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zhayujie/cowagent

📈 Trending★ 44,571PythonAudience · developerComplexity · 4/5ActiveLicenseSetup · hard

TLDR

Python framework that turns an AI assistant into an autonomous agent across WeChat, Feishu, and other messaging platforms, it plans multi-step tasks, accesses files, runs commands, and remembers conversations.

Mindmap

mindmap
  root((CowAgent))
    What it does
      Multi-step task planning
      File and web access
      Persistent memory
      24/7 autonomous operation
    Messaging platforms
      WeChat personal
      Feishu and DingTalk
      QQ and web interface
    LLM backends
      OpenAI and Claude
      DeepSeek and Qwen
      Gemini and MiniMax
    Extensibility
      Built-in tools
      Community skill hub
      Conversation-driven skills
    Tech stack
      Python 3.7-3.13
      Docker support
      CLI management tool
    Use cases
      Automate document workflows
      Schedule and coordinate tasks
      Research and summarization

Things people build with this

USE CASE 1

Deploy a 24/7 AI assistant on WeChat or Feishu that summarizes documents, schedules meetings, and sends emails without manual intervention.

USE CASE 2

Build a multi-step workflow automation tool that reads files, browses the web, and executes terminal commands based on natural language requests.

USE CASE 3

Create a persistent AI teammate that remembers past conversations and context from weeks ago to provide smarter, contextual responses.

USE CASE 4

Extend the agent with custom skills via the Skill Hub or by instructing it to create new capabilities through conversation.

Tech stack

PythonDockerOpenAIClaudeDeepSeek

Getting it running

Difficulty · hard Time to first run · 1day+

Requires API keys (OpenAI/Claude/DeepSeek), messaging platform credentials (WeChat/Feishu), Docker setup, and multi-component orchestration (agent, messaging adapters, file/command execution).

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

In plain English

CowAgent (originally known as chatgpt-on-wechat) is an AI-powered assistant framework written in Python that lets you deploy a capable, autonomous AI agent across multiple Chinese and international messaging platforms. The core problem it solves is that most large language model (LLM) chat experiences are purely reactive, you ask a question and get an answer. CowAgent goes further by giving the AI the ability to plan multi-step tasks, access files and run terminal commands, browse the web, and remember past conversations persistently. When you set it up, the agent connects to your chosen messaging platform, WeChat (personal or official account), Feishu (Lark), DingTalk, enterprise WeChat, QQ, or even a simple web interface, and listens for messages. When you send a request like "summarize this PDF and email a draft to my colleague," the agent breaks that into steps, calls the relevant built-in tools (file reader, browser, scheduler), and completes the work without you micromanaging each step. All of this happens 24 hours a day on your personal computer or a server. The system supports a wide range of LLM backends, including DeepSeek, OpenAI, Claude, Gemini, MiniMax, Qwen, and GLM. You can switch models in the configuration file. Memory is layered into core memory, daily notes, and longer-term knowledge graphs so the agent can recall relevant context from weeks ago when you revisit a topic. A Skills system lets you extend the agent's capabilities: install community-contributed skills from the Skill Hub, import from GitHub with one command, or instruct the agent to create a new skill through conversation. The tech stack is pure Python (3.7-3.13), deployable on Linux, macOS, or Windows. Docker is supported for setups where you want to skip the manual Python environment configuration. A CLI tool called cow manages starting, stopping, and updating the service.

Copy-paste prompts

Prompt 1
I want to set up CowAgent on my WeChat account so it can handle multi-step tasks like summarizing PDFs and sending emails. Walk me through the installation and configuration steps.
Prompt 2
How do I add a custom skill to my CowAgent instance? Show me an example of creating a skill that fetches data from an API and formats it for my team.
Prompt 3
I'm using OpenAI but want to switch to DeepSeek. How do I change the LLM backend in CowAgent's config file?
Prompt 4
Explain how CowAgent's memory system works, how does it store and recall information from past conversations across days or weeks?
Prompt 5
I want to deploy CowAgent on a Linux server using Docker. What's the quickest way to get it running with persistent storage?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.