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python - Extracting AI Responses from a Multi-Agent Graph using . . . events = graph stream(lang_input, config=thread_config, stream_mode="updates", subgraphs=True) How do I extract just the AI-generated responses from this? The return type seems arbitrary, making it unclear which part contains the actual AI outputs, especially since my graph contains LLM nodes nested in subgraphs There does not seem to be a structured response from graph stream ( ) so im a
visualization - Langfuse trace graph for multi-agent runs is ambiguous . . . Any recommended trace structure or fields that Lang fuse needs for correct multi-agent graphs (e g , specific parent_id formatting, span metadata, agent vs tool role tags)? Workarounds if this is a Lang fuse visualization issue? If helpful, I can share a sanitized trace JSON, please let me know fields you need to inspect
Openai gym environment for multi-agent games - Stack Overflow Yes, it is possible to use OpenAI gym environments for multi-agent games Although in the OpenAI gym community there is no standardized interface for multi-agent environments, it is easy enough to build an OpenAI gym that supports this For instance, in OpenAI's recent work on multi-agent particle environments they make a multi-agent environment that inherits from gym Env which takes the