by zou-group · Agent Tool · ★ 241
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AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning (NeurIPS 2024) NeurIPS paper | DSPy Implementation AvaTaR is a novel and automatic framework that optimizes an LLM agent to effectively use the provided tools and improve its performance on a given task/domain. During optimization, we design a comparator module to iteratively provide insightful and holistic prompts to the LLM agent via reasoning between positive and negative examples sampled from training data. News [July 2024] 🔥 Avatar is integrated into DSPy - Credit to Herumb Shandilya! You can try out the example on jupyter notebook. (For general QA）Using Avatar with DSPy Avatar is now integrated with DSPy as Module for agent execution and for Actor optimization. To use Avatar you'll need: Task Signature and Tools. Task Signature is a class defining the structure of your task. So if your task is of QA type you can create a signature with input field and output field. Tools is a list of containing all the tools of langchain tool format. Here is an example python from dspy.predict.avatar i
| Stars | 241 |
| Forks | 27 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 69.739615888711/100 |
| Open Issues | 2 |
| Last Updated | 2025-06-10 |
| Created | 2024-06-14 |
| Platforms | python |
| Est. Tokens | ~914k |
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avatar is (NeurIPS 2024) AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning. It is categorized as a Agent Tool with 241 GitHub stars.
avatar is primarily written in Python. It covers topics such as agents, knowledge-base, llms.
You can find installation instructions and usage details in the avatar GitHub repository at github.com/zou-group/avatar. The project has 241 stars and 27 forks, indicating an active community.
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