Observed since 21 Oct, 09:14 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
Aglet memory technique: rolling LLM-summarised history for long conversations.
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.
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
pip install --pre aglet-builtin-memory-summary
| Key | Default | Description |
|---|---|---|
model | default | Model alias (from agent.yaml models:) used for compression |
trigger_chars | 6000 | Compress when the conversation buffer exceeds this many chars |
keep_recent | 6 | Number of most-recent messages kept verbatim |
summary_prefix | "[Prior conversation summary]" | Label prepended to recalled summaries |
Apache-2.0
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.1.0a1 | 22 Apr 2026 | 1 |
This describes our collected data; it is not a security assessment.
Observed since 21 Oct, 09:14 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:1f5e605998f4eeda118330c6bd86a3e8aee0ef0a1a88fb3f726602fbbcc0765e