LibreFang Model Catalog

Community-maintained model metadata catalog for LibreFang -- the open-source Agent Operating System.

This repository is the source of truth for model metadata (pricing, context windows, capabilities). When new models are released (e.g. GPT-5.5, Claude 5), anyone can submit a PR here without touching the LibreFang binary.

Structure

model-catalog/
├── providers/          # One TOML file per provider
│   ├── anthropic.toml
│   ├── openai.toml
│   ├── gemini.toml
│   └── ...
├── aliases.toml        # Global alias mappings (e.g. "sonnet" -> "claude-sonnet-4-6")
├── schema.toml         # Reference schema documenting all fields
├── scripts/
│   └── validate.py     # Validation script
├── CONTRIBUTING.md     # How to add a new model
└── LICENSE             # MIT

How LibreFang Uses This Catalog

LibreFang ships with a built-in model catalog compiled into the binary. This repository serves as the upstream source. To update your local catalog:

librefang catalog update

This fetches the latest TOML files from this repository and merges them into your local catalog.

Custom Local Models

You can also add custom models locally without submitting a PR:

# Add to your personal config
# ~/.librefang/model_catalog.toml

[[models]]
id = "my-custom-model"
display_name = "My Custom Model"
provider = "ollama"
tier = "local"
context_window = 32768
max_output_tokens = 4096
input_cost_per_m = 0.0
output_cost_per_m = 0.0
supports_tools = true
supports_vision = false
supports_streaming = true

Schema Reference

Each provider file contains a [provider] section and one or more [[models]] entries:

[provider]
id = "provider-id"                  # Unique provider identifier
display_name = "Provider Name"      # Human-readable name
api_key_env = "PROVIDER_API_KEY"    # Environment variable for API key
base_url = "https://api.example.com"  # Default API endpoint
key_required = true                 # Whether an API key is needed

[[models]]
id = "model-id"                    # Unique model identifier (API model ID)
display_name = "Human Name"        # Human-readable display name
tier = "smart"                     # frontier | smart | balanced | fast | local
context_window = 128000            # Maximum input tokens
max_output_tokens = 16384          # Maximum output tokens
input_cost_per_m = 2.50            # USD per million input tokens
output_cost_per_m = 10.0           # USD per million output tokens
supports_tools = true              # Tool/function calling support
supports_vision = true             # Vision/image input support
supports_streaming = true          # Streaming response support
aliases = ["alias1", "alias2"]     # Short names for this model

Tier Definitions

Tier	Description	Examples
frontier	Most capable, cutting-edge models	Claude Opus, GPT-4.1, Gemini 2.5 Pro
smart	Smart, cost-effective models	Claude Sonnet, GPT-4o, Gemini 2.5 Flash
balanced	Balanced speed/cost	GPT-4.1 Mini, Llama 3.3 70B
fast	Fastest, cheapest	GPT-4o Mini, Claude Haiku
local	Local models (zero cost)	Ollama, vLLM, LM Studio

How to Add a New Model

1. Edit the appropriate provider file in providers/
2. Run validation: python scripts/validate.py
3. Submit a Pull Request

See CONTRIBUTING.md for detailed instructions.

Validation

python scripts/validate.py

This checks all TOML files for correctness: required fields, valid tiers, non-negative costs, no duplicate IDs.

Current Stats

• 30+ providers including Anthropic, OpenAI, Google, DeepSeek, Groq, Mistral, xAI, and more
• 190+ models with pricing, context windows, and capability flags
• 80+ aliases for quick model selection

License

MIT License. See LICENSE.