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    agentkits

    agentkits

    Deepleaper April 12, 2026
    1 copies 0 downloads

    🧰 Multi-provider AI toolkit — 18 modules: Embedding, LLM, RAG, Agent, Vision, TTS, STT, Image Gen, Tool Calling, Structured Output, Smart Router, Failover, and more. One interface, any model.

    <div align="center">

    🧰 AgentKits

    多模型 AI 智能体工具库 · Multi-provider AI toolkit for agents

    npm version License TypeScript Tests Node.js

    20 个大模型 · 15 个向量化引擎 · 38 个功能模块 · 零锁定

    🇨🇳 完整中文文档 · Quick Start · Providers · Modules

    </div>

    为什么选择 AgentKits?

    构建 AI 智能体不应被锁定在单一供应商。AgentKits 提供统一接口,覆盖 20+ 大模型和 15+ 向量化引擎。切换模型只需改一个配置,无需重写代码。

    🇨🇳 深度支持中国大模型生态:通义千问、智谱AI、月之暗面、零一万物、硅基流动、阶跃星辰、百川智能、DeepSeek深度求索、MiniMax — 一等公民,不是附加品。

    import { createChat, createEmbedding } from 'agentkits';
    
    // 切换供应商只需改一个词
    const chat = createChat({ provider: 'deepseek' });  // 深度求索
    const reply = await chat.complete('解释量子计算');
    
    const emb = createEmbedding({ provider: 'dashscope' });  // 通义千问
    const vector = await emb.embed('你好世界');
    

    快速开始 Quick Start

    npm install agentkits
    

    对话补全 Chat

    import { createChat } from 'agentkits';
    
    // 使用任意供应商
    const chat = createChat({ provider: 'deepseek', model: 'deepseek-chat' });
    const reply = await chat.complete('你好!');
    
    // 流式输出
    for await (const chunk of chat.stream('给我讲个故事')) {
      process.stdout.write(chunk.content ?? '');
    }
    

    向量化 Embedding

    import { createEmbedding } from 'agentkits';
    
    const emb = createEmbedding({ provider: 'dashscope' });  // 通义千问向量化
    const vector = await emb.embed('语义搜索');
    const batch = await emb.embedBatch(['文档1', '文档2', '文档3']);
    

    函数调用 Tool Calling

    import { createToolChat, defineTool } from 'age
    

    Tags

    agentaideepseekembeddinggeminillmmulti-providerollamaopenairag

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