Manual Install - Chatgpt On Wechat

Source Code Deployment

1. Clone the project

bash

git clone https://github.com/zhayujie/CowAgent
cd CowAgent/

<Tip> For network issues, use the mirror: https://gitee.com/zhayujie/CowAgent </Tip>

2. Install dependencies

Core dependencies (required):

bash

pip3 install -r requirements.txt

Optional dependencies (recommended):

bash

pip3 install -r requirements-optional.txt

3. Install Cow CLI

Install the command-line tool for managing services and skills:

bash

pip3 install -e .

Then use the cow command:

bash

cow help

<Note> This step is recommended. After installation you can use `cow start`, `cow stop`, `cow update` to manage the service, and `cow skill` to manage skills. Without the CLI, you can use `./run.sh` or `python3 app.py` to run. </Note>

4. Configure

Copy the config template and edit:

bash

cp config-template.json config.json

Fill in model API keys, channel type, and other settings in config.json. See the model docs for details.

5. Run

Using Cow CLI (recommended):

bash

cow start

Or run locally in foreground:

bash

python3 app.py

By default, the Web console starts. Access http://localhost:9899 to chat.

Background run on server (without CLI):

bash

nohup python3 app.py & tail -f nohup.out

<Tip> If deploying on a server, open port `9899` in your firewall or security group to access the Web console. It's recommended to restrict access to specific IPs for security. </Tip>

Docker Deployment

Docker deployment does not require cloning source code or installing dependencies. For Agent mode, source deployment is recommended for broader system access.

<Note> Requires [Docker](https://docs.docker.com/engine/install/) and docker-compose. </Note>

1. Download config

bash

curl -O https://cdn.link-ai.tech/code/cow/docker-compose.yml

Edit docker-compose.yml with your configuration.

2. Start container

bash

sudo docker compose up -d

3. View logs

bash

sudo docker logs -f chatgpt-on-wechat

<Tip> If deploying on a server, open port `9899` in your firewall or security group to access the Web console. It's recommended to restrict access to specific IPs for security. </Tip>

Core Configuration

json

{
  "channel_type": "web",
  "model": "deepseek-v4-flash",
  "deepseek_api_key": "",
  "agent": true,
  "agent_workspace": "~/cow",
  "agent_max_context_tokens": 40000,
  "agent_max_context_turns": 30,
  "agent_max_steps": 15
}

Parameter	Description	Default
`channel_type`	Channel type	`web`
`model`	Model name	`deepseek-v4-flash`
`agent`	Enable Agent mode	`true`
`agent_workspace`	Agent workspace path	`~/cow`
`agent_max_context_tokens`	Max context tokens	`40000`
`agent_max_context_turns`	Max context turns	`30`
`agent_max_steps`	Max decision steps per task	`15`

<Tip> Full configuration options are in the project [`config.py`](https://github.com/zhayujie/CowAgent/blob/master/config.py). </Tip>