User Analysis Memory

Overview

User Analysis Memory extracts user traits from conversations and builds persistent profiles. This enables personalization across sessions and adaptive agent behavior.

Save vs Load

Flag	Purpose
`user_analysis_memory`	Save: Analyze conversations and persist user profiles
`load_user_analysis_memory`	Load: Inject user profile into subsequent runs (defaults to `user_analysis_memory`)

Basic Usage

from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

storage = SqliteStorage(db_file="users.db")

memory = Memory(
    storage=storage,
    session_id="session_001",
    user_id="user_abc",
    user_analysis_memory=True,
    model="anthropic/claude-sonnet-4-5"
)

agent = Agent("anthropic/claude-sonnet-4-5", memory=memory)

result1 = agent.do(Task("I'm a data scientist with 5 years of ML experience"))
result2 = agent.do(Task("What do you know about me?"))
print(result2)  # "You're a data scientist with 5 years of ML experience"

Standalone Usage

User analysis memory works without full session memory. The agent saves and loads user profiles independently:

from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

storage = SqliteStorage(db_file="profiles.db")

memory = Memory(
    storage=storage,
    session_id="session_001",
    user_id="user_abc",
    full_session_memory=False,     # No raw history
    user_analysis_memory=True,     # Save user profile
    model="anthropic/claude-sonnet-4-5"
)

agent = Agent("anthropic/claude-sonnet-4-5", memory=memory)

result1 = agent.do(Task("I'm Alice, a Python developer who prefers dark mode"))
result2 = agent.do(Task("What do you know about me?"))
print(result2)  # Recalls user profile without raw history

Custom Profile Schema

Define specific fields to track:

from pydantic import BaseModel, Field
from typing import Optional, List
from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

class DeveloperProfile(BaseModel):
    name: Optional[str] = Field(None, description="Developer name")
    languages: Optional[List[str]] = Field(None, description="Programming languages")
    experience_years: Optional[int] = Field(None, description="Years of experience")
    preferred_editor: Optional[str] = Field(None, description="Preferred code editor")

storage = SqliteStorage(db_file="developers.db")

memory = Memory(
    storage=storage,
    session_id="session_001",
    user_id="dev_123",
    user_analysis_memory=True,
    user_profile_schema=DeveloperProfile,
    model="anthropic/claude-sonnet-4-5"
)

agent = Agent("anthropic/claude-sonnet-4-5", memory=memory)

result = agent.do(Task("I'm Alice, I use Python and Go, been coding for 8 years, love VS Code"))
print(result)

Dynamic Profile Generation

Let the agent discover and create custom fields:

from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

storage = SqliteStorage(db_file="dynamic.db")

memory = Memory(
    storage=storage,
    session_id="session_001",
    user_id="user_456",
    user_analysis_memory=True,
    dynamic_user_profile=True,  # Agent creates custom fields
    model="anthropic/claude-sonnet-4-5"
)

agent = Agent("anthropic/claude-sonnet-4-5", memory=memory)

result = agent.do(Task("I prefer concise answers, work night shifts, and love hiking"))
print(result)  # Agent adapts to learned preferences

Cross-Session Persistence

User profiles persist across sessions:

from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

storage = SqliteStorage(db_file="profiles.db")

# Session 1: Learn about user
memory1 = Memory(
    storage=storage,
    session_id="session_001",
    user_id="customer_789",
    user_analysis_memory=True,
    model="anthropic/claude-sonnet-4-5"
)
agent1 = Agent("anthropic/claude-sonnet-4-5", memory=memory1)
agent1.do(Task("I'm John, I run a small bakery in Seattle"))

# Session 2: New session, same user - profile is loaded
memory2 = Memory(
    storage=storage,
    session_id="session_002",  # Different session
    user_id="customer_789",     # Same user
    user_analysis_memory=True,
    model="anthropic/claude-sonnet-4-5"
)
agent2 = Agent("anthropic/claude-sonnet-4-5", memory=memory2)
result = agent2.do(Task("Remind me what you know about me"))
print(result)  # "You're John, you run a bakery in Seattle"

Save-Only Mode

Save profiles for analytics without injecting them into the agent’s context:

from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

storage = SqliteStorage(db_file="analytics.db")

memory = Memory(
    storage=storage,
    session_id="session_001",
    user_id="user_789",
    user_analysis_memory=True,          # Save profiles
    load_user_analysis_memory=False,    # Don't inject into context
    model="anthropic/claude-sonnet-4-5"
)

agent = Agent("anthropic/claude-sonnet-4-5", memory=memory)
result = agent.do(Task("I also speak Spanish and French"))
print(result)

Update Modes

Mode	Behavior
`'update'`	Merge new traits with existing profile (default)
`'replace'`	Replace entire profile with new traits

from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

storage = SqliteStorage(db_file="modes.db")

memory = Memory(
    storage=storage,
    session_id="session_001",
    user_id="user_123",
    user_analysis_memory=True,
    user_memory_mode='update',
    model="anthropic/claude-sonnet-4-5"
)

agent = Agent("anthropic/claude-sonnet-4-5", memory=memory)
result = agent.do(Task("I also speak Spanish and French"))
print(result)

Parameters

Parameter	Type	Default	Description
`user_analysis_memory`	`bool`	`False`	Save user profiles
`load_user_analysis_memory`	`bool \| None`	`None`	Inject profiles into runs (defaults to `user_analysis_memory`)
`user_id`	`str`	auto-generated	User identifier
`model`	`str \| Model`	(required)	Model for trait extraction
`user_profile_schema`	`BaseModel \| None`	`None`	Custom profile schema
`dynamic_user_profile`	`bool`	`False`	Auto-generate profile fields
`user_memory_mode`	`Literal['update', 'replace']`	`'update'`	Profile update strategy

​Overview

​Save vs Load

​Basic Usage

​Standalone Usage

​Custom Profile Schema

​Dynamic Profile Generation

​Cross-Session Persistence

​Save-Only Mode

​Update Modes

​Parameters