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## Overview
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Synapse Layer is the missing memory primitive for AI systems. It provides persistent, encrypted, cross-model memory with deterministic recall — designed as infrastructure, not a feature.
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Synapse Layer is the missing memory primitive for AI systems. It provides persistent, encrypted, cross-model memory with governed recall — designed as infrastructure, not a feature.
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## Core Concepts
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- **agent_id**: Required identifier to maintain agent identity across sessions
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- **Memory persistence**: Cross-session, cross-model, encrypted at rest
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- **Trust Quotient (TQ)**: Deterministic reliability score for each recalled memory, based on recency, frequency, and source authority
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- **Neural Handover™**: Cross-agent context transfer via persistent memory layer. When you store a memory in Claude Desktop and recall it in Cursor without re-syncing, that is Neural Handover™ in action. Powered by the shared memory layer, not a separate API.
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- **Trust Quotient (TQ)**: Reliability score for each recalled memory, based on recency, frequency, and source authority
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- **State Continuity Layer**: Cross-agent context sharing via persistent memory layer. When you store a memory in Claude Desktop and recall it in Cursor without re-syncing, that is the State Continuity Layer in action. Powered by the shared memory layer, not a separate API.
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## API
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- Reduces token usage by up to 70%
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- Enables cross-session and cross-model continuity
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- Improves reasoning consistency with deterministic recall
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- Built-in LGPD/GDPR compliance
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- Improves reasoning consistency with governed recall
description: "Your Synapse Connect Token. Get it at synapselayer.org → Dashboard → Connect."
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agent_id:
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type: "string"
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description: "Identifier for the agent using the memory layer. Defaults to 'default'."
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default: "default"
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tools:
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- name: recall
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description: "Deterministically retrieves past contextand decisions. Call before responding when prior context may exist."
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description: "Retrieves past context, decisions, and preferences from encrypted memory. Call before responding when prior context may exist."
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inputSchema:
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type: object
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required: [query]
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description: "Maximum memories to return (1-50, default: 10)"
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- name: save_to_synapse
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description: "Persists user preferences, task progress, and facts with Zero-Knowledge encryption."
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description: "Persists user preferences, task progress, and facts with AES-256-GCM encryption. Server never sees plaintext."
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inputSchema:
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type: object
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required: [content]
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description: "Agent identifier. Defaults to 'default'."
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- name: process_text
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description: "Self-extracting memory engine. Automatically updates long-term memory from text. Never stores secrets, credentials, or financial data."
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description: "Self-extracting memory engine. Automatically identifies and stores relevant facts from text. Never stores secrets, credentials, or financial data."
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inputSchema:
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type: object
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required: [text]
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description: "Agent identifier. Defaults to 'default'."
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- name: search
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description: "Cross-agent memory search with TRUTH QUOTIENT™ ranking. Returns results across all agents matching the query."
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description: "Cross-agent semantic memory search with TRUTH QUOTIENT™ ranking. Returns results across all agents matching the query."
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inputSchema:
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type: object
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required: [query]
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properties:
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query:
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type: string
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description: "Search query — natural language or keywords to find across all stored memories"
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description: "Search query — natural language or keywords"
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agent_id:
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type: string
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description: "Optional agent filter. Omit to search across all agents."
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