How do I convert a GitHub repo to a text file?

Paste the repository URL above and the file tree appears as a checkbox list. Untick what the model does not need, generate, and you get one text file: directory structure at the top, then each selected file under a header with its path. Nothing to clone, nothing to install, free and no sign-up.

How do I convert a private GitHub repository?

Paste a personal access token alongside the URL. Create a fine-grained token scoped to just that one repository with read-only Contents permission and a short expiry — that is the least privilege the API will accept for this. The token is used to call GitHub from your browser and is not stored on our side.

How many tokens is my codebase, and will it fit?

The counter updates as you tick files, so you can see the number before you paste anything. As a rough guide, source code runs around 3 to 4 characters per token, so a 500 KB selection lands near 140,000 tokens. That fits a 200K context window and will not fit into a single chat message on most interfaces.

Which files should I exclude before feeding a repo to an LLM?

node_modules, vendor, and any dependency directory first — they are usually the overwhelming majority of the bytes and contribute nothing about your code. Then lockfiles, build output, minified bundles, test fixtures, and binary assets. On a typical JavaScript project that pass alone often removes over ninety percent of the tokens.

How does this compare to gitingest, repomix, or code2prompt?

Those are strong tools and the honest difference is workflow, not capability. They are command-line first, scriptable, and the right answer inside CI or a batch job. This runs in a browser tab with a checkbox tree, so you can see the repo, deselect what you do not want, and watch the token count move — better for the one-off case where you are still deciding what to include.

Is it safe to put my code through this?

Public repositories are read through the GitHub API from your browser, so the code goes from GitHub to you. For anything confidential the stronger option is the local folder converter, which never uploads at all. Either way, scan your selection for API keys and credentials before pasting it into a model — that is the step people skip.

LiveKit Voice Agents with Urdu TTS: Bridging Language Barriers

LiveKit for Voice Agents: An Effortless Approach with Python and Urdu TTS Customization

Introduction: The Rise of Real-Time Voice Agents

Imagine talking to your software applications as naturally as you would with a friend. Voice-based conversational agents are becoming increasingly popular – from virtual assistants and customer support bots to real-time language translators. Developers and businesses are seeking ways to build these real-time voice agent systems that can listen, understand, and respond instantly. The challenge is achieving low-latency, high-quality audio streaming and integrating speech AI (speech-to-text, language understanding, and text-to-speech) in a seamless way.

Luckily, modern platforms make this easier. LiveKit is one such open-source solution that simplifies streaming audio (and video) with minimal delay, enabling interactive voice experiences at scale. In this blog post, we'll dive into how LiveKit empowers developers to build voice agents effortlessly using Python. We'll explore LiveKit’s features (like its ultra-low latency WebRTC engine and scalable SFU architecture), show how to integrate it with Python, and demonstrate customizing your voice agent to speak Urdu using an Urdu Text-to-Speech (TTS) plugin called Uplift.

Whether you want to create a voice-enabled chatbot, a real-time translation service, or an AI assistant that can converse in Urdu, this guide will help you get started in a developer-friendly way. We'll also touch on useful tools (like converting a code repository to text for AI with repo2txt) and how companies such as Its IT Group can assist with full-stack development, DevOps, and AI/ML integration for these solutions.

Let's dive in and see how LiveKit, Python, and a bit of customization can bring voice agents to life!

What is LiveKit and Why Use It for Voice Agents?

LiveKit is a cutting-edge, open-source platform for real-time audio and video communication. Think of it as a building block for live voice/video features – it provides the heavy lifting of streaming media between users (or between a user and an AI agent) with sub-100ms latency, which is practically instantaneous. LiveKit's technology is built on WebRTC and a server component using SFU architecture, optimized for routing media efficiently to many participants.

