llm-aggregator 0.0.2

A model aggregator service for multiple LLM backends.

LLM Aggregator

A comprehensive model aggregator service for discovering, enriching, and cataloging Large Language Models (LLMs) from multiple local backends. Provides a web interface to browse and manage your model collection with detailed metadata.

Features

• Multi-Provider Discovery: Automatically discovers models from multiple LLM servers running on different ports
• AI-Powered Enrichment: Uses a configurable "brain" LLM to enrich model metadata with details like model family, context size, quantization, and capabilities
• Web Catalog Interface: Clean web UI for browsing your model collection with filtering and sorting
• Real-time Statistics: Monitors system resources like RAM usage
• REST API: Programmatic access to model data and statistics
• Background Processing: Continuous model discovery and enrichment without blocking the UI
• OpenAI-Compatible: Works with any LLM server that implements the OpenAI /v1/models API

Installation

Prerequisites

• Python 3.10 or higher
• One or more running LLM servers (Ollama, llama.cpp, nexa, etc.) with OpenAI-compatible APIs

Install from Source

git clone https://github.com/Wuodan/llm-aggregator.git
cd llm-aggregator
pip install -e .

Install from PyPI

pip install llm-aggregator

Configuration

See config.yaml.

Configuration Options

• host/port: Server binding address
• brain: Configuration for the enrichment LLM
  • host: Base URL of the enrichment model server
  • port: Port where enrichment model runs
  • id: Model identifier for enrichment
  • api_key: Optional API key for authentication
  • max_batch_size: Models to enrich per batch
• time: Timing configurations for background tasks
• providers: List of LLM servers to monitor
  • base_url: Base endpoint for the provider
  • port: Port number for model discovery

Usage

Set the LLM_AGGREGATOR_CONFIG environment variable to point at your config.yaml and the service will load it on startup.

Starting the Service

export LLM_AGGREGATOR_CONFIG=/path/to/config.yaml
llm-aggregator

Or run directly:

export LLM_AGGREGATOR_CONFIG=/path/to/config.yaml
python -m llm_aggregator

The web interface will be available at http://localhost:8888

Web Interface

The web catalog displays:

• Model: Model identifier
• Port: Provider port
• Types: Model capabilities (llm, vlm, embedder, etc.)
• Family: Model architecture family
• Context: Context window size
• Quant: Quantization level
• Param: Parameter count
• Summary: Brief model description