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AGISHub Memory Search
x402
AGISHub Memory Search is a paid API for AI agents from api.agishub.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-31).
Semantically searches a named memory namespace and returns the most relevant stored entries using vector similarity — the retrieval step of a RAG pipeline.
Price / call$0.01
Success—
StatusUnknown
How this service works
One install → every tool for your AI agent. Wallet-native, pay-per-call, no keys (x402). Connect Claude, Cursor, OpenClaw or any MCP client with a single command, or call the x402 HTTP API and pay per request in USDC on Base. 34 tools: AI, web search & scraping, data & analytics, market data, media generation, developer tools, memory — all discoverable and free to try over MCP.
Output
Returns a ranked list of the most semantically similar stored entries from the specified namespace, ordered by relevance to the query. The number of results is controlled by top_k (default 5, max 20).
Example use cases
RAG context retrieval for chatbot
“Search the 'product-docs' namespace for the 5 most relevant stored chunks matching the question 'what are the system requirements for installation?' so I can augment my answer.”
Personal knowledge base recall
“Look in my 'personal-notes' namespace for anything semantically related to 'annual budget planning' — give me the top 3 results.”
Agent memory lookup before responding
“Before you reply to this user, search the 'conversation-history' namespace for the top 5 entries most similar to 'user's dietary preferences' so you can personalize the response.”
Technical details
Endpoint
GEThttps://api.agishub.com/v1/memory-search?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to retrieve the most semantically relevant stored entries from a named memory namespace as part of a RAG pipeline or agent memory recall loop. It is specifically designed for the retrieval step — prefer it over generic full-text search when you need similarity-ranked results based on meaning rather than keyword overlap. Ideal when entries were upserted via the paired memory upsert endpoint on the same platform.
Side effects
- Namespace not found — returns empty results or error if the namespace hasn't been populated
- Query too short or empty — rejected due to minLength:1 validation
- top_k out of range — rejected if below 1 or above 20
- No matching entries — returns empty results if no stored content is semantically close to the query
- Network timeout — if the vector search backend is slow or unavailable
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| outputrequired | object |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "Semantically search a memory collection (namespace) and return the most relevant stored entries. The retrieval half of R"
}
}Tags
x402
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