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langPeanut Uses AST Parsing and Gemini AI to Automate App Localization at Low Cost

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A developer built langPeanut, a multi-agent localization and SEO platform written in Go, as a submission for the Google Cloud Agentic Hackathon. Unlike traditional LLM-based approaches that rewrite entire source files and introduce syntax errors, langPeanut restricts AI involvement to linguistic judgment only, using Tree-Sitter AST parsing to identify exact byte-level string positions at zero token cost. Google's Gemini 3.7 Flash model is then called solely to handle context disambiguation and ICU-safe translation, while a deterministic patch engine handles all code modifications. The architecture is split across three coordinated systems covering localization, an AI chat copilot with 19 tools, and an SEO optimization pipeline targeting translated locale files. On an adversarial 10-case benchmark spanning React, Dart, and SwiftUI codebases, the platform achieved a 100% AST compilation pass rate with no formatting drift.

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langPeanut Uses AST Parsing and Gemini AI to Automate App Localization at Low Cost · ShortSingh