Tool calling

Tool calling makes LLMs smarter.

LLMs use tool calling to interact with the outside world. Mistral.rs has OpenAI compatible support for tool calling in all APIs, HTTP, Python, and Rust.

Note that some models, such as Mistral Small/Nemo models, require a chat template to be specified. For example:

mistralrs serve -p 1234 --isq 4 --jinja-explicit chat_templates/mistral_small_tool_call.jinja -m mistralai/Mistral-Small-3.1-24B-Instruct-2503

OpenAI docs: https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models

We support the following models’ tool calling in OpenAI-compatible and parse native tool calling:

• Llama 4
• Llama 3.1/3.2/3.3
• Mistral Small (including 3.1 + multimodal)
• Mistral Nemo
• Hermes 2 Pro
• Hermes 3
• DeepSeek V2/V3/R1
• Qwen 3

All models that support tool calling will respond according to the OpenAI tool calling API.

OpenAI compatible HTTP example

Please see our example here.

OpenAI docs: https://platform.openai.com/docs/api-reference/chat/create?lang=curl

Rust example

Please see our example here.

Python example

Please see our notebook here.

Tool callbacks

You can override tool execution using a tool callback. The callback receives the tool name and a dictionary of arguments and must return the tool output as a string.

Python

def tool_cb(name: str, args: dict) -> str:
    if name == "local_search":
        return json.dumps(local_search(args.get("query", "")))
    return ""

runner = Runner(
    which=Which.Plain(model_id="YourModel/ID", arch=Architecture.Llama),
    tool_callback=tool_cb,
)

See custom_search.py for a full example. In Rust pass .with_tool_callback(...) to the builder as demonstrated in custom_search/main.rs.

Search callbacks

Web search uses a DuckDuckGo-based callback by default. Provide your own search function with search_callback in Python or .with_search_callback(...) in Rust. Each callback should return a list of results with title, description, url and content fields. See WEB_SEARCH.md for more details and examples.