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Documentation Index

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Overview

Summary Memory generates and maintains an evolving summary of key conversation points. It works both alongside conversation memory and independently.

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Save vs Load

FlagPurpose
summary_memorySave: Generate and persist session summaries
load_summary_memoryLoad: Inject summary into subsequent runs (defaults to summary_memory)

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Standalone Usage

Summary memory works without full session memory. The agent recalls context through generated summaries instead of raw message history: 
from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

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

memory = Memory(
    storage=storage,
    session_id="session_001",
    full_session_memory=False,  # No raw history
    summary_memory=True,        # Summary generated after each run
    model="anthropic/claude-sonnet-4-5"
)

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

result1 = agent.do(Task("My name is Alice and I work on project Falcon"))
result2 = agent.do(Task("What project am I working on?"))
print(result2)  # Recalls via summary

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Combined with Conversation Memory

Use both for detailed context + cost-efficient summaries: 
from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

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

memory = Memory(
    storage=storage,
    session_id="session_001",
    full_session_memory=True,
    summary_memory=True,
    num_last_messages=10,  # Recent history + summary for older context
    model="anthropic/claude-sonnet-4-5"
)

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

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

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Save History, Load Summary Only

Save full history for auditing but only inject the summary to reduce token usage: 
from upsonic import Agent, Task
from upsonic.storage.memory import Memory
from upsonic.storage.sqlite import SqliteStorage

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

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

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

result1 = agent.do(Task("Let's discuss Python web frameworks"))
result2 = agent.do(Task("How does Django compare to Flask?"))
result3 = agent.do(Task("What have we discussed so far?"))
print(result3)  # Uses summary for context, full history saved in storage

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How It Works

  1. After each completed run, the session summary is updated by a sub-agent
  2. Summary includes key points, user preferences, and topics discussed
  3. Summary is injected as context for subsequent interactions (when load_summary_memory is enabled)
  4. Works independently of full_session_memory — can be the sole source of recall

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Parameters

ParameterTypeDefaultDescription
summary_memoryboolFalseSave and generate summaries
load_summary_memorybool | NoneNoneInject summary into runs (defaults to summary_memory)
session_idstrauto-generatedSession identifier
modelstr | Model(required)Model for generating summaries