Architecture - CowAgent

CowAgent is an out-of-the-box super AI assistant and a complete Agent harness framework, featuring complex task planning, long-term memory, skill extensibility, self-evolution, and multi-agent collaboration.

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
Evolution	Reviews a conversation in an isolated environment after it goes idle, improving skills, following up on unfinished tasks, and backfilling memory and knowledge so the Agent keeps growing through everyday use
Multi-agent	Multiple Agents form a team, each with its own responsibilities, model, and workspace. Supports group-chat collaboration, task delegation, and sub-Agents; a channel can bind a single Agent or the whole team to serve externally
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

System Workspace

The Agent’s system 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/
└── agents/            # Multi-agent team
    ├── team.json      # Team roster & channel bindings
    └── <agent-id>/    # Non-default agent's own workspace

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

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

Project Workspace

Besides the default workspace, each session can be bound to its own project directory. The Agent’s file reads/writes and command execution happen inside that directory, giving you multi-project isolation; memory, skills, and the like still live in the default workspace. When more than one project is in use, the Web/desktop history list automatically groups sessions by project.

Multi-Agent Workspace

Once you build an Agent team, each Agent has a complete workspace of its own. The default Agent uses the instance root ~/cow, while every other member lives under ~/cow/agents/<agent-id>/. This splits storage into three categories — system-shared, per-member isolated, and optionally shared:

System-shared: the secrets file (~/.cow/.env) and the team config (~/cow/agents/team.json) are shared across the whole instance.
Per-member isolated: each Agent’s core files (SYSTEM.md, USER.md, AGENT.md, RULE.md), memory (MEMORY.md, memory/), and output files live inside its own workspace and are not visible to other members.
Optionally shared: skills and the knowledge base can either be shared with the team or dedicated to a specific member, and the two modes can be switched at any time.

A sub-Agent is a temporary execution unit spun up by the lead Agent; it shares the lead Agent’s workspace and has no memory of its own. A team member is a persistent, full Agent with a completely independent workspace and memory. See Sub-Agents for details.

Per-Session Model & Permission

Workspace, model, and permission can all be set per session, falling back to the global default when unset:

Model: different sessions can switch to different models, making it easy to pick the right one per task.
Permission: controls what the Agent is allowed to do, in three levels — read-only, workspace-write, and full-access. You can set a global default (agent_permission_mode) in the config; new sessions inherit it and can be adjusted individually as needed. Permissions reduce the risk of accidental changes; for strong isolation, run inside a container.

Core Configuration

Configure Agent mode parameters in config.json:

{
  "agent": true,
  "agent_workspace": "~/cow",
  "agent_max_context_tokens": 50000,
  "agent_max_context_turns": 20,
  "agent_max_steps": 20,
  "agent_permission_mode": "full-access",
  "enable_thinking": false,
  "cow_lang": "auto"
}

Parameter	Description	Default
`agent`	Enable Agent mode	`true`
`agent_workspace`	Workspace path	`~/cow`
`agent_max_context_tokens`	Max context tokens	`50000`
`agent_max_context_turns`	Max context turns	`20`
`agent_max_steps`	Max decision steps per task	`20`
`agent_permission_mode`	Global default permission inherited by new sessions: `read-only` / `workspace-write` / `full-access`	`full-access`
`enable_thinking`	Enable deep-thinking mode	`false`
`knowledge`	Enable personal knowledge base	`true`
`self_evolution_enabled`	Enable Self-Evolution (on by default for new installs)	`false`
`cow_lang`	Language for the UI, command text and system prompts; `auto` to detect, or set `zh` / `en`	`auto`

​System Architecture

​Agent Mode Workflow

​Workspace

​System Workspace

​Project Workspace

​Multi-Agent Workspace

​Per-Session Model & Permission

​Core Configuration