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๐Ÿง  Synapse Layer

RAG retrieves. Synapse remembers.

Persistent memory infrastructure for AI agents โ€” 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 ยท Docs ยท PyPI ยท Forge


โšก 30-Second Quickstart

pip install synapse-layer
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 โ†’ Dashboard โ†’ Connect


Related MCP server: Sylex Memory

What is Synapse Layer?

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

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

Feature

Description

๐Ÿ” Encrypted at rest

AES-256-GCM with per-operation random IV and HMAC-SHA-256 integrity

๐Ÿงฉ One-click connect

Claude Desktop, Cursor, LangChain, CrewAI, n8n

๐ŸŒ Cross-agent memory

Save in ChatGPT, recall in Claude

โšก MCP-native

Any MCP-compatible agent

๐Ÿ”’ Header-first auth

Tokens never in URLs or logs

๐ŸŽฏ Trust Quotient

Deterministic recall โ€” memories ranked by confidence, not recency alone


Why Synapse Layer?

Your AI agents forget everything between sessions. Synapse Layer fixes that โ€” 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 โ€” persist user preferences, facts, and prior decisions across sessions.

  • Cross-agent continuity โ€” save context in one agent and recall it in another.

  • Secure memory for MCP clients โ€” connect Claude Desktop, Cursor, and other MCP-compatible tools to a governed memory layer.

  • Operational memory for teams โ€” maintain structured context, trust scoring, and searchable recall for production agents.


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 โ†’ Dashboard โ†’ 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 (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


API โ€” 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) โ€” 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.


Project

Description

synapse-sdk-python

Python SDK โ€” 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 ยฉ Synapse Layer

Install Server
A
license - permissive license
B
quality
B
maintenance

Maintenance

โ€“Maintainers
2dResponse time
5dRelease cycle
18Releases (12mo)
Commit activity
Issues opened vs closed

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  • A
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Related MCP Connectors

  • Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.

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