from synapse_layer import SynapseMemory

memory = SynapseMemory(agent_id="agent-1")

await memory.store(
    content="User prefers secure systems",
    confidence=0.95
)

results = await memory.recall("user preferences")

# Each result includes a Trust Quotient (TQ) — a deterministic score based on:
# - Recency (how current the info is)
# - Frequency (how often it was reinforced)
# - Source Authority (how reliable the input source is)

That's the entire API. Encryption, PII redaction, differential privacy, intent validation, and trust scoring — all happen under the hood. No configuration. No boilerplate. No amnesia.

🧠 Why Synapse Layer?

AI agents are stateless by design.

They forget everything between sessions, lose context when switching models, and continuously reprocess the same information — increasing cost, latency, and inconsistency.

This is the missing layer in modern AI systems.

Synapse Layer introduces persistent, encrypted, cross-model memory with deterministic recall.

Not as a feature — but as infrastructure.

🎯 Who Is This For

• AI agent builders — give your agents persistent context across sessions
• LLM infrastructure teams — reduce token waste, increase reasoning consistency
• Production AI systems — deterministic recall with zero hallucination risk
• Privacy-first organizations — built-in LGPD/GDPR compliance with zero-knowledge architecture

Before vs After

🏢 Real-World Impact

Used in production

Synapse Layer powers real systems in production:

• 📉 Up to 70% reduction in token usage via persistent context recall
• 🔁 Cross-session and cross-model memory continuity
• 🔐 Built-in privacy and compliance (LGPD/GDPR ready)
• 🧠 Zero context loss across 10K+ agent sessions

"Synapse Layer transformed our agents from stochastic parrots into reliable professional partners by giving them an immutable expert memory." — Ismael Marchi, Founder @ [GoArqIA](https://goarqi

... View full README on GitHub