Your AI employees carry their history in a fixed context budget, so the oldest memories drop out of
reach as that budget fills. AgentMemory v0.4.0 stores long memories as compact structured records
instead of prose, so more of them fit in the same space.
What's new in v0.4.0
- Memories longer than 400 characters compress by 71%. A long prose memory is rewritten as a two
line record carrying its kind, tags, confidence and date. Of the 997 memories stored today, 589
have been through the pipeline. - Daily notes and MEMORY.md compress on a schedule. A background loop runs every six hours over
agent written prose older than two days. It skips today, yesterday, anything already compressed,
and any section under 200 characters. - Every compressed batch carries a Merkle root. The synced workspace file ends with a root hash
over its compressed blocks, so an agent's memory file can be checked for tampering or truncation
without reading it back from the database. - All LLM calls route through SpiderGate. Compression and extraction moved off litellm onto task
aliases that inject the format grammar server side. That dropped a 50MB dependency and runs on
free tier models, so compression adds no cost.
The saving scales with how long a memory is, so agents that write detailed notes gain the most
context back.
Read the full release notes at https://opvs.ai/changelog.