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Deeper Seeker

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Basic Information

Deeper Seeker is an open-source research assistant designed to automate and structure deep web research tasks such as market research, competitor analysis, and preparation of investment memos. The tool accepts a user research query, generates follow-up questions to refine scope, creates a multi-step research plan, produces JSON-structured search queries, executes parallel web searches via the Exa API, processes and highlights results with citations, and synthesizes findings into a formatted final report using Google Gemini. It is implemented in Python, run via app.py, and saves outputs as final_report.md. The repository documents installation, required environment variables for Exa, OpenAI, Groq, and Gemini API keys, and describes an iterative follow-up loop that refines context and repeats research steps until completion.

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App URL
https://github.com/HarshJ23/Deeper-Seeker
Github Repository
https://github.com/HarshJ23/Deeper-Seeker/blob/main/README.md

App Details

Features
Iterative research workflow that breaks a query into logical steps, generates follow-up questions, and refines context across iterations. Structured output which creates JSON-formatted search queries for API calls and formats search results with highlights, citations, and summaries. Parallel execution of multiple search queries to reduce latency during plan execution. Comprehensive reporting that synthesizes processed search results into a well-formatted report produced with Google Gemini and saved as final_report.md. Multi-model support that leverages OpenAI and Groq for reasoning and planning and Google Gemini for report generation. Clear code structure with functions for follow-up generation, research planning, query generation, plan execution, and report generation. Documentation for installing dependencies and configuring API keys.
Use Cases
The tool helps researchers, analysts, product managers, and investors by automating repetitive research steps and producing consistent, actionable outputs. It accelerates scoping through generated follow-up questions, ensures coverage by breaking topics into multiple queries and steps, and reduces manual search work by executing parallel web searches. Processed results include highlights and citations to simplify source verification, and the final synthesized report provides a concise deliverable for decision making or further analysis. Built-in multi-model use and JSON-structured queries make it adaptable for integration or extension. The repository includes setup and usage instructions and saves results locally so users can review and iterate on research output.

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