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arxiv: 2502.11271 · v2 · submitted 2025-02-16 · 💻 cs.LG · cs.CL· cs.CV· cs.MA

OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning

Pan Lu , Bowen Chen , Sheng Liu , Rahul Thapa , Joseph Boen , James Zou This is my paper

Pith reviewed 2026-05-23 02:36 UTC · model grok-4.3

classification 💻 cs.LG cs.CLcs.CVcs.MA
keywords multi-agent frameworkstandardized tool cardscomplex reasoningplanner executortraining-freeLLM tool usebenchmark evaluationextensible agents
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The pith

OctoTools uses standardized tool cards plus a planner-executor split to raise accuracy 9.3 percent over GPT-4o on 16 tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a multi-agent framework that augments language models for complex reasoning tasks mixing visual understanding, domain knowledge, numerical steps, and multi-step logic. It claims that standardized tool cards, which describe each tool's function, inputs, and outputs in a fixed format, together with a planner that creates both high-level and low-level plans and an executor that carries them out, produce better results than direct model use or other agent systems. The design requires no training and can be extended simply by writing new tool cards. A sympathetic reader would care because the reported gains appear on benchmarks spanning math, medicine, professional knowledge, and general agent tasks, suggesting that better organization of existing tools can lift performance across unrelated domains.

Core claim

OctoTools is a training-free multi-agent framework that introduces standardized tool cards to encapsulate tool functionality, a planner for high-level and low-level planning, and an executor to carry out tool usage. Validated across 16 diverse tasks including MathVista, MMLU-Pro, MedQA, and GAIA-Text, the system achieves an average accuracy gain of 9.3 percent over GPT-4o and outperforms AutoGen, GPT-Functions, and LangChain by up to 10.6 percent when given the same tools. Ablations and tests with compact backbones and noisy tool environments show advantages in task planning, tool usage, and multi-step solving.

What carries the argument

Standardized tool cards that encapsulate each tool's functionality, inputs, and outputs, used by a planner for high-level and low-level plans and an executor that calls the tools.

If this is right

  • The same set of tools produces higher accuracy than AutoGen, GPT-Functions, or LangChain on the tested tasks.
  • Performance gains hold when using compact model backbones or when tool outputs contain noise.
  • The framework can be extended to new tools simply by adding new standardized cards without retraining.
  • Gains arise from improved task planning and effective multi-step tool selection rather than other factors.
  • The design supports visual, numerical, knowledge-retrieval, and reasoning tasks within one system.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The card format could be adopted by other agent systems to reduce the engineering needed for new domains.
  • Public release of the code allows direct testing on additional task types such as code synthesis or scientific hypothesis generation.
  • If the planning-execution split remains effective at larger scale, it could influence how future agent designs allocate responsibilities between components.
  • One could measure whether adding cards for highly specialized tools preserves the average gains without extra tuning.

Load-bearing premise

The standardized tool cards and planner-executor design can be applied across 16 unrelated domains without domain-specific engineering or post-hoc adjustments to the evaluation protocol.

What would settle it

A new complex reasoning task outside the 16 tested domains where OctoTools requires custom tool cards or fails to show the reported accuracy gains over the base model without adjustments would challenge the claim.

Figures

Figures reproduced from arXiv: 2502.11271 by Bowen Chen, James Zou, Joseph Boen, Pan Lu, Rahul Thapa, Sheng Liu.

