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87 lines
4.0 KiB
Plaintext
87 lines
4.0 KiB
Plaintext
---
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title: Architecture
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description: CowAgent 2.0 system architecture and core design
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---
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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.
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## System Architecture
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CowAgent's architecture consists of the following core modules:
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<img src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/architecture/en/architecture.jpg" alt="CowAgent Architecture" />
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| Module | Description |
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| --- | --- |
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| **Plan** | Understands user intent, decomposes complex tasks into multi-step plans, and iteratively invokes tools until the goal is achieved |
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| **Memory** | Automatically persists important information as core memory and daily memory, with hybrid keyword and vector retrieval for cross-session context continuity |
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| **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 |
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| **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 |
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| **Skills** | Loads and manages Skills. Supports one-click installation from Skill Hub, GitHub, and more, or custom skill creation through conversation |
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| **Models** | Model layer with unified access to OpenAI, Claude, Gemini, DeepSeek, MiniMax, GLM, Qwen, and other mainstream LLMs |
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| **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 |
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| **CLI** | Command-line system providing terminal commands (`cow`) and chat commands (`/`) for process management, skill installation, configuration, knowledge base management, and more |
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## Agent Mode Workflow
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When Agent mode is enabled, CowAgent runs as an autonomous agent with the following workflow:
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1. **Receive Message** — Receive user input through channels
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2. **Understand Intent** — Analyze task requirements and context
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3. **Plan Task** — Break complex tasks into multiple steps
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4. **Invoke Tools** — Select and execute appropriate tools for each step
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5. **Update Memory & Knowledge** — Store important information in long-term memory and organize structured knowledge into the knowledge base
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6. **Return Result** — Send execution results back to the user
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## Workspace Directory Structure
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The Agent workspace is located at `~/cow` by default and stores system prompts, memory files, and skill files:
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```
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~/cow/
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├── SYSTEM.md # Agent system prompt
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├── USER.md # User profile
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├── MEMORY.md # Core memory
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├── memory/ # Long-term memory storage
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│ └── YYYY-MM-DD.md # Daily memory
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├── knowledge/ # Personal knowledge base
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│ ├── index.md # Knowledge index
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│ └── <category>/ # Topic-based pages
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└── skills/ # Custom skills
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├── skill-1/
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└── skill-2/
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```
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Secret keys are stored separately in `~/.cow` directory for security:
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```
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~/.cow/
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└── .env # Secret keys for skills
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```
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## Core Configuration
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Configure Agent mode parameters in `config.json`:
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```json
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{
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"agent": true,
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"agent_workspace": "~/cow",
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"agent_max_context_tokens": 50000,
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"agent_max_context_turns": 20,
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"agent_max_steps": 20,
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"enable_thinking": false
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}
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```
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| Parameter | Description | Default |
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| --- | --- | --- |
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| `agent` | Enable Agent mode | `true` |
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| `agent_workspace` | Workspace path | `~/cow` |
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| `agent_max_context_tokens` | Max context tokens | `50000` |
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| `agent_max_context_turns` | Max context turns | `20` |
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| `agent_max_steps` | Max decision steps per task | `20` |
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| `enable_thinking` | Enable deep-thinking mode | `false` |
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| `knowledge` | Enable personal knowledge base | `true` |
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| `knowledge` | Enable personal knowledge base | `true` |
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