@just-every/ensemble

A simple interface for interacting with multiple LLM providers during a single conversation.

🚀 Quick Demo

Try the interactive demos to see Ensemble in action:

npm run demo

This opens a unified demo interface at http://localhost:3000 with all demos:

Demo Interface

Navigate to http://localhost:3000 to access all demos through a unified interface.

See the demo README for detailed information about each demo.

Features

• 🤝 Unified Streaming Interface - Consistent event-based streaming across all providers
• 🔄 Model/Provider Rotation - Automatic model selection and rotation
• 🛠️ Advanced Tool Calling - Parallel/sequential execution, timeouts, and background tracking
• 📝 Automatic History Compaction - Handle unlimited conversation length with intelligent summarization
• 🤖 Agent Orientated - Advanced agent capabilities with verification and tool management
• 🔌 Multi-Provider Support - OpenAI, Anthropic, Google, DeepSeek, xAI, OpenRouter, ElevenLabs
• 🖼️ Multi-Modal - Support for text, images, embeddings, and voice generation
• 📊 Cost & Quota Tracking - Built-in usage monitoring and cost calculation
• 🎯 Smart Result Processing - Automatic summarization and truncation for long outputs

Model Updates (Dec 2025)

• OpenAI: Added GPT-5.2 (base + chat-latest + pro) and refreshed GPT-5.1/GPT-5/Codex pricing
• Anthropic: Claude 4.5 (Sonnet/Haiku, incl. 1M context) and Claude Opus 4.1
• Google: Gemini 3 (Pro/Flash/Ultra) and refreshed Gemini 2.5 pricing incl. image/TTS/native-audio
• xAI: Grok 4.1 Fast and Grok 4 Fast with tiered pricing; updated Grok 4/3/mini variants plus Grok Imagine image generation/editing support (grok-imagine-image, grok-imagine-image-pro)

*Codex-Max pricing reflects current published rates and may change if OpenAI updates pricing.

Installation

npm install @just-every/ensemble

Environment Setup

Copy .env.example to .env and add your API keys:

cp .env.example .env

Available API keys (add only the ones you need):

# LLM Providers
OPENAI_API_KEY=your-openai-key
ANTHROPIC_API_KEY=your-anthropic-key
GOOGLE_API_KEY=your-google-key
XAI_API_KEY=your-xai-key
DEEPSEEK_API_KEY=your-deepseek-key
OPENROUTER_API_KEY=your-openrouter-key

# Voice & Audio Providers
ELEVENLABS_API_KEY=your-elevenlabs-key

# Search Providers
BRAVE_API_KEY=your-brave-key

Note: You only need to configure API keys for the providers you plan to use. The system will automatically select available providers based on configured keys.

Quick Start

import { ensembleRequest, ensembleResult } from '@just-every/ensemble';

const messages = [{ type: 'message', role: 'user', content: 'How many of the letter "e" is there in "Ensemble"?' }];

// Perform initial request
for await (const event of ensembleRequest(messages)) {
    if (event.type === 'response_output') {
        // Save out to continue conversation
        messages.push(event.message);
    }
}

// Create a validator agent
const validatorAgent = {
    instructions: 'Please validate that the previous response is correct',
    modelClass: 'code',
};
// Continue conversation with new agent
const stream = ensembleRequest(messages, validatorAgent);
// Alternative method of collecting response
const result = await ensembleResult(stream);
console.log('Validation Result:', {
    message: result.message,
    requestStatus: result.requestStatus,
    failure: result.failure,
    cost: result.cost,
    completed: result.completed,
    duration: result.endTime ? result.endTime.getTime() - result.startTime.getTime() : 0,
    messageIds: Array.from(result.messageIds),
});

Documentation

• Request Lifecycle & Failures - Outer request status, retries, timeouts, verification, and failure handling
• Tool Execution Guide - Advanced tool calling features
• Interactive Demos - Web-based demos for core features
• Generated API Reference with npm run docs

Run npm run docs to regenerate the HTML documentation.

