Agent patterns
Agent memory compression
Agent memory stores facts about users, tasks, and previous interactions. Over time, this memory grows beyond any context window. Compression keeps the most relevant memories for each new interaction.
Memory is unbounded
An agent serving customers might accumulate 100+ facts per customer: preferences, account details, issue history, resolved tickets. Sending all 100 facts with every new query is expensive and dilutes focus. Compression keeps only the facts relevant to the current query.
Frequently asked questions
Does compression lose long-term memory?
No. The full memory is stored externally. Compression selects the relevant subset for each query.
Can I use it with persistent agent frameworks?
Yes. Compress the retrieved memory subset before injecting it into the agent prompt.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.