microsoft/Trace
🏗️ Frameworkmicrosoft
A framework for end-to-end generative optimization of AI agent workflows and prompt systems.
Trace represents a significant shift in how developers build and refine AI agents. Rather than relying on trial-and-error prompt engineering, Trace provides a structured approach to 'Generative Optimization.' It conceptualizes agentic workflows as compound systems where individual components can be optimized for specific performance metrics. The framework leverages concepts from automatic differentiation to propagate improvements through the agent's logic, effectively 'training' the agent's behavior. This allows for the automated tuning of prompts, tool-use strategies, and decision-making paths. By treating the agent as a differentiable graph, Trace enables developers to optimize for complex objectives, such as reducing latency, improving accuracy, or minimizing token usage. This is particularly useful for complex, multi-step agentic workflows where traditional gradient-based learning is not applicable. The framework is built in Python and is designed to integrate seamlessly into existing agentic development pipelines, providing a robust toolkit for researchers and engineers aiming to build more reliable and efficient autonomous systems.
💡Highlights
- ├─End-to-end generative optimization
- ├─Differentiable agentic workflows
- └─Automated prompt & logic tuning
🎯For
- ├─AI Research Engineers
- └─Agentic Workflow Developers