feat: initial model catalog with 196 models across 39 providers
Community-maintained TOML catalog for LibreFang. New models can be added via PR without requiring a LibreFang binary release. Includes validation script, bilingual docs, and GitHub templates.
This commit is contained in:
Evan committed
commit
21c82e335c
47 files changed
+3668
No files matched your search
@@ -0,0 +1,188 @@
|
||||
# LibreFang Model Catalog
|
||||
|
||||
Community-maintained model metadata catalog for [LibreFang](https://github.com/librefang/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:
|
||||
|
||||
```bash
|
||||
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:
|
||||
|
||||
```bash
|
||||
# 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:
|
||||
|
||||
```toml
|
||||
[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](CONTRIBUTING.md) for detailed instructions.
|
||||
|
||||
## Validation
|
||||
|
||||
```bash
|
||||
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](LICENSE).
|
||||
|
||||
---
|
||||
|
||||
# LibreFang 模型目录
|
||||
|
||||
社区维护的 [LibreFang](https://github.com/librefang/librefang) 模型元数据目录 -- 开源 Agent 操作系统。
|
||||
|
||||
本仓库是模型元数据（定价、上下文窗口、能力标记）的唯一数据源。当新模型发布时（如 GPT-5.5、Claude 5），任何人都可以在这里提交 PR，而无需修改 LibreFang 二进制文件。
|
||||
|
||||
## 目录结构
|
||||
|
||||
```
|
||||
model-catalog/
|
||||
├── providers/ # 每个提供商一个 TOML 文件
|
||||
│ ├── anthropic.toml
|
||||
│ ├── openai.toml
|
||||
│ ├── gemini.toml
|
||||
│ └── ...
|
||||
├── aliases.toml # 全局别名映射（如 "sonnet" -> "claude-sonnet-4-6"）
|
||||
├── schema.toml # 字段定义参考
|
||||
├── scripts/
|
||||
│ └── validate.py # 验证脚本
|
||||
├── CONTRIBUTING.md # 如何添加新模型
|
||||
└── LICENSE # MIT 许可证
|
||||
```
|
||||
|
||||
## LibreFang 如何使用此目录
|
||||
|
||||
LibreFang 内置了编译到二进制文件中的模型目录。本仓库作为上游数据源。更新本地目录：
|
||||
|
||||
```bash
|
||||
librefang catalog update
|
||||
```
|
||||
|
||||
### 本地自定义模型
|
||||
|
||||
您也可以在本地添加自定义模型，无需提交 PR：
|
||||
|
||||
```bash
|
||||
# 编辑个人配置文件
|
||||
# ~/.librefang/model_catalog.toml
|
||||
|
||||
[[models]]
|
||||
id = "my-custom-model"
|
||||
display_name = "我的自定义模型"
|
||||
provider = "ollama"
|
||||
tier = "local"
|
||||
context_window = 32768
|
||||
max_output_tokens = 4096
|
||||
input_cost_per_m = 0.0
|
||||
output_cost_per_m = 0.0
|
||||
```
|
||||
|
||||
## 如何添加新模型
|
||||
|
||||
1. 编辑 `providers/` 中对应的提供商文件
|
||||
2. 运行验证：`python scripts/validate.py`
|
||||
3. 提交 Pull Request
|
||||
|
||||
详细说明请参考 [CONTRIBUTING.md](CONTRIBUTING.md)。
|
||||
|
||||
## 验证
|
||||
|
||||
```bash
|
||||
python scripts/validate.py
|
||||
```
|
||||
|
||||
检查所有 TOML 文件的正确性：必填字段、有效的层级值、非负成本、无重复 ID。
|
||||
|
||||
## 许可证
|
||||
|
||||
MIT 许可证。详见 [LICENSE](LICENSE)。
|
||||
Reference in new issue
Block a user