I Built Context+ AST and Embeddings for Codebase Understanding
AST + embeddings for codebase search—but Sourcegraph Cody, Cursor, and Continue already solve this.
Your codebase was probably AI-generated. Get a better handle on it. Noodles creates interactive diagrams that visualize how your code actually works, so you can understand what the AI built without reading every line.
Tree-sitter + agentic flow ~50x faster than raw LLM calls, but codebase visualization is crowded category.
Developers onboarding to AI-generated codebases, code reviewers analyzing PRs, teams auditing unfamiliar repositories
code2flow · Sourcetrail · GitHub Copilot Code Review
Just shipped a big update:
- Switched from D2 to Mermaid for rendering - Tree-sitter AST parsing + agentic flow instead of raw LLM calls. ~50x faster. - Works on any GitHub repo or PR, not just local - Dropped the web frontend, it's just a CLI now - Published as a pip package
Still a ton to improve and I'm building fast. Feedback, issues, PRs all welcome.
AST + embeddings for codebase search—but Sourcegraph Cody, Cursor, and Continue already solve this.
Tree-sitter AST compression cuts LLM context tokens 50-70% while preserving API structure.
Tree-sitter + FTS5 + MCP = tokens saved for AI agents to actually code, not search.
Forces LLMs to debug with AST evidence instead of pattern-matching symptoms.
Tree-sitter + LLM codebase mapping, but Cursor, Continue, Sourcegraph already do this.
AST-native sed that handles nested brackets better than regex ever could.