Synapse Layer — Zero-Knowledge Memory

io.github.SynapseLayer由 GitHub 帳號 SynapseLayer 發布v1.0.7更新於 Oct 24, 2026

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

發布方自己的服務:Memory.fit

概覽

AI 產生的概覽

讓助理擁有持久、加密的長期記憶,支援語意檢索與跨代理上下文交接。

可以做什麼
Synapse Layer 是針對 AI 代理的持久記憶層。它提供 13 個 MCP 工具,涵蓋記憶寫入、語意召回、結構化儲存、回饋評分、代理交接與運作觀測,包括 recall、save_memory、store_memory、list_memories、memory_feedback、neural_handover 與 slo_report。記憶以 AES-256-GCM 靜態加密並建立語意檢索索引,README 表示可在一個代理中儲存、在另一個代理中召回。
適用情境
當助理需要跨工作階段記住使用者偏好、事實與先前的決定,或多個代理需要共享上下文時適合使用。也適合想為正式環境代理提供可檢索、帶評分召回能力的團隊,而不必每次工作階段都從零開始。
執行需求
以本機 stdio 程序執行,從 PyPI 安裝 synapse-layer 並以 uvx 啟動。README 也展示透過 mcp-remote 存取遠端 Forge 端點並帶上 x-connect-token 標頭,以及使用連線權杖的 Python SDK。環境變數包括 SYNAPSE_AGENT_ID、LOG_LEVEL、SYNAPSE_PRIVACY_EPSILON,以及密鑰 SYNAPSE_ENCRYPTION_KEY(未提供時自動產生)。使用託管 Forge 服務需要網路連線。
安裝前請注意
README 要求使用者從廠商 Forge 主控台取得連線權杖,並以 x-connect-token 標頭或傳給 SDK 使用,該權杖應視為憑證。密鑰 SYNAPSE_ENCRYPTION_KEY 用於保護靜態記憶,遺失後可能導致已存記憶無法讀取。README 的安裝方式是把遠端指令稿透過管線交給 shell 執行,會在本機執行第三方程式碼。記憶內容會傳送到託管 Forge 服務,save_memory、store_memory、neural_handover 等工具會寫入並共享資料。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Synapse Layer — Zero-Knowledge Memory,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

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

來源:README.md,提交 96d0706

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v1.0.7最新Sep 16, 2026

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