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Orchestrate Swarm Agents & GPT-5 Synthesis

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Linked challenge: Multimodal Video Intelligence with Qwen3-VL, GPT-5 & LlamaIndex

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Linked challenge

Multimodal Video Intelligence with Qwen3-VL, GPT-5 & LlamaIndex

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Design and implement your OpenAI Swarm-like agent collective. Define 3-4 specialized agents (e.g., 'Visual Search', 'Transcript Analyst', 'Synthesizer'). How will they use A2A protocol to collaborate on queries, retrieve info from the LlamaIndex, and how will GPT-5 synthesize the final answer?

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Multimodal Video Intelligence with Qwen3-VL, GPT-5 & LlamaIndex

Inspired by advancements in long-context multimodal understanding, this challenge tasks you with building a cutting-edge video intelligence system. You will integrate the Qwen3-VL model for robust video and image analysis with GPT-5 for higher-level reasoning and synthesis. The system will leverage LlamaIndex for advanced RAG over multimodal data, allowing it to accurately answer complex 'needle-in-a-haystack' queries spanning long video durations. The core of the system will involve processing entire 30-minute video segments, extracting key visual and auditory information, generating multimodal embeddings, and indexing them using LlamaIndex. An OpenAI Swarm-like orchestration will manage specialized agents that collaborate using an A2A protocol to perform visual search, event detection, and generate comprehensive summaries. MCP could be used to facilitate access to external video processing tools or contextual databases.

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Related prompts

Browse library

Design Multimodal RAG Pipeline

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Implement Video Processing & Qwen3-VL Integration

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Execute 'Needle-in-a-Haystack' Queries

testing