Introduction

Mnemo is an MCP-native memory database for AI agents. It provides persistent, structured memory with semantic search, access control, hash-chain verification, and multi-agent collaboration primitives.

Key Features

• 21 MCP Tools: core memory ops (remember, recall, forget, forget_subject, share, consolidate), git-like state (checkpoint, branch, merge, replay), delegation & verification (delegate, verify, trajectory_audit), attention state, agent-controlled mem_*, and plan memory — see the tools reference
• Hybrid Retrieval: Vector similarity (USearch/pgvector) + BM25 full-text (Tantivy) + recency + graph signals fused via Reciprocal Rank Fusion
• Access Control: Owner-based permissions, ACL sharing, transitive delegation with time bounds
• Integrity Verification: SHA-256 hash chains over memory records with tamper detection
• Git-like State Management: Checkpoint, branch, merge, and replay agent memory states
• Cognitive Forgetting: Ebbinghaus decay curves, consolidation, archival strategies
• Memory Poisoning Detection: Anomaly scoring with automatic quarantine
• Multiple Backends: DuckDB (embedded) or PostgreSQL (distributed)
• REST API: Full HTTP API alongside MCP stdio transport
• SDKs: Python (with LangGraph, CrewAI, OpenAI Agents integrations), TypeScript, Go

Use Cases

• Agent Memory: Give LLM agents persistent memory across conversations
• Multi-Agent Collaboration: Share memories between agents with fine-grained permissions
• Audit Trails: Immutable event logs with hash-chain integrity verification
• Knowledge Management: Store, retrieve, and organize agent-generated knowledge

Architecture

Mnemo is built in Rust for performance and safety. The workspace contains: