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Basic Information

AvaTaR is a research codebase that implements a framework for optimizing large language model agents to use external tools more effectively, as presented in a NeurIPS 2024 paper. The repository provides the Avatar module and an AvatarOptimizer teleprompter that iteratively improves an agent by contrastive reasoning between positive and negative examples sampled from training data. It is intended for researchers and developers who want to adapt or reproduce the AvaTaR optimization pipeline, run experiments, or integrate the optimizer into existing agent systems. The README documents integration with the DSPy library, required task signatures and tool formats, instructions to set API keys, and dataset-specific setup for STaRK and Flickr30k Entities to reproduce experiments.

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App URL
https://github.com/zou-group/avatar
Github Repository
https://github.com/zou-group/avatar/blob/main/README.md

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App Details

Features
The project exposes an Avatar agent module and an AvatarOptimizer component that implements a comparator module for contrastive reasoning and actor optimization. It integrates with DSPy and accepts langchain-style tools wrapped as Tool objects. The repo includes example usage code showing how to define a task signature, construct tools, instantiate an Avatar agent, and compile an optimized agent with a user-provided metric. It contains scripts to download embeddings, run optimization and evaluation pipelines, and reproduce experiments on STaRK and Flickr30k Entities. Additional assets include a ReAct baseline implementation, configuration defaults, logging of reasoning traces, and evaluation scripts to compare optimized and baseline agents.
Use Cases
AvaTaR helps developers and researchers improve LLM agent performance on tool-using tasks by providing a concrete, automated optimization loop that leverages contrastive examples. It makes it straightforward to integrate existing tools and task signatures via DSPy, to define custom evaluation metrics, and to compile optimized actors for downstream use. The included scripts, dataset preparation guidance, and baseline implementations support reproducible experiments and comparisons. Users can run provided pipelines to optimize actor actions for groups of queries, evaluate optimized policies, and examine reasoning and action logs to diagnose and iterate on agent behavior.

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