ProactiveAgent

A proactive agent based on large language models that predicts user needs and offers assistance.

CommonProductProgrammingArtificial IntelligenceMachine Learning

ProactiveAgent is a proactive agent project powered by a Large Language Model (LLM), designed to build an intelligent agent that predicts user needs and provides proactive help. This project achieves its goals through data collection and generation pipelines, automatic evaluators, and training agents. Key advantages of ProactiveAgent include environmental awareness, annotation assistance, dynamic data generation, and construction pipelines, with a reward model achieving an F1 score of 0.918 on the test set, indicating strong performance. This product is suitable for programming, writing, and daily life scenarios, and adheres to the Apache License 2.0.

Environmental Awareness: Collects environmental scenes and user activities via Activity Watcher to automatically recommend tasks.
Annotation Assistance: Provides a platform for annotating agent-generated responses to align with human annotation results.
Dynamic Generation: A dynamic data generation pipeline where user feedback influences subsequent events.
Building Pipeline: Includes the environment Gym
Proactive Agent
and reward model in the generation pipeline
achieving an F1 score of 0.918 on the reward model test set.
Datasets and Evaluation Scripts: Provides a complete data collection and generation pipeline
datasets
and corresponding evaluation scripts.

\The target audience includes developers
data scientists
and AI researchers who need an intelligent agent capable of understanding user needs and providing proactive assistance to enhance work efficiency and improve user experience. ProactiveAgent anticipates user requirements
reducing explicit requests and allowing users to focus more on their core tasks.\

Developers use ProactiveAgent to automatically recommend code snippets and fix suggestions while coding.
Data scientists leverage ProactiveAgent to automatically suggest data preprocessing steps during data analysis.
Researchers utilize ProactiveAgent to monitor experiment progress and propose adjustments automatically.

1. Clone the repository and navigate to the ProactiveAgent folder.
2. Set up the Python environment and install the required packages.
3. Install Activity Watcher and verify its proper installation.
4. Configure the private.toml file to customize it for personal settings.
5. Run Proactive Agent and interact according to the prompts.

Visit

ProactiveAgent Visit Over Time

Monthly Visits

515580771

Bounce Rate

37.20%

Page per Visit

5.8

Visit Duration

00:06:42

ProactiveAgent Visit Trend

ProactiveAgent Visit Geography

ProactiveAgent Traffic Sources

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ProactiveAgent

ProactiveAgent Visit Over Time

ProactiveAgent Visit Trend

ProactiveAgent Visit Geography

ProactiveAgent Traffic Sources

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West Lake AI Model — A multimodal model with high emotional and intellectual intelligence

RWKV v6 Finch 14B — RWKV v6 Finch 14B is an open-source large model that efficiently processes long texts.

C4AI Command R+ 08-2024 — A multilingual advanced dialogue generation model with 104 billion parameters.

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