Can you help me with writing agents. 1. which tak...
# ask-aiw
witty-yak-62929
07/24/2025, 1:18 PMCan you help me with writing agents. 1. which takes the user message and translates to English 2. another agent to say it in other language
t
tall-airline-16756
07/24/2025, 1:18 PMThanks for reaching out, Chiranjeevi. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
tall-airline-16756
07/24/2025, 1:19 PMTo create agents for translating user messages to English and then to another language, here are some approaches you can use:
1. Using LLM-based Translation with Two-Step Process:
• Create an agent that translates the user message to English first and then to the target language.
• Example:
Copy code
from livekit.agents import Agent
class TwoStepTranslator(Agent):
def __init__(self, target_language: str = "French"):
super().__init__(
instructions=f"""
You are a two-step translator. For each user message:
1. Translate the user's speech to English if it's not already in English.
2. Translate the English version to {target_language}.
Format your response as:
English: [English translation]
{target_language}: [{target_language} translation]
""",
stt=deepgram.STT(),
llm=openai.LLM(model="gpt-4o"),
tts=elevenlabs.TTS(model="eleven_multilingual_v2"),
vad=silero.VAD.load()
) Copy code
class SequentialTranslator(Agent):
async def translate_text(self, text: str, target_lang: str):
# Use Google Translate API for translation
passw
witty-yak-62929
07/24/2025, 1:22 PMHow are you handling handoff between these 2 agents?
t
tall-airline-16756
07/24/2025, 1:23 PM1. Tool-Based Handoff: Hey there! Let me walk you through how we can smoothly pass translation tasks between agents in LiveKit. First up, we've got a super handy tool-based handoff method. - Imagine you want to switch agents using a tool call - it's as easy as returning an
Agent Copy code
from livekit.agents import Agent, function_tool
class TranslationCoordinatorAgent(Agent):
def __init__(self):
super().__init__(
instructions="You coordinate translation requests and hand off to appropriate translation agents."
)
@function_tool()
async def handoff_to_translator(self, target_language: str):
"""Hand off to a specialized translation agent for the target language."""
translator_agent = SpecializedTranslatorAgent(target_language)
return translator_agent Copy code
class SpecializedTranslatorAgent(Agent):
def __init__(self, target_language: str, chat_ctx: Optional[ChatContext] = None):
super().__init__(
instructions=f"You are a specialized translator for {target_language}.",
chat_ctx=chat_ctx # Preserve conversation history
)update_agent Copy code
session.update_agent(SpecializedTranslatorAgent(target_language)) Copy code
@function_tool()
async def handoff_to_translator(self, target_language: str):
translator_agent = SpecializedTranslatorAgent(target_language)
return translator_agent, f"Switching to {target_language} translation specialist."AgentHandoffEventw
witty-yak-62929
07/24/2025, 1:37 PMWhat is the best option from these 4, when to use which way of hand off?
t
tall-airline-16756
07/24/2025, 1:38 PMHey there! Let's break down agent handoff methods in a way that's super clear and easy to understand. 1. Tool-Based Handoff (LLM-Driven): - Think of this like having a smart assistant who knows exactly when to pass the conversation baton. - Perfect for: - Getting consent smoothly at the start of a call - Collecting info like addresses or payment details - Guiding you through step-by-step processes - Check out this cool example:
Copy code
@function_tool()
async def on_consent_given(self):
return HelpfulAssistant() Copy code
session.update_agent(HelpfulAssistant())w
witty-yak-62929
07/24/2025, 1:40 PMCan you give one real world example for the direct agent update?
t
tall-airline-16756
07/24/2025, 1:41 PMDynamic Tool Updates During Runtime: - Hey there! Let me walk you through a cool way of updating an agent's tools on the fly in LiveKit. Imagine you've got a voice agent that can adapt its skills in real-time – pretty neat, right?
Copy code
async def entrypoint(ctx: JobContext):
# Create agent with initial tools
agent = MyAgent(
instructions="You are a helpful assistant that can answer questions and help with tasks.",
tools=[
function_tool(
_get_course_info,
name="get_course_info",
description="Get information about a course",
)
],
)
# Direct agent update: Adding new tools after creation
async def _random_number() -> int:
num = random.randint(0, 100)
<http://logger.info|logger.info>(f"random_number called: {num}")
return num
await agent.update_tools(
agent.tools
+ [function_tool(_random_number, name="random_number", description="Get a random number")]
)w
witty-yak-62929
07/24/2025, 1:44 PMYou are answering about dynamic tool, I am asking about example for dynamic agent update
t
tall-airline-16756
07/24/2025, 1:45 PMDynamic Agent Update for Real-Time Configuration: - Hey there! Let me walk you through a super cool way to update an agent's settings on the fly during a live session. It's like giving your digital buddy a quick wardrobe change mid-conversation!
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@ctx.room.local_participant.register_rpc_method("pg.updateConfig")
async def update_config(data: rtc.rpc.RpcInvocationData):
new_config = parse_session_config(json.loads(data.payload))
if config != new_config:
session = model.sessions[0]
session.session_update(
instructions=new_config.instructions,
voice=new_config.voice,
temperature=new_config.temperature,
max_response_output_tokens=new_config.max_response_output_tokens,
turn_detection=new_config.turn_detection,
modalities=new_config.modalities,
)
return json.dumps({"changed": True})2 Views