Agent work can lose useful context between sessions, while accumulated notes become hard to retrieve and maintain.
My own ongoing work with local-first agent tools.
A two-loop design: a per-turn path for retrieving and recording relevant memory, paired with scheduled maintenance for auditing and connecting stored knowledge.
Author of this adaptation; based on rand/mnemosyne and materially extended for Hermes and related agent workflows.
2026
Python, SQLite, Hermes.
Agent runtimes and model capabilities vary. Memory quality depends on the available context and the maintenance process; personal notes are not a public benchmark dataset.
The code and an explanation of the design are public. Recall quality, reliability gains, and adoption have not been measured or published.
No quantified recall or reliability improvement and no public adoption metrics. Personal memory data is not included as benchmark evidence.
Publish reproducible retrieval benchmarks using non-sensitive test data.