OpenClaw LLM Tools
by @michealxie001
Universal Tool Definition System for LLM function calling. Define tools once, use with any LLM provider (OpenAI, Anthropic, Gemini, etc.). JSON Schema valida...
clawhub install oc-llm-tools📖 About This Skill
name: llm-tools description: Universal Tool Definition System for LLM function calling. Define tools once, use with any LLM provider (OpenAI, Anthropic, Gemini, etc.). JSON Schema validation and automatic format conversion. tools: - read - write - exec
LLM Tools - 通用工具定义系统
基于 Bytebot Tool Definition 模式实现的 LLM 函数调用工具定义系统。
Version: 1.0.0 Features: JSON Schema 定义、多 LLM 格式转换、工具注册中心、参数验证
Purpose
让 OpenClaw 能够:
Quick Start
1. 定义工具
from llm_tools import ToolRegistry, Tool创建工具注册表
registry = ToolRegistry()定义工具
@registry.register(
name="get_weather",
description="Get current weather for a location",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City name"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"default": "celsius"
}
},
"required": ["location"]
}
)
def get_weather(location: str, unit: str = "celsius"):
return {"temperature": 22, "unit": unit}
2. 转换为不同 LLM 格式
# OpenAI format
openai_tools = registry.to_openai()Anthropic format
anthropic_tools = registry.to_anthropic()Google Gemini format
gemini_tools = registry.to_gemini()Ollama format
ollama_tools = registry.to_ollama()
3. 验证工具调用
# 验证参数
is_valid, error = registry.validate_call(
"get_weather",
{"location": "Beijing", "unit": "celsius"}
)执行工具
result = registry.execute("get_weather", {"location": "Beijing"})
CLI Usage
转换工具格式
# 从 JSON 定义转换
python3 scripts/main.py convert --input tools.json --format openai
python3 scripts/main.py convert --input tools.json --format anthropic验证工具定义
python3 scripts/main.py validate --input tools.json列出所有工具
python3 scripts/main.py list --input tools.json
工具定义 JSON 格式
{
"tools": [
{
"name": "search_web",
"description": "Search the web for information",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query"
},
"limit": {
"type": "integer",
"default": 10
}
},
"required": ["query"]
}
}
]
}
Supported LLM Formats
| Provider | Format | Features |
|----------|--------|----------|
| OpenAI | Function Calling | tools / functions |
| Anthropic | Tool Use | computer_* 命名空间 |
| Google | Function Calling | function_declarations |
| Ollama | Tools | Native tool support |
| Mistral | Function Calling | OpenAI-compatible |
| Cohere | Tool Use | Custom format |
Installation
pip3 install -r requirements.txt
API Reference
ToolRegistry
from llm_tools import ToolRegistryregistry = ToolRegistry()
注册工具
registry.register_tool(tool_definition)装饰器方式
@registry.register(name="...", description="...", parameters={...})
def my_tool():
pass批量注册
registry.register_from_dict({"tools": [...]})
registry.register_from_json_file("tools.json")导出格式
openai_format = registry.to_openai()
anthropic_format = registry.to_anthropic()
gemini_format = registry.to_gemini()
ollama_format = registry.to_ollama()验证和执行
registry.validate_call(name, arguments)
registry.execute(name, arguments)
Tool Definition
from llm_tools import Tooltool = Tool(
name="calculate",
description="Perform mathematical calculation",
parameters={
"type": "object",
"properties": {
"expression": {"type": "string"}
},
"required": ["expression"]
},
handler=lambda expr: eval(expr) # 可选
)
Integration with OpenClaw
在 Skill 中使用:
from llm_tools import ToolRegistryclass MySkill:
def __init__(self):
self.tools = ToolRegistry()
@self.tools.register(name="read_file", ...)
def read_file(path: str):
return Path(path).read_text()
def get_llm_tools(self, provider: str):
if provider == "openai":
return self.tools.to_openai()
elif provider == "anthropic":
return self.tools.to_anthropic()
Architecture
llm-tools/
├── SKILL.md
├── requirements.txt
├── lib/
│ ├── __init__.py
│ ├── registry.py # ToolRegistry 核心
│ ├── tool.py # Tool 类定义
│ ├── formats/
│ │ ├── __init__.py
│ │ ├── openai.py # OpenAI 格式转换
│ │ ├── anthropic.py # Anthropic 格式转换
│ │ ├── gemini.py # Google Gemini 格式
│ │ └── ollama.py # Ollama 格式
│ └── validators.py # JSON Schema 验证
├── scripts/
│ └── main.py # CLI 入口
└── examples/
├── tools.json # 示例工具定义
└── registry_example.py # 注册表示例
Use Cases
1. 多 LLM 支持 - 一次定义，多处使用 2. 工具共享 - 在 Skills 间共享工具定义 3. 参数验证 - 自动验证 LLM 输出的参数 4. 格式转换 - 迁移到不同 LLM 提供商
License
MIT License - 基于 Bytebot Tool Definition 模式实现
⚡ When to Use
💡 Examples
1. 定义工具
from llm_tools import ToolRegistry, Tool创建工具注册表
registry = ToolRegistry()定义工具
@registry.register(
name="get_weather",
description="Get current weather for a location",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City name"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"default": "celsius"
}
},
"required": ["location"]
}
)
def get_weather(location: str, unit: str = "celsius"):
return {"temperature": 22, "unit": unit}
2. 转换为不同 LLM 格式
# OpenAI format
openai_tools = registry.to_openai()Anthropic format
anthropic_tools = registry.to_anthropic()Google Gemini format
gemini_tools = registry.to_gemini()Ollama format
ollama_tools = registry.to_ollama()
3. 验证工具调用
# 验证参数
is_valid, error = registry.validate_call(
"get_weather",
{"location": "Beijing", "unit": "celsius"}
)执行工具
result = registry.execute("get_weather", {"location": "Beijing"})