Zero-dependency agent memory + MCP server. Value-ranked recall, consolidation, and a first-class correction & erasure channel (revert, lineage-aware retraction, tamper-evident receipts). Measured inte
A memory layer for AI agents — the one that already runs an autonomous research OS over ~6,000 notes.
Memory is the mother of the Muses. An agent with no memory has no ideas.
pip install agora-mnemo · PyPI · Hugging Face · DOI 10.5281/zenodo.21128549 · Homepage · MIT · v1.7.0
mnemo is the recall + consolidation core of Agora — an autonomous research system — distilled into a single file with no required dependencies. It does the four things agent memory actually needs, the way that held up running in production for weeks.
Most "agent memory" libraries are demos. This one is extracted from a system that has used it daily to curate a 6,000-note knowledge base, and whose consolidation behaviour we have measured, not assumed (see Provenance below).
That is the whole loop most agents need: remember, recall, correct, and audit — in one zero-dependency file. Add embed=yourmodel for semantic recall; everything below is depth (governance, poison-resistance, bitemporal, multi-tenancy) you can reach for when you need it.
Runnable examples live in examples/: basics · correction & erasure · semantic recall.
Jump to: Correction (measured) · Governance & erasure · Install · MCP server · The four operations · Five rules · Provenance & receipts · Threat model
Correction is a first-class operation (measured across systems)
Any memory layer can store a fact and retrieve it. The harder, less-benchmarked property is integrity: when a fact is corrected, can the store undo the correction on command, and does restating a retired value resurrect it? mnemo treats correction as a first-class
From the project README.
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