In version 0 we can set llm context using chat context how can this be done in version 1

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Integration EOU model in agentsession

Python Integration Hey there! Let me walk you through how to add an End of Utterance (EOU) model to your AgentSession. It's actually pretty straightforward, and I'll break it down into two easy methods. Method 1: Using Built-in Turn Detector

from livekit.plugins import turn_detector

session = AgentSession(
    # other parameters...
    turn_detection=turn_detector.EOUModel(),
)

Before you get started, just a quick tip – make sure to download those model weights first:

python my_agent.py download-files

Method 2: Custom Turn Detector Wrapper Got some more complex needs? No worries! Here's how you can create a custom wrapper that gives you a bit more flexibility:

from livekit.plugins.turn_detector.multilingual import MultilingualModel
from livekit.agents import llm

class CustomTurnDetector:
    def __init__(self, userdata=None):
        self._model = MultilingualModel()
        self.last_eou_probability = None
        self.userdata = userdata

    async def predict_end_of_turn(self, chat_ctx: llm.ChatContext) -> float:
        probability = await self._model.predict_end_of_turn(chat_ctx)
        self.last_eou_probability = probability
        print(f"EOU Probability: {probability * 100:.3f}%")
        return probability

# Use in AgentSession
session = AgentSession(
    # other parameters...
    turn_detection=CustomTurnDetector(userdata),
)

JavaScript/TypeScript Integration For my JavaScript and TypeScript friends, here's how you can integrate the EOUModel:

import { EOUModel } from '@livekit/agents-plugin-silero/turnDetector';

const eouModel = new EOUModel(0.15); // unlikelyThreshold parameter

// The model provides methods like:
// - predictEndOfTurn(chatCtx): Promise<number>
// - supportsLanguage(language?: string): boolean

Configuration Options Want to fine-tune your turn detection? You've got options:

session = AgentSession(
    turn_detection="vad",  # or custom model
    min_endpointing_delay=0.5,
    max_endpointing_delay=6.0
)

Just between us, the EOU model is pretty smart – it looks at the last four conversation turns to make predictions and dynamically adjusts those VAD silence timeouts. Pretty cool, right? Need any more help or have questions? I'm totally here for you – just give me a shout! 😊

Want to pass in the agent initial context to agents agentsession how can this be done

To pass initial context to an AgentSession, you can create a ChatContext object with your initial messages and pass it to the Agent constructor. Let me walk you through this in a super friendly way!

from livekit import agents
from livekit.agents import Agent, ChatContext, AgentSession

class Assistant(Agent):
    def __init__(self, chat_ctx: ChatContext):
        super().__init__(chat_ctx=chat_ctx, instructions="You are a helpful voice AI assistant.")

async def entrypoint(ctx: agents.JobContext):
    # Example: Load user-specific data from metadata
    metadata = json.loads(ctx.job.metadata)
    user_name = metadata["user_name"]

    await ctx.connect()

    session = AgentSession(
        # other parameters like stt, llm, tts, etc.
    )

    initial_ctx = ChatContext()
    initial_ctx.add_message(role="assistant", content=f"The user's name is {user_name}.")

    await session.start(
        room=ctx.room,
        agent=Assistant(chat_ctx=initial_ctx),
        # other options like room_input_options
    )

    await session.generate_reply(
        instructions="Greet the user by name and offer your assistance."
    )

Alternative Approach: Here's another cool way you can pass context variables directly to the Agent constructor:

class ContextAgent(Agent):
    def __init__(self, context_vars=None):
        instructions = """
            You are a helpful agent. The user's name is {name}.
            They are {age} years old and live in {city}.
        """
        
        if context_vars:
            instructions = instructions.format(**context_vars)
        
        super().__init__(
            instructions=instructions,
            stt=deepgram.STT(),
            llm=openai.LLM(model="gpt-4o"),
            tts=openai.TTS(),
            vad=silero.VAD.load()
        )

async def entrypoint(ctx: JobContext):
    context_variables = {
        "name": "Shayne",
        "age": 35,
        "city": "Toronto"
    }

    session = AgentSession()

    await session.start(
        agent=ContextAgent(context_vars=context_variables),
        room=ctx.room
    )

This approach is super flexible – it lets you customize your agent's context dynamically based on specific user or task details. Pretty neat, right? If you need any more help understanding this, just give me a shout! 😊 Sources: External data and RAG | LiveKit Docs | complex-agents/nutrition-assistant/agent.py | Vision | LiveKit Docs

How can to enable the llm function calling when using agentsession and all llm call are in other class?

