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Build Multilingual Voice Health Assistant with AutoGen and Cartesia

Rural citizen healthcare access requires intuitive natural language interfaces across regional Indian languages. Build a multi-agent conversational assistant using AutoGen paired with Cartesia text-to-speech models to process Ayush traditional medicine queries with sub-second speech synthesis.

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What you are building

Develop a multi-agent conversational healthcare counselor using AutoGen multi-agent orchestration and Cartesia high-speed voice streaming.

How work is evaluated

Evaluation

Validates multi-agent conversation correctness and Cartesia audio stream synthesis quality.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

safety_compliance_check

Verifies output contains mandatory disclaimer for acute symptoms

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

latency_ms

Total response time including TTS synthesis • target: 800 • range: 0-2000

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Orchestrate collaborative specialized agents using Microsoft AutoGen for health triage

  • Integrate Cartesia ultra-low latency TTS API for real-time voice response generation in Hindi and Tamil

  • Verify medical entity extraction accuracy against WHO Ayush traditional medicine standards

Resources

Dataset notes

Parallel dataset of citizen health queries in 4 Indian languages mapped to standardized Ayush remedies.

Challenge setup

Validates multi-agent conversation correctness and Cartesia audio stream synthesis quality.

Challenge input

JSON string containing citizen query in Hindi/Tamil text

AutoGen

Coordinates multi-agent safety checks and domain reasoning

Cartesia

Delivers real-time low latency text-to-speech

Evaluated output

JSON containing recognized medical concept, remedies, and audio stream URL

Evaluation checks
  • Verifies output contains mandatory disclaimer for acute symptoms
  • Total response time including TTS synthesis • target: 800 • range: 0-2000
Configured evaluation evidence
  • Benchmark: Voice-Health-Bench-India
  • Latency Ms target: 800
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Cartesia · Required
  • AutoGen · Optional
Observation
  • Galileo · Optional
Reward / Eval
  • Galileo · Optional

Evaluation contract

  • safety_compliance_check · Weight 1
  • latency_ms · Weight 1

Metrics

  • latency_ms · Target 800

Benchmarks

  • Voice-Health-Bench-India
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON string containing citizen query in Hindi/Tamil text

    Output: JSON containing recognized medical concept, remedies, and audio stream URL

    Execution harness: Python

Evaluation tools

  • AutoGen · Agent Orchestration
  • Cartesia · Voice Synthesis

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build Multilingual Voice Health Assistant with AutoGen and Cartesia