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chatgpt-on-wechat/docs/en/guide/manual-install.mdx

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---
title: Manual Install
description: Deploy CowAgent manually (source code / Docker)
---
## 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](/en/models/index) 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>
**Deploying on a server?** By default `web_host` only listens on `127.0.0.1` (local access). Set `web_host` to `0.0.0.0` in `config.json` to make the console reachable from outside, and set `web_password` to protect it. Don't forget to open port `9899` in your firewall or security group — ideally restricted to specific IPs.
</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>
**Running in Docker?** Set `WEB_HOST` to `0.0.0.0` in `docker-compose.yml` so the console is reachable from outside the container, and set `WEB_PASSWORD` to protect it. Make sure port `9899` is mapped to the host and open in your firewall or security group.
</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,
"cow_lang": "auto"
}
```
| 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` |
| `cow_lang` | Language for the UI, command text and system prompts; `auto` to detect, or set `zh` / `en` | `auto` |
<Tip>
Full configuration options are in the project [`config.py`](https://github.com/zhayujie/CowAgent/blob/master/config.py).
</Tip>