Mnemosyne

Mnemosyne is a local-first memory system for AI agents that uses SQLite to store and retrieve data without network calls or cloud dependencies.

Mnemosyne serves as a persistent, local-first memory layer for AI agents, replacing cloud-based memory services. Built on SQLite, it stores data directly on the user's machine to eliminate network overhead and API dependencies. The system utilizes a Beam architecture consisting of working memory for hot context, episodic memory for long-term storage, and a scratchpad for agent reasoning. It features native vector search via sqlite-vec and full-text search capabilities to facilitate accurate information retrieval. The software operates as a Python library and is designed for individual agents and single-machine deployments.

Mnemosyne at a glance

Best for: Software engineer
Runs on: CLI, Python
Pricing: Open source; free to use
Released: April 2026
Category: AI agent tool
Website: mnemosyne.site

Key features

Sub-millisecond query latency via local SQLite access
Hybrid search combining vector similarity and full-text search
Beam architecture with working, episodic, and scratchpad memory
Automatic memory consolidation into episodic storage
Streaming updates via DeltaSync
Smart filtering to block irrelevant content
One-pip installation with zero external dependencies

Use it for

Persistent memory for individual AI agents
Reducing memory latency in local LLM applications
Storing agent reasoning workspaces
Managing local-first knowledge graphs

Alternatives to Mnemosyne

MemPalace — Achieves high recall with local-first storage and zero API calls.
LongMemory — Provides immutable, temporal, and governed recall instead of simple vector retrieval.
Supermemory — Integrates long-term memory into AI coding agents with a single prompt.
AgentMemory — It runs locally with zero external databases and achieves high retrieval recall.
Tigerless Agent Memory — Maintains agent history in portable, greppable Markdown files without API keys.
Mnemolith — It keeps AI memory accurate by retiring stale facts automatically when files change.
TenseMem — Keeps one current value per slot and refuses to learn from its own output.
claude-mem — It shares persistent, searchable agent memory across sessions and devices.
Memvid — Single-file architecture replaces complex RAG pipelines with portable storage.
ReMe — It enables local-first, file-native long-term memory sharing across multiple AI agents.
EverOS — It enables agents to learn from experience and improves over time without manual coding.
Mem0 — It provides drop-in persistent memory for AI agents without changing pipelines.

All Mnemosyne alternatives