# neural-maze/realtime-phone-agents-course

Build realtime AI voice agents using FastRTC for low-latency streaming, Superlinked for vector search, Twilio for live phone calls, and Runpod for scalable GPU deployment.

Repository: https://github.com/neural-maze/realtime-phone-agents-course
Canonical: https://ross.abutalabs.com/products/realtime-phone-agents-course
Language: Python
License: MIT
License Family: permissive
Last push: 2026-01-10T17:45:50+00:00

## Health v2 (maintenance only)
Score: 53/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 61, release rhythm 61, longevity 21
- inputs: {"age_days": 295, "days_push": 235, "days_rel": 262, "gap_med": 8.0, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1085, forks 248 (observed 2026-08-28T04:03:32.030236+00:00)

## What it is
A hands-on course repository teaching how to build realtime AI voice agents that handle live phone calls. It covers FastRTC for low-latency audio streaming, Superlinked for vector search, Twilio telephony, speech-to-text and text-to-speech models, and GPU deployment on Runpod.

## Use cases
- build an AI agent that answers inbound phone calls
- make outbound calls with a voice agent
- stream realtime audio conversations with low latency
- transcribe speech instantly for a voice bot
- generate lifelike speech with open-source TTS models
- search property data with vector search for a voice agent
- deploy open-source voice models on a GPU cloud

## When to choose
- you want a structured, project-based course on realtime voice agents
- you need to integrate Twilio phone calls with AI agents
- you want to learn the full STT/TTS/LLM voice pipeline end to end

## When to avoid
- you need a production-ready call center product rather than learning material
- you want a plug-and-play voice agent library instead of a course
- you are not working in Python

## Facets
- artifact type: learning-resource
- maturity: active
- function: speech-recognition, tts, rag, agent-framework, llm-inference, gpu-computing
- domain: artificial-intelligence, speech-processing, tutorials
- platform: python, cloud
- tags: voice-agents, phone-calls, twilio, fastrtc, course, realtime-audio, vector-search, runpod, ai-agents, retrieval-augmented-generation, gpu

## Member repositories
- neural-maze/realtime-phone-agents-course (main) score 53

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:32.030236+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:49:52.466013+00:00, confidence not recorded.
  - readme: https://github.com/neural-maze/realtime-phone-agents-course (fetched 2026-08-28T04:03:32.030236+00:00, sha 0e335b6714c2)
- Data as of 2026-08-30T08:39:29.467469+00:00.