autourgos-summary-memory 1.0.0

LLM-compressed rolling summary memory for Autourgos agents — SummaryBufferedMemory.

autourgos-summary-memory

LLM-compressed rolling summary memory for Autourgos agents.

Keeps the last N messages in full. When the buffer overflows, older messages are fed to an LLM for compression and merged into a rolling summary. The summary + recent messages are both included in every LLM prompt.

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Install

pip install autourgos-summary-memory

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Quick Start

from autourgos_summary_memory import SummaryBufferedMemory
from autourgos_openaichat import OpenAIChatModel
from autourgos_react_agent import ReactAgent

# Use a cheap model for summarization
summarizer_llm = OpenAIChatModel(model="gpt-4o-mini")

memory = SummaryBufferedMemory(
    llm=summarizer_llm,
    max_messages=10,   # keep last 10 messages in full; compress the rest
)
agent = ReactAgent(llm=my_llm, memory=memory)
agent.invoke("Start a long research task...")

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Without an LLM

If no LLM is provided, overflow messages are concatenated verbatim (no AI compression). Still prevents unbounded growth:

memory = SummaryBufferedMemory(max_messages=10)

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Parameters

Parameter	Type	Default	Description
llm	any	None	LLM with .invoke(prompt). Falls back to raw concat if not set.
max_messages	int	10	Recent messages kept in full before compression triggers.
moving_summary	str	""	Seed summary to start with (optional).

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What format_for_llm returns

--- Summary of Past Conversation ---
[compressed history here]
------------------------------------

--- Recent Conversation Context ---
[2024-...] user: latest messages
[2024-...] agent: in full
-----------------------------------

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Pair with autourgos-summarizer

For scratchpad compression (not memory), see autourgos-summarizer — it compresses the agent's reasoning chain, not the conversation history.

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Links

• PyPI: https://pypi.org/project/autourgos-summary-memory/
• GitHub: https://github.com/devxjitin/autourgos-summary-memory
• Issues: https://github.com/devxjitin/autourgos-summary-memory/issues

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License

MIT — see LICENSE