Figure 2
Figure 2. Figure 2: Performance comparison across 16 bench￾marks. Our OctoTools framework achieves an average accuracy gain of 9.3% over GPT-4o without function plugins and 7.3% over LangChain, using the same tools under the same configuration. *Equal contribution. PL and RT started the project. PL completed the early framework. PL, BC refined the framework. PL, BC, and SL contributed to experiments and paper writing. Corresp… view at source ↗
Figure 3
Figure 3. Figure 3: The demonstration of a self-contained example from [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: a. Tool usage distribution in our OctoTools framework and agent baselines by averaging results from 16 tasks. b. Tool usage distribution across 16 tasks in OctoTools. OctoTools takes advantage of different external tools to address task-specific challenges. CLEVR-Math PathCLS SciFIBench SLAKE PuzzleVQA MathVista Game of 24 GAIA-Text Hallusion-VD MedQA MMLU-Pro Omni-MATH GPQA AlgoPuzzleVQA Average Number of… view at source ↗
Figure 5
Figure 5. Figure 5: Benchmark distribution across average number of steps and fraction of external tool usage (tools that exclude the base tool Generalist Solution Generator) in OctoTools. Python Code Generator for precise calculations. External tool usage v.s. multiple steps [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Benchmark distribution across two dimensions. Tasks that show high improvement from task decomposition likely re￾quire multi-step reasoning, while tasks that show high improve￾ment from specialized tools likely require specialized skills [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Performance of OctoTools on 16 tasks and various cat￾egories using a weaker LLM, GPT-4o-mini, as the base en￾gine. OctoToolsbase is the configuration in which only the base Generalist Solution Generator tool is enabled. The number in parentheses indicates # tasks in each category. 6. Conclusion In this paper, we introduced OctoTools, a training-free, ex￾tensible agentic framework for complex reasoning. Oct… view at source ↗
Figure 10
Figure 10. Figure 10: Performance ours vs. other agents. Our framework consistently outperforms agent baselines across all benchmarks. Bar values represent accuracy and error bars represent standard deviation. AlgoPuzzleVQA Hallusion-VD PuzzleVQA VQA 2.0 0 20 40 60 80 100 General Game of 24 Omni-MATH CLEVR-Math MathVista Mathematical GPQA MMLU-Pro SciFIBench Scientific MedQA PathCLS PathVQA SLAKE Medical GAIA-Text Agentic Base… view at source ↗
Figure 11
Figure 11. Figure 11: Performance with vs. without tool selection. While toolset optimization increases performance over using the full toolset in most tasks, even without it, our framework achieves similar performance by naively enabling all possible tools. Bar values represent accuracy and error bars represent standard deviation. AlgoPuzzleVQA Hallusion-VD PuzzleVQA VQA 2.0 0 20 40 60 80 General CLEVR-Math Game of 24 MathVis… view at source ↗
Figure 12
Figure 12. Figure 12: Performance on a weaker LLM (GPT-4o-mini). We observe similar trends using OctoTools with a weaker base LLM. Bar values represent accuracy and error bars represent standard deviation. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Distribution of tools usage. Frequency of tools used by the AutoGen agent for each benchmark. GPQA MedQA MMLU-Pro Omni-MATH PuzzleVQA Hallusion-VD AlgoPuzzleVQA MathVista SciFIBench PathVQA PathCLS CLEVR-Math SLAKE VQA 2.0 GAIA-Text Game of 24 0 20 40 60 80 100 Base Tool Image_Captioner Relevant_Patch_Zoomer Text_Detector Wikipedia_Knowledge_Searcher Google_Search Url_Text_Extractor Python_Code_Generator … view at source ↗
Figure 14
Figure 14. Figure 14: Distribution of tools usage. Frequency of tools used by the GPT-Functions agent for each benchmark. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Distribution of tools usage. Frequency of tools used by the LangChain agent for each benchmark. AlgoPuzzleVQA Hallusion-VD PuzzleVQA VQA 2.0 Game of 24 Omni-MATH CLEVR-Math MathVista GPQA MMLU-Pro SciFIBench MedQA PathCLS PathVQA SLAKE GAIA-Text 7.4 11.3 8.8 4.2 22.5 5.2 5.0 14.5 2.0 1.0 5.8 7.0 22.2 17.2 4.5 9.7 [PITH_FULL_IMAGE:figures/full_fig_p021_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Performance gains across different benchmarks from our OctoTools framework over the base GPT-4o model. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Distribution of number of steps used. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Accuracy vs number of maximum steps. The change in accuracy from a maximum step of 1 is plotted. Most benchmarks improve in performance with the number of allowed steps. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_18.png] view at source ↗
read the original abstract

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/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper presents OctoTools, a training-free multi-agent framework using standardized tool cards, a planner for high- and low-level planning, and an executor for tool calls. It claims this design enables complex reasoning across 16 diverse tasks (MathVista, MMLU-Pro, MedQA, GAIA-Text, etc.), delivering a 9.3% average accuracy improvement over GPT-4o and up to 10.6% gains over AutoGen, GPT-Functions, and LangChain when using identical tools. The work includes ablations, robustness tests with compact models and noisy environments, and releases code and demos.

