Building Kairos AI: My 10-Day Journey Building a Voice Coding Mentor with Murf Falcon & LiveKit

🔗 Project Repository
The complete source code for Kairos AI is available on GitHub:
https://github.com/AMLAN-07/murf-livekit-starter
Meet Kairos AI
Kairos AI handling a voice conversation with the user.

Specialist Agent Handoff
Kairos handing a DSA problem to the dedicated DSA Specialist Agent.

Call Analytics Dashboard
Kairos AI call analytics showing call outcomes.

LiveKit signal connection
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Timeout
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Tested network connectivity
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IPv6 connection failed
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IPv4 connection succeeded
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Adjusted network configuration
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LiveKit worked

Challenges Along the Way

"The project definitely didn't work perfectly on the first attempt."
LiveKit timeout → IPv6 investigation

Building Kairos was not always smooth. I faced issues with LiveKit connection timeouts, networking, agent handoffs, and tool integration. Debugging these problems helped me understand that building a voice agent requires not only good code, but also careful testing of real-time communication and system configuration.

What Didn't Go as Planned

Kairos was not built in one smooth run.

I had issues with LiveKit connectivity, IPv6 networking, agent handoffs, tool execution, and outbound calling. Some features worked in isolated tests but behaved differently when connected to the complete voice workflow.

Those problems were actually some of the most useful parts of the challenge. They taught me to debug the entire system instead of assuming every problem was caused by the Python code.

Building a voice agent is as much about debugging real-time systems as it is about writing prompts and integrating AI models.

1. The Problem I Wanted to Solve I wanted Kairos to provide a more natural interface: Talk → Think → Discuss → Practice
2. Meet Kairos AI Kairos can also communicate in English, Hindi, and Hinglish, allowing users to interact in a more natural way.
3. Murf Falcon — Giving Kairos a Voice tts=murf.TTS( voice="Samar", locale="en-IN", style="Conversational", tokenizer=tokenize.basic.SentenceTokenizer( min_sentence_len=2 ), text_pacing=True, )
4. Human Help / Escalation An AI mentor shouldn't pretend it can solve every situation.
5. Specialist Agent Handoff Instead of making Kairos an expert at everything, I created a separate specialist whose only responsibility is DSA.
6. Outbound Calling I also added an outbound-call capability.
7. Final Architecture After 10 days, Kairos evolved into something much more interesting than the simple voice agent I started with. ┌─────────────────┐ │ User │ └────────┬────────┘ │ Voice / Phone │ ▼ ┌─────────────────┐ │ LiveKit │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Kairos AI │ │ Main Agent │ └───────┬─────────┘ │ ┌──────────────────┼──────────────────┐ │ │ │ ▼ ▼ ▼ Memory Tools DSA Specialist │ │ │ │ ┌───────┴──────┐ │ │ │ │ │ │ Codeforces Human Help │ │ │ │ │ └──────────┴───────┬──────┘ │ │ │ ▼ │ Analytics / Calls ◄────────┘ │ ▼ Murf Falcon TTS │ ▼ 🔊 User Conclusion

When I started this challenge, the goal was simply to build a voice agent.
Ten days later, I had built Kairos AI, a voice coding mentor with:

🎙️ Real-time voice interaction
🇮🇳 Indian English voice
🧠 Persistent memory
💻 Coding-problem tools
🛡️ Guardrails
🧑‍💻 Human escalation
📞 Outbound calling
📊 Call analytics
🤝 Specialist-agent handoffs
🌐 Multilingual interaction

The biggest takeaway for me is that voice AI is not just about making an LLM speak.
It's about designing an entire interaction system around the user.

And that's what made these 10 days so valuable.