evotraders/tuner/README.md

AgentScope Tuner

This directory contains several examples of how to use the AgentScope Tuner for tuning AgentScope applications. The table below summarizes the available examples:

Example Name	Description	Example Path	Multi-step Interaction	LLM-as-a-Judge	Tool-use	Multi-Agent	Data Augmentation
Math Agent	A quick start example for tuning a math-solving agent to enhance its capabilities.	math_agent	✅	❌	❌	❌	❌
Frozen Lake	Make an agent to navigate the Frozen Lake environment in multi-step interactions.	frozen_lake	✅	❌	❌	❌	❌
Learn to Ask	Using LLM as a judge to provide feedback to facilitate agent tuning.	learn_to_ask	✅	✅	❌	❌	❌
Email Search	Enhance the tool use ability of your agent on tasks without ground truth.	email_search	✅	✅	✅	❌	❌
Werewolf Game	Enhance the agent's performance in a multi-agent game setting.	werewolf_game	✅	✅	✅	✅	❌
Data Augment	Data augmentation for better tuning results.	data_augment	❌	❌	❌	❌	✅

Each example contains a README file with detailed instructions on how to set up and run the tuning process for that specific scenario. Feel free to explore and modify the examples to suit your needs!

Prerequisites

AgentScope Tuner requires:

• Python 3.10 or higher
• agentscope>=1.0.12
• trinity-rft>=0.4.1

AgentScope Tuner is built on top of Trinity-RFT. Please refer to the Trinity-RFT installation guide for detailed instructions on how to set up the environment.