Summary Deep Search Agent is a Python implementation of an AI agent designed for automated, in-depth research report generation. It addresses the need for high-quality synthesized information by employing multi-turn web searches and iterative reflection, offering a framework-agnostic alternative to heavy orchestration libraries. The agent targets developers and researchers seeking a flexible tool for complex information gathering and analysis.

How It Works The agent follows a phased research methodology: query decomposition, report structure generation, initial search and summarization per paragraph, followed by a multi-turn reflection loop. This loop iteratively refines summaries through targeted searches, ensuring depth and completeness. Key architectural choices include a modular design with distinct processing nodes, support for multiple LLMs (DeepSeek, OpenAI), and integration with the Tavily search engine. Its core advantage lies in its framework-agnostic implementation, focusing on core agent logic and extensibility.

Quick Start & Requirements Requires Python 3.9+. Installation involves cloning the repo, setting up a Python environment, and running pip install -r requirements.txt. Users must configure API keys for DeepSeek, OpenAI, and Tavily in config.py. Usage is demonstrated via command-line scripts (examples/basic_usage.py, examples/advanced_usage.py) and a Streamlit web interface (streamlit run examples/streamlit_app.py). Programmatic access is available via the DeepSearchAgent class.

Highlighted Details

• Framework-Agnostic Core: Built from scratch, avoiding dependencies on large frameworks like LangChain for core functionality.
• Multi-LLM Support: Integrates with DeepSeek and OpenAI models, extensible via an abstract base class.
• Iterative Reflection: Employs a multi-turn reflection mechanism to progressively deepen research and optimize content.
• Integrated Search: Leverages the Tavily search engine for high-quality web data retrieval.

Maintenance & Community The project welcomes contributions via Pull Requests. Specific details regarding maintainers, community channels (e.g., Discord/Slack), or a public roadmap are not detailed in the README.

Licensing & Compatibility Released under the permissive MIT License, allowing for broad use, modification, and distribution, including in commercial and closed-source applications.

Limitations & Caveats Users must obtain and configure API keys for LLMs and search services. Integrating alternative search engines requires code modification. Report quality is influenced by LLM choice and configuration parameters like max_reflections and max_search_results.