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Mnemosyne
An open-source memory engine born from Hermes. Sub-ms recall
Mnemosyne is a hermes-first, native, sub-millisecond memory system for AI agents using SQLite. No HTTP, no servers, no API keys. 500x faster than cloud alternatives. Open source.
Mnemosyne, the Titaness of Memory, mother of the nine Muses. Every poet and hero answered to her first.
Now she's a memory engine for your agents.
Mnemosyne started as the memory layer for Hermes, the open-source agent framework from Nous Research. Hermes agents needed somewhere to store conversations, preferences, long-running context. The existing options were either SaaS APIs or Docker+Postgres pipelines with a dozen moving parts.
So we built our own. Then open-sourced it.
What it does:
- 65.2% on BEAM at 100K scale — ICLR 2026 benchmark. We iterated from 35.4% in v2.5 to 65.2% in v3 through architecture work. Not done yet. - Three-tier memory architecture — working memory, episodic summaries, semantic knowledge graph. All in one portable database. - Hybrid search — vector + FTS5 + importance + temporal scoring. No separate vector DB. - Built-in MCP server — plug into Cursor, Claude Code, Codex, Windsurf, OpenWebUI in 30 seconds. - 23 Hermes plugin tools — lifecycle, validation, graph traversal, collaborative memory, export/import, diagnostics. - Local-first, deployable anywhere — laptop, homelab, server.
Built for Hermes, built to last:
Mnemosyne was forged for Hermes agents by Hermes. That's where the 23-tool plugin got battle-tested. But MCP + Python SDK means Cursor, Codex, or your own stack plugs in just as easily.
The community:
800+ GitHub stars. 17K+ PyPI downloads. 26 contributors. An active Discord where people ask questions, share integrations, and push the project forward.
Memory is the last unsolved primitive in agent infrastructure. We built Mnemosyne so you don't have to rebuild it yourself!
pip install mnemosyne-memory[all]
About Mnemosyne on Product Hunt
“An open-source memory engine born from Hermes. Sub-ms recall”
Mnemosyne was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #121 on the daily leaderboard. Mnemosyne is a hermes-first, native, sub-millisecond memory system for AI agents using SQLite. No HTTP, no servers, no API keys. 500x faster than cloud alternatives. Open source.
On the analytics side, Mnemosyne competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Mnemosyne performed against the three products that launched closest to it on the same day.
Who hunted Mnemosyne?
Mnemosyne was hunted by Abdias J. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Mnemosyne including community comment highlights and product details, visit the product overview.
Abdias J
Mnemosyne, the Titaness of Memory, mother of the nine Muses. Every poet and hero answered to her first.
Now she's a memory engine for your agents.
Mnemosyne started as the memory layer for Hermes, the open-source agent framework from Nous Research. Hermes agents needed somewhere to store conversations, preferences, long-running context. The existing options were either SaaS APIs or Docker+Postgres pipelines with a dozen moving parts.
So we built our own. Then open-sourced it.
What it does:
- 65.2% on BEAM at 100K scale — ICLR 2026 benchmark. We iterated from 35.4% in v2.5 to 65.2% in v3 through architecture work. Not done yet.
- Three-tier memory architecture — working memory, episodic summaries, semantic knowledge graph. All in one portable database.
- Hybrid search — vector + FTS5 + importance + temporal scoring. No separate vector DB.
- Built-in MCP server — plug into Cursor, Claude Code, Codex, Windsurf, OpenWebUI in 30 seconds.
- 23 Hermes plugin tools — lifecycle, validation, graph traversal, collaborative memory, export/import, diagnostics.
- Local-first, deployable anywhere — laptop, homelab, server.
Built for Hermes, built to last:
Mnemosyne was forged for Hermes agents by Hermes. That's where the 23-tool plugin got battle-tested. But MCP + Python SDK means Cursor, Codex, or your own stack plugs in just as easily.
The community:
800+ GitHub stars. 17K+ PyPI downloads. 26 contributors. An active Discord where people ask questions, share integrations, and push the project forward.
Memory is the last unsolved primitive in agent infrastructure. We built Mnemosyne so you don't have to rebuild it yourself!
pip install mnemosyne-memory[all]