Leilani – SIP client that streams call audio to the OpenAI Realtime API

Hacker News

We built a SIP user agent that registers to any PBX just like a soft-phone and streams audio to OpenAI’s real-time API. No SIP trunking, call forwarding or unintegrated voice systems. Leilani behaves like a normal extension… because it’s just a normal SIP extension. How it works - Implements bog standard SIP over TCP to connect just like a normal desk phone or spftphone. - Streams RTP (mu-law) bidirectionally to OpenAI’s realtime API - Handles function calls for external actions (webhooks) Use cases - After-hours auto-attendant - Voicemail/intent capture with structured output - Internal system lookup (CRM, scheduling, ticket creation, etc.) via function calls - Replaces IVR’s with natural conversation Why? Most AI voice systems expect you to hand over call routing, use their SIP trunk, or are an entirely separate voice stack all together. Every company (nearly) already has a SIP PBX, so we thought operating as a normal SIP extension was the simplest integration point. Tech Stack - The backend is built in asynchronous Rust. - We connect to the realtime API using websockets rather than SIP trunking or WebRTC - Hosted on a simple AWS EC2 instance Limitations / gotchas - Currently only supports SIP over TCP, we have TLS support coming soon - There are some NAT traversal assumptions (we behave like a softphone) - Latency depends on PBX and model RTT and audio frame sizes (currently seeing ~300ms across most deployments) - You still need your own OpenAI key. Could be a positive or negative, depends how you look at it :) Link https://leilani.dev

Visit product View on Hacker NewsOct 30, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, user · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon, calls · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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