can the agent give a response and call a tool simultaneously

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

i want that agent gives a response and in the same response or just after that calls a tool without giving the turn to user. and after the tool is called it should pass the turn to user

Thanks — responding about your desired flow: agent speaks, immediately calls a tool without giving the turn to the user, then returns the turn after the tool finishes. Short answer: Yes — but only if the tool call is triggered within the same ReAct cycle that was started by the user input or by an external trigger you control. The agent cannot autonomously start that sequence without being triggered. How to implement this (high-level, actionable): 1. Trigger point ◦ Ensure the sequence is started by a user utterance or by an external programmatic trigger (timer, event, or server-side RPC) that calls the agent’s session. 1. In the same handling flow: ◦ Have the agent generate a spoken reply first (e.g.,

session.generate_reply("I'll run that check now.")

). ◦ Immediately after (in the same async flow), invoke the tool function directly (e.g., call the

@function_tool

or your tool method). ◦ Optionally use

context.session.say("Working on it...")

inside the tool to provide live feedback while it runs. 1. Ending the sequence and passing the turn ◦ When the tool finishes, return its result to the agent logic if you want to include it in a follow-up message. ◦ Stop calling

generate_reply()

again; once the agent stops speaking and no further replies are generated, VAD will hand the turn back to the user. Important constraints and tips: • The tool call must be initiated by the agent’s reasoning during that triggered handling — there’s no built-in flag to “always call a tool after speaking” without wiring that logic. • If you need the agent to do this autonomously (without a user turn), trigger the same sequence programmatically from your server or a background task by calling

session.generate_reply()

and then the tool. • Use

context.session.say()

for filler speech during long-running tool calls so users hear progress while the tool runs. If you want, tell me: • Which tool you plan to call (name/purpose) and whether you want the tool’s output announced to the user immediately or only used internally — I’ll provide a concise code snippet tailored to that tool. Sources: The ReAct Pattern for Voice Agents and How AI Agents Think, Act, and Respon... | Function tool definition | LiveKit Documentation | Audio customization | LiveKit Documentation, with interactive elements

suppose i collect the user's name. and in prompt i have written. when the user provides name then greet by saying thank you <user's name>. and in the same response call a tool to save the user's name called save_name

Thanks — responding to your example where the agent should say "thank you name" and in the same turn call the

save_name

tool before returning the turn to the user. Yes — you can do this by triggering both actions within the same handling flow (the same ReAct cycle). Pattern: 1) generate the spoken reply, 2) immediately call the

save_name

tool, 3) finish without calling another

generate_reply()

so the turn returns to the user. Example approach (high-level): 1. Prompt / system instruction (guide the LLM): ◦ Tell the model to greet with the provided name and to call the

save_name

tool right after producing the greeting. Example instruction: “When the user gives their name, reply: ‘Thank you name.’ Then call the

save_name

tool with the name. Do not wait for user confirmation.” 1. Implementation flow (async pseudocode):

# 1) receive user input with name
user_name = extract_name_from_input(user_input)  # or let LLM parse

# 2) generate the spoken reply immediately
await session.generate_reply(instructions=f"Say: 'Thank you {user_name}.'")

# 3) call the tool in the same handler (no user turn in between)
result = await save_name_tool({"name": user_name})

# optional: provide internal feedback while saving
# await context.session.say("Saving your name now...")

# 4) end handler — do not call generate_reply() again unless you want another spoken message
# Turn is now returned to the user (VAD will listen)

1. Tool signature example (for clarity):

@function_tool
async def save_name_tool(params):
    # params = {"name": "Alice"}
    # store the name in your DB
    return {"status": "ok"}

Notes and tips: • Ensure this handler is triggered by the user input (or programmatically) so the agent is in an active ReAct cycle. • Use

context.session.say()

if you want brief filler speech while the tool runs. • If you want the agent to report the save result back immediately, call

generate_reply()

after the tool finishes to speak that result; otherwise omit it to pass control back to the user. If you want, I can provide a concrete code example matching your runtime (Node/Python) or a sample system prompt that instructs the LLM to both greet and call

save_name

. Which would you prefer? , with interactive elements

i am only providing this instruction in prompt

Acknowledging: you’re giving only a prompt and want the agent to say “Thank you name.” and in that same turn call the

save_name

tool (without handing control to the user until the tool finishes). Short answer: You can instruct the model in the prompt to both speak the greeting and call

save_name

in the same handling flow, but prompt-only instructions have limits — the runtime must allow the tool to be invoked as part of the model’s action. To be reliable, include a clear tool schema and an explicit instruction pattern in the prompt. Prompt-only example (system + user instructions) • System instruction (high-level rule): ◦ "When the user provides their name, respond with:

Thank you <name>.

Then immediately call the tool

save_name

with the parameter

name: <name>

. Do not ask for confirmation. After calling the tool, end the turn (do not wait for further input). If the tool returns an error, include a brief failure message." • Tool schema (tell the model how to call it): ◦ Name:

save_name

◦ Arguments:

{ "name": "<string>" }

◦ Return:

{ "status": "ok" }

or

{ "status": "error", "reason": "<string>" }

User example message: • "My name is Alice." Expected model behavior when following prompt: 1. Output the user-facing greeting text: "Thank you Alice." 2. Emit a function/tool call to

save_name

with

{"name":"Alice"}

immediately (in the same response cycle). 3. After the tool completes, do not prompt the user — turn returns to the user. If you want the result announced, instruct the model to say the save result after the tool returns. Notes and reliability tips • Make the instruction deterministic and explicit (show exact greeting format and exact tool argument). • Include the tool schema so the model knows the function name and argument structure. • Some runtimes separate textual reply and function calls; ensure your runtime supports generating a text reply plus a function call in the same output. If not, you must programmatically generate the reply then call the tool immediately in your handler. • If you want filler speech while saving, instruct the model to say a short interim phrase (e.g., "Saving now...") before the tool call. • If you need absolute reliability, combine the prompt instruction with minimal orchestration code that: 1) asks the model to produce the greeting and a structured instruction to call

save_name

, and 2) invokes the tool according to that structured output. If you’d like, I can draft the exact system/user prompt JSON for your runtime (OpenAI function-style or another agent framework). Which runtime are you using? , with interactive elements

but is it possible in livekit to work this way, if i only provide instruction in prompt

Acknowledging: you want to know if LiveKit will reliably both speak a greeting and call

save_name

in the same turn when you only supply a prompt (no extra code/orchestration). Short answer: Not reliably. A prompt can instruct the model to both speak and call a tool, but LiveKit’s runtime requires the tool to be invoked as part of the agent’s handling cycle (ReAct). Simply providing a prompt alone does not guarantee the runtime will execute the tool call unless the agent is configured with that tool schema and the runtime supports function/tool calls from the LLM response. What is required for reliability • Register the tool (name/signature) with the agent so the model knows how to call

save_name

. • Ensure the runtime supports model-initiated function/tool calls in the same output (some runtimes separate text replies and function calls). • The sequence must be triggered by a user input or an external programmatic trigger so the agent is in an active ReAct cycle. • If you need guaranteed behavior, add minimal orchestration: parse the model’s structured output (or function call), invoke

save_name

programmatically, and then optionally let the agent continue. If you want, I can: • Draft the exact system + user prompt format plus a tool schema for LiveKit-compatible function calls, or • Provide a short code snippet showing minimal orchestration (Python/Node) that ensures the greeting is spoken and

save_name

is executed in the same flow. Which would you prefer? , with interactive elements