Significance. If the empirical gains prove robust, the framework offers a practical, extensible approach to tool-augmented reasoning that avoids per-task training. Public release of code, demos, and visualizations is a clear strength that supports reproducibility.

major comments (3)
  1. [Abstract] Abstract: The headline 9.3% average accuracy gain over GPT-4o is reported only in aggregate form with no per-task accuracy table, error bars, or statistical tests. Without these, it is impossible to determine whether gains are consistent across domains or driven by a subset of the 16 tasks.
  2. [§3] §3 (Tool Cards and Planner-Executor Design): The claim that standardized tool cards require no domain-specific engineering is central to the generality argument, yet the manuscript supplies no explicit protocol or examples showing how cards for MedQA (domain knowledge) were constructed identically to those for MathVista (visual reasoning) without task-specific examples or adjustments.
  3. [Evaluation] Evaluation section: The paper does not describe whether tool outputs were manually verified, how tasks were selected, or whether the 16-task suite was finalized before or after observing performance, leaving open the possibility of post-hoc selection bias in the reported averages.
minor comments (2)
  1. [Abstract] Abstract contains a typo: 'comprehensive analysi' should be 'comprehensive analysis'.
  2. [§3] Notation for tool-card format is introduced without a clear running example that readers can follow across sections.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate where revisions will be made to improve clarity and transparency.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline 9.3% average accuracy gain over GPT-4o is reported only in aggregate form with no per-task accuracy table, error bars, or statistical tests. Without these, it is impossible to determine whether gains are consistent across domains or driven by a subset of the 16 tasks.

    Authors: We agree that presenting only the aggregate figure in the abstract limits interpretability. The full manuscript contains per-task results in the evaluation tables, but we will revise the abstract to reference these and add a consolidated per-task accuracy table (with standard deviations from repeated runs where available) plus a note on statistical significance testing in the revised version. revision: yes

  2. Referee: [§3] §3 (Tool Cards and Planner-Executor Design): The claim that standardized tool cards require no domain-specific engineering is central to the generality argument, yet the manuscript supplies no explicit protocol or examples showing how cards for MedQA (domain knowledge) were constructed identically to those for MathVista (visual reasoning) without task-specific examples or adjustments.

    Authors: We will add an appendix providing the explicit construction protocol for tool cards, including side-by-side examples for a MedQA knowledge-retrieval card and a MathVista visual-reasoning card. This will demonstrate that the process relies only on the tool's documented interface and does not incorporate task-specific examples or adjustments beyond the tool's core functionality. revision: yes

  3. Referee: [Evaluation] Evaluation section: The paper does not describe whether tool outputs were manually verified, how tasks were selected, or whether the 16-task suite was finalized before or after observing performance, leaving open the possibility of post-hoc selection bias in the reported averages.

    Authors: We will expand the evaluation section to explicitly state that tool outputs were not manually verified (the framework operates in a fully automated loop) and to detail the a-priori task selection criteria based on diversity of reasoning types. The 16-task suite was finalized prior to running the main experiments; we will add this timeline description to remove ambiguity about selection bias. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical gains on public benchmarks with no self-referential derivations

full rationale

The paper introduces OctoTools as a training-free multi-agent framework and reports accuracy improvements on 16 public benchmarks (MathVista, MMLU-Pro, MedQA, GAIA-Text, etc.). These are direct empirical measurements against fixed external test sets rather than quantities derived from internal equations, fitted parameters, or self-citations that reduce the headline gains to the authors' own choices. No load-bearing steps match the enumerated circularity patterns; the central claims rest on observable performance deltas, not on renaming or self-definition.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that existing LLMs can reliably interpret and invoke the standardized tool cards without fine-tuning or domain-specific prompt engineering.

axioms (1)
  • domain assumption Standardized tool cards can encapsulate functionality across unrelated domains without loss of performance
    Invoked by the claim that the same framework works on math, medicine, and general tasks.

