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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