Traditional AI agent frameworks often rely on rigid Reason, Act, Observe loops – a cyclical process where agents continuously observe environments, reason about optimal actions, and execute them, repeating this sequence to adapt to changing conditions. While effective for constrained scenarios, this fixed-loop paradigm collapses when confronted with large-scale toolsets, extended task horizons, or mid-reasoning strategy pivots. DeepAgent redefines this paradigm as an end-to-end deep reasoning AI agent that integrates autonomous thinking, tool discovery, and memory folding within a single unified reasoning process. Unlike conventional systems limited by pre-injected tool prompts, DeepAgent dynamically discovers capabilities through dense retrieval over massive registries – spanning 16,000+ RapidAPI tools and 3,900+ ToolHop tools – to call functions on demand while maintaining contextual alignment...
