Context-aware memory
Agents that remember user preferences across sessions, enabling truly personalized automation.
Use cases
The application changes. The context layer doesn't.
Everything below runs on the same API, in a different mixture of the same five jobs. That mixture is what makes a support desk a different piece of software from a coding agent, and it is the reason context is infrastructure rather than a feature.
01 · 5 use cases
Persistent memory
Preferences and corrections survive the session, the handoff and the model swap.
context.memory.add02 · 4 use cases
Current context
Superseded versions are subtracted before the model ever sees them.
subtraction · versioned fileName03 · 3 use cases
Traceable decisions
Every answer carries the exact context it was served, and why.
context.traces05 · 4 use cases
Scoped retrieval
Intersection, subtraction and ranking decide what enters the window, not similarity alone.
context arithmeticIn every use case
What the context layer gives every agent
Whichever job leads, the same capabilities sit underneath: memory, sync, tooling and agents that act on context.
Real-time sync
Your data is always up to date. Information syncs across teams and applications in real time.
Customer support
Add a human touch to your chatbots with memory, so they retain context.
Integrated tooling
Connect your existing stack through a single, powerful API layer.
LLMs with memory
Give large language models long-term memory for richer, continuous conversations.
Agentic AI
Build autonomous agents that reason, plan and execute complex tasks using context.
Don't see yours?
It is probably one of the five.
The applications differ. The jobs underneath them do not. Tell us what you are building and which one it leans on, and we will show you the mixture.