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aglet-builtin-memory-summary

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Aglet memory technique: rolling LLM-summarised history for long conversations.

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Direct dependencies1From release 0.1.0a1
Last observed21 Oct, 09:14 UTC

README

Version 0.1.0a1 documentation

aglet-builtin-memory-summary

Rolling LLM-summarised conversation memory — the right Memory Technique for long chats where a sliding window would drop important context.

Part of the Aglet pluggable Agent runtime.

What it does

When the per-conversation buffer grows past trigger_chars, the technique asks an LLM to compress the oldest half of the history into a single short paragraph and recalls that summary on every subsequent turn. Recent turns are kept verbatim.

Best combined with memory.sliding_window under routing: parallel_merge:

memory:
  techniques:
    - { name: sliding_window, config: { max_messages: 10 } }
    - name: summary
      config:
        model: cheap
        trigger_chars: 6000
        keep_recent: 6
  routing: parallel_merge

Install

pip install --pre aglet-builtin-memory-summary

Config

KeyDefaultDescription
modeldefaultModel alias (from agent.yaml models:) used for compression
trigger_chars6000Compress when the conversation buffer exceeds this many chars
keep_recent6Number of most-recent messages kept verbatim
summary_prefix"[Prior conversation summary]"Label prepended to recalled summaries

License

Apache-2.0

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VersionPublishedDirect dependenciesOpen version
0.1.0a122 Apr 20261

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zyssyz123/agentkit

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package:1f5e605998f4eeda118330c6bd86a3e8aee0ef0a1a88fb3f726602fbbcc0765e