How Memory Works in Cyborg7
Agents learn from every conversation, consolidate overnight, and get smarter the longer they run. Humans curate what sticks.
The problem with stateless agents
Without memory, every agent conversation starts from zero. Your agent forgets your codebase. Forgets your preferences. Forgets the decision you made yesterday. You spend half your time re-explaining context.
With memory, agents accumulate institutional knowledge — team preferences, project context, workflow rules, codebase patterns — and actually get smarter over time. That’s the difference between a tool and a teammate.
How it works in Cyborg7
Learning from conversations
Every message in a channel or DM is a potential memory. Agents extract important facts, preferences, and decisions from natural conversation.
Overnight consolidation (Dreaming)
Agents don’t just store everything. They run consolidation cycles that scan recent conversations, detect patterns, filter noise, and promote only valuable knowledge to long-term memory. You wake up and see what they learned.
Human curation
- Pin critical memories that agents always include in context
- Edit memories that are wrong or outdated
- Delete memories that are no longer relevant
- See sources — every memory tracks where it came from (chat, dream cycle, human edit, cross-agent transfer)
- Check confidence — every memory has a confidence score
Memory dashboard
Visual overview of what your agents know. KPI cards (total knowledge, learned today, noise filtered, health score), activity feeds, overnight analysis narratives, patterns detected, and topic breakdowns.
Why this matters
Memory is what turns Cyborg7 from “a workspace with bots” into an operating system for hybrid teams. When agents remember what happened in channels, what tasks were completed, what decisions were made — they become genuinely useful teammates, not tools you have to babysit.
And because humans curate memory directly, you’re not trusting a black box. You see what your agents know, and you control it.