Running AI agents in a local script is straightforward. Running them reliably in production across teams, across restarts, with isolated environments per context is a different problem entirely. BerriAI, the company behind the LiteLLM AI Gateway, is now open-sourcing a purpose-built answer to that problem: the LiteLLM Agent Platform. The platform is described as a simple, self-hosted infrastructure platform for running multiple agents in production.
What Problem Does it Solve?
It helps to understand what happens when you try to scale agents beyond a single process. Agents are stateful: they carry session history, tool call results, and intermediate reasoning across turns. If the container running your agent crashes, restarts, or gets replaced during a deployment, that session state is gone unless something is explicitly managing it. At the same time, different teams often need different runtime environments, different tools, different secrets, different access scopes which means you cannot throw all agents into one shared container.
The platform manages two things: per-team and per-context sandboxes, and session continuity across pod restarts and upgrades. These two capabilities are the core infrastructure primitives the platform provides.
