RAG retrieves relevant external knowledge for a current question. Agent memory stores or reconstructs state that persists across steps, sessions or interactions. Use RAG for governed sources, short-term state for the active thread and durable memory only for information worth retaining with consent and lifecycle controls.
Three context stores
| Layer | Content | Lifecycle |
|---|---|---|
| Working context | Prompt, messages, tool results | One run |
| Durable memory | Preferences, summaries, task state | Across sessions |
| RAG corpus | Documents, records, policies | Source controlled |
MemGPT frames finite context as a memory hierarchy problem. Context-engineering guidance recommends loading high-signal material just in time.
Why the distinction matters
A policy document should keep source identity, permissions and update lifecycle; it belongs in the knowledge layer. A preferred report format may belong in long-term memory. Today’s intermediate results may need only checkpointed task state.
Mixing these creates stale “memories” of source facts, weak deletion controls and claims that cannot show provenance.
Write memory selectively
- Define events eligible for memory.
- Require consent for sensitive persistence.
- Store source and timestamp with learned facts.
- Provide review, correction and deletion.
- Expire volatile information.
- Do not let untrusted text write durable memory without validation.
Combine safely
Retrieve the minimum relevant memory, source evidence and task state. Label each class so a model can distinguish preference from authoritative document. Resolve conflicts by policy, not recency alone.
SPYN3 currently provides governed workspace knowledge through REST and MCP. Durable agent memory is direction, not a current beta claim. See the product facts page.
Built for the next step
Build on a durable context layer
Start with source-aware workspace knowledge; keep memory and knowledge as deliberate layers.
See current capabilitiesSPYN3 is AI-ready data infrastructure for technical solopreneurs and AI builders. It connects business data into a workspace-isolated knowledge layer and exposes it through a unified REST API and hosted MCP server, with permission-scoped access and source-aware returns. SPYN3 is currently in private beta; current capabilities and product direction are documented on the product facts page.
Sources
- Packer et al.: MemGPT — hierarchical context and memory tiers. Verified 13 Sep 2026
- Anthropic: effective context engineering — high-signal context and progressive disclosure. Verified 13 Sep 2026
- Anthropic: contextual retrieval — adding chunk-specific context before indexing. Verified 13 Sep 2026