CowAgent – Open‑source AI Assistant Platform

What it is

CowAgent is a self‑hosted AI assistant that can plan tasks, run tools on your computer, keep a personal knowledge base, and cooperate with other agents. It is positioned as a reference implementation of an Agent Harness: a modular system that connects LLMs, memory, tools, and communication channels.

Core capabilities (as described in the README)

Feature	What it does
Planning	Breaks a complex request into sub‑steps and iteratively calls tools until the goal is met.
Multi‑Agent teams	You can spin up several agents, each with its own role, memory and skills, and let them collaborate in a shared conversation.
Memory	Three‑tier memory (short‑term context → daily → long‑term MEMORY.md). Nightly Deep Dream runs distill conversation logs into concise long‑term entries.
Knowledge base	Auto‑curates structured markdown wiki and builds a visual knowledge‑graph from the agent’s discoveries.
Self‑evolution	The agent reviews its own chats, improves skills, follows up unfinished tasks and expands its memory/knowledge over time.
Skills	Re‑usable workflows defined by a manifest. Install from the public Skill Hub, GitHub, or create new ones via natural‑language conversation.
Tools	Built‑in atomic tools (file I/O, terminal, web fetch, browser automation, scheduler, search, vision, etc.) and an MCP (Model Context Protocol) layer for custom tool services.
Channels	Connect the same agent to many front‑ends: Web console, Telegram, Slack, Discord, WeChat, Feishu/Lark, DingTalk, QQ, WeCom, etc.
Multimodal	Handles text, images, voice and arbitrary files – both as input and output.
Model flexibility	Supports dozens of commercial LLM providers (Claude, OpenAI, Gemini, DeepSeek, Qwen, etc.) and custom/local models; each capability (chat, vision, embeddings, TTS, ASR) can be routed to a different provider.
Deployment	One‑line installer for Linux/macOS, PowerShell script for Windows, or Docker compose. A web UI (http://localhost:9899) serves as a unified console for chat, configuration, skill management and knowledge browsing.

Typical workflow

1. Install – run the one‑line script (or Docker) which pulls dependencies, creates config.json and starts the service.
2. Open the Web console – log in, add your LLM API keys, pick a default model, and enable the channels you need.
3. Chat – type a request. The agent plans, calls tools (e.g., bash, web_fetch, browser), stores results in memory, and replies.
4. Extend – install a skill from the Skill Hub (/skill install <name>) or author a new one via the /skill creator dialogue.
5. Scale – create additional agents in the UI, assign them specialized skills, and let them cooperate on larger projects.

Who might use it

• Developers who want a locally‑run, extensible AI assistant that can execute code, browse the web, or manage files.
• Power users looking for a personal knowledge‑base that automatically captures useful information from conversations.
• Teams that need a self‑hosted alternative to cloud‑only agents and want to integrate the assistant with existing IM platforms.
• Researchers interested in experimenting with agent architectures, memory strategies, or multi‑agent collaboration.

Getting started resources (links from the README)

• Docs & Quick‑start – https://docs.cowagent.ai/guide/quick-start
• Skill Hub – https://skills.cowagent.ai/
• Web console – http://localhost:9899 after installation
• Desktop client – downloadable for macOS/Windows
• Community – Discord, WeChat group, and GitHub issues for support

License & disclaimer

• MIT License.
• The README warns that the agent can execute arbitrary commands on your machine, so it should only be run in trusted environments. Token usage can be high because the agent repeatedly calls LLM APIs.

---

All information above is taken directly from the repository’s README; no additional features have been inferred.