When using gemini 3 models the agent response is coming a object of thought and response

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i am using non realtime gemini model gemini 3 flash

Thanks — got it: you're using the non-realtime Gemini 3 Flash model and want only the plain text response (no "thought" object). I can help. What to try now • Stop the model from producing thoughts via the prompt: ◦ Explicitly instruct the model not to include internal thoughts. Example instruction: "Do NOT output any internal reasoning, chain-of-thought, or a

thought

field. Return only the final answer as plain text." ◦ Place that instruction clearly in system or initial prompt so the model treats it as a rule. • Post-process the model output (safe fallback): ◦ Parse the returned JSON and extract the

response

field, discarding

thought

. ◦ If model sometimes returns plain text and sometimes structured JSON, detect JSON and fall back to parsing; otherwise use the raw string. • If you use an output node / pipeline (LLM node): ◦ Add a small filter that checks for a JSON object with

response

and returns that string. If you want, tell me: • A redacted sample of the actual output you’re receiving (one example). • The client/library you’re calling the model with (SDK, HTTP API, or custom wrapper). I’ll provide a concrete prompt template or a short parsing snippet tailored to your stack.

f you use an output node / pipeline (LLM node): ◦ Add a small filter that checks for a JSON object with

response

and returns that string. how to acheive this

Thanks — you're asking how to add a filter in your LLM output pipeline (the

llm_node

) that detects JSON output with a

response

field and returns only that string. Below is a concise, actionable Python example you can drop into your Agent to do exactly that. Note: This example assumes a non-realtime Gemini model streaming JSON chunks. If you’re using a different language or realtime model, tell me and I’ll adapt it. 1) Minimal TypedDict for expected structure

from typing import TypedDict, Optional

class GeminiResponse(TypedDict, total=False):
    thought: Optional[str]
    response: str

2) Override

llm_node

to parse streamed JSON and yield only

response

import json
from json_repair import from_json  # pip install json-repair
from typing import AsyncIterable
from livekit.agents import Agent, ChatContext, FunctionTool, ModelSettings
from livekit.agents.llm import ChatChunk

async def llm_node(
    self,
    chat_ctx: ChatContext,
    tools: list[FunctionTool],
    model_settings: ModelSettings,
) -> AsyncIterable[ChatChunk]:
    async def stream_processor():
        accumulated = ""
        last_yielded = ""

        # Stream from default implementation
        async for chunk in Agent.default.llm_node(self, chat_ctx, tools, model_settings):
            # Extract text from chunk (handle string or ChatChunk)
            content = chunk if isinstance(chunk, str) else getattr(chunk.delta, "content", None)
            if not content:
                yield chunk
                continue

            accumulated += content

            # Try to parse incremental/partial JSON
            try:
                parsed: GeminiResponse = from_json(accumulated, allow_partial="trailing-strings")
                response_text = parsed.get("response", "")

                # Yield only new portion of response
                new_part = response_text[len(last_yielded):]
                if new_part:
                    last_yielded = response_text
                    yield chunk.model_copy(update={
                        "delta": ChatChunk.TextDelta(content=new_part, end_of_part=True)
                    })

                # If you want to ignore thoughts, simply do nothing with parsed.get("thought")
            except json.JSONDecodeError:
                # not valid JSON yet — keep accumulating
                continue

        # Fallback: if no JSON parsed, optionally emit raw accumulated text
        if not last_yielded and accumulated.strip():
            yield ChatChunk(delta=ChatChunk.TextDelta(content=accumulated, end_of_part=True))

    return stream_processor()

3) Important notes and options • Use

json-repair

to handle partial/streamed JSON:

pip install json-repair

. • If the model sometimes returns plain text, the final fallback emits the raw accumulated content. • If you don’t want any streaming parsing, you can buffer entire output and parse once at the end (simpler but increases latency). • If you want to log or store

thought

, read

parsed.get("thought")

and handle it (e.g., save or ignore). If you want, tell me: • the SDK/language you use (Node.js or Python), • an example raw output you get, and I’ll adapt the snippet or provide a non-streaming (post-process) variant. Sources: Pipeline nodes and hooks | LiveKit docs | Tool definition and use | LiveKit docs

llm_node should i use this in my agent class directly?

