ThinkDepth.ai deep researcher uses multiple deep agents that automatically balance thinking depth, cost, number of sub-agents and other factors. We define deep agents as follows:​

• Are goal-oriented, stateful, and autonomous LLM-based systems that can plan, reason, and execute over long horizons.

• They maintain memory, adapt strategies, and self-reflect to refine their output or actions.

• Orchestrate multiple tools, APIs, sub-agents, or workflows to iteratively achieve complex tasks.

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Key Advantages of Deep Agents in Deep Research

• Hierarchical Planning and Decomposition: deep agents can break down a high-level research question into structured sub-tasks, such as literature review, hypothesis generation, evidence synthesis, counterexample search, etc.

• Memory and Iteration: deep agents remember previous steps, track what’s been explored, and iterate as new information is uncovered.

• Self-Critique and Meta-Reasoning: deep agents can self-evaluate their answers (“Was this synthesis comprehensive?”), spot gaps in their knowledge, and re-plan.

• Workflow Automation: deep agents can autonomously chain together tools (e.g., search → extract → analyze → write → verify) across many steps.​

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With those self-balancing deep agents, ThinkDepth.ai deep researcher is ranked #1 on the DeepResearch Bench. ThinkDepth.ai deep research established a new state-of-art result on DeepResearch Bench on Oct 29th, 2025. It outperformed gemini-2.5-pro-deepresearch by 2.78%; OpenAI deep research by 6.04%; Anthropic Claude deep research by 7.45%.

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