Synapse Layer — Zero-Knowledge Memory

io.github.SynapseLayerPublished by GitHub account SynapseLayerv1.0.7Updated Oct 24, 2026

Zero-Knowledge Context™ & Neural Handover™ for AI Agents. The Trust Layer for AI Memory.

The publisher's own server: Memory.fit

Overview

AI-generated overview

Gives an assistant persistent, encrypted long-term memory with semantic recall and cross-agent context handover.

What you can do
Synapse Layer is a persistent memory layer for AI agents. It exposes 13 MCP tools covering memory capture, semantic recall, structured storage, feedback scoring, agent handoff, and operational reporting, including recall, save_memory, store_memory, list_memories, memory_feedback, neural_handover, and slo_report. Memories are encrypted at rest with AES-256-GCM and indexed for semantic search, and the README states they can be saved in one agent and recalled in another.
When to use it
Worth adding when an assistant should remember user preferences, facts, and earlier decisions across sessions, or when several agents need to share context. It also suits teams wanting searchable, scored recall for production agents rather than per-session statelessness.
Requirements
Runs as a local stdio process, installed from PyPI as synapse-layer and launched with uvx. The README also shows a remote Forge endpoint reached through mcp-remote with an x-connect-token header, and a Python SDK using a connect token. Environment variables include SYNAPSE_AGENT_ID, LOG_LEVEL, SYNAPSE_PRIVACY_EPSILON, and the secret SYNAPSE_ENCRYPTION_KEY, which is auto-generated if unset. Network access is needed for the hosted Forge service.
Before you install
The README directs users to obtain a connect token from the vendor's Forge dashboard and pass it as the x-connect-token header or to the SDK; treat that token as a credential. The secret SYNAPSE_ENCRYPTION_KEY protects memory at rest, so losing it may make stored memories unreadable. The README's install instructions pipe a remote script into a shell, which runs third-party code on your machine. Memory content is sent to the hosted Forge service, and tools such as save_memory, store_memory…

Installation

In SourceWeft

  1. Open Synapse Layer — Zero-Knowledge Memory in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

🧠 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] [Official MCP Registry] [CI] [License: Apache-2.0] [Smithery]

Website · Docs · PyPI · Forge


⚡ 30-Second Quickstart

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

bash
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:

python
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


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.

FeatureDescription
🔐 Encrypted at restAES-256-GCM with per-operation random IV and HMAC-SHA-256 integrity
🧩 One-click connectClaude Desktop, Cursor, LangChain, CrewAI, n8n
🌐 Cross-agent memorySave in ChatGPT, recall in Claude
⚡ MCP-nativeAny MCP-compatible agent
🔒 Header-first authTokens never in URLs or logs
🎯 Trust QuotientDeterministic 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 LayerWith Synapse Layer
Agent forgets context every sessionPersistent memory across all sessions
Memory locked to one modelCross-agent: save in ChatGPT, recall in Claude
No audit trailTrust Quotient scoring on every memory
Complex integrationpip install synapse-layer + 3 lines of code
Plaintext stored on serversAES-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

bash
pip install synapse-layer

Quick Start

Python Script

python
from synapse_layer import Synapse
client = Synapse(token="sk_connect_YOUR_TOKEN")
# Storeclient.store("User prefers dark mode and concise answers")
# Recallresults = client.recall("user preferences")for r in results:    print(r["content"], r["trust_quotient"])

With Context Manager

python
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


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

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

MCP Integration (one tested path per client)

Claude Code (CLI)

bash
claude mcp add --transport http synapse-layer \  https://forge.synapselayer.org/api/mcp \  --header "x-connect-token: sk_connect_YOUR_TOKEN"

The URL is a positional argument — claude mcp add --transport http <name> <url> — there is no --url flag.

Claude Desktop

Add to claude_desktop_config.json:

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

Cursor

Add to .cursor/mcp.json in your project root (same JSON block as above).

Any MCP client

Any host that runs npx can use the mcp-remote block above unchanged — it bridges local stdio to the remote endpoint.

Config file locations:

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

API — Header-First Auth

bash
# Health checkcurl -H "x-connect-token: sk_connect_YOUR_TOKEN" \  https://forge.synapselayer.org/api/connect/health
# Save memorycurl -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

FeatureImplementation
EncryptionAES-256-GCM at rest with per-operation random IV
IntegrityHMAC-SHA-256 on content
AuthHeader-first (x-connect-token) — tokens never in URLs or logs
PrivacyContent sanitization + tenant-scoped encrypted storage
Isolation1 user = 1 tenant = 1 private mind

See SECURITY.md for vulnerability reporting.


Related Projects

ProjectDescription
synapse-sdk-pythonPython SDK — LangChain, CrewAI, and A2A protocol adapters
synapse-layer-skillMCP skill configuration for Claude Desktop, Cursor, Windsurf
synapse-layer-langgraphLangGraph 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

Source: README.md at commit 96d0706

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v1.0.7LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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