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Gemini Executive Synthesis

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.

Technical Positioning
Enhancing user experience, perceived latency, and debuggability for long-running multi-agent tasks.
SaaS Insight & Market Implications
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.
Proprietary Technical Taxonomy
Streaming output agent execution real-time LLM responses AgentRunner adapter.chat() long-running tasks progress feedback

Raw Developer Origin & Technical Request

Source Icon GitHub Issue Apr 1, 2026
Repo: JackChen-me/open-multi-agent
[Feature] Streaming output for agent execution

## Summary

Support real-time streaming output during agent execution, so users can see LLM responses as they are generated.

## Motivation

Currently `AgentRunner` uses `adapter.chat()` which waits for the full response. For long-running tasks, users have no visibility into what the agent is doing until it finishes. Streaming would enable:

- Real-time progress feedback in CLI or web UI
- Lower perceived latency
- Early termination if the agent goes off track

## Proposed Approach

- Add `stream` mode option to `AgentRunner.run()`
- Use `adapter.stream()` instead of `adapter.chat()` when enabled
- Emit events via callback or AsyncIterable for consumer integration
- Handle tool calls within streaming context

## Open Questions

- Should streaming be opt-in per agent or per team?
- How to handle tool execution interleaved with streaming output?

Developer Debate & Comments

No active discussions extracted for this entry yet.

Adjacent Repository Pain Points

Other highly discussed features and pain points extracted from JackChen-me/open-multi-agent.

Extracted Positioning
Integration of local LLM support via Ollama. Specifically, implementing an OllamaAdapter for the multi-agent framework.
Expanding the framework's compatibility to include local models, reducing reliance on cloud APIs, and catering to the 'r/LocalLLaMA' community.
Extracted Positioning
Gathering user feedback on use cases, agent team configurations, LLM provider preferences, and missing features for the open-multi-agent framework.
A versatile, lightweight multi-agent framework supporting various LLMs, aiming to meet diverse real-world needs.
Extracted Positioning
Robust error handling and fault tolerance for multi-agent tasks. Specifically, configurable retry logic and error recovery strategies for failed LLM API calls.
A production-ready, resilient multi-agent framework capable of handling transient failures gracefully.
Extracted Positioning
Real-time visualization dashboard for multi-agent task execution. Specifically, a web UI to display the Task Directed Acyclic Graph (DAG), agent status, and progress.
Enhancing the usability, observability, and debuggability of complex multi-agent workflows.
Extracted Positioning
Discussion around 'leaked source code' related to Claude Code.
N/A (This issue is a statement about a leak, not a product feature or positioning of open-multi-agent).

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Real-time Real-time Computing