SuperMemory vs Alchemyst: Memory + RAG vs Deterministic Context

What is SuperMemory's five-layer approach?

SuperMemory provides a unified context stack:

1. Memory Graph — stores facts extracted from conversations with temporal validity.
2. User Profiles — maintains stable facts + recent activity for personalization.
3. Knowledge Base — RAG over documents and external data.
4. Session Context — conversation continuity within sessions.
5. Enterprise Compliance — SOC 2, HIPAA, GDPR controls.

This works well for coding agents and personal assistants where the agent needs both memory and document retrieval.

What is Alchemyst AI's deterministic approach?

Alchemyst AI focuses exclusively on institutional context with three primitives:

• Context Arithmetic — set operations over meaning at query time.
• Semantic Consensus — resolve contested definitions before retrieval.
• Context Traces — every retrieval is fully auditable.

Key differences

When to choose which?

Choose SuperMemory if:

• You are building a coding agent or personal assistant.
• You need both user preferences AND document retrieval in one API.
• You want connectors to Google Drive, Slack, Notion, GitHub.

Choose Alchemyst AI if:

• You are building multi-agent systems with shared institutional knowledge.
• You need audit trails for every context retrieval.
• You want to enforce semantic consensus across teams ("revenue" means one thing).

The trade-offs

SuperMemory covers more ground in a single service—memory, user profiles, knowledge base, and compliance. This reduces integration complexity but can mean you are paying for capabilities you may not need.

Alchemyst does one thing (context) but does it with surgical precision—deterministic, traceable, and consensual. For enterprises where wrong answers cost millions, this precision matters more than breadth.