# AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI

arXiv: https://arxiv.org/abs/2602.21251 · HTML: https://mlsanity.com/paper/2602.21251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 9 of 9)

- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [TypeWriter: Neural Type Prediction with Search-based Validation](https://mlsanity.com/paper/1912.03768/) (2019)
- [Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors](https://mlsanity.com/paper/2306.01394/) (2023)
- [OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints](https://mlsanity.com/paper/2004.00348/) (2020)
- [PyTy: Repairing Static Type Errors in Python](https://mlsanity.com/paper/2401.06619/) (2024)
- [Do Machine Learning Models Produce TypeScript Types That Type Check?](https://mlsanity.com/paper/2302.12163/) (2023)
- [Type Prediction With Program Decomposition and Fill-in-the-Type Training](https://mlsanity.com/paper/2305.17145/) (2023)
- [CodeCureAgent: Automatic Classification and Repair of Static Analysis Warnings](https://mlsanity.com/paper/2509.11787/) (2025)

## Built on by (most cited first, 2 of 2)

- [From Question Answering to Task Completion: A Survey on Agent System and Harness Design](https://mlsanity.com/paper/2606.20683/) (2026)
- [SDAD: Spec-Driven Agentic Development for the AI-Native SDLC](https://mlsanity.com/paper/2608.20341/) (2026)

## Abstract (arXiv)

Legacy JavaScript systems lack type safety, making maintenance risky. While TypeScript can help, manually adding types is expensive. Previous automated typing research focuses on type inference but rarely addresses type checking setup, definition generation, bug identification, or behavioral correctness at repository scale. We present AgenticTyper, a Large Language Model (LLM)-based agentic system that addresses these gaps through iterative error correction and behavior preservation via transpilation comparison. Evaluation on two proprietary repositories (81K LOC) shows that AgenticTyper resolves all 633 initial type errors in 20 minutes, reducing manual effort from one working day.