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what is deepresearch-datagen-cli fr?

oqura-ai/deepresearch-datagen-cli — explained in plain English

Analysis updated 2026-05-18

40PythonAudience · dataComplexity · 3/5Setup · moderate

tl;dr

A Python command-line tool that automates building structured training datasets for LLMs by researching a topic across the web.

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  root((deepresearch datagen cli))
    What it does
      Builds training datasets
      Splits topics into subtopics
    Tech Stack
      Python
      LangChain
      LangGraph
      OpenAI
      Tavily
    Use Cases
      Dataset generation for finetuning
      Automated web research
    Audience
      Data teams
      Developers

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what do people make with this?

VIBE 1

Automatically build a structured training dataset on a specific topic for finetuning an LLM.

VIBE 2

Break a broad research topic into subtopics and gather web sourced information for each.

VIBE 3

Control research depth and results per section to match your dataset size needs.

what's the stack?

PythonLangChainLangGraphOpenAITavily

how it stacks up fr

oqura-ai/deepresearch-datagen-cliasimons81/hermes-dreamingbaskduf/harness-starter-kit
Stars404040
LanguagePythonPythonPython
Setup difficultymoderatemoderateeasy
Complexity3/53/52/5
Audiencedatadeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · moderate time til it works · 30min

Requires Python 3.9+, an OpenAI API key, and a Tavily API key.

in plain english

deepresearch-datagen-cli is a Python command-line tool that automates the process of building structured datasets for training AI language models. The problem it solves is the tedious manual work of gathering, organizing, and formatting data. Instead, you describe the kind of dataset you want, and the tool takes it from there. It breaks your request into focused subtopics, assigns a research agent to each one, has each agent search the web and extract relevant information, then merges everything into a single clean dataset file saved to your machine. You configure the depth of research, how many results to gather per section, and the AI model to use. It is built on LangChain and LangGraph, uses OpenAI as its AI backbone, and Tavily for web search. You would use this when you need a training dataset on a specific topic but do not want to spend hours manually collecting and structuring the data yourself. It requires Python 3.9 or newer, an OpenAI API key, and a Tavily API key to run.

prompts (copy fr)

prompt 1
Use deepresearch-datagen-cli to generate a dataset about renewable energy policy for finetuning.
prompt 2
Help me configure the research depth and per-section result count in deepresearch-datagen-cli.
prompt 3
Explain how deepresearch-datagen-cli merges multiple subtopic research agents into one dataset file.

Frequently asked questions

what is deepresearch-datagen-cli fr?

A Python command-line tool that automates building structured training datasets for LLMs by researching a topic across the web.

What language is deepresearch-datagen-cli written in?

Mainly Python. The stack also includes Python, LangChain, LangGraph.

How hard is deepresearch-datagen-cli to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is deepresearch-datagen-cli for?

Mainly data.

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