LLM runtime
The environment that turns model output into real work: executing tool calls, managing context and memory, enforcing permissions, retrying failures, recording what happened. Models reason; runtimes act.
Models reason; runtimes act
A model emits text, including text that says "call this tool". Something has to parse that, check permission, execute it, catch the failure, retry sensibly, record it and hand the result back. That something is the runtime, and it is where nearly all the reliability of an agent system actually lives.
The unglamorous checklist
Tool execution with permissions. Persistent memory. Context assembly and trimming. Triggers and scheduling. Human approval gates. Credential storage. Receipts. None of it is research. Shipping all of it, coherently, is the product — which is why teams that start by writing their own runtime usually end up rebuilding this list badly.
Where it runs decides who owns it
The runtime is what holds your API keys, your agents’ memory of your operations and the log of every decision made on your behalf. Whether that corpus sits on your hardware or somebody else’s is settled by the architecture, not by the privacy policy printed on top of it.
In AGNT
AGNT is a local-first LLM runtime — the layer where agents, tools, memory and receipts actually live.
LLM runtime — common questions
Is a runtime the same as a framework?
No. A framework is a library you assemble into an application. A runtime is the running system — with triggers, storage, permissions and a UI — that your agents live inside.
Can one runtime use several model providers?
Yes. AGNT normalises tool calling across frontier APIs and local models, so workflows are provider-portable by default.
What does AGNT add on top of the model?
Identity, tools, memory, scheduling, approval gates and receipts — everything between a model that can reason and a system that can be trusted with work.