my agent generate reply before calling the desired tool, i want to call it immediately without mid responce

Thanks for reaching out, Aleh. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

python

@function_tool

Thanks — I understand: you’re using Python

@function_tool

and want the agent to call the tool immediately without generating any intermediate text. Below are focused, practical steps and a minimal code + prompt example to achieve that. 1) Make the tool intention explicit • Put a clear instruction in the tool docstring telling the model to call this tool immediately and not produce any text before calling it. • Keep parameters strict so the model recognizes when to use it. 2) Force behavior in the system prompt • Add a system prompt rule that says: “If the user’s input matches X, call tool Y immediately and do not generate any natural language before the tool call.” 3) Optional: route deterministically before the LLM • For fully deterministic cases, run a lightweight pre-check in Python (intent classifier or regex). If matched, call the tool directly without sending the message to the LLM. Minimal example

from livekit import Agent, function_tool, RunContext

agent = Agent(
    system_prompt="""
You are an assistant that must call tools when appropriate.
If the user requests an action that matches a tool, call the tool immediately and do not produce any text before the tool result.
"""
)

@function_tool()
async def immediate_tool(ctx: RunContext, action: str):
    """Perform 'action'.
    IMPORTANT: Call this tool immediately when user asks for this action.
    Do NOT generate any assistant message before calling this tool.
    """
    # tool logic here
    return {"status": "done", "action": action}

Example pre-routing (optional, deterministic)

def should_call_tool(user_text: str) -> bool:
    return "run tool" in user_text.lower()  # replace with robust check

if should_call_tool(user_input):
    result = await immediate_tool(... )  # call tool directly
else:
    response = await agent.generate_reply(user_input)

Testing tips • Use simple test prompts that clearly trigger the tool. • Use mock tools to assert the agent emits a tool call event before any assistant text. • If you still see intermediate text, tighten the system prompt and tool docstring or move the check upstream (pre-routing). If you want, share: • one example user input that currently triggers the mid-response, and • your current tool decorator + system prompt text; I’ll give a tailored prompt or regex/router snippet you can drop in. Sources: https://docs.livekit.io/agents/logic/tools/definition.md| | Audio customization | LiveKit Documentation | The ReAct Pattern for Voice Agents and How AI Agents Think, Act, and Respon... | Function tool definition | LiveKit Documentation | How to Use the Supervisor Pattern for Multi-Agent Voice AI Systems | LiveKi..., with interactive elements

Here's a precise description you can use: --- Issue: Realtime model generates spoken text before executing a function tool call We're using RealtimeModel (GPT-4o realtime via Azure) with LiveKit Agents. When the user triggers a specific action (e.g. "I don't want this loan"), the model generates and speaks an acknowledgment message before calling the intended function tool. This results in two separate spoken turns: 1. Pre-tool speech (unwanted): "I'll go ahead and withdraw your application for you..." 2. Post-tool speech (correct): the farewell message generated inside the tool via session.generate_reply(instructions=...) Desired behavior: The model should call the tool immediately as its first action, with no spoken text before the tool executes. What we tried: Adding prompt instructions like "call the tool immediately, without saying anything first" — inconsistently followed by the realtime model. Question: Is there a framework-level mechanism (session config, tool option, hook, or API parameter) to suppress or prevent the model from generating spoken text in the same response that contains a function tool call? Or is there a recommended pattern for "silent tool calls" in the realtime pipeline?

Thanks — I understand: you’re using GPT‑4o Realtime (via Azure) with LiveKit Agents and want the model to invoke a

@function_tool

immediately with no spoken text before the tool runs. Short answer • There is no framework-level flag or session config that forces realtime models to never produce pre-tool speech. Realtime models decide speech vs. tool-calling internally, so you can’t reliably suppress the model’s pre-tool utterance purely via an API parameter. What you can do (recommended patterns) • Make the tool intent explicit: ◦ Put an unmistakable instruction in the tool docstring and parameters: “Call this tool as the FIRST action. Do not produce any assistant message before calling it.” • Tighten the system prompt: ◦ Add a rule like: “If the user requests X, call tool Y immediately and do not say anything before the tool result.” • Pre-route deterministically (most reliable): ◦ Run a lightweight intent check (regex or small classifier) before sending text to the realtime model. If matched, call the tool directly from your code instead of sending the utterance to the model. • Silent tool response pattern: ◦ Perform the action inside the tool and generate user-facing speech from inside the tool (e.g., via

context.session.generate_reply(instructions=...)

) while returning an empty assistant message so you avoid a double spoken turn. • If precise timing and guaranteed silence are required: ◦ Consider switching to a pipeline (STT → non-realtime LLM → tool → TTS) where you control exactly when TTS is invoked. Why prompt-only fixes are inconsistent • Realtime models prioritize low-latency conversational behavior; they sometimes “acknowledge” before calling tools despite explicit prompts. Prompt engineering helps but isn’t 100% reliable. If you’d like, I can: • Review one example user utterance and your current system prompt + tool docstring, then suggest a tightened prompt and a concrete pre-routing regex/classifier you can add. Sources: Function tool definition | LiveKit Documentation | Realtime models overview | LiveKit Documentation | Sequential Pipeline Architecture for Voice Agents | LiveKit, with interactive elements

how i can say to an agnet to now generate reply before calling the tool

I hear you — you want the agent to call the tool immediately and not generate any assistant reply before the tool runs. Short answer: realtime models don’t guarantee that behavior, but you can greatly reduce/prevent pre-tool replies using these practical options. What to do now (pick one or combine) 1) Tighten system prompt (direct instruction) • Add a hard rule in your Agent’s system prompt: ◦ "If the user requests cancellation or any action matched to tool

withdraw_loan_application

, call that tool immediately and do NOT produce any assistant text before the tool result." 2) Make the tool instruction explicit • In the

@function_tool

docstring: ◦ "IMMEDIATELY call this tool when the user requests X. Do not speak before calling this tool." 3) Pre-route before the LLM (most reliable) • Run a small intent check in Python (regex or classifier). If matched, call the tool directly from your code instead of sending the utterance to the realtime model. ◦ Example rule: if "I don't want this loan" in user_text.lower(): await withdraw_loan_application(...) 4) Silent-tool pattern inside the tool • Do the action inside the tool and generate the user-facing message from inside the tool (e.g.,

context.session.generate_reply(instructions=...)

) while returning an empty assistant response to avoid a separate pre-tool utterance. Why prompts alone are inconsistent • GPT‑4o Realtime decides speech vs. tool call internally for latency/naturalness. Even precise prompts can be ignored sometimes — pre-routing is the only deterministic solution. If you want, paste: • one exact user input that currently triggers the unwanted pre-speech, and • your system prompt + current

@function_tool

docstring, and I’ll return a tightened prompt and a simple pre-routing snippet you can copy-paste. , with interactive elements