modelmix（modelmix 搜索）

ModelMix Library Skill

Overview

ModelMix is a Node.js library providing a unified fluent API to interact with multiple AI LLM providers. It handles automatic fallback between models, round-robin load balancing, structured JSON output, streaming, MCP tool integration, custom local tools, rate limiting, and token tracking.

Use this skill when:

• Integrating one or more AI models into a Node.js project
• Chaining models with automatic fallback or round-robin
• Extracting structured JSON from LLMs
• Adding MCP tools or custom tools to models
• Streaming responses from any provider
• Working with templates and file-based prompts
• Tracking token usage and costs

Do NOT use for:

• Python or non-Node.js projects
• Direct HTTP calls to LLM APIs (use ModelMix instead)

Quick Reference

• Installation
• Creating an instance
• Attaching models
• Get a text response
• Get structured JSON
• Stream a response
• Extract a code block
• Get raw response (tokens, thinking, tool calls)
• Access full response with lastRaw
• Add images
• Templates with placeholders
• Round-robin load balancing
• MCP integration
• Custom local tools
• Rate limiting
• Conversation history
• Debug mode
• Free-tier models
• Multi-provider routing

Installation

npm install modelmix

Core Concepts

Import

import { ModelMix } from 'modelmix';

Creating an Instance

const model = ModelMix.new();

const model = ModelMix.new({
    options: { max_tokens: 4096, temperature: 0.7 },
    config: {
        system: "You are a helpful assistant.",
        max_history: 5,   // -1 = unlimited, 0 = none (default), N = keep last N
        debug: 0,          // 0=silent, 1=minimal, 2=summary, 3=full, 4=verbose
        roundRobin: false  // false=fallback, true=rotate models
    }
});

Attaching Models

Chain shorthand methods to attach providers. First model is primary; others are fallbacks (or rotated if roundRobin: true):

const model = ModelMix.new()
    .sonnet46()        // primary
    .gpt52()           // fallback 1
    .gemini3flash()    // fallback 2
    .addText("Hello!")

If sonnet46 fails, it automatically tries gpt52, then gemini3flash.

Available Model Shorthands

OpenAI

gpt52() gpt52chat() gpt51() gpt5() gpt5mini() gpt5nano() gpt45() gpt41() gpt41mini() gpt41nano() o3() o4mini()

Anthropic

opus46() opus45() opus41() sonnet46() sonnet45() sonnet4() haiku45() haiku35()

Thinking variants: append think — e.g. opus46think() sonnet46think() sonnet45think() sonnet4think() opus45think() opus41think() haiku45think()

Google

gemini3pro() gemini3flash() gemini25pro() gemini25flash()

Grok

grok4() grok41() grok41think() grok3() grok3mini()

Perplexity

sonar() sonarPro()

Groq

scout() maverick()

Together

qwen3() kimiK2() kimiK2think() kimiK25think() gptOss()

MiniMax

minimaxM25() minimaxM21() minimaxM2() minimaxM2Stable()

Fireworks

deepseekV32() GLM5() GLM47()

Cerebras

GLM46()

OpenRouter

GLM45()

Multi-provider (auto-fallback across free/paid tiers)

deepseekR1() hermes3() scout() maverick() kimiK2() GLM47()

Local

lmstudio() — for LM Studio local models

Each method accepts optional {options, config} to override per-model settings.

Common Tasks

Get a text response

const answer = await ModelMix.new()
    .gpt5mini()
    .addText("What is the capital of France?")
    .message();

Get structured JSON

const result = await ModelMix.new()
    .gpt5mini()
    .addText("Name and capital of 3 South American countries.")
    .json(
        { countries: [{ name: "", capital: "" }] },
        { countries: [{ name: "country name", capital: "in uppercase" }] },
        { addNote: true }
    );

json() signature: json(schemaExample, schemaDescription?, {addSchema, addExample, addNote}?)

