LLM Agents Making Agent Tools
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Tool use has turned large language models (LLMs) into powerful agents that can perform complex multi-step tasks by dynamically utilising external software components. However, these tools must be implemented in advance by human developers, hindering the applicability of LLM agents in domains demanding large numbers of highly specialised tools, like in life sciences and medicine. Motivated by the growing trend of scientific studies accompanied by public code repositories, we propose ToolMaker, an agentic framework that autonomously transforms papers with code into LLM-compatible tools. Given a GitHub URL and short task description, ToolMaker autonomously installs dependencies and generates code to perform the task, using a closed-loop self-correction mechanism for debugging. To evaluate our approach, we introduce a benchmark comprising 15 complex computational tasks spanning various domains with over 100 unit tests to assess correctness and robustness. Our method correctly implements 80% of the tasks, substantially outperforming current state-of-the-art software engineering agents. ToolMaker therefore is a step towards fully autonomous agent-based scientific workflows. Our code and benchmark are publicly available at https://github.com/KatherLab/ToolMaker.
Key facts. Cited by 48 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 31 of its 61 references that are in the map. First posted on arXiv 17 Feb 2025.
Builds on
31 of 61 references.
Closest ancestors · 4
Same direction, newest first
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- 2023ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code16 Nov
- 2023ChatDev: Communicative Agents for Software Development16 Jul
Other references · 27
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- 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 2,004
- 2022Large Language Models Encode Clinical Knowledgecited by 1,830
- 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
- 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 631
- 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 554
- 2024BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructionscited by 546
- 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 425
- 2023RepoBench: Benchmarking Repository-Level Code Auto-Completion Systemscited by 353
15 more
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- 2024Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agentscited by 218
- 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 161
- 2023CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsetscited by 129
- 2024Empowering Biomedical Discovery with AI Agentscited by 118
- 2023On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)cited by 85
- 2024Phikon-v2, A large and public feature extractor for biomarker predictioncited by 74
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- 2024LegalAgentBench: Evaluating LLM Agents in Legal Domaincited by 59
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- 2024AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodologycited by 46
- 2024AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learningcited by 33
- 2024Automating Exploratory Proteomics Research via Language Modelscited by 11
- 2024Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncologycited by 6
- 2023What is the Impact of Releasing Code with Publications? Statistics from the Machine Learning, Robotics, and Control Communitiescited by 4
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Citing papers per year: 2025: 21; 2026: 27.
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