pith-pipeline@v0.9.0 · 5776 in / 1239 out tokens · 37271 ms · 2026-05-23T02:36:31.937346+00:00 · methodology

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Reference graph

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    Converted the Cuniform to Arabic ( 8 56)

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    Since Babylonian is a base 60 system, converted the 60’s place to decimal (8*60=480)

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    Wrong Prediction from GPT-4o The given symbols are written in Sumerian cuneiform and follow the Babylonian number system, which is a

    Added 56 to 480 (536). Wrong Prediction from GPT-4o The given symbols are written in Sumerian cuneiform and follow the Babylonian number system, which is a . Let’s analyze the symbols: 1. : This symbol represents 60 in the Babylonian numeral system. 2. : - represents 10. - represents 2. - Together, represents 12. Now, interpreting the full number: - In a ...

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    Knowledge of Ancient Number Systems: Understanding the Mesopotamian/Babylonian number system and Sumerian cuneiform symbols is crucial for interpreting the given symbols

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    Historical Linguistics: Ability to translate ancient scripts into modern numerical systems

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    Relevant tools:

    Research Skills: Ability to find reliable sources or references that explain the conversion process from cuneiform to Arabic numerals. Relevant tools:

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    Babylonian number system Sumerian cuneiform symbols

    Wikipedia Knowledge Searcher Tool: This tool can be used to find detailed articles on the Babylonian number system and Sumerian cuneiform, providing background information and conversion methods. Additional considerations: The task requires a deep understanding of ancient numeral systems, which may not be directly supported by the available tools. Therefo...

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    - represents the number 50

    Identify the Symbols: - represents the number 8. - represents the number 50. - represents the number 6

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    Each position represents a power of 60

    Understand the Structure: - Babylonian numbers are written in a positional system similar to our decimal system but based on 60. Each position represents a power of 60

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    This suggests two separate groupings, which can be interpreted as two different positional values

    Analyze the Given Symbols: - The symbols are written as . This suggests two separate groupings, which can be interpreted as two different positional values

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    - The second group (50 + 6) represents 56

    Convert Each Group: - The first symbol (8) is in the higher position, so it represents 8 × 60 = 480. - The second group (50 + 6) represents 56

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    Therefore, the Sumerian cuneiform symbols convert to the Arabic numeral 536

    Calculate the Total: - Add the values from each group: 480 + 56 = 536. Therefore, the Sumerian cuneiform symbols convert to the Arabic numeral 536. 76 OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning F.4. Example for Medical Reasoning The Query Image path: source.jpg Question: What is the organ on the left side of this image? Gr...

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    Image Analysis: Ability to interpret and analyze medical images to identify organs

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    Relevant tools:

    Medical Knowledge: Understanding of human anatomy to accurately identify organs in medical images. Relevant tools:

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    However, it may have limitations in complex medical images

    Image Captioner Tool: This tool can generate a description of the image, which might help in identifying the organ on the left side. However, it may have limitations in complex medical images

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    source.jpg

    Relevant Patch Zoomer Tool: This tool can zoom into specific areas of the image, which might help in focusing on the left side to better identify the organ. Step 1: Action from Action Predictor Justification: The Relevant Patch Zoomer Tool is the best choice for the next step because it allows us to focus specifically on the left side of the image, which ...

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    Histopathology Knowledge: Understanding of osteosarcoma and its histological features

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    Image Analysis: Ability to interpret H&E stained images

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    Relevant tools:

    Classification Skills: Ability to categorize images based on visual features. Relevant tools:

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    tissue.jpg

    Path Generalist Classifier Tool: This tool is suitable for classifying histopathology images into predefined categories. It can be used to determine whether the image is of a non-tumor, necrotic tumor, or viable tumor. Additional considerations: Ensure the options provided for classification are clear and specific. Cross-reference the tool’s output with e...

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    Critical Thinking: To analyze and understand the descriptions of logical fallacies and identify the correct one

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    Knowledge of Logical Fallacies: To accurately recognize and differentiate between various logical fallacies, including the appeal to indignation

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    Relevant tools:

    Decision Making: To choose the correct option from the given list based on the analysis. Relevant tools:

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Showing first 80 references.