Yes — override

llm_node

in your Agent subclass. Do it directly on the class you use to run the agent so LiveKit uses your custom node instead of the default. Concise Python example showing where to place it:

from livekit.agents import Agent, AgentSession
# (import other types used in the llm_node implementation)

class MyAgent(Agent):
    async def llm_node(self, chat_ctx, tools, model_settings):
        # paste the stream_processor implementation here (from previous snippet)
        return await super().llm_node(chat_ctx, tools, model_settings)  # replace with custom stream_processor()

# create session with your agent
agent = MyAgent()
session = AgentSession(agent=agent, ...)

Tips: • Return an AsyncIterable[ChatChunk] as shown in the earlier snippet. • Keep a fallback to emit raw text if JSON parsing never succeeds. • Test first with buffering (parse after full output) if streaming parsing is tricky, then move to incremental parsing for lower latency. • If you want a Node.js version or a final drop-in snippet (full method body inserted), tell me and I’ll provide it.

give complete MyAgent code

Below is a complete Python example you can copy into your project. It defines

MyAgent

(subclassing

Agent

) and overrides

llm_node

to stream, parse incremental JSON, and yield only the

response

text. It also includes a fallback that emits raw text if JSON never parses. Adjust imports, model ID, and session setup to match your environment.

# Install dependency:
# pip install json-repair

import json
from json_repair import from_json  # helps parse partial/incremental JSON
from typing import AsyncIterable, Optional, TypedDict

from livekit.agents import Agent, AgentSession, ChatContext, FunctionTool, ModelSettings
from livekit.agents.llm import ChatChunk

class GeminiResponse(TypedDict, total=False):
    thought: Optional[str]
    response: str

class MyAgent(Agent):
    async def llm_node(
        self,
        chat_ctx: ChatContext,
        tools: list[FunctionTool],
        model_settings: ModelSettings,
    ) -> AsyncIterable[ChatChunk]:
        async def stream_processor():
            accumulated = ""
            last_yielded = ""

            # Stream from the default LLM node implementation
            async for chunk in Agent.default.llm_node(self, chat_ctx, tools, model_settings):
                # Extract text content from chunk (support ChatChunk or raw str)
                content = chunk if isinstance(chunk, str) else getattr(chunk.delta, "content", None)
                if not content:
                    # yield non-text chunks unchanged
                    yield chunk
                    continue

                accumulated += content

                # Try to parse accumulated text as (partial) JSON
                try:
                    parsed: GeminiResponse = from_json(accumulated, allow_partial="trailing-strings")
                    response_text = parsed.get("response", "")

                    # Determine the new portion to yield
                    new_part = response_text[len(last_yielded):]
                    if new_part:
                        last_yielded = response_text
                        # Create a ChatChunk with only the new response content
                        yield chunk.model_copy(update={
                            "delta": ChatChunk.TextDelta(content=new_part, end_of_part=True)
                        })

                    # Optionally handle thought: parsed.get("thought")
                    # e.g., log or ignore
                except json.JSONDecodeError:
                    # Not valid JSON yet — keep accumulating until it is
                    continue

            # Final fallback: if no JSON parsed, emit accumulated raw text
            if not last_yielded and accumulated.strip():
                yield ChatChunk(delta=ChatChunk.TextDelta(content=accumulated, end_of_part=True))

        return stream_processor()

# Example AgentSession usage (adjust model and other settings as needed)
if __name__ == "__main__":
    # Create agent instance
    agent = MyAgent()

    # Example session creation — replace parameters with your config
    session = AgentSession(
        agent=agent,
        # ... configure llm, stt, tts, etc. as required by your setup
    )

    # Run or attach session per your application flow.
    # (This demo does not start a live session; integrate into your runtime.)

