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turboshop.ai Agent Memory Service
x402
turboshop.ai Agent Memory Service is a paid API for AI agents from turboshop.ai, paid per call via x402, $0.03/call, status unknown (last checked 2026-10-19).
Stores and semantically retrieves text memories with metadata for autonomous agents using vector search and knowledge graph technology.
Price / call$0.03
Success—
StatusUnknown
How this service works
Persistent vector memory and knowledge graph as a service for autonomous agents. Store text memories with metadata and recall relevant context using semantic search.
Output
Returns a list of the top-k semantically matched memory entries, each with the original text content, associated metadata, similarity scores, and namespace information. When storing, returns confirmation of the persisted memory entry with its assigned identifier.
Example use cases
Persistent context across agent sessions
“Save the memory 'Completed onboarding step 3: API keys configured on 2024-01-15' to namespace 'onboarding-agent-007' with metadata tag stage=onboarding, so my agent can pick up where it left off next session.”
Semantic recall for task planning
“Search my agent's memory namespace 'project-alpha' for the top 5 memories most relevant to 'pending deliverables and blockers' so the agent can plan today's work.”
Multi-agent shared knowledge base
“Store the insight 'Supplier X has a 3-week lead time on orders over 500 units' in the shared namespace 'supply-chain-agents' with metadata source=negotiation-agent, so all agents in the fleet can access it.”
Technical details
Endpoint
GEThttps://turboshop.ai/api/v1/products/prod-agent-memory-service/buy?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need persistent, semantically searchable memory for AI agents across sessions — particularly when exact keyword matching is insufficient and you need meaning-based recall. Ideal for autonomous agents that accumulate knowledge over time, multi-agent systems sharing a knowledge namespace, or any LLM application requiring stateful context beyond a single conversation window.
Side effects
- Missing both 'text' and 'query' fields returns an error — at least one must be provided
- Invalid or empty namespace may result in memory isolation failures
- top_k values exceeding system limits may be capped or return an error
- Malformed metadata objects may be rejected
- Payment failure via x402 protocol returns 402 status before any memory operation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Memory content string to persist |
| query | string | Semantic search query to recall relevant memories |
| top_k | integer | Number of top memories to retrieve (default 5) |
| metadata | object | Optional metadata key-value pairs |
| client_id | string | Optional calling agent ID |
| namespace | string | Agent memory partition / namespace |
Tags
x402
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