cowagent

CowAgent is an open-source super AI assistant that proactively plans tasks, controls your computer and external services, creates and runs Skills, builds a personal knowledge base and long-term memory, forms multi-agent teams, and grows alongside you through self-evolution — a reference implementation of Agent Harness engineering.

CowAgent is lightweight, easy to deploy, and built to extend. Plug in any major LLM provider and run it 24/7 on a personal computer or server, across the web and all major IM platforms.

🎬 Demo

🌟 Highlights

Capability	Description
Planning	Decomposes complex tasks and executes them step by step, looping over tools until the goal is reached
Multi-Agent	Build a team of Agents, each with its own role, model, skills, and knowledge, collaborating within a shared conversation
Memory	Three-tier architecture (context → daily → core), automatic Deep Dream distillation, hybrid keyword + vector retrieval
Knowledge	Auto-curates structured knowledge into a Markdown wiki, builds an evolving knowledge graph with visual browsing
Evolution	Self-Evolution reviews conversations automatically to improve skills, follow up on unfinished tasks, and consolidate memory and knowledge, growing through everyday use
Skills	One-click install from Skill Hub, GitHub, ClawHub; or create custom skills via natural-language conversation
Tools	Built-in file I/O, terminal, browser, scheduler, memory retrieval, web search, and 10+ more tools — with native MCP integration
Channels	Integrates with Web, WeChat, Feishu, DingTalk, WeCom, QQ, Official Accounts, Telegram, and Slack
Multimodal	First-class support for text, images, voice, and files — recognition, generation, and delivery
Models	Claude, GPT, Gemini, DeepSeek, Qwen, GLM, Kimi, MiniMax, Doubao, and more — swap providers from the Web console with one click
Deploy	One-line installer, unified Web console, multiple deployment modes (local, Docker, server)

🏗️ Architecture

CowAgent is a complete Agent Harness: messages flow in through Channels; the Agent Core plans and reasons over memory, knowledge, and the available tools and skills; Models generate the response, which is sent back through the originating channel. Every layer is decoupled and independently extensible.

Read more in Architecture.

🚀 Quick Start

A one-line installer takes care of dependencies, configuration, and startup:

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

Once started, open http://localhost:9899 to access the Web console — your one-stop hub to chat with the Agent, configure models, connect channels, and install skills.

Deploying on a server? 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.

📖 Detailed guides: Quick Start · Install from Source · Upgrade

After installation, manage the service with the cow CLI:

cow start | stop | restart        # service control
cow status | logs                  # status and logs
cow update                         # pull latest code and restart
cow skill install <name>           # install a skill
cow install-browser                # install browser automation

💻 Desktop client: download the CowAgent Desktop client (macOS / Windows) — the backend is bundled, ready to use out of the box.

👥 Multi-Agent Team

Create a team of Agents, each with its own memory, capabilities, knowledge, and workspace. Each can focus on its own domain, or collaborate within a shared conversation to tackle complex tasks together. Multi-Agent teams are available on the Web, the desktop client, and IM channel integrations.

See Agent Team for details.

🤖 Models

CowAgent supports all mainstream LLM providers. Chat, vision, image generation, ASR/TTS, and embeddings can each be routed to a different vendor. Providers are configured directly in the Web console — no manual file editing required.

Provider	Featured Models	Chat	Vision	Image Gen	ASR	TTS	Embedding
DeepSeek	deepseek-flash (V4.1) / pro	✅	✅
Claude	claude-opus-5-5 / fable-5.1	✅	✅
OpenAI	gpt-6.1-sol / gpt-6 series	✅	✅	✅	✅	✅	✅
Gemini	gemini-3.8-flash	✅	✅	✅
MiniMax	MiniMax-M3	✅	✅	✅		✅
GLM	glm-5.3-flash, glm-5v-turbo	✅	✅		✅		✅
Qwen	qwen3.8-flash	✅	✅	✅	✅	✅	✅
Kimi	kimi-k3	✅	✅
Doubao	doubao-seed-2.1 series	✅	✅	✅			✅
ERNIE	ernie-5.1	✅	✅
MiMo	mimo-v2.5 / pro	✅	✅			✅
LinkAI	One key for 100+ models	✅	✅	✅	✅	✅	✅
Custom	Local models / third-party proxy	✅

For details on each provider, see the Models overview.

💬 Channels

A single Agent instance can serve multiple channels in parallel. Most channels can be onboarded right from the Web console.

Channel	Text	Image	File	Voice	Group
Web Console (default)	✅	✅	✅	✅
Telegram	✅	✅	✅	✅	✅
Slack	✅	✅	✅		✅
Discord	✅	✅	✅		✅
WeChat	✅	✅	✅	✅
Feishu / Lark	✅	✅	✅	✅	✅
DingTalk	✅	✅	✅	✅	✅
WeCom Bot	✅	✅	✅	✅	✅
QQ	✅	✅	✅		✅
WeCom App	✅	✅	✅	✅
WeChat Customer Service	✅	✅	✅	✅
WeChat Official Account	✅	✅		✅

See the Channels overview for setup details.

The Web console is the default channel and the unified entry point to configure models, channels, skills, memory, and more.

🧠 Memory & Knowledge Base

Long-term memory uses a three-tier architecture: conversation context (short-term) → daily memory (mid-term) → MEMORY.md (long-term). A nightly Deep Dream pass distills scattered memories into refined long-term entries and a narrative journal. See Long-term Memory · Deep Dream.

Personal knowledge base complements the time-ordered memory by organizing structured knowledge by topic. The Agent automatically curates valuable information from conversations, maintains cross-references and indexes, and the Web console offers an interactive knowledge-graph view. See Personal Knowledge Base.