Deep Search Agent is an experimental demonstration project that showcases an autonomous AI agent designed to perform multi-step research and information gathering tasks. The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses. It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured outputs. The project is positioned primarily as a proof of concept for deep research agents rather than a production-ready system. Its architecture highlights agent loops, tool calling, and stepwise execution, which are increasingly important patterns in modern AI automation. Overall, the demo serves as a practical reference for developers exploring autonomous research agents and multi-tool LLM orchestration.
Features
- Autonomous multi-step research workflow
- LLM-driven planning and reasoning loop
- Integrated search and retrieval pipeline
- Tool-calling agent architecture
- Proof-of-concept research agent design
- Extensible framework for experimentation