LedgerMind
True zero-touch autonomous memory for AI agents
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for LedgerMind, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.
Overview
LedgerMind is a next-generation autonomous memory core for AI agents that achieves genuine zero-touch operation. Using client-side hooks, it automatically: • Searches and injects the most relevant memories before every user prompt • Logs all agent actions including tool calls, file reads, script executions, document analysis and their results • Performs self-healing every 5 minutes • Maintains a full immutable Git-based audit trail • Resolves memory conflicts autonomously It combines hybrid SQLite + vector storage with powerful reasoning capabilities. Currently 100% stable and working out-of-the-box with Gemini CLI. Support for Claude Desktop and Cursor is in active development.
Key features
- True zero-touch automation via client-side hooks
- Automatic memory search + context injection before every prompt
- Automatic logging of all agent actions and tool results
- Self-healing memory every 5 minutes
- Full Git-based immutable audit trail
- Autonomous conflict resolution
Use cases
- Long-term autonomous AI agents that need reliable persistent memory
- Complex multi-step research, coding and data analysis agents
- Production-grade agentic workflows requiring consistent context
- Agents in Gemini CLI, Claude Desktop and Cursor
- Multi-agent systems with shared reliable memory