Enhanced descriptors

Descriptions can be strings or descriptor objects with metadata:

const result = await model.json(
    { name: 'martin', age: 22, sex: 'Male' },
    {
        name: { description: 'Name of the actor', required: false },
        age: 'Age of the actor',
        sex: { description: 'Gender', enum: ['Male', 'Female', null] }
    }
);

Descriptor properties: description (string), required (boolean, default true — if false, field becomes nullable), enum (array — if includes null, type auto-becomes nullable), default (any).

Array auto-wrap

Top-level arrays are auto-wrapped as {out: [...]} for better LLM compatibility, and unwrapped on return:

const result = await model.json([{ name: 'martin' }]);
// result is an array: [{ name: "Martin" }, { name: "Carlos" }, ...]

Stream a response

await ModelMix.new()
    .gpt5mini()
    .addText("Tell me a story.")
    .stream(({ delta, message }) => {
        process.stdout.write(delta);
    });

Extract a code block

const code = await ModelMix.new()
    .gpt5mini()
    .addText("Write a hello world function in JavaScript.")
    .block();
// Returns only the content inside the first code block

block() accepts {addSystemExtra} (default true) — adds system instructions that tell the model to wrap output in a code block.

Get raw response

const raw = await ModelMix.new()
    .sonnet45think()
    .addText("Solve this step by step: 2+2*3")
    .raw();
// raw.message, raw.think, raw.tokens, raw.toolCalls, raw.response

Access full response with lastRaw

After calling message(), json(), block(), or stream(), use lastRaw to access the complete response:

const model = ModelMix.new().gpt5mini().addText("Hello!");
const text = await model.message();
console.log(model.lastRaw.tokens);
// { input: 122, output: 86, total: 541, cost: 0.000319, speed: 38 }
console.log(model.lastRaw.think);    // reasoning content (if available)
console.log(model.lastRaw.response); // raw API response

Add images

const model = ModelMix.new().sonnet45();
model.addImage('./photo.jpg');                          // from file
model.addImageFromUrl('https://example.com/img.png');   // from URL
model.addImageFromBuffer(imageBuffer);                  // from Buffer
model.addText('Describe this image.');
const description = await model.message();

All image methods accept an optional second argument {role} (default "user").

Templates with placeholders

const model = ModelMix.new().gpt5mini();
model.setSystemFromFile('./prompts/system.md');
model.addTextFromFile('./prompts/task.md');
model.replace({
    '{role}': 'data analyst',
    '{language}': 'Spanish'
});
model.replaceKeyFromFile('{code}', './src/utils.js');
console.log(await model.message());

Round-robin load balancing

const pool = ModelMix.new({ config: { roundRobin: true } })
    .gpt5mini()
    .sonnet45()
    .gemini3flash();

const r1 = await pool.new().addText("Request 1").message();
const r2 = await pool.new().addText("Request 2").message();

MCP integration

const model = ModelMix.new({ config: { max_history: 10 } }).gpt5nano();
model.setSystem('You are an assistant. Today is ' + new Date().toISOString());
await model.addMCP('@modelcontextprotocol/server-brave-search');
model.addText('Use Internet: What is the latest news about AI?');
console.log(await model.message());

Requires BRAVE_API_KEY in .env for Brave Search MCP.

Custom local tools

const model = ModelMix.new({ config: { max_history: 10 } }).gpt5mini();

model.addTool({
    name: "get_weather",
    description: "Get weather for a city",
    inputSchema: {
        type: "object",
        properties: { city: { type: "string" } },
        required: ["city"]
    }
}, async ({ city }) => {
    return `The weather in ${city} is sunny, 25C`;
});

model.addText("What's the weather in Tokyo?");
console.log(await model.message());

Register multiple tools at once:

model.addTools([
    { tool: { name: "tool_a", description: "...", inputSchema: {...} }, callback: async (args) => {...} },
    { tool: { name: "tool_b", description: "...", inputSchema: {...} }, callback: async (args) => {...} }
]);

Manage tools: model.removeTool("tool_a") and model.listTools() → {local, mcp}.

