CodingGenie: A Proactive LLM-Powered Programming Assistant
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Abstract (arXiv)
While developers increasingly adopt tools powered by large language models (LLMs) in day-to-day workflows, these tools still require explicit user invocation. To seamlessly integrate LLM capabilities to a developer's workflow, we introduce CodingGenie, a proactive assistant integrated into the code editor. CodingGenie autonomously provides suggestions, ranging from bug fixing to unit testing, based on the current code context and allows users to customize suggestions by providing a task description and selecting what suggestions are shown. We demonstrate multiple use cases to show how proactive suggestions from CodingGenie can improve developer experience, and also analyze the cost of adding proactivity. We believe this open-source tool will enable further research into proactive assistants. CodingGenie is open-sourced at https://github.com/sebzhao/CodingGenie/ and video demos are available at https://sebzhao.github.io/CodingGenie/.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2025.
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Citing papers per year: 2025: 4; 2026: 6.
10 papers.
- 2025ContextAgent: Context-Aware Proactive LLM Agents with Open-World Sensory Perceptionscited by 44
- 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
- 2025ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wildcited by 7
- 2026Humans are Missing from AI Coding Agent Researchcited by 3
- 2026Agentic Coding Needs Proactivity, Not Just Autonomycited by 2
- 2025DiscussLLM: Teaching Large Language Models When to Speakcited by 1
- 2026Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learningcited by 1
- 2026An Empirical Study of Proactive Coding Assistants in Real-World Software Developmentcited by 1
- 2026VibeJam: A User Study Platform for Web Development with Agentscited by 0
- 2026Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generationcited by 0
- 2026Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation28 Sep 2026
- 2026VibeJam: A User Study Platform for Web Development with Agents30 Aug 2026
- 2026Humans are Missing from AI Coding Agent Research4 Jul 2026
- 2026Agentic Coding Needs Proactivity, Not Just Autonomy7 May 2026
- 2026An Empirical Study of Proactive Coding Assistants in Real-World Software Development7 May 2026
- 2026Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning17 Jan 2026
- 2025ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild7 Dec 2025
- 2025Large Language Models for Security Operations Centers: A Comprehensive Survey13 Sep 2025
- 2025DiscussLLM: Teaching Large Language Models When to Speak25 Aug 2025
- 2025ContextAgent: Context-Aware Proactive LLM Agents with Open-World Sensory Perceptions20 May 2025