llm-registry 0.3.2

A centralized registry for discovering and managing LLM model capabilities. Track model features, costs, and limitations across providers like OpenAI and Anthropic. Supports both verified model definitions and user-managed entries with local storage.

🤖 LLM Registry

Your Central Hub for LLM Model Management

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LLM Registry is a Python package that provides a unified interface for discovering and managing the capabilities of various Large Language Models (LLMs). It includes a robust API, a rich CLI, and supports both package-included and user-managed model registries with local storage. The package supports multi-provider models, allowing a single model to be associated with multiple providers.

Table of Contents

• Overview
• Features
• Installation
• Library Usage
• CLI Usage
• Model Capabilities
• Configuration
• Development
• License

🎯 Overview

Manage and discover LLM model capabilities across multiple providers like OpenAI, Anthropic, and more in a centralized registry. Use this package to check model capabilities before initializing provider clients and to manage model metadata efficiently.

💡 Perfect for teams managing multiple LLM providers and wanting to standardize their model interactions.

✨ Features

🔗 Unified API

• Single interface for capability discovery and management
• Consistent experience across all providers

🏢 Multiple Providers

• Support for OpenAI, Anthropic, Google, Cohere, Mistral, Meta, and more
• Multi-provider model support - associate a single model with multiple providers

💾 Smart Storage

• Local storage for model metadata
• Package-included and user-managed registries
• Efficient caching mechanism

🖥️ Rich CLI Experience

• Intuitive commands for model management
• Beautiful terminal output with rich formatting
• Quick access to model information

📊 Comprehensive Tracking

• Token costs (standard and cached)
• Model features and capabilities
• API parameters and limitations
• Training cutoff dates

🗂️ Organization

• Group models by family and provider
• Easy filtering and search

🚀 Installation

Install via uv:

uv add llm-registry

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Install via pip:

pip install llm-registry

📚 Library Usage

Integrate the package in your Python projects by following these steps:

📋 Listing Models

from llm_registry import CapabilityRegistry, Provider
registry = CapabilityRegistry()
models = registry.get_models()
for model in models:
    print(model)

🔍 Retrieve a Specific Model's Capabilities

model = registry.get_model("gpt-4")
if model and model.api_params.stream:
    from openai import OpenAI  # Replace with actual OpenAI client import
    client = OpenAI()  # Initialize client with streaming enabled
    response = client.chat.completions.create(
        model=model.model_id,
        messages=[{"role": "user", "content": "Hello"}],
        stream=True
    )

➕ Add a New Model Capability

from llm_registry.utils import create_model_capability
from llm_registry import Provider

# Single provider model
new_model = create_model_capability(
    model_id="gpt-4",
    provider=Provider.OPENAI,  # Automatically converted to list internally
    model_family="GPT-4",
    input_cost=0.01,
    output_cost=0.03,
    cache_input_cost=0.005,  # Optional cached token costs
    cache_output_cost=0.015,
    context_window=8192,
    training_cutoff="2023-04",
    supports_streaming=True,
    supports_tools=True,
    supports_json_mode=True,
    supports_system_prompt=True
)

# Multi-provider model
multi_provider_model = create_model_capability(
    model_id="llama-2-70b",
    provider=[Provider.META, Provider.GITHUB],
    model_family="Llama 2",
    input_cost=0.0007,
    output_cost=0.0009,
    context_window=4096
)

from llm_registry import CapabilityRepository
repo = CapabilityRepository()
repo.save_model_capabilities(new_model)
repo.save_model_capabilities(multi_provider_model)

💻 CLI Usage

The CLI tools llmr and llm-registry allow you to interact with model capabilities directly from the terminal.

📋 List Models

View all available models:

llmr list

The above screenshot demonstrates how the CLI tool (llmr) currently looks like when listing models.

To filter models by provider:

llmr list --provider openai

Additional filtering options:

# Show only user-defined models
llmr list --user-only

# Show only package-included models
llmr list --package-only

🔍 Get Detailed Model Information

Get detailed information about a specific model:

llmr get gpt-4

For JSON output:

llmr get gpt-4 --json

Output

❯ llmr get o1 --json
{
  "model_id": "o1",
  "providers": [
    "openai"
  ],
  "model_family": "o1",
  "base_model": null,
  "api_params": {
    "max_tokens": true,
    "temperature": false,
    "top_p": false,
    "stream": true
  },
  "features": {
    "vision": true,
    "tools": true,
    "json_mode": true,
    "system_prompt": false
  },
  "token_costs": {
    "input_cost": 15.0,
    "output_cost": 60.0,
    "cache_input_cost": 7.5,
    "cache_output_cost": null,
    "context_window": 200000,
    "training_cutoff": "2023-10"
  }
}

➕ Add Model

Add a new model:

llmr add gpt-4 \
    --provider openai \
    --model-family GPT-4 \
    --input-cost 0.01 \
    --output-cost 0.03 \
    --cache-input-cost 0.005 \
    --cache-output-cost 0.015 \
    --context-window 8192 \
    --training-cutoff 2023-04 \
    --stream \
    --tools \
    --json-mode \
    --system-prompt

🔄 Update Model

Update an existing model:

llmr update gpt-4 \
    --provider openai \
    --model-family "GPT-4 Turbo" \
    --input-cost 0.005 \
    --output-cost 0.015 \
    --vision

🗑️ Delete Model

Remove an existing model:

llmr delete gpt-4 --provider openai

Use -f or --force to bypass confirmation.

🎯 Model Capabilities

Each model entry tracks:

🏷️ Basic Information

• Providers (supports multiple providers per model)
• Model ID and Model Family

💰 Cost Details

• Input/Output token costs (per 1M tokens)
• Cached Input/Output token costs (per 1M tokens)
• Context window size
• Training data cutoff date

⚙️ API Parameters

• Max tokens support
• Temperature support
• Top-p support
• Streaming support

✨ Feature Support

• Streaming responses
• Tools/Function calling
• Vision/Image input
• JSON mode
• System prompt support

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👥 Contributing

Contributions are welcome! Feel free to:

• Report bugs
• Suggest new features
• Submit pull requests

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Configuration

Default model data is stored in ~/.llm-registry. You can override the directory by:

• Passing a data_dir parameter to CapabilityRepository in code
• Using the --data-dir option in CLI commands

Development

Requirements

• Python 3.13+
• uv for dependency management

Setup

# Create virtual environment and sync dependencies
uv venv
uv sync --group dev

# Run tests with coverage analysis
pytest -v --cov=llm_registry

License

Distributed under the MIT License. See LICENSE for more information.