Key reasons LiveKit stands out for voice AI applications:

Ultra-Low Latency Audio/Video: LiveKit ensures that audio streams with minimal lag. In a voice agent scenario, low latency is crucial so the AI can listen and respond in real-time. LiveKit’s use of WebRTC and an SFU (Selective Forwarding Unit) server means audio travels from user to agent (and back) very quickly, even handling the ~300ms round-trip needed for smooth conversations.
Scalable SFU Architecture: Instead of a peer-to-peer mesh or a heavy mixing server, LiveKit uses an SFU server that efficiently forwards audio/video streams to all participants who need them. This means it can scale from 1:1 conversations to large group sessions without bogging down clients or sacrificing performance. The SFU approach optimizes bandwidth and keeps the system flexible – each audio track remains separate, allowing fine-grained control (like adjusting a specific stream’s volume or processing it individually).
WebRTC Support and Cross-Platform SDKs: LiveKit leverages WebRTC, which is supported in browsers and native platforms, so your voice agent can interface with web apps, mobile apps, or other clients easily. SDKs exist for many languages and platforms (JavaScript/TypeScript, Swift, Android, React, etc.), and importantly for us – Python. This cross-platform nature means you could have a user on a mobile app talking to an AI agent running in a Python server, all connected via LiveKit.
High Quality and Adaptive Streaming: LiveKit provides high-quality audio (and video) with adaptive streaming. It can adjust quality based on network conditions automatically. For voice agents, this means even on spotty networks the audio tries to remain clear, and if bandwidth improves, the quality ramps up.
Open-Source and Self-Hostable: Being open-source, developers have full control. You can run LiveKit on your own servers or use the managed LiveKit Cloud. It’s customizable and you’re not locked into proprietary limitations. This is great for tweaking it to specific use cases (and also cost-effective since there are no license fees).
Additional Features (Recording, Data, Security): LiveKit supports features like recording sessions (useful if you want to save conversations), data channels for sending real-time text or signals, and robust security (encryption, access tokens for authentication, etc.). For enterprise or production use, these features ensure your voice agent system is secure and can be audited or integrated with other systems.

In short, LiveKit provides the real-time streaming backbone needed for voice agents, so you don't have to build low-level WebRTC or networking code. Instead, you can focus on the fun part – the AI logic that makes your agent listen and talk. And since LiveKit is developer-focused, it even offers higher-level frameworks (like the Agents SDK) to simplify common patterns for voice AI.

Fun fact: LiveKit is battle-tested – it’s known to power voice features in high-profile applications (rumor has it that OpenAI’s ChatGPT voice mode uses LiveKit under the hood to stream audio between users and the AI!). This gives confidence that it can handle demanding real-time AI conversations.

Integrating LiveKit with Python for Real-Time Voice AI

One of the best parts about LiveKit for AI developers is its Python integration. Python is the lingua franca of machine learning and AI, so being able to control LiveKit from Python means you can seamlessly connect your speech recognition, language model, and speech synthesis in one workflow. LiveKit provides a Python SDK and an Agents Framework that let you connect to a LiveKit server (cloud or self-hosted) and build a voice pipeline with just a few lines of code.

Let’s break down how you would set up a basic voice agent in Python using LiveKit:

1. Installation of LiveKit SDK and Agents – First, install the necessary packages. You’d typically install the core LiveKit Python SDK and the agents framework, plus any plugins for the AI services you plan to use (speech-to-text, TTS, etc.). For example:

  pip install livekit livekit-agents livekit-plugins-openai

In this command:

livekit is the core SDK to interface with LiveKit servers (managing rooms, participants, tracks, etc.).
livekit-agents is a high-level framework that provides classes like VoicePipelineAgent to orchestrate audio AI pipelines easily.
livekit-plugins-openai might be used for OpenAI integration (e.g. using Whisper for STT or GPT-4 for generating responses, and maybe text-to-speech if using OpenAI’s voice).

We will also later install the Uplift plugin for Urdu TTS, but let's start with a simple agent pipeline.

