limbiq 0.1.1

Neurotransmitter-inspired adaptive learning layer for LLMs

Limbiq

Neurotransmitter-inspired adaptive learning layer for LLMs.

Limbiq makes any LLM appear to learn and adapt across conversations — without touching a single weight. It sits between the user and the LLM, modifying what the model sees through five discrete signal types inspired by human brain chemistry.

User → Limbiq → Modified Context → Any LLM → Response → Limbiq observes → Loop

Installation

pip install limbiq

Quick Start

from limbiq import Limbiq

# Initialize
lq = Limbiq(
    store_path="./neuro_data",
    user_id="dimuthu",
)

# Before sending to LLM — get enriched context
result = lq.process(
    message="What's my wife's name?",
    conversation_history=[
        {"role": "user", "content": "Hi there"},
        {"role": "assistant", "content": "Hello! How can I help?"},
    ],
)

# Inject result.context into your system prompt
messages = [
    {"role": "system", "content": f"You are a helpful assistant.\n\n{result.context}"},
    {"role": "user", "content": "What's my wife's name?"},
]
response = my_llm(messages)  # Any LLM

# After getting response — let Limbiq observe and learn
lq.observe("What's my wife's name?", response)

# End session — triggers memory compression
lq.end_session()

Signals (v0.1)

Dopamine — "This matters, remember it"

Fires when the user shares personal info, corrects the model, or gives positive feedback. Tagged memories are always included in context.

lq.dopamine("User's wife is named Prabhashi")

GABA — "Suppress this, let it fade"

Fires when memories are denied, contradicted, or go stale. Suppression is soft — memories can be restored.

lq.gaba(memory_id="abc123")
lq.restore_memory("abc123")  # Undo suppression

Corrections

Combines both signals — stores new info as priority, suppresses the old.

lq.correct("User works at Bitsmedia, not Google")

Inspection

lq.get_stats()              # Memory counts per tier
lq.get_signal_log()         # Full history of signals fired
lq.get_priority_memories()  # All dopamine-tagged memories
lq.get_suppressed()         # All GABA-suppressed memories
lq.export_state()           # Full JSON export for debugging

How It Works

• LLM-agnostic — works with any LLM (OpenAI, Anthropic, Ollama, llama.cpp, etc.)
• Zero weight modification — all adaptation through context manipulation
• SQLite persistence — memories survive across sessions
• Semantic search — uses sentence-transformers for embedding-based retrieval (falls back to TF-IDF if not installed)
• Transparent — every signal is logged with trigger, timestamp, and effect
• Reversible — suppressed memories can be restored, nothing is permanently destructive

License

MIT