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DyFlow is a dynamic workflow framework for agentic reasoning that splits tasks into high-level planning (Designer) and low-level execution (Executor). It uses execution-adaptive workflows with dynamic operators that re-plan subgoals during execution based on intermediate feedback, improving robustness across domains. The project provides installation and deployment guidance, including running a local DyPlanner model via vLLM and required API keys (OpenAI, Anthropic, DeepInfra) using Hugging Face models.
AI named DyFlow in December 2025.