@just-every/ensemble v0.2.56

@just-every/ensemble

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

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

Installation

npm install @just-every/ensemble

Environment Setup

Set your provider API keys as environment variables before running examples or using the library:

export OPENAI_API_KEY=your-openai-key
export ANTHROPIC_API_KEY=your-anthropic-key
export GOOGLE_API_KEY=your-google-key
export XAI_API_KEY=your-xai-key
export DEEPSEEK_API_KEY=your-deepseek-key
export OPENROUTER_API_KEY=your-openrouter-key
export ELEVENLABS_API_KEY=your-elevenlabs-key

These variables enable access to the respective providers. Only the keys you need are required.

Quick Start

import { ensembleRequest } 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 === 'message_complete') {
        // Write response
        console.log(event.content);
    }
    else 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
for await (const event of ensembleRequest(messages, validatorAgent)) {
    if (event.type === 'message_complete') {
        console.log(event.content);
    }
}

Documentation

• Tool Execution Guide - Advanced tool calling features
• Examples - Complete working examples
• 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
• error - Error handling

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.

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, ensembleVoiceStream } 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 ensembleVoiceStream('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
• 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

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