Architecture - CowAgent

CowAgent 2.0 has evolved from a simple chatbot into a super intelligent assistant with Agent architecture, featuring autonomous thinking, task planning, long-term memory, and skill extensibility.

System Architecture

CowAgent’s architecture consists of the following core modules: CowAgent Architecture

Module	Description
Plan	Understands user intent, decomposes complex tasks into multi-step plans, and iteratively invokes tools until the goal is achieved
Memory	Automatically persists important information as core memory and daily memory, with hybrid keyword and vector retrieval for cross-session context continuity
Knowledge	Organizes structured knowledge by topic. The Agent autonomously distills valuable information into Markdown pages, maintaining indexes and cross-references to build a growing knowledge network
Tools	Core capability for Agent to access OS resources. 10+ built-in tools including file read/write, terminal, browser, scheduler, memory search, web search, and more
Skills	Loads and manages Skills. Supports one-click installation from Skill Hub, GitHub, and more, or custom skill creation through conversation
Models	Model layer with unified access to OpenAI, Claude, Gemini, DeepSeek, MiniMax, GLM, Qwen, and other mainstream LLMs
Channels	Message channel layer for receiving and sending messages. Supports Web console, WeChat, Feishu, DingTalk, WeCom, WeChat Official Account, and more with a unified protocol
CLI	Command-line system providing terminal commands (`cow`) and chat commands (`/`) for process management, skill installation, configuration, knowledge base management, and more

Agent Mode Workflow

When Agent mode is enabled, CowAgent runs as an autonomous agent with the following workflow:

Receive Message — Receive user input through channels
Understand Intent — Analyze task requirements and context
Plan Task — Break complex tasks into multiple steps
Invoke Tools — Select and execute appropriate tools for each step
Update Memory & Knowledge — Store important information in long-term memory and organize structured knowledge into the knowledge base
Return Result — Send execution results back to the user

Workspace Directory Structure

The Agent workspace is located at ~/cow by default and stores system prompts, memory files, and skill files:

~/cow/
├── system.md          # Agent system prompt
├── user.md            # User profile
├── MEMORY.md          # Core memory
├── memory/            # Long-term memory storage
│   └── YYYY-MM-DD.md  # Daily memory
├── knowledge/         # Personal knowledge base
│   ├── index.md       # Knowledge index
│   └── <category>/    # Topic-based pages
└── skills/            # Custom skills
    ├── skill-1/
    └── skill-2/

Secret keys are stored separately in ~/.cow directory for security:

~/.cow/
└── .env               # Secret keys for skills

Core Configuration

Configure Agent mode parameters in config.json:

{
  "agent": true,
  "agent_workspace": "~/cow",
  "agent_max_context_tokens": 40000,
  "agent_max_context_turns": 30,
  "agent_max_steps": 15
}

Parameter	Description	Default
`agent`	Enable Agent mode	`true`
`agent_workspace`	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`
`knowledge`	Enable personal knowledge base	`true`

Overview

​System Architecture

​Agent Mode Workflow

​Workspace Directory Structure

​Core Configuration

System Architecture

Agent Mode Workflow

Workspace Directory Structure

Core Configuration