Synapse Layer — Trust Infrastructure for AI Agents MCP Server

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🧠 Synapse Layer

RAG retrieves. Synapse remembers.

Persistent memory infrastructure for AI agents — AES-256-GCM encrypted at rest, semantic search, MCP-native.

Synapse Layer is open-source persistent memory infrastructure for AI agents and assistants. Memories are encrypted at rest with AES-256-GCM, indexed via pgvector HNSW for semantic recall, and exposed through MCP JSON-RPC for native integration with Claude, GPT, Gemini, and any MCP-compatible client. Apache 2.0 licensed.

Website · Docs · PyPI · Forge

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⚡ 30-Second Quickstart

Get your Connect Token at forge.synapselayer.org → Connect, then install into any MCP client:

curl -fsSL https://forge.synapselayer.org/install/smithery | bash

On the Forge dashboard the Smithery card already copies the command with your token embedded (… | bash -s -- sk_connect_…) — a single paste in your terminal is enough.

Or use the Python SDK:

from synapse_layer import Synapse

s = Synapse(token="sk_connect_YOUR_TOKEN")

s.store("user likes coffee")
print(s.recall("what does user like?"))

Get your token at forge.synapselayer.org → Dashboard → Connect

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What is Synapse Layer?

The persistent memory layer for AI agents — the missing piece between stateless LLMs and real continuity of context.

Your AI agents forget everything between sessions. Synapse Layer fixes that.

Feature	Description
🔐 Encrypted at rest	AES-256-GCM with per-operation random IV and HMAC-SHA-256 integrity
🧩 One-click connect	Claude Desktop, Cursor, LangChain, CrewAI, n8n
🌐 Cross-agent memory	Save in ChatGPT, recall in Claude
⚡ MCP-native	Any MCP-compatible agent
🔒 Header-first auth	Tokens never in URLs or logs
🎯 Trust Quotient	Deterministic recall — memories ranked by confidence, not recency alone

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Why Synapse Layer?

Your AI agents forget everything between sessions. Synapse Layer fixes that — in one line.

Without Synapse Layer	With Synapse Layer
Agent forgets context every session	Persistent memory across all sessions
Memory locked to one model	Cross-agent: save in ChatGPT, recall in Claude
No audit trail	Trust Quotient scoring on every memory
Complex integration	pip install synapse-layer + 3 lines of code
Plaintext stored on servers	AES-256-GCM encrypted at rest

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Use Cases

• Long-term assistant memory — persist user preferences, facts, and prior decisions across sessions.
• Cross-agent continuity — save context in one agent and recall it in another.
• Secure memory for MCP clients — connect Claude Desktop, Cursor, and other MCP-compatible tools to a governed memory layer.
• Operational memory for teams — maintain structured context, trust scoring, and searchable recall for production agents.

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Install

pip install synapse-layer

Quick Start

Python Script

from synapse_layer import Synapse

client = Synapse(token="sk_connect_YOUR_TOKEN")

# Store
client.store("User prefers dark mode and concise answers")

# Recall
results = client.recall("user preferences")
for r in results:
    print(r["content"], r["trust_quotient"])

With Context Manager

from synapse_layer import Synapse

with Synapse(token="sk_connect_YOUR_TOKEN") as client:
    client.store("User prefers dark mode and concise answers")
    results = client.recall("user preferences")
    for r in results:
        print(r["content"])

Get your token at forge.synapselayer.org → Dashboard → Connect

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13 MCP Tools at a Glance

Synapse Layer currently exposes 13 MCP tools for persistent memory workflows:

• recall
• save_to_synapse
• process_text
• search
• health_check
• initialize_context
• save_memory
• store_memory
• recall_memory
• list_memories
• memory_feedback
• neural_handover
• slo_report

These tools cover memory capture, semantic recall, structured storage, feedback loops, agent handoff, and operational observability.

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Deployment Modes

Python Script Mode

Use the SDK when you want direct Python access to Forge memory from your application.

Best for:

• prototypes and scripts
• Python-native workflows
• fast integration into existing apps

Cloud / Forge API

Use Forge when you need persistent, cross-session, and cross-agent memory with managed access tokens.

Best for:

• production assistants
• multi-agent systems
• MCP-based integrations
• shared memory across tools and sessions

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MCP Integration (Claude Desktop / Cursor)

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "synapse-layer": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://forge.synapselayer.org/api/mcp",
        "--header",
        "x-connect-token: sk_connect_YOUR_TOKEN"
      ]
    }
  }
}

Config file location:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

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API — Header-First Auth

# Health check
curl -H "x-connect-token: sk_connect_YOUR_TOKEN" \
  https://forge.synapselayer.org/api/connect/health

# Save memory
curl -X POST \
  -H "x-connect-token: sk_connect_YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"content": "User is a Python developer"}' \
  https://forge.synapselayer.org/api/v1/capture

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Security

Feature	Implementation
Encryption	AES-256-GCM at rest with per-operation random IV
Integrity	HMAC-SHA-256 on content
Auth	Header-first (x-connect-token) — tokens never in URLs or logs
Privacy	Content sanitization + tenant-scoped encrypted storage
Isolation	1 user = 1 tenant = 1 private mind

See SECURITY.md for vulnerability reporting.

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Related Projects

Project	Description
synapse-sdk-python	Python SDK — LangChain, CrewAI, and A2A protocol adapters
synapse-layer-skill	MCP skill configuration for Claude Desktop, Cursor, Windsurf
synapse-layer-langgraph	LangGraph checkpoint saver with encrypted state persistence

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Governance

• All public claims follow the Public Claims Matrix.
• Architecture details that reveal benefits are public; mechanisms that enable them are private.
• Claim = Reality. If it's not implemented, it's not in the README.

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License

Apache-2.0 © Synapse Layer