autourgos-summarizer 1.0.1

Scratchpad compression middleware for Autourgos agents — prevents token overflow by auto-summarizing long reasoning chains.

autourgos-summarizer

Scratchpad compression middleware for Autourgos agents.

Automatically summarizes long reasoning chains so your agent never runs out of token space — even on tasks with dozens of iterations.

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Why use this?

LLM agents accumulate a scratchpad — a growing log of thoughts, tool calls, and observations. On long tasks this scratchpad can:

• Hit the LLM's context window limit and crash
• Slow down responses (more tokens = more cost and latency)
• Confuse the LLM with too much irrelevant history

AutoSummarizeMiddleware solves this by compressing the scratchpad in the background every N iterations (or when it exceeds a size limit), keeping only what matters: key findings, tool results, and current status.

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Install

pip install autourgos-summarizer

Zero dependencies. Works with any Autourgos agent.

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Quick Start

from autourgos_summarizer import AutoSummarizeMiddleware
from autourgos_react_agent import ReactAgent

summarizer = AutoSummarizeMiddleware(
    summarize_every=5,          # compress every 5 iterations
    max_scratchpad_chars=15000, # also compress if scratchpad exceeds 15k chars
)

agent = ReactAgent(llm=my_llm, middleware=[summarizer])
result = agent.invoke("Research the latest breakthroughs in quantum computing")
print(result)

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How it works

AutoSummarizeMiddleware hooks into on_iteration_start. Before each iteration it checks:

1. Is this a multiple of summarize_every? (e.g. iteration 5, 10, 15…)
2. Is the scratchpad longer than max_scratchpad_chars?

If either is true, it calls the LLM (middleware's own llm if set, otherwise agent.llm) with a compression prompt that asks it to distill the scratchpad into:

[Summary of steps 1-N]
Key findings: ...
Tool results: ...
Current status: ...

This summary replaces the full scratchpad before the next LLM call. The agent continues from where it left off, but with a much smaller context.

Summarization runs synchronously (the agent loop is paused at this point), and a threading.Lock prevents duplicate summarizations when parallel tool callbacks fire.

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Use a dedicated LLM for summarization

You can pass a separate llm to AutoSummarizeMiddleware. This LLM is used only for compressing the scratchpad — great for keeping costs low by using a cheaper/faster model just for this job.

from autourgos_summarizer import AutoSummarizeMiddleware
from autourgos_openaichat import OpenAIChatModel

# Main agent uses a powerful model
main_llm = OpenAIChatModel(model="gpt-4o")

# Summarizer uses a cheap fast model
cheap_llm = OpenAIChatModel(model="gpt-4o-mini")

summarizer = AutoSummarizeMiddleware(
    summarize_every=5,
    llm=cheap_llm,  # overrides agent.llm for summarization
)

agent = ReactAgent(llm=main_llm, middleware=[summarizer])
result = agent.invoke("Research the latest breakthroughs in quantum computing")
print(result)

If llm is not provided, it falls back to agent.llm automatically.

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Parameters

Parameter	Type	Default	Description
summarize_every	int | None	5	Summarize every N iterations. None = disable iteration-based trigger.
max_scratchpad_chars	int	15000	Also trigger if scratchpad exceeds this many characters.
llm	any	None	LLM for summarization. Needs .invoke(prompt). Falls back to agent.llm if not set.

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Combine with other middleware

from autourgos_summarizer import AutoSummarizeMiddleware
from autourgos_history import AgentHistoryMiddleware

history    = AgentHistoryMiddleware()
summarizer = AutoSummarizeMiddleware(summarize_every=5)

agent = ReactAgent(llm=my_llm, middleware=[summarizer, history])

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Requirements

• Python 3.9+
• Any Autourgos agent that exposes agent.scratchpad, agent.llm, and agent.query

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License

MIT — see LICENSE