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Persistent AI Memory
Let agents recall relevant history across sessions instead of starting over.
Live workload cutawayArchitecture in context
Persistent AI Memory
Signal path active4 deployment units
What this replaces
Even powerful agents lose continuity when a session ends, forcing people to repeat project history, decisions and context.
What your team gains
A semantic session-history layer that can be searched through MCP or HTTP and injected into active work.
What is inside the blueprint
Session capture and chunking
Vectorized historical recall
Semantic similarity retrieval
Bearer-auth HTTP access
MCP access
Historian persona/workflows
Cross-session project continuity
Foundation for user/team long-term memory
How it fits the platform
Conversation/session history -> embeddings -> recall chunks in the vector store -> session-recall-mcp -> AI clients/personas.
Included services
session-recall-mcp, Relational database, Vector store, embedding endpoint
Platform requirements
- AICORTEX Core PlatformSession history and embeddings persist in Core's data tier.
From prerequisites to operation
Prerequisites
- Captured session history
- Embedding pipeline
- Memory access/privacy policy
Deployment
- Create recall schema/index
- Ingest historical sessions
- Deploy MCP/HTTP service
- Register with supported clients
- Create memory retrieval instructions
Configuration
- Chunking
- Namespaces/tenants
- Similarity threshold
- Maximum recall context
- Retention and deletion rules
- Authentication
Operations
- Index growth
- Privacy boundaries
- Duplicate/stale memories
- Embedding migrations
- Retention enforcement
What this unlocks with other layers
Institutional Memory
The AI can retrieve documents, recall prior work and trace relationships and provenance.
AI SRE Foundation
Agents can reason over the same telemetry and history humans use for diagnosis.
Technology behind this capability
Relational databaseVector storeLocal embedding runtimeModel Context Protocol (MCP)Session recall MCP serverAICORTEX native