Agent Runtime Environments

At the foundation level, the framework provides a runtime environment, which facilitates communication between agents, manages their identities and lifecycles, and enforce security and privacy boundaries.

It supports two types of runtime environment: standalone and distributed. Both types provide a common set of APIs for building multi-agent applications, so you can switch between them without changing your agent implementation. Each type can also have multiple implementations.

Standalone Agent Runtime

Standalone runtime is suitable for single-process applications where all agents are implemented in the same programming language and running in the same process. In the Python API, an example of standalone runtime is the SingleThreadedAgentRuntime.

The following diagram shows the standalone runtime in the framework.

Here, agents communicate via messages through the runtime, and the runtime manages the lifecycle of agents.

Developers can build agents quickly by using the provided components including routed agent, AI model clients, tools for AI models, code execution sandboxes, model context stores, and more. They can also implement their own agents from scratch, or use other libraries.

Distributed Agent Runtime

Distributed runtime is suitable for multi-process applications where agents may be implemented in different programming languages and running on different machines.

A distributed runtime, as shown in the diagram above, consists of a host servicer and multiple workers. The host servicer facilitates communication between agents across workers and maintains the states of connections. The workers run agents and communicate with the host servicer via gateways. They advertise to the host servicer the agents they run and manage the agents’ lifecycles.

Agents work the same way as in the standalone runtime so that developers can switch between the two runtime types with no change to their agent implementation.