# An approach for API synthesis using large language models

arXiv: https://arxiv.org/abs/2502.15246 · HTML: https://mlsanity.com/paper/2502.15246/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions](https://mlsanity.com/paper/2510.25445/) (2025)
- [Semantic Voting: Execution-Grounded Consensus for LLM Code Generation](https://mlsanity.com/paper/2605.08680/) (2026)
- [APRIL: API Synthesis with Automatic Prompt Optimization and Reinforcement Learning](https://mlsanity.com/paper/2509.25196/) (2025)
- [Sketch-and-Verify: Structured Inference-Time Scaling via Program Sketching](https://mlsanity.com/paper/2605.08658/) (2026)

## Abstract (arXiv)

APIs play a pivotal role in modern software development by enabling seamless communication and integration between various systems, applications, and services. Component-based API synthesis is a form of program synthesis that constructs an API by assembling predefined components from a library. Existing API synthesis techniques typically implement dedicated search strategies over bounded spaces of possible implementations, which can be very large and time consuming to explore. In this paper, we present a novel approach of using large language models (LLMs) in API synthesis. LLMs offer a foundational technology to capture developer insights and provide an ideal framework for enabling more effective API synthesis. We perform an experimental evaluation of our approach using 135 real-world programming tasks, and compare it with FrAngel, a state-of-the-art API synthesis tool. The experimental results show that our approach completes 133 of the tasks, and overall outperforms FrAngel. We believe LLMs provide a very useful foundation for tackling the problem of API synthesis, in particular, and program synthesis, in general.