2. Connecting to a LiveKit Room – A voice agent operates inside a LiveKit room (just like a participant in a call). You’ll need to have a LiveKit server running or an account in LiveKit Cloud, and create a room with an access token for your agent. For this example, assume you have environment variables LIVEKIT_URL, LIVEKIT_API_KEY, and LIVEKIT_API_SECRET to create tokens. You can use the LiveKit server SDK (livekit-api package) to generate tokens, or use the CLI, but for brevity we'll assume you have a token ready.

Here's a snippet showing a Python agent connecting to a LiveKit room and preparing to handle audio:

  from livekit import Room, RTCConfiguration
  from livekit import connect  # method to connect to a LiveKit room
  
  # Configuration for LiveKit server
  LIVEKIT_URL = "ws://localhost:7880"  # e.g., your LiveKit server Websocket URL
  LIVEKIT_TOKEN = "<your_generated_access_token>"
  
  # Connect to the LiveKit room
  room: Room = connect(LIVEKIT_URL, LIVEKIT_TOKEN, rtc_config=RTCConfiguration())
  print("Agent connected to room:", room.name)

In this code:

We connect to a LiveKit server via WebSocket (on localhost in this case, port 7880 is the default for LiveKit server). LiveKit uses WebSocket signaling to set up peer connections.
connect returns a Room object that gives us access to the session. The agent will appear as a participant in this room.
Ensure that the token used has publish/subscribe permissions for audio if the agent will both listen and speak.

3. Setting Up the Voice Pipeline – Once connected, we want the agent to start listening for user audio, transcribe it, generate a response (via an LLM), and then speak it out. LiveKit Agents SDK provides VoicePipelineAgent which streamlines this setup. We just need to configure it with the building blocks:

Voice Activity Detection (VAD): To detect when the user stops talking (silence) so the agent knows when to respond. LiveKit offers a plugin for VAD (for example, using Silero VAD).
Speech-to-Text (STT): To transcribe user speech. You can use any STT service; OpenAI’s Whisper, Deepgram, Google Cloud STT, etc. (LiveKit has plugins for various providers).
LLM (Language Model): The brain of the agent that takes transcribed text and comes up with a response. This could be OpenAI GPT-4, a local Llama 2 model, etc., as long as you have an interface to call it. For our example, we'll assume an OpenAI LLM for simplicity.
Text-to-Speech (TTS): To convert the LLM’s response text back into spoken audio. This is where the Uplift Urdu TTS plugin will come in if we want the agent to speak Urdu. If not using Uplift, one could use English TTS from providers like ElevenLabs, Google, or even OpenAI’s new voice, but Uplift gives us a unique capability to speak fluent Urdu.

Let’s see how we put these together in code using LiveKit’s agent framework. Below is a code snippet that creates a voice agent with these components:

  import asyncio
  # Import LiveKit agent components and plugins
  from livekit_agents import VoicePipelineAgent, JobContext, AutoSubscribe
  # Import plugins (assuming installed via pip)
  import livekit_plugins_silero as silero   # Silero VAD plugin
  import livekit_plugins_openai as openai   # OpenAI STT/LLM plugin
  # (We'll import Uplift plugin later for the TTS part)
  
  async def run_voice_agent():
      # LiveKit job context (provided when running inside LiveKit Agents framework)
      ctx = JobContext()
      await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
      print("Connected to LiveKit and subscribed to audio stream.")
  
      # Prepare an initial system prompt for the LLM to establish voice persona or language
      initial_ctx = openai.ChatContext().append(
          role="system",
          text=(
              "You are a helpful AI voice assistant on a LiveKit call. "
              "Respond conversationally and briefly. "
              "Speak in English for now."  # We'll adjust this for Urdu later
          )
      )
  
      # Create the voice agent pipeline
      agent = VoicePipelineAgent(
          vad=silero.VAD.load(),            # Voice Activity Detection model
          stt=openai.STT(language="en"),    # Speech-to-Text (English for now)
          llm=openai.LLM(model="gpt-3.5-turbo"),  # Language model (OpenAI GPT)
          tts=openai.TTS(voice="Joanna"),   # Text-to-Speech (placeholder voice)
          chat_ctx=initial_ctx             # Initial context for the conversation
      )
  
      # Start the agent to begin listening and responding
      agent.start(ctx.room)
      print("Voice agent is now running and awaiting user speech...")
  
