Conversation Memory - Upsonic AI

Overview

Conversation Memory persists the complete chat history for a session, enabling agents to reference previous messages and maintain context across interactions.

Save vs Load

Flag	Purpose
`full_session_memory`	Save: Persist raw messages to storage
`load_full_session_memory`	Load: Inject message history into subsequent runs (defaults to `full_session_memory`)

You can save history without injecting it — useful when pairing with summary memory to reduce token usage while keeping a full audit trail.

Basic Usage

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

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

memory = Memory(
    storage=storage,
    session_id="session_001",
    full_session_memory=True
)

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

result1 = agent.do(Task("My name is Alice"))
result2 = agent.do(Task("What's my name?"))
print(result2)  # "Your name is Alice"

Save-Only Mode

Save full history for auditing but don’t inject it into context:

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

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

memory = Memory(
    storage=storage,
    session_id="session_001",
    full_session_memory=True,         # Save raw history
    load_full_session_memory=False,   # Don't inject into context
    summary_memory=True,              # Use summary for context instead
    load_summary_memory=True,
    model="anthropic/claude-sonnet-4-5"
)

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

result = agent.do(Task("Continue our discussion"))
print(result)

With Message Limiting

Control memory size by limiting to the last N conversation turns:

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

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

memory = Memory(
    storage=storage,
    session_id="session_001",
    full_session_memory=True,
    num_last_messages=10  # Keep last 10 turns only
)

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

result = agent.do(Task("Let's continue our discussion"))
print(result)

With Tool Call Results

Include tool execution results in the conversation history:

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

@tool
def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"Weather in {city}: 72°F, Sunny"

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

memory = Memory(
    storage=storage,
    session_id="session_001",
    full_session_memory=True,
    feed_tool_call_results=True  # Include tool outputs in history
)

agent = Agent("anthropic/claude-sonnet-4-5", tools=[get_weather], memory=memory)

result1 = agent.do(Task("What's the weather in NYC?"))
result2 = agent.do(Task("What was the weather you just told me?"))
print(result2)  # Can reference previous tool results

Parameters

Parameter	Type	Default	Description
`full_session_memory`	`bool`	`False`	Save conversation history
`load_full_session_memory`	`bool \| None`	`None`	Inject history into runs (defaults to `full_session_memory`)
`session_id`	`str`	auto-generated	Session identifier
`num_last_messages`	`int \| None`	`None`	Limit to last N turns
`feed_tool_call_results`	`bool`	`False`	Include tool outputs

​Overview

​Save vs Load

​Basic Usage

​Save-Only Mode

​With Message Limiting

​With Tool Call Results

​Parameters