Provides modular, graph-based agents powered by large language models (LLMs) for intelligent task execution in R. 
      Supports structured workflows for tasks such as forecasting, data visualization, feature engineering, data wrangling, data cleaning, 'SQL', code generation, weather reporting, and research-driven question answering. 
      Each agent performs iterative reasoning: recommending steps, generating R code, executing, debugging, and explaining results. 
      Includes built-in support for packages such as 'tidymodels', 'modeltime', 'plotly', 'ggplot2', and 'prophet'. Designed for analysts, developers, and teams building intelligent, reproducible AI workflows in R. 
      Compatible with LLM providers such as 'OpenAI', 'Anthropic', 'Groq', and 'Ollama'. Inspired by the Python package 'langagent'.

Detects and suggests fixes for semantic inconsistencies in data
    frames by calling large language models (LLMs) through a unified,
    provider-agnostic interface. Supported providers include 'OpenAI'
    ('GPT-4o', 'GPT-4o-mini') <https://platform.openai.com>,
    'Anthropic' ('Claude') <https://www.anthropic.com>,
    'Google' ('Gemini') <https://ai.google.dev>,
    'Groq' (free-tier 'LLaMA' and 'Mixtral') <https://groq.com>,
    and local 'Ollama' models <https://ollama.com>.
    The package identifies issues that rule-based tools cannot detect:
    abbreviation variants, typographic errors, case inconsistencies, and
    malformed values. Results are returned as tidy data frames with column,
    row index, detected value, issue type, suggested fix, and confidence
    score. An offline fallback using statistical and fuzzy-matching methods
    is provided for use without any application programming interface (API)
    key. Interactive fix application with human review is supported via
    'apply_fixes()'. Methods follow de Jonge and van der Loo (2013)
    <https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>
    and Chaudhuri et al. (2003) <doi:10.1145/872757.872796>.