      # For demonstration, let's have the agent proactively say something at start:
      await agent.say("Hello! I am your voice assistant. How can I help you today?", allow_interruptions=True)
  
  # In an actual application, you'd run the above coroutine in an asyncio event loop.

Let’s unpack what’s happening in this example:

We used VoicePipelineAgent from livekit_agents to set up our voice AI pipeline. We provided it:
- A VAD from Silero (a pre-trained model to detect silence or speech segments).
- An STT from OpenAI with language="en" (this could utilize OpenAI’s Whisper model or the new OpenAI voice API to transcribe English speech).
- An LLM using OpenAI’s GPT model (we specify GPT-3.5 here for quick responses).
- A TTS using OpenAI’s TTS (if OpenAI provides one; for example, voice "Joanna" here is just a placeholder name referencing perhaps an AWS Polly or ElevenLabs voice – in practice, you would use whatever TTS engine is configured; OpenAI’s new voice API might have specific voice IDs).
- A chat context that starts with a system prompt instructing the assistant how to behave. We told it to be helpful and speak English in this initial setup.
We call agent.start(ctx.room) to attach the agent to the LiveKit room so it starts processing incoming audio. The agent will:
1. Listen on the room’s audio (incoming user speech).
2. Use VAD to detect when the user finishes a sentence (or pauses).
3. Once speech segment is captured, use STT to get text.
4. Feed the text to the LLM to generate a response.
5. Take the LLM’s text response and send it to TTS to get audio.
6. Play the audio back into the LiveKit room so the user hears the agent's response.
We also demonstrated await agent.say("Hello!...") which makes the agent speak an initial greeting proactively. We allowed interruptions, meaning the user could start talking and the agent would stop its speech if needed (LiveKit’s agent framework can handle barge-in/interrupts, which is important for natural conversations).

This high-level API means we did not have to manually handle audio buffers, WebRTC connections, or multi-threaded audio processing – LiveKit and the plugins handle all that. In a few lines, we integrated speech services and got a conversational loop running. Pretty effortless, right?

Now, the above example was configured for English. But what if we want our voice agent to converse in another language, say Urdu? Maybe our end-users are more comfortable in Urdu, or we’re building an agent for the Pakistani market. We’d need our STT to understand Urdu and our TTS to speak Urdu. STT for Urdu could be handled by some providers (OpenAI’s Whisper supports many languages including Urdu, if you specify the language code or use a multilingual model). The bigger challenge is finding a good Urdu TTS voice – this is where the Uplift plugin comes into play.

Urdu Text-to-Speech Customization with the Uplift Plugin

To give our voice agent a truly localized persona, we want it to respond in spoken Urdu. Uplift AI is a service specializing in high-quality Urdu speech synthesis. They have an AI model (codenamed Orator) that can generate extremely natural Urdu speech, far surpassing generic text-to-speech engines in terms of accent and clarity for Urdu language. The LiveKit Uplift plugin integrates Uplift’s Urdu TTS into the LiveKit Agents framework as a drop-in component.

What does the Uplift plugin do?
The Uplift plugin provides a TTS module that hooks into LiveKit. Under the hood, it calls Uplift AI’s API to synthesize audio from text. By installing the plugin, you gain a new TTS option (we used openai.TTS earlier; now we’ll use uplift.TTS). This allows your agent to speak Urdu with a lifelike voice.

How to get it: The plugin is available as a Python package livekit-plugins-uplift on PyPI, and its source is on GitHub for reference. You can install it with:

  pip install livekit-plugins-uplift

(Make sure you have your Uplift AI API key ready – you can get one by signing up on Uplift AI’s website. Set the key in your environment as UPLIFT_AI_API_KEY so the plugin can authenticate.)

