Cowagent AI Assistant

CowAgent AI Assistant

Skill by ara.so — AI Agent Skills collection.

CowAgent is an open-source autonomous AI assistant framework that plans tasks, executes tools and skills, and grows through memory and knowledge. It supports multiple LLM providers (Claude, GPT, Gemini, DeepSeek, etc.) and channels (Web, WeChat, Feishu, DingTalk), with a three-tier memory architecture and personal knowledge base.

Installation

Quick Install (Recommended)

Linux / macOS:

bash <(curl -fsSL https://cdn.link-ai.tech/code/cow/run.sh)

Windows (PowerShell):

irm https://cdn.link-ai.tech/code/cow/run.ps1 | iex

Docker:

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

Manual Installation from Source

# Clone repository
git clone https://github.com/zhayujie/CowAgent.git
cd CowAgent

# Install dependencies (Python 3.8+)
pip3 install -r requirements.txt

# Copy and configure
cp config-template.json config.json

# Start the agent
python3 app.py

After starting, access the Web console at http://localhost:9899.

CLI Commands

The cow CLI manages the CowAgent service:

# Service control
cow start              # Start CowAgent
cow stop               # Stop CowAgent
cow restart            # Restart CowAgent
cow status             # Check service status
cow logs               # View logs

# Updates and skills
cow update             # Pull latest code and restart
cow skill install <name>   # Install a skill from Skill Hub
cow install-browser    # Install browser automation dependencies

# Usage examples
cow skill list         # List installed skills
cow skill search weather   # Search for skills

Configuration

Model Configuration

Configure via Web console (recommended) or manually edit config.json:

{
  "model": "claude-opus-4",
  "claude_api_key": "${CLAUDE_API_KEY}",
  "openai_api_key": "${OPENAI_API_KEY}",
  "gemini_api_key": "${GEMINI_API_KEY}",
  
  "vision_model": "gpt-4o",
  "image_create_model": "dall-e-3",
  "speech_recognition_model": "whisper-1",
  "text_to_speech_model": "tts-1",
  "embedding_model": "text-embedding-3-small"
}

Channel Configuration

Set channel_type to switch channels:

{
  "channel_type": "wx",  // Options: terminal, wx, web, feishu, dingtalk, wecom_bot, qq
  
  // Web channel (default)
  "web": {
    "port": 9899,
    "admin_password": "your_password"
  },
  
  // WeChat
  "wechat": {
    "single_chat_prefix": ["bot", "@bot"],
    "single_chat_reply_prefix": "[bot] ",
    "group_chat_prefix": ["@bot"],
    "group_name_white_list": ["ChatGroup1", "ChatGroup2"]
  },
  
  // Feishu
  "feishu": {
    "app_id": "${FEISHU_APP_ID}",
    "app_secret": "${FEISHU_APP_SECRET}"
  }
}

Memory Configuration

{
  "memory": {
    "enable_long_term": true,
    "deep_dream_time": "03:00",  // Daily Deep Dream time
    "max_context_messages": 20,
    "enable_hybrid_search": true
  }
}

Knowledge Base Configuration

{
  "knowledge": {
    "enable": true,
    "auto_curate": true,
    "update_threshold": 3
  }
}

Skills System

Installing Skills

Via CLI:

cow skill install weather
cow skill install stock-query
cow skill install github-repo-search

Via Chat:

/skill search weather
/skill install weather
/skill list

Creating Custom Skills

Skills are defined in a skill.json manifest:

{
  "name": "custom-api-caller",
  "version": "1.0.0",
  "description": "Call external API and process results",
  "author": "Your Name",
  "triggers": ["call api", "fetch data from api"],
  "parameters": [
    {
      "name": "endpoint",
      "type": "string",
      "description": "API endpoint URL",
      "required": true
    },
    {
      "name": "method",
      "type": "string",
      "description": "HTTP method (GET/POST)",
      "default": "GET"
    }
  ],
  "steps": [
    {
      "action": "web_fetch",
      "params": {
        "url": "{{endpoint}}",
        "method": "{{method}}"
      }
    },
    {
      "action": "write",
      "params": {
        "path": "result.json",
        "content": "{{web_fetch.response}}"
      }
    }
  ]
}

Place in skills/custom-api-caller/skill.json and restart.

