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Persistent memory + emergent identity engine for any LLM

Project description

kore-mind

Persistent memory + emergent identity engine for any LLM.

One file = one mind. SQLite-based. Zero config. Runtime-agnostic.

Install

pip install kore-mind

Usage

from kore_mind import Mind

mind = Mind("agent.db")

# Register experiences
mind.experience("User works on complexity theory proofs")
mind.experience("User prefers direct, concise answers")

# Recall relevant memories
memories = mind.recall("proof techniques")

# Reflect: decay old memories, consolidate, update identity
identity = mind.reflect()
print(identity.summary)

# Forget: explicit pruning
mind.forget(threshold=0.1)

Core concepts

  • Memory has a lifecycle: salience decays over time. Unused memories fade. Accessed memories strengthen.
  • Identity is emergent: not configured, but computed from accumulated memories.
  • reflect() is the key operation: decay + consolidation + identity update.

API (5 methods)

Method Description
experience(text) Something happened. Record it.
recall(query) What's relevant now?
reflect(fn) Consolidate. Decay. Evolve.
identity() Who am I now?
forget(threshold) Explicit pruning.

License

MIT

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