DanceNitra/inspeximus
by Various
The self-correcting memory layer for AI agents. Zero-dependency Python memory and MCP server. Supersede, revert, or forget a value deterministically. Verifiable erasure, witness-ba
MCP
DanceNitra/inspeximus
Added 13 Sept 2026
Overview
A self-correcting memory layer for AI agents, implemented as a zero-dependency Python library and MCP server. It allows agents to deterministically supersede, revert, or forget stored values, with verifiable erasure and witness-backed tamper-evident receipts. The design targets EU AI Act readiness.
Best for
Best for
Developers building compliant, auditable memory systems for AI agents
Use cases
- Track and revert state changes made by an AI agent
- Provide tamper-evident audit logs for agent memory operations
- Implement compliant data erasure for AI systems
How to use
Install
pip install inspeximusClients listed in this record (not verified here)
Claude Code, Cursor, Windsurf, Codex, Cline
Notes
6 stars on GitHub. Last updated 2026-09-13. Licensed MIT.
Pros
- Zero dependencies, easy to integrate into Python projects
- Deterministic operations for reliable state management
- Built-in audit receipts support compliance requirements
Cons
- Very low adoption with 6 GitHub stars, so maturity and community support are unproven
- Niche scope as a memory layer, requiring MCP or Python integration to be useful
- Limited documentation and usage examples due to low project visibility
Open-source & AI alternatives
Swap-in tools that solve the same job. Weigh the trade-offs before you commit.
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