Conversational Skill Creation

Use the built-in skill-creator skill:

Create a skill that fetches GitHub repository info and saves it to a markdown file.

The agent will generate the skill manifest interactively.

Tools System

Built-in Tools

File Operations:

# Agent uses these tools automatically
read(path="/path/to/file.txt")
write(path="output.txt", content="data")
edit(path="config.json", replacements=[{"old": "value1", "new": "value2"}])
ls(path="./data")

Terminal:

bash(command="ls -la")
bash(command="python script.py")

Memory & Knowledge:

memory(query="what did user say about project X")
knowledge_search(query="API documentation")

Web & Browser:

web_fetch(url="https://api.example.com/data")
web_search(query="Python async best practices")
browser(action="navigate", url="https://example.com")
browser(action="click", selector="#submit-button")

Scheduling:

scheduler(action="add", time="2026-05-25 14:00", task="Send report")
scheduler(action="list")

MCP Integration

Configure MCP servers in mcp.json:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"],
      "transport": "stdio"
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "transport": "stdio",
      "env": {
        "GITHUB_TOKEN": "${GITHUB_TOKEN}"
      }
    },
    "puppeteer": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-puppeteer"],
      "transport": "stdio"
    }
  }
}

Hot reload: edit mcp.json and restart CowAgent.

Working Code Examples

Python Plugin Example

Create a custom plugin in plugins/my_plugin.py:

import plugins
from bridge.context import ContextType
from bridge.reply import Reply, ReplyType
from common.log import logger

@plugins.register(
    name="MyPlugin",
    desc="Custom functionality plugin",
    version="1.0",
    author="Your Name"
)
class MyPlugin(plugins.Plugin):
    def __init__(self):
        super().__init__()
        self.handlers[Event.ON_HANDLE_CONTEXT] = self.on_handle_context
        logger.info("[MyPlugin] initialized")

    def on_handle_context(self, e_context: EventContext):
        context = e_context['context']
        if context.type != ContextType.TEXT:
            return
        
        content = context.content.strip()
        
        if content.startswith("/hello"):
            reply = Reply()
            reply.type = ReplyType.TEXT
            reply.content = "Hello from custom plugin!"
            e_context['reply'] = reply
            e_context.action = EventAction.BREAK_PASS
            return

    def get_help_text(self, **kwargs):
        return "MyPlugin: Use /hello to get a greeting"

Custom Channel Implementation

from channel.channel import Channel
from bridge.context import Context, ContextType
from bridge.reply import Reply

class CustomChannel(Channel):
    def __init__(self):
        super().__init__()
        
    def startup(self):
        # Initialize your channel (websocket, HTTP server, etc.)
        logger.info("[CustomChannel] starting...")
        
    def handle_message(self, message):
        context = Context()
        context.type = ContextType.TEXT
        context.content = message['text']
        context['session_id'] = message['user_id']
        
        # Process through agent
        reply = super().build_reply_content(message['text'], context)
        
        # Send reply through your channel
        self.send_message(message['user_id'], reply.content)
        
    def send_message(self, user_id, content):
        # Implement sending logic
        pass

Register in channel/channel_factory.py:

from channel.custom.custom_channel import CustomChannel

def create_channel(channel_type):
    if channel_type == "custom":
        return CustomChannel()
    # ... existing channels

Advanced Skill with Multiple Tools

{
  "name": "github-issue-reporter",
  "version": "1.0.0",
  "description": "Search GitHub repos, analyze issues, generate report",
  "triggers": ["analyze github issues", "report on github repository"],
  "parameters": [
    {
      "name": "repo",
      "type": "string",
      "description": "GitHub repository (owner/repo)",
      "required": true
    }
  ],
  "steps": [
    {
      "action": "web_fetch",
      "params": {
        "url": "https://api.github.com/repos/{{repo}}/issues",
        "headers": {
          "Authorization": "token ${GITHUB_TOKEN}"
        }
      },
      "output": "issues_data"
    },
    {
      "action": "bash",
      "params": {
        "command": "echo '{{issues_data}}' | jq '[.[] | {title: .title, state: .state, comments: .comments}]' > /tmp/issues.json"
      }
    },
    {
      "action": "read",
      "params": {
        "path": "/tmp/issues.json"
      },
      "output": "processed_issues"
    },
    {
      "action": "write",
      "params": {
        "path": "github_report_{{repo | replace('/', '_')}}.md",
        "content": "# GitHub Issues Report for {{repo}}\n\n{{processed_issues}}\n\nGenerated at {{now}}"
      }
    }
  ]
}

