rememberber/wepush

专注批量推送的小而美的工具,目前支持:模板消息-公众号、模板消息-小程序、微信客服消息、微信企业号/企业微信消息、阿里云短信、阿里大于模板短信 、腾讯云短信、云片网短信、E-Mail、HTTP请求、钉钉、华为云短信、百度云短信、又拍云短信、七牛云短信

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Summary Information

Updated 11 minutes ago
Added to GitGenius on September 14th, 2026
Created on June 23rd, 2017
Open Issues & Pull Requests: 11 (+0)
GitHub issues: Enabled
Number of forks: 1,017
Total Stargazers: 4,681 (+0)
Total Subscribers: 148 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 544.8 days
Mean response time: 791.0 days
90th percentile: 2216.0 days
Tracked items: 25

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How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 3% of issues opened in the past year have been closed. Three people close 100% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 1
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 2,561 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

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

Top labels

  • 计划中 (1)

Most active issues this week

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Detailed Description

WePush is a desktop application for batch message delivery that supports multiple channels including WeChat template messages, WeChat Work, SMS providers, email, HTTP requests, DingTalk, and others.

The tool addresses the need to send messages in bulk across diverse platforms through a unified interface. It provides a graphical client that lets users configure channel credentials, compose messages, define recipient audiences, and execute batch sending tasks. The project maintains two independent product lines: Classic, positioned as a stable desktop client for mature use cases like WeChat, SMS, and email; and Next, which adds cross-platform offline deployment, a web UI alongside desktop options, extensible provider architecture, and resource management features.

Classic suits teams already comfortable with desktop applications who need reliable batch sending for established channels. Next targets deployments requiring self-hosted infrastructure, multi-platform support, or extensibility. Both versions now support integration with AI assistants through Model Context Protocol and Skill interfaces, allowing assistants to query tasks, preview messages, perform dry runs, and execute authorized sends. The tool's dual-track approach means users can choose stability with Classic or adopt Next's newer capabilities without affecting existing Classic installations.

Development activity shows sustained maintenance with parallel evolution of both product lines. The project has introduced AI assistant integration across both versions with independent implementations. Recent work includes expanding provider support and adding resource governance features to the Next line. The codebase receives updates addressing both the stable Classic branch and the actively developed Next branch, with documentation covering quick-start procedures for both paths.