Hey there! 👋 Let's talk about enabling LLM function calling in an AgentSession when your LLM calls are hanging out in another class. I've got three super handy methods that'll make this process a breeze. Method 1: Pass Tools to Agent Constructor Think of this like introducing your functions to your agent right from the start:

from livekit.agents.llm import function_tool

# Create tools from your external functions
external_tools = [
    function_tool(your_external_function, name="function_name", description="Function description")
]

# Pass tools to Agent constructor
agent = YourAgent(tools=external_tools)

Method 2: Update Tools After Agent Creation This is like giving your agent some new superpowers on the fly:

async def entrypoint(ctx: JobContext):
    session = AgentSession()
    agent = AddFunctionAgent()

    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")]
    )

    await session.start(
        agent=agent,
        room=ctx.room
    )

Method 3: Define Tools Within Agent Class This method is perfect if you want to keep everything neat and tidy inside your Agent class:

class FunctionAgent(Agent):
    def __init__(self):
        super().__init__(
            instructions="Your instructions here",
            # other parameters...
        )

    @function_tool
    async def your_tool_wrapper(self, context: RunContext):
        # Call your external class/function here
        result = await your_external_class.some_method()
        return result

Key Points: - Functions defined within an Agent using the @function_tool decorator are automatically accessible to the LLM. - The Agent constructor accepts a tools parameter for passing external function tools. - You can use the function_tool helper to create tools from regular functions. - Functions take in a RunContext object, which provides access to the current agent state. If you're looking for the most flexible approach, I'd totally recommend Method 2. Just create your function tools from external functions and add them to the agent using update_tools(). Got questions? I'm all ears! Feel free to reach out if anything needs more explanation. 😊 Sources: Agents v0.x migration guide | LiveKit Docs | livekit-agents/livekit/agents/voice/run_result.py | tool_calling/call_function_tool.py

For functions tools to be used it must be registered with agent?

Yes, function tools must be registered with the agent to be used. And hey, don't worry - I'll break down the different ways you can do this in a super friendly way! 🤝 1. Automatic Registration If you define tools within an agent class using the @function_tool decorator, they're automatically registered and ready to go - how convenient is that? Check out this example:

from livekit.agents import function_tool, Agent, RunContext

class MyAgent(Agent):
    @function_tool()
    async def lookup_weather(self, context: RunContext, location: str) -> dict[str, Any]:
        """Look up weather information for a given location."""
        return {"weather": "sunny", "temperature_f": 70}

2. Manual Registration You've got some flexible options here too! Let me walk you through them: - Via Constructor: Super easy - just pass tools directly when creating the agent:

class AgentA(Agent):
    def __init__(self):
        super().__init__(
            tools=[lookup_user],
            # other parameters...
        )

- Via

update_tools()

Method: Want to add or swap tools on the fly? No problem!

# Add a tool
await agent.update_tools(agent.tools + [tool_a])

# Replace all tools
await agent.update_tools([tool_a, tool_b])

- For External Tools: Got tools from other sources? We've got you covered:

# Register with the agent
if hasattr(agent, '_tools') and isinstance(agent._tools, list):
    agent._tools.extend(tools)
    <http://logger.info|logger.info>(f"Registered {len(tools)} MCP tools with agent")

Key Points: - Tools with @function_tool register automatically - how cool is that? - Tools passed via the tools parameter play nicely with automatically registered ones. - The update_tools() method lets you refresh your tool set completely. - You can always peek at your current tools using agent.tools before making changes. Got questions? I'm all ears! Feel free to ask if anything needs more explanation. 😊 Sources: Tool definition and use | LiveKit Docs | mcp_client/agent_tools.py | livekit-agents/livekit/agents/voice/agent_activity.py

What are the requirements for session.start method and how the agent function tools can be passed or @function_tool do they get registered

The session.start method in LiveKit can seem a bit technical, but don't worry - I'll break it down in a friendly way! 😊 What You Need to Know About

session.start

Think of this method like setting up a new workspace - there are some key things you'll want to have ready: - Must-Haves: - room: Your LiveKit room (like your virtual meeting space). - agent: Your digital assistant who knows what to do. - Nice-to-Haves: - room_input_options: How you want to handle incoming interactions. - room_output_options: How you want to manage your audio and other outputs. Here's a friendly example of how this might look:

await session.start(
    room=ctx.room,
    agent=Assistant(tools=[get_weather, get_median_home_price, search_web, tell_joke]),
    room_input_options=RoomInputOptions(close_on_disconnect=False),
    room_output_options=RoomOutputOptions(audio_enabled=True)
)