Core Concepts

Tools

Define tools that LLMs can call:

const agent = {
    model: 'o3',
    tools: [
        {
            definition: {
                type: 'function',
                function: {
                    name: 'get_weather',
                    description: 'Get weather for a location',
                    parameters: {
                        type: 'object',
                        properties: {
                            location: { type: 'string' },
                        },
                        required: ['location'],
                    },
                },
            },
            function: async (location: string) => {
                return `Weather in ${location}: Sunny, 72°F`;
            },
        },
    ],
};

Streaming Events

All providers emit standardized events:

• message_start / message_delta / message_complete - Message streaming
• tool_start / tool_delta / tool_done - Tool execution
• cost_update - Token usage and cost tracking
• operation_status - Authoritative outer request lifecycle (started, retrying, completed, failed)
• error - Failure details for the current request round (error, code, details, recoverable)

Agent Configuration

Configure agent behavior with these optional properties:

const agent = {
    model: 'claude-4-sonnet',
    maxToolCalls: 200,              // Maximum total tool calls (default: 200)
    maxToolCallRoundsPerTurn: 5,    // Maximum sequential rounds of tool calls (default: Infinity)
    tools: [...],                   // Available tools for the agent
    modelSettings: {                // Provider-specific settings
        temperature: 0.7,
        max_tokens: 4096
    }
};

Key configuration options:

• maxToolCalls - Limits the total number of tool calls across all rounds
• maxToolCallRoundsPerTurn - Limits sequential rounds where each round can have multiple parallel tool calls
• modelSettings - Provider-specific parameters like temperature, max_tokens, etc.
• modelSettings.timeout_ms - Whole outer-request timeout budget shared across retries and tool completion
• retryOptions - Outer retry/backoff policy for recoverable request failures

Multimodal Input (Images)

For multimodal models, pass content as an array of typed parts. In addition to input_text and input_image, Ensemble now accepts a simpler image part that can take base64 data or a URL.

Supported image fields:

• type: 'image'
• data: base64 string or full data:<mime>;base64,... URL
• url: http(s) URL
• file_id: provider file reference (when supported)
• mime_type: image mime type (recommended when passing raw base64)
• detail: high | low | auto (for providers that support detail hints)

import { ensembleRequest } from '@just-every/ensemble';

const messages = [
    {
        type: 'message',
        role: 'user',
        content: [
            { type: 'input_text', text: 'Describe this image.' },
            { type: 'image', data: myPngBase64, mime_type: 'image/png' },
            // or: { type: 'image', url: 'https://example.com/cat.png' }
        ],
    },
];

for await (const event of ensembleRequest(messages, { model: 'gemini-3-flash-preview' })) {
    if (event.type === 'message_complete' && 'content' in event) {
        console.log(event.content);
    }
}

Structured JSON Output

Use modelSettings.json_schema to request a JSON-only response. Ensemble treats this as the authoritative structured-output contract. When strict: true is enabled, the final response is validated by the outer request lifecycle and invalid JSON/schema violations fail the request instead of silently completing.

Prefer modelSettings.json_schema. The older jsonSchema agent property is still accepted as a compatibility alias and is mapped onto modelSettings.json_schema.

The example below combines image input with JSON output:

import { ensembleRequest, ensembleResult } from '@just-every/ensemble';

const agent = {
    model: 'gemini-3-flash-preview',
    modelSettings: {
        temperature: 0.2,
        json_schema: {
            name: 'image_analysis',
            type: 'json_schema',
            schema: {
                type: 'object',
                properties: {
                    dominant_color: { type: 'string' },
                    confidence: { type: 'number' },
                },
                required: ['dominant_color', 'confidence'],
            },
        },
    },
};

const messages = [
    {
        type: 'message',
        role: 'user',
        content: [
            { type: 'input_text', text: 'Analyze this image and return JSON.' },
            { type: 'image', data: myPngBase64, mime_type: 'image/png' },
        ],
    },
];

const result = await ensembleResult(ensembleRequest(messages, agent));
if (!result.completed) {
    throw new Error(result.failure?.error || 'Request failed');
}
const parsed = JSON.parse(result.message);
console.log(parsed.dominant_color, parsed.confidence);

ensembleResult(...) also exposes requestStatus and failure, so callers can distinguish the final outer request outcome from earlier recoverable errors in the stream.

Request Lifecycle

operation_status is the authoritative outer lifecycle for one ensembleRequest(...) call:

• started is emitted once when the outer request begins
• retrying is emitted after a recoverable failure and before the next attempt
• completed is the only authoritative success outcome
• failed is the only authoritative terminal failure outcome

Recoverable error events can appear before a successful completed status. Callers that want the final outcome should prefer operation_status or ensembleResult(...) over treating the first error event as terminal.

const stream = ensembleRequest(messages, {
    model: 'claude-4-sonnet',
    modelSettings: { timeout_ms: 15_000 },
    retryOptions: { maxRetries: 1 },
});

for await (const event of stream) {
    if (event.type === 'operation_status') {
        console.log(event.status, event.reason, event.attempt, event.max_attempts);
    }

    if (event.type === 'error') {
        console.log(event.recoverable ? 'recoverable' : 'terminal', event.code, event.error);
    }
}

