synapse-layer
MCP-native persistent memory for AI agents. Stores and retrieves encrypted memories with Trust Quotient scoring, cross-agent handover protocol, and immutable audit trail. 13 tools. Remote endpoint available.
README
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๐ง 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.
Website ยท Docs ยท PyPI ยท Forge
</div>
โก 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
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 |
|---|---|
| ๐ Encrypted at rest | AES-256-GCM with per-operation random IV and HMAC-SHA-256 integrity |
| ๐งฉ One-click connect | Claude Desktop, Cursor, LangChain, CrewAI, n8n |
| ๐ Cross-agent memory | Save in ChatGPT, recall in Claude |
| โก MCP-native | Any MCP-compatible agent |
| ๐ Header-first auth | Tokens never in URLs or logs |
| ๐ฏ 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:
recallsave_to_synapseprocess_textsearchhealth_checkinitialize_contextsave_memorystore_memoryrecall_memorylist_memoriesmemory_feedbackneural_handoverslo_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.
Related Projects
| 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
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