Once installed, using the Uplift TTS in your Python code is straightforward. Let's modify our voice agent code to use Uplift for TTS and also switch the STT and prompts to Urdu:

  import livekit_plugins_uplift as uplift  # Import the Uplift plugin after installation
  
  async def run_voice_agent_urdu():
      ctx = JobContext()
      await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
    
      # System prompt forcing the assistant to respond in Urdu script
      initial_ctx = openai.ChatContext().append(
          role="system",
          text=(
              "آپ ایک ذہین اردو بولنے والے معاون ہیں۔ ہمیشہ اپنے جوابات اردو زبان اور نستعلیق اسکرپٹ میں دیں۔" 
              "اگر صارف انگریزی میں پوچھیں تب بھی آپ اردو میں (اصل اردو رسم الخط میں) جواب دیں گے۔"
          )
      )
    
      agent = VoicePipelineAgent(
          vad=silero.VAD.load(),
          stt=openai.STT(language="ur"),        # Speech-to-Text configured for Urdu language
          llm=openai.LLM(model="gpt-4"),        # Using a powerful LLM for better responses (if available)
          tts=uplift.TTS(voice="v_30s70t3a"),   # Uplift TTS with a specified Urdu voice
          chat_ctx=initial_ctx
      )
      agent.start(ctx.room)
      print("Urdu voice agent is running.")
      # Greet the user in Urdu:
      await agent.say("السلام علیکم، میں آپ کی کیسے مدد کر سکتا ہوں؟", allow_interruptions=True)

In this updated snippet:

We changed the system prompt to an Urdu message. It instructs the assistant that it is an intelligent Urdu-speaking assistant and must always respond in Urdu (Nastaliq script). We explicitly mention even if user asks in English, answer in Urdu script. This prompt engineering is important because the LLM (especially if it's an English-trained model like GPT-4) might default to English; we want to force it to output Urdu text. Uplift’s TTS requires the input text in Urdu script (not romanized Urdu). By guiding the LLM, we ensure the text it generates is in Urdu script so that TTS can pronounce it correctly.
We set the STT language to "ur" for Urdu. If using OpenAI Whisper via the OpenAI plugin, it will then transcribe Urdu spoken input into Urdu text. (Some STT services might return romanized text – Whisper typically returns the original script if language is specified as Urdu, which is what we need.)
We swapped the tts component to uplift.TTS(voice="v_30s70t3a"). The voice="v_30s70t3a" is an identifier for the specific Urdu voice model provided by Uplift (they might have multiple voices; this is an example ID). You can choose different voice profiles if Uplift offers them, but we'll stick with this one for now. Under the hood, this will send the text to Uplift's API and get back audio (probably in Opus or PCM format) which LiveKit will play to the user. The result is a very natural Urdu voice response.
Finally, we greet the user with an Urdu phrase: "Assalam-o-Alaikum, main aap ki kaise madad kar sakta hoon?" written in Urdu script "السلام علیکم، میں آپ کی کیسے مدد کر سکتا ہوں؟" which means "Peace be upon you, how can I assist you?". The agent will say this out loud in Urdu at the start of the call.

With these changes, we now have a fully Urdu-capable voice agent. The heavy lifting of TTS is handled by Uplift’s specialized service, but from the developer’s perspective, it was as easy as changing one line to use uplift.TTS. This demonstrates the power of LiveKit’s plugin architecture – you can customize the AI components of your voice agent by swapping plugins. Today it might be Urdu TTS; tomorrow you could plug in a different STT service, or a different LLM, etc., with minimal code changes.

A note on Uplift plugin usage: As mentioned, ensure your environment variable UPLIFT_AI_API_KEY is set (or you provide the API key through a config) before running the agent. The plugin will use that to authenticate with Uplift AI’s API when synthesizing speech. Also, always provide proper Urdu text to the TTS. If you accidentally send Roman Urdu (Urdu written with Latin characters) or English text to uplift.TTS, it may either fail or produce incorrect speech. So, controlling the LLM’s output language via the prompt (as shown above) is essential.

