Complete guide to integrating and using the Vesora API.
Welcome to Vesora, the AI Memory Layer. Vesora enables persistent, portable memory for all intelligent systems. This documentation covers integration, API reference, and best practices.
Key Features:
from vesora import MemoryClient
client = MemoryClient(
api_key='your_api_key',
endpoint='https://api.vesora.io'
)
# Authenticate
client.authenticate(api_key='your_api_key')# Store user preferences
memory = {
'type': 'preference',
'user_id': 'agent_123',
'data': {
'language': 'en',
'timezone': 'UTC',
'preferences': ['tech', 'ai', 'startup']
}
}
response = client.memory.store(memory)
print(f"Memory stored: {response.id}")# Retrieve user preferences
preferences = client.memory.retrieve(
user_id='agent_123',
memory_type='preference'
)
print(f"User preferences: {preferences.data}")# Query memory with filters
results = client.memory.query(
user_id='agent_123',
query='What are the user preferences?',
filters={'type': 'preference'},
limit=10
)
for result in results:
print(f"Memory: {result.content}")# Update existing memory
updated = client.memory.update(
memory_id='mem_12345',
data={
'language': 'es',
'preferences': ['tech', 'ai', 'startup', 'web3']
}
)
print(f"Memory updated at: {updated.updated_at}")# Delete memory
response = client.memory.delete(memory_id='mem_12345')
if response.success:
print("Memory deleted successfully")POST /memory/store
Store a new memory object
GET /memory/retrieve
Retrieve memory by ID or user_id
POST /memory/query
Query memory with semantic search
PUT /memory/update
Update existing memory
DELETE /memory/delete
Delete memory by ID