Overview
Search and retrieve relevant memories from your agent’s memory bank using natural language queries or specific filters.
Request Body
The search query to find relevant memories. Can be natural language or keywords.
Search strategy to use. Options: “llm” (semantic search).
Maximum number of memories to return. Default is 5.
Filter memories by specific user ID.
Filter memories by specific session ID.
Additional filters to apply to the search as key-value pairs.
Response
Returns an array of relevant memories matching your query, sorted by relevance.
Examples
Basic Search
import requests
url = "https://api.agent-mind.com/api/v1/memories/recall"
headers = {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
}
data = {
"query": "user preferences",
"limit": 10
}
response = requests.post(url, json=data, headers=headers)
memories = response.json()
User-Specific Search
data = {
"query": "previous purchases",
"user_id": "customer_123",
"limit": 5,
"strategy": "llm"
}
response = requests.post(url, json=data, headers=headers)
With Custom Filters
data = {
"query": "support issues",
"user_id": "user_456",
"session_id": "session_789",
"filters": {
"category": "support",
"priority": "high",
"resolved": False
},
"limit": 20
}
response = requests.post(url, json=data, headers=headers)
Response Example
{
"memories": [
{
"id": "mem_abc123",
"content": "User prefers email notifications over SMS",
"user_id": "user_123",
"metadata": {
"category": "preferences",
"type": "communication"
},
"relevance_score": 0.95,
"created_at": "2024-03-20T10:30:00Z"
},
{
"id": "mem_def456",
"content": "User's timezone is PST",
"user_id": "user_123",
"metadata": {
"category": "preferences",
"type": "settings"
},
"relevance_score": 0.87,
"created_at": "2024-03-19T15:45:00Z"
}
],
"count": 2
}
Error Responses
400 Bad Request
{
"error": "Query parameter is required"
}
401 Unauthorized
{
"error": "Invalid or missing API key"
}
500 Internal Server Error
{
"error": "Failed to recall memories"
}