liter-llm-tool-calling

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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, json, os
from liter_llm import create_client
from liter_llm._internal_bindings import ChatCompletionRequest

payload = {
    "model": "openai/gpt-4o",
    "messages": [{"role": "user", "content": "What is the weather in Berlin?"}],
    "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"],
                },
            },
        }
    ],
    "tool_choice": "auto",
}

async def main() -> None:
    client = create_client(api_key=os.environ["OPENAI_API_KEY"])
    request = ChatCompletionRequest.from_json(json.dumps(payload))
    response = await client.chat(request)
    for call in response.choices[0].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:

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

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.