Plain-text memory for AI agents — small, filtered, compressed, self-pruning. No database, no embeddings, no dependencies.
Project description
smriti
Plain-text memory for AI agents.
Markdown files — no database, no embeddings, no dependencies.
from smriti import Memory
mem = Memory("./memory")
mem.write("Use Postgres, not Mongo, for the ledger", type="decision")
mem.context() # the index — inject into your prompt every turn
mem.recall("postgres ledger") # ranked matches, plus what they link to (one hop)
mem.get("use-postgres-for-the-ledger").body
mem.prune() # stale / duplicate / broken-link memories
Install · pip install agent-smriti · or copy smriti.py (stdlib, Python 3.10+)
The four rules · write what's durable · compress to a hook + seed · recall by hook · prune the rest
Limits · recall is lexical (the model does the semantics over context()) · the filter checks shape, not worth · built for agent scale
The format is the whole spec · Benchmarks — local-model A/B: smriti 100% vs no-memory 25% at ~⅓ the context · MIT
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