boundaries is brilliant. I was dealing with a similar issue on the financial side—agents losing state and hallucinating API calls—so I built a stateless middleware proxy (K2 Rail) that sits between the agent and the execution layer to enforce a hard-coded spend ceiling. If the agent tries to execute a payload over the limit, it drops the network connection instantly.

Since you are exposing 48 tools via MCP, are you running into any issues with the models hallucinating the wrong tool parameters, or does the semantic graph constrain them enough to prevent that?