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Synapse Layer — Zero-Knowledge Memory

Officialdeprecated

by SynapseLayer · Python

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

This MCP server provides persistent memory infrastructure for AI agents and assistants. It supports zero-knowledge style “Trust Layer” concepts for memory, with encrypted storage, semantic recall, and MCP-native JSON-RPC exposure for integration with MCP-compatible clients.

🛠️ Key Features

  • Encrypted memory at rest using AES-256-GCM
  • Semantic recall via pgvector HNSW indexing
  • Exposes memory through MCP JSON-RPC
  • Built as open-source persistent memory infrastructure
  • Category coverage includes context-engineering, RAG, and vector-search

🚀 Use Cases

  • Cross-agent memory for AI agents
  • Long-term memory and persistent-memory workflows
  • RAG-style retrieval combined with semantic-search
  • Semantic lookup using vector-search over stored memories
  • Integration with Claude, GPT, Gemini, and other MCP clients

⚡ Developer Benefits

  • MCP-native access through JSON-RPC
  • pgvector-based semantic-search/indexing
  • Python ecosystem alignment (as referenced by repository topics)
  • Designed for agent-infrastructure and developer-tools contexts

⚠️ Limitations

  • Tool list details are not provided in the available data (toolCount missing).

Topics

ai-agentsencrypted-memorymcpmemorycross-agent-memorypersistent-memorytrust-quotientagent-infrastructurecontext-engineeringdeveloper-toolsllmlong-term-memorymodel-context-protocolopen-sourcepgvectorpythonragsemantic-searchvector-searchagent-memory

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