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).
How the Memory Search tool works in practice
Memory Search is an agent core & quality tool you assign to a VDF AI agent. It runs a semantic search over an agent’s stored memories and returns the most relevant ones with similarity scores. Its hallmarks — Semantic, Scored, Relevant — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, memories are embedded and matched by cosine similarity within the tenant’s scope, so the agent gets meaning-based recall without leaking across tenants. It expects user_id and query as required inputs, so calls are explicit and easy to audit. Every call is scoped to the requesting tenant and written to an audit log, so the capability is safe to run inside a regulated, on-premise environment — the same governance model behind every VDF AI tool.
Teams reach for Memory Search when they need to handle relevant recall, cross-project insight, and deduplication. It rarely works alone — pair it with Memory Store, Memory Recall, and RAG Vector Query to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
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.
What inputs does the Memory Search tool need?
It requires user_id and query, and optionally accepts top_k and namespace. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Memory Search?
Memory Search is commonly assigned alongside Memory Store, Memory Recall, and RAG Vector Query. On VDF AI you compose several tools and agents into a single governed, on-premise network.
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.
Assign Memory Search to these agents
These VDF AI agents can be assigned this tool. Open an agent to see the full toolkit it can run.
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.