Advanced Features

• Parallel Tool Execution - Tools run concurrently by default within each round
• Sequential Mode - Enforce one-at-a-time execution
• Timeout Handling - Automatic timeout with background tracking
• Result Summarization - Long outputs are intelligently summarized
• Abort Signals - Graceful cancellation support

Voice Generation

Generate natural-sounding speech from text using Text-to-Speech models:

import { ensembleVoice, ensembleVoice } from '@just-every/ensemble';

// Simple voice generation
const audioData = await ensembleVoice('Hello, world!', {
    model: 'tts-1', // or 'gemini-2.5-flash-preview-tts'
});

// Voice generation with options
const audioData = await ensembleVoice(
    'Welcome to our service',
    {
        model: 'tts-1-hd',
    },
    {
        voice: 'nova', // Voice selection
        speed: 1.2, // Speech speed (0.25-4.0)
        response_format: 'mp3', // Audio format
    }
);

// Streaming voice generation
for await (const event of ensembleVoice('Long text...', {
    model: 'gemini-2.5-pro-preview-tts',
})) {
    if (event.type === 'audio_stream') {
        // Process audio chunk
        processAudioChunk(event.data);
    }
}

Supported Voice Models:

• OpenAI: tts-1, tts-1-hd
• Google Gemini: gemini-2.5-flash-preview-tts, gemini-2.5-pro-preview-tts

Image generation

Use OpenAI GPT-Image-1 (or the new cost-efficient GPT-Image-1 Mini) or Google Gemini image models:

import { ensembleImage } from '@just-every/ensemble';

const images = await ensembleImage(
    'A serene lake at dawn',
    { model: 'gemini-2.5-flash-image-preview' },
    { size: 'portrait' }
);

// Gemini 3.1 Flash Image: grounded generation + thinking controls + metadata callback
const grounded = await ensembleImage(
    'A detailed painting of a Timareta butterfly resting on a flower',
    { model: 'gemini-3.1-flash-image-preview' },
    {
        size: '16:9',
        quality: 'high', // 4K
        grounding: {
            web_search: true,
            image_search: true,
        },
        thinking: {
            level: 'high',
            include_thoughts: true,
        },
        on_metadata: metadata => {
            // metadata.citations includes containing-page URLs for attribution compliance
            console.log(metadata.citations);
        },
    }
);

• ElevenLabs: eleven_multilingual_v2, eleven_turbo_v2_5

Development

# Install dependencies
npm install

# Run tests
npm test

# Build
npm run build

# Generate docs
npm run docs

# Lint
npm run lint

Additional image providers

New providers added

• Fireworks AI (FLUX family: Kontext/Pro/Schnell) – async APIs with result polling. Docs: Fireworks Image API.
• Stability AI (Stable Image Ultra/SDXL) – REST v2beta endpoints supporting text-to-image and image-to-image.
• Runway Gen-4 Image – via FAL.ai.
• Recraft v3 – via FAL.ai (supports text-to-vector and vector-style outputs).

Environment

FIREWORKS_API_KEY=your_key
STABILITY_API_KEY=your_key
FAL_KEY=your_key

Fallbacks

• If Fireworks returns 401/403 or is not configured, requests for Flux-family models automatically fall back to FAL.ai equivalents when FAL_KEY is set.

• Luma Photon (official): set LUMA_API_KEY and use luma-photon-1 or luma-photon-flash-1.

• Ideogram 3.0 (official): set IDEOGRAM_API_KEY and use ideogram-3.0.

• Midjourney v7 (3rd-party): set MIDJOURNEY_API_KEY (or KIE_API_KEY) and optional MJ_API_BASE; use midjourney-v7.

Notes

• Gemini 3.1 Flash Image supports 0.5K/1K/2K/4K tiers, explicit aspect ratios, Google Image Search grounding, and thinking controls.
• Gemini 3 Pro Image supports explicit 1K/2K/4K resolution presets mapped to official aspect-ratio tables.
• Luma Photon and Ideogram return URLs; we pass them through without altering pixels.

Architecture

Ensemble provides a unified interface across multiple LLM providers:

1. Provider Abstraction - All providers extend BaseModelProvider
2. Event Streaming - Consistent events across all providers
3. Tool System - Automatic parameter mapping and execution
4. Message History - Intelligent conversation management
5. Cost Tracking - Built-in usage monitoring

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Submit a pull request

Troubleshooting

Provider Issues

• Ensure API keys are set correctly
• Check rate limits for your provider
• Verify model names match provider expectations

Tool Calling

• Tools must follow the OpenAI function schema
• Ensure tool functions are async
• Check timeout settings for long-running tools

Streaming Issues

• Verify network connectivity
• Check for provider-specific errors in events
• Enable debug logging with DEBUG=ensemble:*

License

MIT