by quantifylabs · MCP Server · ★ 27
Your agent's context is your attack surface. Act accordingly. Secure context engineering for production AI agents. Content security. Integrity verification. Trust hierarchy. Context that improves itself. <img src="https://api.scorecard.dev/projects/github.com/quantifylabs/
| Stars | 27 |
| Forks | 6 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 68.8749044088395/100 |
| Open Issues | 12 |
| Last Updated | 2026-07-05 |
| Created | 2025-12-25 |
| Platforms | mcp, python |
| Est. Tokens | ~18k |
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aegis-memory is Secure context engineering for AI agents. Content security · integrity verification · trust hierarchy · ACE patterns. Self-hosted, Apache 2.0.. It is categorized as a MCP Server with 27 GitHub stars.
aegis-memory is primarily written in Python. It covers topics such as agent-security, agents, ai.
You can find installation instructions and usage details in the aegis-memory GitHub repository at github.com/quantifylabs/aegis-memory. The project has 27 stars and 6 forks, indicating an active community.
aegis-memory is released under the Apache-2.0 license, making it free to use and modify according to the license terms.