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Mem0 Cloud Memory Search
x402
Mem0 Cloud Memory Search is a paid API for AI agents from mem0-cloud.saastemly.com, paid per call via x402, $0.004/call, status unknown (last checked 2026-10-31).
Semantic search over stored memories using embedding similarity, returning the closest matches with relevance scores filtered by a secret memory key and entity identifiers.
Price / call$0.004
Success—
StatusUnknown
How this service works
Search your stored memories by meaning, like mem0's search: embeds the query and returns the closest memories with a relevance score from 0 to 1. Only memories stored under the same memory_key (your secret string; required) are searched. filters must name at least one of user_id, agent_id, app_id, run_id. Returns {results:[{id, memory, score, metadata, ...}]}.
Output
Returns a JSON object with a 'results' array, each element containing: memory id, memory text, relevance score (0–1), metadata fields, and any expiration or entity tagging. Results are ranked by semantic similarity to the query, filtered by the provided entity identifiers and minimum threshold.
Example use cases
Personalized assistant context retrieval
“Before responding to this user, search my stored memories for anything about 'coffee preferences and morning routines' for user_id 'user_42' — give me the top 10 most relevant memories above a 0.2 relevance score.”
Agent recall for ongoing project
“Look up what my agent has remembered about 'project deadlines and milestones' for agent_id 'planner_agent_01' and give me the top 5 closest matches using my memory key 'zP8mQ3nL5wX2rT7vC9bK4dY6'.”
Session-scoped memory lookup
“Find any memories about 'user onboarding steps completed' for run_id 'session_789xyz' — I want everything with a relevance score above 0.4, up to 15 results.”
Technical details
Endpoint
POSThttps://mem0-cloud.saastemly.com/v3/memories/search?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to semantically retrieve previously stored facts or memories for a specific user, agent, app, or session — especially when keyword search is insufficient and meaning-based similarity ranking is needed. Prefer it over general vector search services when the data was stored via the companion Mem0 Cloud add endpoint and access is controlled by a shared memory_key secret.
Side effects
- Missing or invalid memory_key causes no results or unauthorized error
- Filters object missing all of user_id/agent_id/app_id/run_id returns a validation error
- Query string exceeding 500 characters is rejected
- top_k outside 1–100 range causes a parameter error
- Threshold set too high returns an empty results array
- Memory key loss makes stored memories permanently unreachable
- Expired memories excluded by default unless show_expired is true
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | What to look for, in natural language (up to 500 characters) |
| top_k | integer | How many memories to return, 1-100 (default 10) |
| filters | object | mem0 filters: at least one of user_id, agent_id, app_id, run_id, each a string, "*" or {"in": [up to 20 strings]}; AND lists of those. OR and NOT are not supported. |
| threshold | number | Minimum relevance score, 0-1 (default 0.1; 0 disables) |
| memory_key | string | Your secret: a random string of 16-256 characters that you generate once and keep (also accepted as the X-Memory-Key header). Memories are stored and searched only under its hash, so nobody who lacks it can read them, whatever user_id they send. Lose it and the memories are unreachable. |
| show_expired | boolean | Include memories whose expiration_date has passed (default false) |
Tags
x402
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