{"uid":"cap_DgNzhEgcCsNIXXA7lGsw0","slug":"agentrxi-vector-memory-c67e01f2","name":"AgentRXI Vector Memory","description":"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), $0.003 per query or delete. Also callable as GET with the same fields as query parameters.","url":"https://agentrxi.com/v1/vectors?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","properties":{"ids":{"type":"string","description":"For delete"},"items":{"type":"string","description":"[{id, text, metadata}]; separate items with |"},"query":{"type":"string"},"top_k":{"type":"integer"},"action":{"type":"string","description":"upsert, query (default), delete"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_iNTydTn0ZpiZG8hav3sYs","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"Save these 3 notes to my long-term agent memory — upsert them: 'User prefers dark mode | source: preferences', 'User is vegetarian | source: dietary', 'User lives in Berlin | source: profile' — then later I want to search by meaning with a query like 'what are the user's food restrictions' and get the top 2 results.","exampleUseCases":[{"title":"Persistent agent memory across sessions","prompt":"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."},{"title":"Semantic search over stored knowledge base","prompt":"Search my agent's vector memory for anything related to 'renewable energy investments' and return the top 5 most relevant stored entries."},{"title":"Cleaning up stale memory entries","prompt":"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."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:12.001Z","isFirstParty":false,"canonicalSlug":"agentrxi-vector-memory-c67e01f2"}