ToolCoder revolutionizes how LLMs interact with external tools by reimagining tool learning as a systematic code generation task rather than prompt engineering.

• Leverages software engineering principles with Python function scaffolds for tool integration
• Enables multi-step planning through structured programming constructs
• Incorporates error diagnosis and reflection mechanisms for improved reliability
• Achieves superior performance compared to prompt-based approaches

This engineering breakthrough provides a more structured, maintainable approach to tool learning that could significantly enhance LLMs' ability to solve complex real-world tasks through external tool interaction.

ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models