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Glossary

Non-LLM Memory Server

A non-LLM memory server stores and retrieves facts for AI agents using deterministic storage (key-value, graph, or vector) without passing stored content through an LLM for reinterpretation, preventing the semantic drift that occurs when models summarize or rephrase remembered information.

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Definition

A non-LLM memory server stores and retrieves facts for AI agents using deterministic storage (key-value, graph, or vector) without passing stored content through an LLM for reinterpretation, preventing the semantic drift that occurs when models summarize or rephrase remembered information.

In Depth

Standard agent memory implementations store information by having the LLM summarize experiences into memory entries, then retrieve by having the LLM interpret those summaries. Each read-write cycle introduces semantic drift: the model subtly rephrases, emphasizes different aspects, or loses precision. After 10+ cycles, a price of $30/mo might drift to 'approximately $30' to 'around $25-35' to 'roughly $30-40.' Non-LLM memory servers (Fidelis, claude-mem, custom key-value stores) bypass this by storing raw text or structured data without LLM processing. Retrieval returns exact stored content. The trade-off: non-LLM memory lacks the flexibility of semantic retrieval and requires more structured queries. The optimal pattern combines non-LLM memory for verified facts (pricing, dates, configuration) with LLM memory for experiential knowledge (what worked, what failed), and search grounding via an API like Scavio to validate both against current reality.

Example Usage

Real-World Example

A Claude Code agent stores 'Tavily pricing: $30/mo Researcher plan, verified 2026-05-07' in a non-LLM memory server. Three months later, the agent retrieves this exact string. Instead of trusting the potentially stale fact, it triggers a Scavio search for current Tavily pricing, finds the price changed post-Nebius acquisition, and updates memory with the verified current price.

Platforms

Non-LLM Memory Server is relevant across the following platforms, all accessible through Scavio's unified API:

  • Google

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Frequently Asked Questions

A non-LLM memory server stores and retrieves facts for AI agents using deterministic storage (key-value, graph, or vector) without passing stored content through an LLM for reinterpretation, preventing the semantic drift that occurs when models summarize or rephrase remembered information.

A Claude Code agent stores 'Tavily pricing: $30/mo Researcher plan, verified 2026-05-07' in a non-LLM memory server. Three months later, the agent retrieves this exact string. Instead of trusting the potentially stale fact, it triggers a Scavio search for current Tavily pricing, finds the price changed post-Nebius acquisition, and updates memory with the verified current price.

Non-LLM Memory Server is relevant to Google. Scavio provides a unified API to access data from all of these platforms.

Standard agent memory implementations store information by having the LLM summarize experiences into memory entries, then retrieve by having the LLM interpret those summaries. Each read-write cycle introduces semantic drift: the model subtly rephrases, emphasizes different aspects, or loses precision. After 10+ cycles, a price of $30/mo might drift to 'approximately $30' to 'around $25-35' to 'roughly $30-40.' Non-LLM memory servers (Fidelis, claude-mem, custom key-value stores) bypass this by storing raw text or structured data without LLM processing. Retrieval returns exact stored content. The trade-off: non-LLM memory lacks the flexibility of semantic retrieval and requires more structured queries. The optimal pattern combines non-LLM memory for verified facts (pricing, dates, configuration) with LLM memory for experiential knowledge (what worked, what failed), and search grounding via an API like Scavio to validate both against current reality.

Non-LLM Memory Server

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