OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning
On the map: LLM reasoning → Reasoning for agents, planning and code
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Abstract (arXiv)
Solving complex reasoning tasks may involve visual understanding, domain knowledge retrieval, numerical calculation, and multi-step reasoning. Existing methods augment large language models (LLMs) with external tools but are restricted to specialized domains, limited tool types, or require additional training data. In this paper, we introduce OctoTools, a training-free, user-friendly, and easily extensible multi-agent framework designed to tackle complex reasoning across diverse domains. OctoTools introduces standardized tool cards to encapsulate tool functionality, a planner for both high-level and low-level planning, and an executor to carry out tool usage. We validate OctoTools' generality across 16 diverse tasks (including MathVista, MMLU-Pro, MedQA, and GAIA-Text), achieving substantial average accuracy gains of 9.3% over GPT-4o. Furthermore, OctoTools also outperforms AutoGen, GPT-Functions, and LangChain by up to 10.6% when given the same set of tools. Through comprehensive analysi, ablations, and robustness tests with compact backbones and noisy tool environments, OctoTools demonstrates advantages in task planning, effective tool usage, and multi-step problem solving. Code, demos, and visualization are publicly available at https://octotools.github.io/.
Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 35 of its 67 references that are in the map. First posted on arXiv 16 Feb 2025.
Builds on
35 of 67 references.
References · 35
- 2023GPT-4 Technical Reportcited by 18,132
- 2021Training Verifiers to Solve Math Word Problemscited by 9,743
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- 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
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- 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
- 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
- 2023GAIA: a benchmark for General AI Assistantscited by 1,084
- 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 609
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- 2020PathVQA: 30000+ Questions for Medical Visual Question Answeringcited by 467
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- 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
- 2022CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoningcited by 119
- 2020Unsupervised Question Decomposition for Question Answeringcited by 118
- 2024Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoningcited by 110
- 2024MMedAgent: Learning to Use Medical Tools with Multi-modal Agentcited by 108
- 2024DINO-X: A Unified Vision Model for Open-World Object Detection and Understandingcited by 85
- 2023PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documentscited by 61
- 2024PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patternscited by 54
- 2024SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretationcited by 47
- 2024Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agentscited by 39
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- 2024Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Basescited by 23
- 2024Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputscited by 22
- 2023WebWISE: Web Interface Control and Sequential Exploration with Large Language Modelscited by 11
- 2024Less is More: Optimizing Function Calling for LLM Execution on Edge Devicescited by 1
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Citing papers per year: 2025: 39; 2026: 30.
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