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\n\n

一个与多种大语言模型（LLM）兼容的开源 AI Agent CLI（Gemini ClI 的 Fork 版本）。

\n\n[English](./README.md) | [简体中文](./README.zh-CN.md)\n\n
\n\n这个仓库包含了 Easy LLM CLI（[Gemini ClI](https://github.com/google-gemini/gemini-cli) 的 Fork 版本），一个连接到您的工具、理解您的代码并加速您工作流程的命令行 AI 工具。它支持多种 LLM 提供商，包括 Gemini、OpenAI 以及任何遵循 OpenAI API 格式的自定义 LLM API。\n\n使用 Easy LLM CLI，您可以：\n\n- 利用先进的 LLM 能力查询和编辑大型代码库，支持大上下文窗口\n- 使用多模态能力从 PDF 或草图生成新应用\n- 自动化操作任务，如查询 Pull Request 或处理复杂的 rebase 操作\n- 使用工具和 MCP 服务器连接新功能\n- 通过简单的环境变量配置和使用您首选的 LLM 提供商\n- 在不改变工作流程的情况下无缝切换不同的 LLM 提供商\n\n
\n\n本方案已经对多种不同提供商、模型以及本地部署的模型进行了多个维度（是否具备思考过程、能否完成简单任务、是否具备工具调用能力、是否具备多模态能力、是否具备复杂任务能力、是否可以统计 Toekn）的测试，以下是测试结果：\n\n| 模型 | 思考过程 | 简单任务 | 工具调用 | MCP 调用 | 复杂任务 | 多模态 | Token 统计 |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| 【Google】Gemini-2.5-pro | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| 【OpenRouter】Claude Sonnet 4 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| 【OpenRouter】Gpt-4.1 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| 【OpenRouter】Grok-4 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| 【Volcengine】Doubao-Seed-1.6 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| 【Bailian】Qwen3-Plus | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| 【Moonshot】kimi-k2 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ |\n| 【Volcengine】DeepSeek-R1 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ |\n| 【Siliconflow】DeepSeek-R1 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ |\n| 【Volcengine】Doubao-1.5-Pro | ❌ | ✅ | ✅ | ✅ | ⚠️ | ❌ | ✅ |\n| 【Volcengine】DeepSeek-V3 | ❌ | ✅ | ✅ | ✅ | ⚠️ | ❌ | ✅ |\n| 【Bailian】Qwen3-235b-a22b | ✅ | ✅ | ✅ | ✅ | ⚠️ | ❌ | ✅ |\n| 【vLLM】Qwen2.5-7B-Instruct | ❌ | ✅ | ✅ | ✅ | ⚠️ | ❌ | ✅ |\n| 【vLLM】DeepSeek-R1--32B | ✅ | ✅ | ✅ | ✅ | ⚠️ | ❌ | ✅ |\n| 【Ollama】Qwen2.5-7B-Instruct | ❌ | ✅ | ✅ | ✅ | ⚠️ | ❌ | ✅ |\n\n## 快速开始\n\n1. **前提条件：** 确保您安装了 [Node.js 20 版本](https://nodejs.org/en/download) 或更高版本。\n2. **运行 CLI：** 在您的终端中执行以下命令：\n\n```bash\n npx easy-llm-cli\n```\n\n或者使用以下命令安装：\n\n```bash\n npm install -g easy-llm-cli\n elc\n```\n\n## 自定义 LLM 配置\n\nEasy LLM 命令行工具支持连接到任何兼容 OpenAI 的 LLM API。你可以使用以下环境变量配置首选的 LLM：\n\n```bash\n# 启用自定义 LLM 支持\nexport USE_CUSTOM_LLM=true\nexport CUSTOM_LLM_API_KEY=\"your-api-key\" # 你的 LLM 提供商 API 密钥\nexport CUSTOM_LLM_ENDPOINT=\"https://api.your-llm-provider.com/v1\" # API 端点\nexport CUSTOM_LLM_MODEL_NAME=\"your-model-name\" # 模型名称\n\n# 可选参数\nexport CUSTOM_LLM_TEMPERATURE=0.7 # 温度参数（默认值：0）\nexport CUSTOM_LLM_MAX_TOKENS=8192 # 最大令牌数（默认值：8192）\nexport CUSTOM_LLM_TOP_P=1 # Top P 参数（默认值：1）\n```\n\n当这些变量设置后，Easy LLM 命令行工具将使用你的自定义 LLM，而非默认的 Gemini 模型。\n\n\n## 示例\n\n一旦 CLI 运行起来，你就可以从 shell 中与 Gemini 交互了。\n\n你可以从新目录开始一个项目：\n\n```sh\ncd new-project/\nelc\n> 帮我写一个 Discord 机器人，它能使用我将要提供的 FAQ.md 文件来回答问题\n```\n\n或者处理一个已有的项目：\n\n```sh\ngit clone https://github.com/ConardLi/easy-llm-cli\ncd easy-llm-cli\nelc\n> 给我总结一下昨天所有的变更内容\n```\n\n## 在代码中运行\n\nEasy LLM 支持直接在代码中通过 NPM 引入并使用：\n\n```js\n import { ElcAgent } from 'easy-llm-cli';\n const agent = new ElcAgent({\n model: 'custom-llm-model-name',\n apiKey: 'custom-llm-api-key',\n endpoint: 'custom-llm-endpoint',\n extension: {\n mcpServers: {\n chart: {\n command: 'npx',\n args: ['-y', '@antv/mcp-server-chart'],\n trust: false\n }\n },\n excludeTools: ['run_shell_command']\n }\n });\n const result = await agent.run('请帮我生成一个销售数据的柱状图');\n console.log(result);\n```\n\n- 查看 API 调用详细文档：[Programmatic API](./docs/programmatic-api.zh-CN.md)\n\n### 后续步骤\n\n- 了解如何[贡献代码或从源码构建](./CONTRIBUTING.md)。\n- 探索可用的**[CLI 命令](./docs/cli/commands.md)**。\n- 如果遇到任何问题，请查看**[故障排除指南](./docs/troubleshooting.md)**。\n- 如需更全面的文档，请参阅[完整文档](./docs/index.md)。\n- 查看一些[热门任务](#热门任务)获取更多灵感。\n\n### 故障排除\n\n如果遇到问题，请查看[故障排除](docs/troubleshooting.md)指南。\n\n## 热门任务\n\n### 探索新代码库\n\n首先进入一个已有的或新克隆的仓库，然后运行 `elc`。\n\n```text\n> 描述这个系统架构的主要组成部分。\n```\n\n```text\n> 有哪些安全机制在运行？\n```\n\n### 处理现有代码\n\n```text\n> 为 GitHub 第 123 号 issue 实现初稿。\n```\n\n```text\n> 帮我把这个代码库迁移到最新版本的 Java。先制定一个计划。\n```\n\n### 自动化工作流\n\n使用 MCP 服务器将本地系统工具与企业协作套件集成。\n\n```text\n> 制作一个幻灯片，展示过去 7 天的 git 历史，按功能和团队成员分组。\n```\n\n```text\n> 制作一个全屏网页应用，用于墙上显示器，展示我们互动最多的 GitHub 问题。\n```\n\n### 与系统交互\n\n```text\n> 将此目录中的所有图像转换为 png 格式，并根据 exif 数据中的日期重命名。\n```\n\n```text\n> 按支出月份整理我的 PDF 发票。\n```\n","readmeFilename":"README.zh-CN.md"}