Can we create a custom voicepipeline agent using a custom agent of langchain createReactAgent

[beingPro007]

Your question

I wanted to create a langchain agent by breaking the llm part of openAI.llm() to a custom reactAgent but there ain't no class/methods anything available to do it..So how to integrate the createReactAgent in voicepipeline agent flow customisably...i know this may be silly question so if possible can anyone help me please...

[CainKilled]

Not a silly question at all — what you’re trying to do is an advanced customization of LangChain’s agent framework. Let me walk you through how to integrate a createReactAgent into a voice pipeline and decouple the default OpenAI LLM while maintaining control over the flow.

⸻

🧠 Understanding the Structure

LangChain provides:
• AgentExecutor: orchestrates the agent execution
• createReactAgent: builds a Reasoning + Action (ReAct) based agent using a set of tools and an LLM
• By default, OpenAI() or similar LLM wrappers are used directly inside createReactAgent, but you want to decouple this

⸻

✅ Steps to Customize the Agent

1. Define Your Custom LLM (Optional but Recommended)

from langchain_core.language_models.llms import BaseLLM

class MyVoiceLLM(BaseLLM):
def _call(self, prompt: str, **kwargs) -> str:
# Connect this to your voice pipeline or speech-to-text NLP
return my_custom_voice_agent_inference(prompt)

@property
def _llm_type(self):
    return "custom-voice-llm"

You can replace my_custom_voice_agent_inference() with your voice-NLP model.

⸻

2. Create Your Tools

Define tools as normal. Each tool must follow the Tool interface with name, description, and func.

from langchain.agents import Tool

def weather_tool(query: str) -> str:
return f"Fetching weather for {query}"

tools = [
Tool(
name="Weather",
func=weather_tool,
description="Gets weather info"
)
]

⸻

3. Create Your Custom ReAct Agent

from langchain.agents import create_react_agent
from langchain.agents import AgentExecutor

Use your custom LLM instead of OpenAI

llm = MyVoiceLLM()

This builds the ReAct-style agent from prompt templates

agent = create_react_agent(llm=llm, tools=tools)

Now use AgentExecutor to run the agent

agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

⸻

4. Run the Agent in Your Voice Pipeline

You now have a complete agent that you can run via audio input:

response = agent_executor.run("What is the weather in Tokyo?")
print(response)

Or, if you want to stream and integrate with a speech interface:

while True:
voice_input = get_transcribed_audio() # Your STT system
print("🗣️ You said:", voice_input)

response = agent_executor.run(voice_input)
speak(response)  # Your TTS system

⸻

🔥 Advanced Flow (VoicePipeline Integration)

If you have a pipeline class like VoicePipelineAgent you want to plug this into, then:

class VoicePipelineAgent:
def init(self, agent_executor):
self.agent_executor = agent_executor

def respond_to_voice(self, voice_text):
    return self.agent_executor.run(voice_text)

And then:

voice_agent = VoicePipelineAgent(agent_executor)
output = voice_agent.respond_to_voice("Turn on the lights")

⸻

🧩 Key Tips
• create_react_agent() builds a chain — you don’t need to subclass anything, just inject your own llm
• You can swap the llm with any subclass of BaseLLM or ChatModel
• You can customize the prompt template if needed using create_react_agent(..., prompt=your_prompt)

⸻

✅ Summary

Goal Method
Replace OpenAI LLM Subclass BaseLLM or use ChatOpenAI config
Use voice pipeline LLM Connect your STT/NLP in _call()
Customize ReAct agent Use create_react_agent() + tools
Wrap in pipeline class Create a class to call agent_executor.run()