TEN-framework / TEN-framework/ten-framework

[FEATURE] [OceanbaseDeveloperChallenge] a Memory-Enabled Voice AI UseCase with TEN + PowerMem

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Python
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Description

Description

Using TEN Framework together with PowerMem, we invite you to build a practical voice AI use case in a specific real-world scenario, where long-term or contextual memory actually matters.

Reference:

https://github.com/TEN-framework/ten-framework/tree/main/ai_agents/agents/examples/voice-assistant-with-PowerMem

What to contribute

  • A concrete voice AI usecase (not just a demo)
  • Memory should play a clear role (e.g. user preferences, conversation history, context accumulation)

Example scenarios (not limited to):

  • Language tutor that remembers learning progress
  • Emotional companion or journaling assistant

You can help developers with

  • Clear example code or runnable workflow
  • Brief explanation of the use case and memory design
  • A screenshot or short demo showing the use case works
Severity

Critical

Additional Information

No response

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the referenced example at ai_agents/agents/examples/voice-assistant-with-PowerMem and understand how TEN Framework and PowerMem are combined. Choose a concrete real-world voice AI use case where long-term or contextual memory matters, then provide runnable example code or a workflow, a brief explanation of the memory design, and a screenshot or short demo showing it works.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
ai, audio-video-rtc
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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