The Uplift plugin is relatively new and is part of the growing LiveKit ecosystem of plugins. You can find more details or updates on its PyPI page or the GitHub repository (open-source). These resources provide documentation on available voice IDs, any additional parameters, and example usage. By leveraging this plugin, developers can reach Urdu-speaking audiences with ease, creating voice agents that feel much more native and engaging to those users.

Enhancing Your Voice Agent with External Knowledge (Using repo2txt for LLM Context)

Our voice agent now has the ability to converse in real-time and even in multiple languages. The next consideration is: what knowledge does your agent have? Out-of-the-box, an LLM like GPT-4 has general knowledge, but you might want your agent to assist with specific domains – for example, answering questions about your company’s products, or even helping developers by answering questions about a codebase. To make the agent knowledgeable about a particular topic or data, you often need to supply that information to the model (either via fine-tuning or via context in prompts).

One practical way is to provide documentation or repository data as part of the prompt context. For instance, if building a voice assistant that helps developers, you might feed it information from a code repository so it can answer questions about the code. This is where repo2txt comes in handy.

repo2txt.com is a tool that converts an entire code repository into plain text. Essentially, it takes all the files (code, documentation, etc.) and produces a structured text output that can be indexed or fed into an LLM. Instead of manually copying code snippets, you can point repo2txt at a repository and get a single text file containing everything. This is extremely useful for Large Language Models (LLMs) that you want to query about the code, because you can then use that text as part of the model’s input (perhaps via prompt engineering or retrieval-based QA).

Some features of repo2txt that could be relevant:

It can work with GitHub URLs directly (crawling the repo content online) – this is referred to as the Crawl4AI functionality. The Crawl4AI tool will fetch the repository from GitHub (even large ones) and convert it to text in a format suitable for AI consumption.
It also supports local repositories or directories. For example, if you have a private repo or code on your machine, you can use the local repository to text converter on repo2txt to upload a zip or select a folder. It will then generate the text from that local source.
The output text is typically organized with file names and code blocks, so an LLM can be prompted effectively (for example: "In file utils.py line 50, what does the function process_data do?").

How does this tie back to our LiveKit voice agent? Suppose our voice agent is an AI assistant for developers (maybe integrated in a dev environment or a documentation site). We can enrich the agent’s knowledge by giving it context from a repository:

We run repo2txt on the relevant codebase (either manually beforehand, or even dynamically if needed) to get a text dump of the repository.
We use that text as part of the LLM’s context. Concretely, we might load portions of that text into the prompt or into a vector database for retrieval. When the user asks a question related to the code, the agent can retrieve the relevant snippet from the repo text and supply it to the LLM to ground the answer.
The agent can then speak the answer out with LiveKit as usual.

For example, you could ask the voice agent: "Can you explain what the function process_data in our repository does?" The agent’s system or retrieval logic would search the repo text, find the process_data function’s code, and then include it in the prompt to GPT-4. GPT-4 reads it and formulates an explanation, which the agent then speaks out. This way, the voice agent behaves like a knowledgeable assistant specific to your project.

Using repo2txt in this pipeline is an efficient way to convert a GitHub repo to text for LLM usage. It saves time and ensures you don’t miss any part of the codebase. The keywords "repo to text for LLM" or "github to txt" basically describe this scenario – turning a repository into a text format that AI can work with.

If you're interested in this approach, check out repo2txt’s documentation. They even have a feature called crawl4ai (as mentioned) which automates a lot of the heavy lifting for you when dealing with public GitHub repositories. For local code or private projects, the local converter is the way to go. With tools like repo2txt, integrating your domain knowledge (code, docs, etc.) into the voice agent becomes much easier.

