MemTensor

MemTensor / MemPrivacy

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MemPrivacy is a privacy-preserving personalized memory management framework for edge-cloud agents.

agent agent-memory ai edge-cloud information-extraction
29
0
100% credibility
Found May 12, 2026 at 29 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

MemPrivacy is a privacy framework for AI agents that detects sensitive information in user messages, replaces it with semantic placeholders locally before cloud transmission, stores mappings securely on-device, and restores originals in responses.

How It Works

1
📰 Discover MemPrivacy

You hear about MemPrivacy, a clever tool that lets AI assistants remember your chats while keeping your personal details safe from the cloud.

2
📥 Bring it to your device

You easily add the tool to your computer, ready to shield your conversations.

3
🛡️ Choose your protections

You pick what to hide, like names, health info, or secret codes, setting simple rules for safety.

4
💬 Chat with peace of mind

You start talking to your AI helper, and it quietly swaps private bits with harmless stand-ins before sharing.

5
☁️ Cloud works its magic

The cloud AI uses the stand-ins to learn and reply smartly, without ever seeing your real secrets.

6
🔄 See your real replies

Responses come back perfectly restored with your original details, feeling just like a normal chat.

🎉 Safe, smart conversations forever

You now have personalized AI chats that remember everything important, all while your privacy stays locked away locally.

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AI-Generated Review

What is MemPrivacy?

MemPrivacy is a Python framework for privacy-preserving personalized memory management in edge-cloud agents. It detects sensitive spans like PII, health data, or credentials in user inputs locally, replaces them with typed placeholders (e.g., ), and stores reversible mappings in a local SQLite DB—ensuring raw data never reaches cloud memory systems. On response, it restores originals locally for seamless personalization without utility loss.

Why is it gaining traction?

Unlike crude masking that mangles semantics and retrieval, MemPrivacy uses typed placeholders to keep context intact, with benchmarks showing just 0.71-1.60% accuracy drops in systems like Mem0 or LangMem. Developers dig the configurable PL1-PL4 policies for fine-tuned control and drop-in integration via simple APIs like mask_dialogue/unmask_dialogue. Open fine-tuned models on HuggingFace make local detection fast and accurate.

Who should use this?

Agent builders handling user convos with sensitive data—think health apps personalizing via LLMs or finance bots with long-term memory. Edge-cloud teams evaluating Mem0/LangMem integrations before production, or researchers benchmarking privacy in multi-turn dialogues.

Verdict

Grab it for prototyping privacy-safe agents; evals and YAML configs make setup painless. But with 29 stars and 1.0% credibility, it's an early research tool—mature it with your own tests before scaling.

(178 words)

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