alingalingling/akasha-wechat

★ 17PythonAudience · developerComplexity · 4/5LicenseSetup · hard

Mindmap

mindmap
  root((akasha-wechat))
    What it does
      WeChat AI chatbot bridge
      Connects WeChat to AI models
      Auto-replies to messages
    Components
      WeFlow hooks WeChat client
      Bridge converts to OneBot
      AstrBot runs AI pipeline
    Features
      Image auto-description
      Group chat reply modes
      Message buffering
    Setup
      Windows only
      WeChat desktop required
      Local web control panel

mindmap root((akasha-wechat)) What it does WeChat AI chatbot bridge Connects WeChat to AI models Auto-replies to messages Components WeFlow hooks WeChat client Bridge converts to OneBot AstrBot runs AI pipeline Features Image auto-description Group chat reply modes Message buffering Setup Windows only WeChat desktop required Local web control panel

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Things people build with this

USE CASE 1

Turn a secondary WeChat account into an AI chatbot that auto-replies to personal or group messages using models like DeepSeek or Claude.

USE CASE 2

Automatically describe images sent in WeChat using a vision AI, so the chatbot understands picture messages.

USE CASE 3

Buffer rapid-fire WeChat messages from the same conversation before sending them to the AI, cutting down on API calls.

USE CASE 4

Control and monitor the bridge, start, stop, view logs, edit config, from a local browser panel without touching config files.

Tech stack

PythonAstrBotOneBot v11WeFlowWebSocket

Getting it running

Difficulty · hard Time to first run · 1h+

Windows only, requires WeChat desktop installed and logged in, plus WeFlow and AstrBot running as separate services before the bridge can connect.

MIT license, use freely for any purpose, including commercial, as long as you keep the copyright notice.

In plain English

Akasha-WeChat is a bridge tool that connects a WeChat personal account to an AI language model, turning a secondary WeChat account into a chatbot that can respond to messages using models like DeepSeek, Kimi, Claude, or any other provider supported by AstrBot. The project is written in Python and runs on Windows, where the desktop WeChat application must be installed and logged in. The system works through a chain of components. WeFlow, a third-party Windows tool, hooks into the local WeChat client and exposes an API that streams incoming messages in real time without polling. The bridge script in this repository picks up those messages, optionally buffers several messages together before forwarding them, and converts them into the OneBot v11 format, which is a standardized protocol for chat bot communication. AstrBot, a separate AI agent platform, receives the converted messages over a WebSocket connection, passes them through its plugin pipeline to call the chosen AI model, and sends back a reply. The bridge then delivers that reply into WeChat by simulating UI interactions on the desktop application. A few practical features are included. Image messages are automatically downloaded, described by a vision-capable AI model (such as llava or Kimi), and the description is injected as text before the message reaches the main AI. Group chat behavior can be set to three modes: reply only when the bot is mentioned, reply to all messages, or process messages in batches. A self-reply guard prevents the AI from responding to its own messages in a loop. The message buffer combines multiple rapid messages from the same conversation before sending them to the AI, reducing the number of API calls. A web control panel is available at a local address and lets you start and stop the bridge, view live logs, check connection status, and edit all configuration options directly in the browser without modifying files by hand. The author notes in the README that they are a high school student preparing for college entrance exams and may not respond to questions promptly. The project uses the MIT license.

Copy-paste prompts

Prompt 1

I've set up Akasha-WeChat and AstrBot. Configure the group chat mode so the bot only replies when mentioned, and explain how to tune the message buffer.

Prompt 2

Write a config for Akasha-WeChat that uses the Claude API as the AI backend and buffers up to 3 messages before forwarding them.

Prompt 3

Walk me through connecting WeFlow to Akasha-WeChat and then to AstrBot on a Windows machine, step by step from scratch.

Prompt 4

My Akasha-WeChat bot is replying to its own messages in a loop. Show me which config setting enables the self-reply guard and how to apply it.

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