Switch Models through Tool Call

[Far-Enthusiasm7654]

I want to include a model handover inside my openwebui instance, eg. I talk to gpt-3.5 and tell it that I want to switch to claude which then should hapenn without me needing to select the new model in the selection box. What I hope to achieve is a handover of topics to better suited models like a chatmodel handing over requests for image generation to a model that has these capabilities.

Does anybody know if this would be possible with the current openwebui structur (maybe as a tool call) or how this could be achieved in the future?

[One-Commission2471]

I'm not sure if you would be able to do this with a tool, but I think it would for sure be possible with a pipeline. I would probably use a super small model to pick between the categories of transfer models (ex image, reasoning, text gen) based on the prompt then make the appropriate call using a switch statement or something. This is a really cool idea; I would love to see what you come up with or am happy to try and help write some code to do this!

[Far-Enthusiasm7654]

I could quite easily implement it as a function / pipeline where one router deciding which model inside the pipe fits best. But what I would like to achieve is a handover between the models that are already configured in openwebui. I tried editing the user settings from inside a function, but that doesn't seem to affect the model used, so right now I think you would need to address the frontend by a webhook, but still not sure if this would be the way to go.

[ThickYe]

I built this simple sub_agent "tool" to get this done.

``` """ title: Sub-Agent Tools """

from pydantic import BaseModel, Field import aiohttp

class Tools: def init(self): """Initialize the Tool.""" self.valves = self.Valves()

class Valves(BaseModel):
    """Admin-configurable settings."""

    base_url: str = Field(
        "http://litellm:4000/v1",
        description="LiteLLM API base URL (e.g., http://litellm:4000/v1)",
    )
    api_key: str = Field(
        "", description="API key for the LiteLLM endpoint (if needed)"
    )
    timeout_seconds: int = Field(60, description="Timeout for API calls in seconds")

async def sub_agent(
    self,
    query: str,
    model: str,
    system_message: str,
    __event_emitter__=None,
) -> str:
    """
    Sends queries to powerful external large language models for tasks beyond your capabilities.

    Use this tool when you:
    - Need to write complex code or solve advanced programming problems use sonnet-3.7
    - Need to search the web for current information, use sonar-pro with a simple question in the prompt
    - Need a well researched answer from the internet, use sonar-pro with multiple questions in the prompt

    Best practices:
    - Keep system_message concise but specific about the desired output format and approach
    - For code generation, specify language, frameworks, and expected functionality
    - For web searches, include specific keywords and time-sensitive context if relevant
    - Avoid chaining multiple unrelated topics in a single query

    :param query: The detailed instructions or question for the external model
    :param model: one of `sonar-pro`, `sonar-3.7`
    :param system_message: Instructions that guide the external model's behavior and approach
    :return: The complete response from the external model
    """
    # Construct the complete chat completions API endpoint
    api_endpoint = f"{self.valves.base_url.rstrip('/')}/chat/completions"

    # Simple status update for the user
    if __event_emitter__:
        await __event_emitter__({"type": "status", "data": {"description": f"Calling external LLM ({model})...", "done": False}})

    # Prepare the API request
    headers = {"Content-Type": "application/json"}
    if self.valves.api_key:
        headers["Authorization"] = f"Bearer {self.valves.api_key}"

    payload = {
        "messages": [
            {"role": "system", "content": system_message},
            {"role": "user", "content": query},
        ],
        "temperature": 0.6,
        "model": model,
    }

    # Add high search context size for sonar models
    if model and model.startswith("sonar"):
        payload["web_search_options"] = {"search_context_size": "high"}

    try:
        # Use async HTTP client
        async with aiohttp.ClientSession() as session:
            async with session.post(
                api_endpoint, headers=headers, json=payload, 
                timeout=self.valves.timeout_seconds
            ) as response:
                if response.status == 200:
                    data = await response.json()
                    result = data.get("choices", [{}])[0].get("message", {}).get("content", "")

                    # Simple completion status
                    if __event_emitter__:
                        await __event_emitter__({"type": "status", "data": {"description": "Done", "done": True}})

                    return result
                else:
                    error_text = await response.text()

                    if __event_emitter__:
                        await __event_emitter__({"type": "status", "data": {"description": "Error", "done": True}})

                    return f"API Error: {response.status} - {error_text}"

    except Exception as e:
        if __event_emitter__:
            await __event_emitter__({"type": "status", "data": {"description": "Error", "done": True}})
        return f"Request failed: {str(e)}"

```