Factor 1: Natural Language to Tool Calls

Principle: Translate natural language into structured tool calls for reliable execution.

How Vel Implements This

Vel uses LLM function calling to convert natural language into structured tool invocations with JSON schema validation:

from vel import Agent, ToolSpec, register_tool

def get_weather_handler(input: dict, ctx: dict) -> dict:
    city = input['city']
    return {'temp_f': 72, 'condition': 'sunny', 'city': city}

weather_tool = ToolSpec(
    name='get_weather',
    input_schema={
        'type': 'object',
        'properties': {'city': {'type': 'string'}},
        'required': ['city']
    },
    output_schema={
        'type': 'object',
        'properties': {
            'temp_f': {'type': 'number'},
            'condition': {'type': 'string'}
        },
        'required': ['temp_f', 'condition']
    },
    handler=get_weather_handler
)

register_tool(weather_tool)

agent = Agent(
    id='weather-agent',
    model={'provider': 'openai', 'model': 'gpt-4o'},
    tools=['get_weather']
)

# Natural language → Structured tool call
answer = await agent.run({'message': 'What is the weather in Tokyo?'})

Benefits

• ✓ JSON schema validation ensures type safety
• ✓ Provider-agnostic tool format works across OpenAI, Gemini, and Claude
• ✓ Clear separation between LLM decision-making and code execution

See: Tools Documentation