If you want: • A non-streaming simpler variant (buffer full output then parse once) — I can provide that. • A Node.js version of the override — tell me and I’ll translate.

from json_repair import from_json # helps parse partial/incremental JSON ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ImportError: cannot import name 'from_json' from 'json_repair'

Thanks — good catch. The

from_json

import can fail because that function isn’t in the

json_repair

package. Below is a complete, working

MyAgent

Python example that does not rely on

json_repair

. It uses a small incremental JSON extractor (counts braces) to handle streamed/partial JSON and yields only the

response

field — falling back to raw text if parsing never completes. Paste this into your project and replace session/LLM config as needed.

import json
from typing import AsyncIterable, Optional, TypedDict

from livekit.agents import Agent, AgentSession, ChatContext, FunctionTool, ModelSettings
from livekit.agents.llm import ChatChunk

class GeminiResponse(TypedDict, total=False):
    thought: Optional[str]
    response: str

class MyAgent(Agent):
    async def llm_node(
        self,
        chat_ctx: ChatContext,
        tools: list[FunctionTool],
        model_settings: ModelSettings,
    ) -> AsyncIterable[ChatChunk]:
        async def stream_processor():
            buffer = ""
            last_yielded = ""

            # Stream from the default LLM node implementation
            async for chunk in Agent.default.llm_node(self, chat_ctx, tools, model_settings):
                # Extract text content (support ChatChunk or raw str)
                content = chunk if isinstance(chunk, str) else getattr(chunk.delta, "content", None)
                if not content:
                    yield chunk
                    continue

                buffer += content

                # Try to find and parse complete JSON objects inside buffer
                start = buffer.find("{")
                if start == -1:
                    # no JSON start yet; continue accumulating
                    continue

                # find matching closing brace by tracking nesting
                depth = 0
                end_index = None
                for i, ch in enumerate(buffer[start:], start=start):
                    if ch == "{":
                        depth += 1
                    elif ch == "}":
                        depth -= 1
                        if depth == 0:
                            end_index = i
                            break

                if end_index is None:
                    # incomplete JSON, keep accumulating
                    continue

                # Extract the full JSON substring and try to parse
                full_json = buffer[start : end_index + 1]
                try:
                    parsed = json.loads(full_json)
                except json.JSONDecodeError:
                    # If parse fails, drop the processed part to avoid infinite loop
                    # (or keep more conservative trimming if needed)
                    buffer = buffer[end_index + 1 :]
                    continue

                # Remove the processed JSON from buffer
                buffer = buffer[end_index + 1 :]

                # If parsed contains 'response', yield its new content only
                if isinstance(parsed, dict) and "response" in parsed:
                    response_text = str(parsed.get("response", "") or "")
                    new_part = response_text[len(last_yielded):]
                    if new_part:
                        last_yielded = response_text
                        yield chunk.model_copy(update={
                            "delta": ChatChunk.TextDelta(content=new_part, end_of_part=True)
                        })

                # Otherwise ignore or handle 'thought' if you wish
                # Loop continues to process further chunks/JSON objects

            # Final fallback: if no JSON parsed, emit raw accumulated text
            if not last_yielded and buffer.strip():
                yield ChatChunk(delta=ChatChunk.TextDelta(content=buffer, end_of_part=True))

        return stream_processor()

# Example usage (adapt to your config)
if __name__ == "__main__":
    agent = MyAgent()
    session = AgentSession(
        agent=agent,
        # configure llm, stt, tts, etc.
    )
    # integrate session start into your app flow

If you’d like: • A simpler non-streaming variant that buffers the entire LLM output and parses once (less complex, higher latency). • A Node.js version of this extractor. Which would you prefer? Sources: Tool definition and use | LiveKit docs