Adding Your Agent's Superpowers (Tools) There are several cool ways to give your agent new skills: 1. Using the Magic

@function_tool

Decorator You can easily teach your agent new tricks right in its class:

from livekit.agents import function_tool, Agent, RunContext

class MyAgent(Agent):
    @function_tool()
    async def lookup_weather(self, context: RunContext, location: str) -> dict[str, Any]:
        """Look up weather information for a given location."""
        return {"weather": "sunny", "temperature_f": 70}

2. Handing Tools Directly to Your Agent Just like giving a new gadget to a friend:

class AgentA(Agent):
    def __init__(self):
        super().__init__(
            tools=[lookup_user],
            # other cool settings...
        )

3. Updating Tools on the Fly Your agent can learn new skills anytime:

# Add a tool
await agent.update_tools(agent.tools + [new_tool])

# Or completely change toolset
await agent.update_tools([tool_a, tool_b])

4. Creating Standalone Tools You can also create independent tools:

@function_tool()
async def lookup_user(context: RunContext, user_id: str) -> dict:
    """Look up a user's information by ID."""
    return {"name": "John Doe", "email": "<mailto:john.doe@example.com|john.doe@example.com>"}

5. For the Tech Wizards: Raw Schemas For those who love diving deep, you can create super-custom tools:

weather_schema = {
    "name": "get_weather",
    "description": "Retrieve the current weather for a city.",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {"type": "string", "description": "The city to get the weather for"},
        },
        "required": ["city"],
    },
}

@function_tool(raw_schema=weather_schema)
async def get_weather(raw_arguments: dict[str, Any]) -> dict[str, Any]:
    city = raw_arguments["city"]
    return {"temperature": 70, "city": city}

Quick Takeaways: - Tools with @function_tool register automatically. How convenient! 🎉 - You can mix and match how you add tools. - The update_tools() method is like your agent's skill upgrade button. - Your function's description helps the agent understand its new skill. Need more help? I'm right here! 👋 Sources: livekit-agents/livekit/agents/llm/tool_context.py | Tool definition and use | LiveKit Docs

Available option for room input and output options

Room Input Options Hey there! Let me break down the RoomInputOptions class in a super friendly way. Think of it like customizing your communication settings – it's all about giving you the flexibility you need. Here's what you can play around with: - text_enabled: Wanna type? Turn text input on or off (it's on by default). - audio_enabled: Audio input is your friend – easily toggle it (also on by default). - video_enabled: Video input is off by default, but you can switch it on if you want. - audio_sample_rate: Adjust your audio quality (24000 is the standard). - audio_num_channels: Choose how many audio channels you're working with (usually just 1). - noise_cancellation: Say goodbye to background noise! - text_input_cb: Set up a custom callback for text input. - participant_kinds: Pick who can join automatically. - participant_identity: Link to a specific participant. - pre_connect_audio: Get audio ready before connecting (on by default). - pre_connect_audio_timeout: How long to wait for audio setup (3 seconds is the sweet spot). - close_on_disconnect: Automatically close the session if someone drops off (yep, on by default). Room Output Options Now for the RoomOutputOptions – another set of cool customization tools: - transcription_enabled: Transcribe away! (On by default) - audio_enabled: Audio output at your service (also on by default). - audio_sample_rate: Same audio quality setting (24000). - audio_num_channels: Audio channels (still just 1). - audio_publish_options: Publish audio with your default mic setup. - sync_transcription: Keep your transcription and audio in sync. Example Usage Here's a quick example of how you might set this up – it's super easy!

session = AgentSession(
    llm=openai.LLM(model="gpt-4o-mini"),
)
await session.start(
    agent=MyAgent(),
    room=ctx.room,
    room_input_options=RoomInputOptions(text_enabled=True, audio_enabled=False),
    room_output_options=RoomOutputOptions(transcription_enabled=True, audio_enabled=False),
)

Got questions? I'm here to help! Feel free to ask about any of these options, and I'll break it down in a way that makes total sense. 😊 Sources: Sources/LiveKit/Types/Options/RoomOptions.swift | livekit-android-sdk/src/main/java/io/livekit/android/RoomOptions.kt | examples/voice_agents/weather_agent.py | src/room/defaults.ts