showcase/shell-docs/src/content/docs/shared-state/streaming.mdx
By default, agent state only updates between LangGraph node transitions, so a long-running tool call (writing a full document, drafting an email) appears to the UI as one big burst at the end. For agent-native apps, that feels broken: users expect to watch the output materialise.
State streaming forwards the value of a specific tool argument
straight into an agent state key as the argument is being generated.
The UI, subscribed via useAgent, re-renders every token.
Use state streaming whenever a tool's output is long-form text or a growing structured value and you want the user to see it assemble in real time. Common shapes:
Without streaming, the user stares at a spinner. With streaming, they see the answer grow token-by-token.
The backend pattern is always the same: map one streaming tool argument
to one shared-state key. In Python prebuilt agents, that is
StateStreamingMiddleware with one or more StateItem(...) entries.
TypeScript graphs use copilotkitCustomizeConfig with an
emitIntermediateState mapping for the same shape. When the LLM streams that
argument, CopilotKit writes every partial value into shared state before the
tool even finishes executing.
A few things to note:
state_key must exist on your AgentState schema (document: str in this demo).tool and tool_argument name the exact LLM-facing tool and
argument to forward.The UI side is identical to any other shared-state subscription:
useAgent with OnStateChanged gives you a reactive agent.state.
Add OnRunStatusChanged if you want a "LIVE" / "done" indicator.
From there, agent.state.document is just a string that grows on every
token, and agent.isRunning tells you whether to show a streaming
indicator.
<IntegrationGrid path="shared-state/streaming" exclude={["agno", "agent-spec", "spring-ai", "langroid"]} />