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Insight for: [Feature] Streaming output for agent execution

Real-time streaming output for multi-agent execution. Specifically, enabling users to see LLM responses as they are generated, rather than waiting for a full response.

Analyzed: Apr 2, 2026

The request for 'streaming output for agent execution' addresses a critical user experience and debugging challenge in multi-agent frameworks: lack of real-time visibility for 'long-running tasks.' Waiting for full LLM responses creates high perceived latency and hinders early intervention if an agent deviates. Implementing streaming, via adapter.stream(), provides immediate 'progress feedback' and enables 'early termination,' significantly improving developer productivity and user satisfaction. This feature is crucial for positioning the framework as responsive and interactive, especially as multi-agent systems tackle increasingly complex and time-consuming problems. It aligns with modern UX expectations for real-time feedback in interactive AI applications.

Streaming output agent execution real-time LLM responses AgentRunner adapter.chat() long-running tasks progress feedback CLI web UI perceived latency early termination stream mode option adapter.stream() emit events callback AsyncIterable consumer integration tool calls interleaved

GitHub Issue

Parent Entity

[Feature] Streaming output for agent execution

State: Open