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AgentRXI Vector Memory
x402
AgentRXI Vector Memory is a paid API for AI agents from agentrxi.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-15).
Private long-term semantic vector memory for AI agents: upsert texts with metadata (auto-embedded, multilingual), then search by meaning or delete by ID, keyed to the paying wallet.
Price / call$0.003
Success—
StatusUnknown
How this service works
Private long-term vector memory for an agent: upsert up to 100 texts with metadata (we embed them, multilingual), then search by meaning. Keyed to the paying wallet. from $0.004 per upsert batch (by size), $0.003 per query or delete. Also callable as GET with the same fields as query parameters.
Output
For queries: a ranked list of semantically similar stored texts with their metadata and similarity scores (up to top_k results). For upserts: confirmation of stored vector IDs. For deletes: confirmation of removed entries. All results are scoped to the authenticated paying wallet.
Example use cases
Persistent agent memory across sessions
“Store these facts about me in your long-term memory so you remember them next time: I prefer concise answers, I work in fintech, and my timezone is CET. Upsert all three with a 'user-profile' metadata tag.”
Semantic search over stored knowledge base
“Search my agent's vector memory for anything related to 'renewable energy investments' and return the top 5 most relevant stored entries.”
Cleaning up stale memory entries
“Delete the memory entries with IDs 'mem-001' and 'mem-047' from my agent's vector store — they're outdated and I don't want them surfacing in future searches.”
Technical details
Endpoint
GEThttps://agentrxi.com/v1/vectors?utm_source=zero.xyzWhen to prefer
Choose this endpoint when an AI agent needs persistent, wallet-scoped semantic memory that survives across sessions and conversations. Ideal for agents that need to remember user preferences, facts, or documents and retrieve them by meaning rather than exact keyword. The multilingual embedding support makes it suitable for cross-language memory. Prefer over in-context storage when data volume exceeds context window limits, or when memory must persist indefinitely between separate agent invocations.
Side effects
- Missing required 'action' parameter defaults to query — may confuse callers expecting upsert behavior
- Exceeding 100 items per upsert batch returns an error
- Invalid or non-existent IDs on delete return empty confirmation rather than error
- Payment failure (x402) blocks all operations
- Malformed items string (not pipe-separated JSON objects) causes parse error
- top_k not specified may return fewer results than expected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
Reviews
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Zero review prompt (fires after every successful call)
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