tool-calling

name	tool-calling
description	Use when defining functions/tools for an LLM to call through liter-llm, or requesting structured JSON outputs. Covers tool schemas, tool_calls handling, and response formats.

Tool Calling

Pass a tools array of function definitions; the model may respond with tool_calls instead of (or alongside) text. Execute the named function and feed the result back as a tool message.

Python

import asyncio, os
from liter_llm import LlmClient

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"},
                },
                "required": ["location"],
            },
        },
    }
]

async def main() -> None:
    client = LlmClient(api_key=os.environ["OPENAI_API_KEY"])
    response = await client.chat(
        model="openai/gpt-4o",
        messages=[{"role": "user", "content": "Weather in Berlin?"}],
        tools=tools,
    )
    choice = response.choices[0]
    for call in choice.message.tool_calls or []:
        print(call.function.name, call.function.arguments)  # arguments is a JSON string

asyncio.run(main())

Structured outputs

Request strict JSON with response_format:

response = await client.chat(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Extract name and age as JSON."}],
    response_format={"type": "json_object"},
)

Notes

• function.arguments is a JSON string — parse it before use.
• Append each tool result as a message with role="tool" and the matching tool_call_id, then call chat again to let the model continue.
• Tool support and JSON-mode availability vary by provider; check the provider reference if a model ignores tools.