Common Patterns

Agent Planning Loop

The agent follows a plan-execute-reflect loop:

1. Plan: Decompose user request into subtasks
2. Execute: Run tools and skills step by step
3. Reflect: Check if goal achieved, adjust plan
4. Loop: Continue until task complete

Memory Retrieval Pattern

# Agent automatically searches memory when relevant
# Manual retrieval in custom code:
from plugins import memory_search

results = memory_search(
    query="user's favorite programming language",
    limit=5
)

Knowledge Base Update Pattern

# Agent auto-curates during conversation
# Manual update:
from plugins import knowledge_update

knowledge_update(
    topic="Project Setup",
    content="New setup steps: 1. Install deps 2. Configure .env",
    operation="append"
)

Multi-Step Workflow Pattern

For complex workflows, chain tools in skills:

{
  "steps": [
    {"action": "web_fetch", "params": {"url": "..."}},
    {"action": "bash", "params": {"command": "process.sh"}},
    {"action": "read", "params": {"path": "result.txt"}},
    {"action": "memory", "params": {"action": "save", "content": "{{read.content}}"}}
  ]
}

Troubleshooting

Installation Issues

Python version mismatch:

python3 --version  # Ensure 3.8+
pip3 install --upgrade pip setuptools wheel

Missing dependencies:

pip3 install -r requirements.txt --force-reinstall

Port already in use:

{
  "web": {
    "port": 9900  // Change from default 9899
  }
}

Model Configuration Issues

API key not working:

• Ensure environment variables are set: export CLAUDE_API_KEY=sk-...
• Or set in config.json directly (not recommended for production)
• Check key has proper permissions and quota

Model not responding:

cow logs  # Check for API errors

Common fixes:

• Verify model field matches provider's model name
• Check provider-specific API key field (claude_api_key, openai_api_key, etc.)
• Test with curl:

curl -X POST https://api.anthropic.com/v1/messages \
  -H "x-api-key: ${CLAUDE_API_KEY}" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{"model":"claude-opus-4","messages":[{"role":"user","content":"test"}],"max_tokens":100}'

Channel Connection Issues

WeChat not connecting:

• QR code expired: restart and scan new code within 60 seconds
• Check wechat config in config.json
• Ensure network allows WeChat web protocol

Feishu/Lark setup:

• Verify app_id and app_secret from Feishu admin console
• Enable bot capabilities in app settings
• Add bot to target groups before testing

Memory & Knowledge Issues

Deep Dream not running:

• Check memory.deep_dream_time in config
• Ensure agent is running at scheduled time
• Verify sufficient conversation history

Knowledge not updating:

• Set knowledge.auto_curate: true
• Check knowledge.update_threshold (default 3 relevant exchanges)

Skill Issues

Skill not triggering:

• Check triggers in skill.json match user input
• List skills: /skill list
• Reinstall: cow skill install <name>

Skill execution fails:

cow logs  # Check for tool errors

Verify:

• Required tools are available
• Parameters match schema
• File paths are accessible

Browser Tool Issues

Browser not installed:

cow install-browser

Headless mode issues:

{
  "browser": {
    "headless": false  // Debug with visible browser
  }
}

Performance Optimization

Slow responses:

• Use faster models: claude-sonnet-4, gpt-4o-mini, deepseek-v4-flash
• Reduce memory.max_context_messages
• Disable unused features

High memory usage:

• Limit conversation history
• Disable Deep Dream if not needed
• Restart agent daily: 0 3 * * * cow restart

Logs and Debugging

cow logs               # View recent logs
cow logs -f            # Follow logs in real-time
tail -f logs/app.log   # Direct log access

Enable debug mode in config.json:

{
  "debug": true,
  "log_level": "DEBUG"
}

For issues, check:

1. cow status - service running
2. cow logs - error messages
3. Config validation: ensure JSON is valid
4. Port conflicts: netstat -tuln | grep 9899