AI Memory OS
Operational memory for agents
Stop rebuilding your agent's brain every morning.
AI Memory OS gives agents durable operational memory, so they remember critical instructions, learn from expensive mistakes, and apply the right procedure before they touch your code, tools, data, or customers.
Live action gate
Before the agent rewrites your ad script
Agent plan
Rewrite the campaign generation script to handle the latest user feedback on hooks, tone, and targeting.
Retrieved procedure
Convert repeat feedback into configurable inputs. Only edit script logic when the current API cannot express the change.
Safer next action
Add a feedback configuration layer, preserve the stable script, and store the lesson for future ad-generation runs.
Pain point
The agent forgets the decision you made 20 minutes ago.
Pain point
It repeats a mistake you already corrected three times.
Pain point
It rewrites the script instead of making the system flexible.
Pain point
It loses folder paths, deployment rules, owner preferences, and project context after compaction.
DNA storage plus brain runtime
Memory that changes agent behavior, not just the prompt.
Vector search can find related text. AI Memory OS decides what matters, what should fade, what replaces an old decision, and what must become a pre-action rule.
Typed Memory Genes
Instructions, decisions, mistakes, facts, procedures, preferences, and context are stored as different memory classes.
Salience Re-Ranking
Recall is not just vector similarity. The system ranks by importance, confidence, age, access history, and memory type.
Procedural Action Gates
Critical lessons become pre-action checks, warnings, and blockers before the agent edits, deploys, or calls tools.
Supersession Graph
When a user changes direction, old decisions are suppressed and linked to the new source of truth.
Built for agents that do real work.
The buyer is not asking for a nicer chatbot. They are asking for agents that stop wasting engineering time, stop losing operational context, and stop making the same dumb mistake after every context reset.
Raw agent run
Extract memories
Score importance
Archive noise
Gate future actions
Cross-session recall
Memories survive tabs, compaction, and restarts.
Selective expression
Only the memories that matter are activated.
For agent teams tired of babysitting
Give every agent a memory layer your team can trust.
Start with the local demo, then wire the SDK into the agents that keep forgetting what your team already taught them.