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Synapse Layer — Zero Knowledge Memory

  • 13 repo stars
  • Updated July 9, 2026
  • SynapseLayer/synapse-layer

Synapse secure-memory is an MCP server that implements Zero-Knowledge Context and Neural Handover for encrypted agent memory.

About

Synapse secure-memory is the Model Context Protocol packaging of Synapse Layer’s zero-knowledge memory story for AI agents. It targets developers who need a explicit trust layer: encrypted at-rest storage, privacy parameters, and agent IDs so multiple clients do not collide. The manifest describes stdio transport through uvx with the synapse-layer PyPI identifier, mirroring the broader Synapse ecosystem on GitHub. Use it when you are wiring MCP into Claude Desktop, Claude Code, or other hosts and want memory tools that advertise PII-aware design rather than ad-hoc JSON files in the workspace. Setup is environment-variable driven with sensible defaults for logging and privacy epsilon.

  • Zero-Knowledge Context and Neural Handover branding for cross-session agent continuity
  • AES-256-GCM encryption with optional SYNAPSE_ENCRYPTION_KEY (auto-generated if omitted)
  • SYNAPSE_PRIVACY_EPSILON differential privacy default 0.5
  • stdio MCP via uvx on PyPI package synapse-layer v1.0.7
  • SYNAPSE_AGENT_ID distinguishes claude-desktop, custom agents, and multi-agent setups

Synapse Layer — Zero Knowledge Memory by the numbers

  • Data as of Jul 20, 2026 (Skillselion catalog sync)
terminal
claude mcp add --env SYNAPSE_AGENT_ID=YOUR_SYNAPSE_AGENT_ID --env SYNAPSE_PRIVACY_EPSILON=YOUR_SYNAPSE_PRIVACY_EPSILON --env SYNAPSE_ENCRYPTION_KEY=YOUR_SYNAPSE_ENCRYPTION_KEY --env LOG_LEVEL=YOUR_LOG_LEVEL synapse-layer -- uvx synapse-layer

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repo stars13
Packagesynapse-layer
TransportSTDIO
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Last updatedJuly 9, 2026
RepositorySynapseLayer/synapse-layer

What it does

Install the Synapse secure-memory MCP package when you want Zero-Knowledge Context and Neural Handover as the trust layer for agent recall.

Who is it for?

Best when you're standardizing on Synapse’s trust-layer narrative for Claude Desktop or custom MCP agents.

Skip if: Quick prototypes that do not need encryption keys or differential privacy tuning.

What you get

Registering synapse-secure-memory lets agents read and write a encrypted memory layer with privacy epsilon and per-agent IDs through standard MCP stdio.

  • MCP-accessible secure memory with encryption key management
  • Privacy-tuned recall via SYNAPSE_PRIVACY_EPSILON
  • Isolated memory namespaces per SYNAPSE_AGENT_ID

By the numbers

  • Manifest version 1.0.7
  • Package identifier synapse-layer on PyPI with stdio transport
  • Four documented environment variables including secret encryption key
README.md

npm version npm version npm downloads License: Apache-2.0

npm downloads License: Apache-2.0

🧠 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.

PyPI Python Downloads MCP Compatible License: Apache-2.0 Smithery

Website · Docs · PyPI · Forge


⚡ 30-Second Quickstart

pip install synapse-layer
from synapse_layer import Synapse

s = Synapse(token="sk_connect_YOUR_TOKEN")

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

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


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

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

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.

Install

pip install synapse-layer

Quick Start

Local SDK — in-process memory

import asyncio
from synapse_layer import SynapseClient

async def main():
    memory = SynapseClient(agent_id="my-agent")

    # Save
    await memory.store("User prefers dark mode and concise answers")

    # Recall
    results = await memory.recall("user preferences")
    for r in results:
        print(f"[TQ={r.trust_quotient:.2f}] {r.content}")

asyncio.run(main())

Cloud — Forge API (persistent, cross-agent)

from synapse_memory.client import Synapse

client = Synapse(token="sk_connect_YOUR_TOKEN")
client.remember("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


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.


Deployment Modes

Local SDK

Use the local SDK when you want in-process memory access inside your Python application.

Best for:

  • local prototypes
  • 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

MCP Integration (Claude Desktop / Cursor)

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "synapse-layer": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://forge.synapselayer.org/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

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

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.


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

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.

License

Apache-2.0 © Synapse Layer

Recommended MCP Servers

How it compares

Sibling MCP package to synapse-layer with the same PyPI runtime—memory trust layer, not a payment or browser QA server.

FAQ

Who is synapse-secure-memory for?

Developers and founders who run MCP agents and want branded zero-knowledge memory instead of rolling their own encrypted store.

When should I use synapse-secure-memory?

During agent stack design when you need durable context handover between tools, sessions, or agent personas.

How do I add synapse-secure-memory to my agent?

Configure MCP stdio with uvx for synapse-layer from PyPI, set SYNAPSE_AGENT_ID, and provide SYNAPSE_ENCRYPTION_KEY if you want a fixed key.

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