The Memory Search Tool
Semantically search an agent’s stored memories to surface the most relevant ones for the current task — even when you don’t know the namespace or the exact words they were saved under.
Autonomous agents fail quietly
An agent that can act is only useful if it remembers, plans, and checks its own work. Without a cognitive core, agents forget context, skip steps, and state wrong answers with full confidence — and you find out too late.
No memory across runs
Agents start from zero every session, re-asking what they were already told.
Acting before thinking
Without an explicit plan, agents take the first path, not the right one.
Confident wrong answers
Nothing checks the output, so mistakes ship as if they were facts.
No accountability
When it goes wrong, there is no trace of why the agent did what it did.
Memory Search, without the risk
Capability
What it does
Find the right memory by meaning, not exact key.
it runs a semantic search over an agent’s stored memories and returns the most relevant ones with similarity scores.
- Semantic, not keyword, recall
- Similarity score per hit
- Cross-namespace search
- Tenant-scoped
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
memories are embedded and matched by cosine similarity within the tenant’s scope, so the agent gets meaning-based recall without leaking across tenants.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
This tool runs inside your perimeter, scoped per user with full audit logging, so the agent’s reasoning, memory, and decisions stay private and accountable — never sent to a third-party service.
Per-tenant, logged
Parameters
The memory_search tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: 10 Optional Maximum number of memories to return (1–50).
Where Memory Search pays back
Relevant recall
Surface the few memories that actually matter for this question.
Cross-project insight
Find a lesson learned on one project while working on another.
Deduplication
Check whether the agent already knows something before storing it again.
Grounded answers
Ground a response in the agent’s own accumulated knowledge.
Assigned to agents, orchestrated as networks
On VDF AI, an industry’s use cases map to agents, and you assign tools like this one to those agents. Compose multiple agents into a governed, on-premise network.
What changes after you assign it
Questions about the Memory Search tool
What is the Memory Search tool?
It runs a semantic search over an agent’s stored memories and returns the most relevant ones with similarity scores. Assigned to a VDF AI agent, it runs under role-based policy with full audit logging so the capability is safe to use in production.
What does it return?
A ranked list of memories with similarity scores, so an agent can use only high-confidence recalls and ignore weak matches.
Do I need to know the namespace?
No. Memory search works across namespaces by default; provide one only to narrow the search.
Does it run on-premise?
Yes. Like every VDF AI tool, it can run on-premise or in your sovereign cloud, scoped per user and audit-logged, so your data never leaves your perimeter.
How do agents use it?
You assign the tool to an agent under a role-based policy; the agent calls it as one step in a task, and several agents and tools can be orchestrated together as a governed VDF AI Network.
Tools that work well alongside this one
Where this tool delivers value
Put Memory Search to work
See the Memory Search tool assigned to an agent and orchestrated in a governed, on-premise network.