A comprehensive survey repository collecting and organizing research papers on how large language models use external tools to solve complex problems.
One-Minute Overview#
LLM-Tool-Survey is a research resource collection focused on tool learning with large language models (LLMs), systematically organizing and categorizing related research literature. This project primarily targets researchers, developers, and learners interested in the AI tool learning field, helping them quickly understand the current research status, core methods, and future directions in tool learning.
Core Value: Provides systematic organization of literature in the tool learning field, lowers barriers to entry for research, and promotes academic exchange and industrial applications.
Quick Start#
Installation Difficulty: Low - This is a survey paper repository requiring no installation; resources can be accessed directly via the GitHub repository
# Clone the repository locally
git clone https://github.com/quchangle1/LLM-Tool-Survey.git
Is this suitable for my scenario?
- ✅ Researchers: Need to systematically understand the latest research progress in the tool learning field
- ✅ AI Developers: Looking for practical methods to enable large language models to use tools
- ✅ Academic Writing: Need to cite relevant papers and survey literature
- ❌ Looking for immediately usable tools: This is a literature survey project, not containing directly usable tool code
Core Capabilities#
1. Literature Organization and Classification - Solving Fragmentation of Research Literature#
- Systematically categorizes papers according to core elements of tool learning ("why" and "how")
- Provides clear literature navigation structure for quickly locating interested research directions Actual Value: Helps researchers save significant time on literature searching and categorization, quickly grasping the full picture of the field
2. Comprehensive Coverage of Tool Learning Field - Solving Limited Research Perspective#
- Covers learning research for various types of tools from search engines and databases to mathematical tools and Python interpreters
- Includes multiple dimensions such as knowledge acquisition, expertise enhancement, automation and efficiency improvement, and interaction enhancement Actual Value: Provides comprehensive reference for researchers and developers across different application scenarios
3. Systematic Methodology Analysis - Solving Fragmented Understanding of Methods#
- Detailed analysis of key stages: task planning, tool selection, tool calling, and response generation
- Classification and comparison of tuning-free and tuning-based methods Actual Value: Helps researchers understand the pros and cons of different methods, selecting research paths suitable for their needs
Tech Stack and Integration#
Development Language: Python Integration Method: Static Documentation Resources / Knowledge Base
Maintenance Status#
- Development Activity: Active, with regular updates to the literature list
- Recent Updates: Still collecting latest 2024 related papers
- Community Response: Open contribution mechanism encouraging community participation in improvement
Documentation and Learning Resources#
- Documentation Quality: Comprehensive
- Official Documentation: GitHub Repository
- Sample Code: Not applicable (literature survey project)