Real-World Applications and Next Steps

By now, we have covered how LiveKit provides the real-time communications layer, how Python + LiveKit Agents framework simplifies building the voice pipeline, and how customizing with the Uplift plugin enables a specialized Urdu voice. We also discussed extending the agent’s knowledge via tools like repo2txt. Here are some real-world applications and final thoughts to illustrate the potential of this setup:

Multilingual Customer Support Bot: Imagine a customer support voice bot that can handle multiple languages. With LiveKit, a user can call in or use a web voice chat, speak in their language, and the AI responds in that language. For Urdu-speaking customers, the Uplift TTS plugin provides a warm, native accent response, improving user satisfaction. At the same time, the underlying LLM could be using company knowledge (fetched via something like repo2txt for product info or internal docs) to give accurate answers.
Developer Voice Assistant: As we hinted, developers could have an AI they can talk to while coding. "Explain this function to me" or "How do I use this API from our codebase?" – the voice agent can listen, quickly retrieve info from the repository text, and answer right away. This hands-free help can boost productivity. LiveKit ensures the audio interaction is smooth, and the Python agent logic can be deeply integrated with dev tools.
Interactive Language Learning App: One could create an app for learning Urdu (or any language) where the AI converses with the user. LiveKit handles the conversation streaming, the LLM handles dialogue, and Uplift’s Urdu TTS makes sure the pronunciation and tone are authentic. This provides an immersive learning experience.
Telephony Integration (Voice IVR): LiveKit also supports dialing in via telephone networks. This means your Python voice agent could even answer phone calls. You could set up an IVR (Interactive Voice Response) system that's far more advanced than the old touch-tone menus – a caller speaks in Urdu, the AI (running via LiveKit agent) understands and responds in Urdu. The plugin architecture would allow swapping to other languages for different callers as well.

To get started with your own LiveKit voice agent, you should:

Set up a LiveKit server or sign up for LiveKit Cloud and get the connection details.
Install the LiveKit Python SDK, Agents, and any plugins needed (as shown in code examples).
Get API keys for any external services (OpenAI, Deepgram, Uplift AI, etc.) and set them in your environment.
Write your agent code, similar to the examples, tailoring the prompt and plugin choices to your use case.
Run the agent and test it out! You can use a web client or LiveKit’s provided React components to create a simple front-end for testing the voice chat. LiveKit even has a playground and example apps to help you demo the functionality.

Finally, building a full-fledged voice AI system might require not just coding the agent, but also designing the user experience, handling scaling in production, and integrating with other systems. This is where professional services can help. Companies like Its IT Group specialize in full-stack development and have expertise in DevOps and AI/ML. They can assist in deploying your LiveKit servers, setting up continuous integration for your agent code, and fine-tuning the AI models for your business needs. Having the right infrastructure and support ensures your voice agent runs reliably for your users.

Conclusion

Real-time voice agents are no longer science fiction – with tools like LiveKit and powerful AI models, any developer can build an interactive voice assistant. In this article, we saw how LiveKit provides an effortless way to stream voice with minimal latency, and how its Python integration allows us to combine speech recognition, an LLM, and speech synthesis into a coherent agent. We also explored customizing the agent to speak a less-served language, Urdu, using the Uplift TTS plugin, showcasing the flexibility of LiveKit’s plugin system. By leveraging these technologies, one can create voicebots that simulate human-like conversation and cater to a global audience.

The combination of LiveKit’s real-time capabilities, Python's simplicity, and specialized plugins means you can focus on your agent’s unique logic and personality rather than the boilerplate of connectivity. Whether you aim to build the next multilingual customer support line, a voice-enabled coding assistant, or an AI tutor, the building blocks are at your fingertips.

We encourage you to experiment with the code snippets provided, check out the references for further details, and start building your own voice agent. The field is moving fast – integrating tools like repo2txt for knowledge and partnering with experts (like the team at Its IT Group) can accelerate your development. With a robust foundation in place, you’ll find that creating a sophisticated LiveKit voice agent (even one that speaks Urdu or any other language) is not only possible but straightforward.

Happy coding, and happy chatting with your new AI voice agent!