Manage Long-Running Agent Memory With Eviction That Preserves What Matters
Long-running agents accumulate memory until retrieval slows or storage explodes. Naive eviction (drop oldest) loses critical context. MemoryLake provides typed eviction strategies — preserve verified facts, archive stale events, promote durable skills — so memory stays useful at scale.
Manage Long-Running Agent Memory With Eviction That Preserves What Matters
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The problem: naive eviction loses what matters
The agent has been running for six months. There's too much memory now. Dropping oldest entries loses founding decisions. Dropping least-accessed loses critical-but-rare facts. Without typed eviction, long-running agents either grind to a halt or amnesiac themselves.
How MemoryLake handles long-running memory eviction
Per-type retention rules
Facts retained longer than ephemeral chat turns; reflections promote to skills.
Importance-weighted retention
Memory used in successful decisions stays; never-retrieved entries archive.
Cold archive instead of deletion
Old memory archived; available on demand, not in active retrieval.
Audit trail on eviction
Compliance can confirm what stayed and what archived.
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How it works for memory eviction
- Connect — Configure retention rules per memory type.
- Structure — Memory tagged by type and importance on write.
- Reuse — Active retrieval pulls fresh memory; archive available for deep queries.
Before vs. after: long-running agent memory management
| Naive eviction | MemoryLake eviction | |
|---|---|---|
| Retention policy | Drop oldest | Per-type with importance |
| Critical-but-rare fact retention | Often lost | Preserved |
| Storage growth | Unbounded or lossy | Bounded with archive |
| Audit "what got dropped?" | None | Eviction log |
Who this is for
Engineering teams running agents that operate for months or years per user — copilots, assistants, agentic systems — where memory accumulation has become an architectural concern.
Try this use case on your own account
An ops agent has run for six months and remembered 36 things; its active memory is capped at 20. Drop the oldest and it loses the founding ledger rule. Drop the least used and it loses the quarter-end freeze it needed once. Each loss shows up weeks later, as a task the agent goes ahead with because nothing in its memory says not to.
What you will see
- 36 memory entries pinned word for word (6 decisions, 4 constraints, 4 preferences, 16 events, 6 chat turns), each with type, written on, use count and last used in metadata — what an eviction job reads
- Three policies over the same metadata, each keeping 20 in its own project: drop-oldest evicts the ledger rule, drop-least-used evicts the quarter-end close freeze, typed retention keeps every decision, constraint and preference and only events still in use
- The same three tasks searched against all three active memories (top 8): the 29 December migration misses the freeze under drop-least-used, the ledger fix misses the append-only rule under drop-oldest, typed retention 3 of 3; the control task is found everywhere
- Typed retention moves its 16 to a cold-archive project with an eviction record (when, policy, reason, original fact id);
search --projects active,archiveanswers an old-incident question at #1 that the active memory alone cannot. Pluscheck,evictionsand a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/eviction-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Can archived memory be retrieved?
Can archived memory be retrieved?
Yes — slower path, but available.
Configurable retention windows?
Configurable retention windows?
Yes, per memory type and scope.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.