深度聊天助手
深度聊天助手 是 AI Skill Hub 本期精选AI工具之一。已获得 5.8k 颗 GitHub Star，综合评分 8.2 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
深度聊天助手 依赖 TypeScript 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 TypeScript 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 深度聊天助手 的版本更新，及时通知重要功能变化。
深度聊天助手 是一款基于 TypeScript 开发的开源工具，专注于 MCP协议、AI助手、Agent框架 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
深度聊天助手 是一款基于 TypeScript 开发的开源工具，专注于 MCP协议、AI助手、Agent框架 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
# 方式一：npm 全局安装 npm install -g deepchat # 方式二：npx 直接运行（无需安装） npx deepchat --help # 方式三：项目依赖安装 npm install deepchat # 方式四：从源码运行 git clone https://github.com/ThinkInAIXYZ/deepchat cd deepchat npm install npm start
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 命令行使用
deepchat --help
# 基本用法
deepchat [options] <input>
# Node.js 代码中使用
const deepchat = require('deepchat');
const result = await deepchat.run(options);
console.log(result);
# deepchat 配置说明 # 查看配置选项 deepchat --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export DEEPCHAT_CONFIG="/path/to/config.yml"
简介
<p align='center'> <img src='./build/icon.png' width="150" height="150" alt="DeepChat AI Assistant Icon" /> </p>
DeepChat - Powerful Open-Source AI Agent Platform
<p align="center">DeepChat is a feature-rich open-source AI agent platform that unifies models, tools, and agents: multi-LLM chat, MCP tool calling, Skills, ACP agent integration, and remote control.</p>
<p align="center"> <a href="https://github.com/ThinkInAIXYZ/deepchat/stargazers"><img src="https://img.shields.io/github/stars/ThinkInAIXYZ/deepchat" alt="Stars Badge"/></a> <a href="https://github.com/ThinkInAIXYZ/deepchat/network/members"><img src="https://img.shields.io/github/forks/ThinkInAIXYZ/deepchat" alt="Forks Badge"/></a> <a href="https://github.com/ThinkInAIXYZ/deepchat/pulls"><img src="https://img.shields.io/github/issues-pr/ThinkInAIXYZ/deepchat" alt="Pull Requests Badge"/></a> <a href="https://github.com/ThinkInAIXYZ/deepchat/issues"><img src="https://img.shields.io/github/issues/ThinkInAIXYZ/deepchat" alt="Issues Badge"/></a> <a href="https://github.com/ThinkInAIXYZ/deepchat/blob/main/LICENSE"><img src="https://img.shields.io/github/license/ThinkInAIXYZ/deepchat" alt="License Badge"/></a> <a href="https://github.com/ThinkInAIXYZ/deepchat/releases/latest"><img src="https://img.shields.io/endpoint?url=https://api.pinstudios.net/api/badges/downloads/ThinkInAIXYZ/deepchat/total" alt="Downloads"></a> <a href="https://deepwiki.com/ThinkInAIXYZ/deepchat"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a> </p>
🚀 Project Introduction
DeepChat is a powerful open-source AI agent platform that brings together models, tools, and agent runtimes in one desktop app. Whether you're using cloud APIs like OpenAI, Gemini, Anthropic, or locally deployed Ollama models, DeepChat delivers a smooth user experience.
Beyond chat, DeepChat supports agentic workflows: rich tool calling via MCP (Model Context Protocol), installable Skills for specialized tasks, unique ACP (Agent Client Protocol) integration that lets you run ACP-compatible agents as first-class “models” with a dedicated workspace UI, and remote control from messaging apps.
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🔥 Main Features
- 🌐 Multiple Cloud LLM Provider Support: DeepSeek, OpenAI, Moonshot/Kimi, Grok, Gemini, Anthropic, and more
- 🏠 Local Model Deployment Support:
- Integrated Ollama with comprehensive management capabilities
- Control and manage Ollama model downloads, deployments, and runs without command-line operations
- 🚀 Rich and Easy-to-Use Chat Capabilities
- Complete Markdown rendering with code block rendering based on industry-leading CodeMirror
- Multi-window + multi-tab architecture supporting parallel multi-session operations across all dimensions, use large models like using a browser, non-blocking experience brings excellent efficiency
- Supports Artifacts rendering for diverse result presentation, significantly saving token consumption after MCP integration
- Messages support retry to generate multiple variations; conversations can be forked freely, ensuring there's always a suitable line of thought
- Supports rendering images, Mermaid diagrams, and other multi-modal content; supports GPT-4o, Gemini, Grok text-to-image capabilities
- Supports highlighting external information sources like search results within the content
- 🔍 Robust Search Extension Capabilities
- Built-in integration with leading search APIs like BoSearch, Brave Search via MCP mode, allowing the model to intelligently decide when to search
- Supports mainstream search engines like Google, Bing, Baidu, and Sogou Official Accounts search by simulating user web browsing, enabling the LLM to read search engines like a human
- Supports reading any search engine; simply configure a search assistant model to connect various search sources, whether internal networks, API-less engines, or vertical domain search engines, as information sources for the model
- 🔧 Excellent MCP (Model Context Protocol) Support
- Complete support for the three core capabilities of Resources/Prompts/Tools in the MCP protocol
- Supports semantic workflows, enabling more complex and intelligent automation by understanding the meaning and context of tasks.
