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Basic Information

OctoTools is an open-source, training-free agentic framework designed to solve complex multi-step reasoning tasks across diverse domains and modalities. It provides a modular system that combines standardized tool cards, a planner for both high-level and low-level reasoning, and an executor that generates and runs tool calls while recording structured intermediate results. The project is intended for researchers and developers who want to build, evaluate, and extend agent-based systems without retraining models. The repository includes example notebooks, benchmark tasks, test scripts for individual tools, installation instructions including a PyPI package, and reproducible benchmark scripts. The framework was evaluated on 16 benchmarks and reported measurable accuracy gains over strong baselines, demonstrating applicability to visual, numerical, retrieval, and reasoning challenges.

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Features
OctoTools centralizes functionality around reusable tool cards that encapsulate tool metadata and usage patterns so new tools can be integrated without changing core agent logic. A planner component handles both strategic and stepwise action planning. An executor instantiates executable commands, runs tools, and stores structured outputs in context, from which a final summarized answer is generated. A task-specific toolset optimization algorithm selects a beneficial subset of tools for downstream tasks. The codebase supports many LLM backends including OpenAI GPT-4o, Anthropic Claude models, TogetherAI, Google Gemini, Grok, vLLM, LiteLLM and Ollama, and includes examples, test scripts for each tool, visualization utilities, and installation options for standard and editable workflows.
Use Cases
OctoTools helps teams accelerate the construction and evaluation of agentic systems by providing a composable, training-free framework that reduces engineering effort when adding diverse tools. Its tool card abstraction and modular toolbox let users swap or extend capabilities without modifying the planner or executor. The executor preserves intermediate steps and structured results, enabling traceability and easier debugging. Built-in examples, notebooks, and benchmark scripts permit reproducible evaluation and comparison with baselines; the authors report gains over GPT-4o and other frameworks. Support for local inference engines like vLLM and Ollama and broad LLM compatibility make the project practical for research and applied workflows involving multimodal and multi-step reasoning.

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