Bettafish — AI Agent Review & Live Stats

Live GitHub stats, community sentiment, and trend data for Bettafish. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Mar 13, 2026 • Sentiment updated: Unknown

GitHub Statistics

• GitHub Stars: 40,107
• Forks: 7,451
• Open Issues: 56
• Primary Language: Python
• License: GPL-2.0
• Weekly Star Velocity: +240 stars/week

Why Bettafish Stands Out

BettaFish is valuable because it provides a unique framework for building and orchestrating multiple AI agents to analyze public opinion from various sources. Its technical approach of using a multi-agent system and natural language processing capabilities sets it apart from alternatives. By providing a platform for building and training machine learning models, BettaFish solves the problem of requiring significant expertise in AI and machine learning to analyze public opinion. The project's focus on providing a simple and easy-to-use interface for non-technical users also makes it stand out from other projects in the field.

Built With

Build a multi-agent system for public opinion analysis — BettaFish enables this by providing a framework for building and orchestrating multiple AI agents to analyze public opinion from various sources, Build a sentiment analysis tool for social media — BettaFish allows this by integrating natural language processing capabilities and machine learning models to analyze sentiment from social media posts, Build a predictive model for future trends — BettaFish facilitates this by providing a platform for building and training machine learning models on large datasets, Build a data visualization dashboard for opinion analysis — BettaFish enables this by providing tools for data visualization and presentation, Build a decision support system for businesses — BettaFish allows this by providing a framework for building and integrating multiple AI agents to support business decision-making

Getting Started

1. Install BettaFish using pip install bettafish
2. Configure the AI agents by modifying the config.json file
3. Train the machine learning models using the train.py script
4. Integrate the AI agents with the data visualization tools using the visualize.py script
5. Try running the predictive model to verify it works by using the predict.py script

About

微舆：人人可用的多Agent舆情分析助手，打破信息茧房，还原舆情原貌，预测未来走向，辅助决策！从0实现，不依赖任何框架。

Official site: https://deepwiki.com/666ghj/BettaFish

Category & Tags

Category: multi-agent

Tags: agent-framework, data-analysis, deep-research, deep-search, llms, multi-agent-system, nlp, public-opinion-analysis, python3, sentiment-analysis