- Extremely user-friendly configuration interface
- Aesthetically pleasing and clear tool call display
- Detailed tool call debugging window with automatic formatting of tool parameters and return data
- Built-in Node.js runtime environment; npx/node-like services require no extra configuration and work out-of-the-box
- Supports StreamableHTTP/SSE/Stdio protocol Transports
- Supports inMemory services with built-in utilities like code execution, web information retrieval, and file operations; ready for most common use cases out-of-the-box without secondary installation
- Converts visual model capabilities into universally usable functions for any model via the built-in MCP service
- 🧠 Skills
- Install Skills from folders, ZIP files, or URLs
- Enable Skills per conversation so DeepChat can load task-specific instructions, references, and optional scripts
- Import and export Skills with other AI coding assistants
- Built-in Skills cover code review, document collaboration, Office/PDF processing, frontend design, MCP development, and more
- 🤝 ACP (Agent Client Protocol) Agent Integration
- Run ACP-compatible agents (built-in or custom commands) as selectable “models”
- ACP workspace UI for structured plans, tool calls, and terminal output when provided by the agent
- 📡 Remote Control
- Control DeepChat sessions from Telegram, Feishu/Lark, QQBot, Discord, and WeChat iLink
- Bind remote endpoints to sessions and manage conversations from messaging apps
- Create or switch sessions, stop generation, open desktop sessions, handle pending interactions, switch models, and check status remotely
- 💻 Multi-Platform Support: Windows, macOS, Linux
- 🎨 Beautiful and User-Friendly Interface, user-oriented design, meticulously themed light and dark modes
- 🔗 Rich DeepLink Support: Initiate conversations via links for seamless integration with other applications. Also supports one-click installation of MCP services for simplicity and speed
- 🚑 Security-First Design: Chat data and configuration data have reserved encryption interfaces and code obfuscation capabilities
- 🛡️ Privacy Protection: Supports screen projection hiding, network proxies, and other privacy protection methods to reduce the risk of information leakage
- 💰 Business-Friendly:
- Embraces open source, based on the Apache License 2.0 protocol, enterprise use without worry
- Enterprise integration requires only minimal configuration code changes to use reserved encrypted obfuscation security capabilities
- Clear code structure, both model providers and MCP services are highly decoupled, can be freely customized with minimal cost
- Reasonable architecture, data interaction and UI behavior separation, fully utilizing Electron's capabilities, rejecting simple web wrappers, excellent performance
For more details on how to use these features, see the documentation index.
Install Dependencies
```bash $ pnpm install $ pnpm run installRuntime
Download and Install
You can install DeepChat using one of the following methods:
Option 1: GitHub Releases
Download the latest version for your system from the GitHub Releases page:
- Windows:
.exeinstallation file - macOS:
.dmginstallation file - Linux:
.AppImageor.debinstallation file
Option 2: Official Website
Download from the official website.
Option 3: Homebrew (macOS only)
For macOS users, you can install DeepChat using Homebrew:
brew install --cask deepchatBuild
```bash
🔍 Use Cases
DeepChat is suitable for various AI application scenarios:
- Daily Assistant: Answering questions, providing suggestions, assisting with writing and creation
- Development Aid: Code generation, debugging, technical problem solving
- Learning Tool: Concept explanation, knowledge exploration, learning guidance
- Content Creation: Copywriting, creative inspiration, content optimization
- Data Analysis: Data interpretation, chart generation, report writing
📦 Quick Start
💻 Development Guide
Please read the Contribution Guidelines
Windows and Linux are packaged by GitHub Action. For Mac-related signing and packaging, please refer to the Mac Release Guide.
Configure Models
- Launch the DeepChat application
- Click the settings icon
- Select the "Model Providers" tab
- Add your API keys or configure local Ollama
Compatible with any model provider in OpenAI/Gemini/Anthropic API format
🧩 ACP Integration (Agent Client Protocol)
DeepChat has built-in support for Agent Client Protocol (ACP), allowing you to integrate external agent runtimes into DeepChat with a native UI. Once enabled, ACP agents appear as first-class entries in the model selector, so you can use coding agents and task agents directly inside DeepChat.
Quick start:
- Open Settings → ACP Agents and enable ACP
- Enable a built-in ACP agent or add a custom ACP-compatible command
- Select the ACP agent in the model selector to start an agent session
To explore the ecosystem of compatible agents and clients, see: https://agentclientprotocol.com/overview/clients
if got err: No module named 'distutils'
$ pip install setuptools ```
- For Windows: To allow non-admin users to create symlinks and hardlinks, enable
Developer Modein Settings or use an administrator account. Otherwisepnpmops will fail.
高��量开源Agent框架，MCP协议支持好，社区活跃维护频繁，适合AI应用开发者快速原型设计和部署。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
- +GitHub 5.8k Star，社区高度认可
- +Apache-2.0 协议，可免费商用
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
经综合评估，深度聊天助手 在AI工具赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | deepchat |
| Topics | MCP协议AI助手Agent框架智能工作流开源 |
| GitHub | https://github.com/ThinkInAIXYZ/deepchat |
| License | Apache-2.0 |
| 语言 | TypeScript |
收录时间：2026-05-25 · 更新时间：2026-05-25 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。