Rate limiting

const model = ModelMix.new({
    config: {
        bottleneck: {
            maxConcurrent: 4,
            minTime: 1000
        }
    }
}).gpt5mini();

Conversation history

const chat = ModelMix.new({ config: { max_history: 10 } }).gpt5mini();
chat.addText("My name is Martin.");
await chat.message();
chat.addText("What's my name?");
const reply = await chat.message();  // "Martin"

max_history: 0 = no history (default), N = keep last N exchanges, -1 = unlimited.

Debug mode

const model = ModelMix.new({
    config: { debug: 2 }  // 0=silent, 1=minimal, 2=summary, 3=full, 4=verbose
}).gpt5mini();

For full debug output, also set: DEBUG=ModelMix* node script.js

Free-tier models

const model = ModelMix.new()
    .gptOss()
    .kimiK2()
    .deepseekR1()
    .hermes3()
    .addText("What is the capital of France?");
console.log(await model.message());

These use providers with free quotas (OpenRouter, Groq, Cerebras). If one runs out of quota, ModelMix falls back to the next.

Multi-provider routing

Some model shorthands register the same model across multiple providers for maximum resilience. Control which providers are enabled via the mix parameter:

const model = ModelMix.new({
    mix: {
        openrouter: true,   // default: true
        cerebras: true,      // default: true
        groq: true,          // default: true
        together: false,     // default: false
        lambda: false,       // default: false
        minimax: false,      // default: false
        fireworks: false     // default: false
    }
}).deepseekR1();

Agent Usage Rules

• Check package.json for modelmix before running npm install.
• Use ModelMix.new() static factory (not new ModelMix()).
• Store API keys in .env and load with dotenv/config or process.loadEnvFile(). Never hardcode keys.
• Chain models for resilience: primary model first, fallbacks after.
• When using MCP tools or addTool(), set max_history to at least 3 — tool call/response pairs consume history slots.
• Use .json() for structured output instead of parsing text manually. Use descriptor objects {description, required, enum, default} for richer schema control.
• Use .message() for simple text, .raw() when you need tokens/thinking/toolCalls.
• For thinking models, append think to the method name (e.g. sonnet45think()).
• Template placeholders use {key} syntax in both system prompts and user messages.
• The library uses CommonJS internally but supports ESM import via {ModelMix}.
• GPT-5+ models automatically use max_completion_tokens instead of max_tokens.
• o-series models (o3, o4mini) automatically strip max_tokens and temperature since those APIs don't support them.
• addText(), addImage(), addImageFromUrl(), and addImageFromBuffer() all accept {role} as second argument (default "user").

API Quick Reference

Available Provider Classes

MixOpenAI MixAnthropic MixGoogle MixPerplexity MixGroq MixTogether MixGrok MixOpenRouter MixOllama MixLMStudio MixCustom MixCerebras MixFireworks MixMiniMax MixLambda

Troubleshooting

Model fails with "API key not found" The provider's API key env var is not set. Add it to .env and ensure it loads before ModelMix runs. Each provider looks for its standard env var (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY).

Tool calls not working Set max_history to at least 3. Tool call/response pairs are stored in history and the model needs to see them to complete the conversation loop.

JSON response parsing fails Add {addNote: true} to the json() options — this injects instructions about JSON escaping that prevent common parsing errors. For complex schemas, also try {addExample: true}.

Model returns empty or truncated response Increase max_tokens in options. Default is 8192 but some tasks need more. For GPT-5+ models, max_completion_tokens is used automatically.

Rate limit errors Configure Bottleneck: config: {bottleneck: {maxConcurrent: 2, minTime: 2000}}. This throttles requests to stay within provider limits.

MCP server fails to connect Ensure the MCP package is installed (npm install @modelcontextprotocol/server-brave-search) and required env vars are set. Call addMCP() with await — it's async.

References

• GitHub Repository