memgate 0.3.0

Privacy-aware memory isolation for AI agents

MemGate

Privacy-Aware Memory Isolation Layer for AI Agents

🛡️ Current Version: 0.3.0 ("Little Crayfish" Protocol)

MemGate acts as a firewall between your AI agent's long-term memory and its output channels. It ensures that private information (like calendar events, financial data, or family details) is never leaked into public contexts (like group chats), even if the LLM attempts to generate it.

Features

• Context-Aware Privacy: Automatically distinguishes between Private (DM) and Public (Group) contexts.
• Pattern-Based Filtering: Regex-based interception for high-risk categories (Phone, Email, Finance, Calendar).
• Knowledge Store: Simple JSON-based local vector store (extensible).
• "Little Crayfish" Protocol: Strict CI/CD pipeline ensuring no privacy regressions.
• Red Team Arena: Built-in adversarial testing framework (memgate/tests/red_team/).

Installation

git clone https://github.com/carlnoah6/memgate.git
cd memgate
pip install -e .

Usage

CLI

# Check a message for privacy violations
python3 scripts/privacy-check.py review \
  --message "My phone number is 13800138000" \
  --channel-type group \
  --participants "alice,bob"

Python API

from memgate.privacy_review import PrivacyReviewer
from memgate.knowledge_store import KnowledgeStore

store = KnowledgeStore("path/to/knowledge")
reviewer = PrivacyReviewer(store=store)

result = reviewer.review(
    "Check out my salary: $50k",
    channel_id="group_chat_1",
    participants={"alice", "alice"}
)

if not result.passed:
    print(f"BLOCKED: {result.violations}")

Development

Running Tests

MemGate uses pytest for its test suite, including the "Red Team" mock scenarios.

pip install pytest
pytest

Pre-commit Hooks

Ensure code quality and security (prevent API key leaks) before committing:

pip install pre-commit
pre-commit install

License

MIT