zhayujie/bot-on-anything

A large model-based chatbot builder that can quickly integrate AI models (including ChatGPT, Claude, Gemini) into various software applications (such as...

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Summary Information

Updated 1 hour ago
Added to GitGenius on September 16th, 2026
Created on February 9th, 2023
Open Issues & Pull Requests: 269 (+0)
GitHub issues: Enabled
Number of forks: 914
Total Stargazers: 4,218 (+0)
Total Subscribers: 40 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 0.7 hours
Mean response time: 33.3 days
90th percentile: 100.0 days
Tracked items: 3

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Issue Activity (beta)

Open issues: 8
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 766 days
Stale 30+ days: 8
Stale 90+ days: 8

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

bot-on-anything is a chatbot builder that integrates large language models into messaging and communication platforms.

The tool addresses the problem of connecting AI models to diverse communication channels without building integrations from scratch. It works by providing a unified framework that abstracts away platform-specific details, allowing developers to configure which AI model and which communication channel to use through configuration rather than custom code. The builder supports multiple leading AI models including ChatGPT, Claude, and Gemini, and can route conversations through Telegram, Gmail, Slack, and web interfaces.

Teams should adopt this tool if they need to deploy conversational AI across multiple platforms without maintaining separate integrations for each combination of model and channel. It suits projects where the primary goal is rapid deployment of a chatbot to existing communication infrastructure rather than building a custom conversational system. The tool is particularly valuable for organizations that want to experiment with different AI models or expand to new platforms without significant rework.

The project shows active development with regular commits addressing bug fixes and feature additions. The codebase demonstrates ongoing refinement of the integration layer between models and platforms. Documentation is maintained to reflect current capabilities and configuration options.