Agentic ETL in YAML: LLM steps with per-record cache, budgets and data checks. Open source, Python. pip install agentl-etl

Our first live example: fx.agentl.com.br Daily euro reference rates from the European Central Bank, fetched, checked and stored by an agentl pipeline on a schedule. Free to use, no sign-up.
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The pipeline status is live on the page: last run, outcome, rows read and changed, checks passed, next run. Runs on GitHub Actions, served by Cloudflare Pages. Zero servers.
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Hello, world. This is agentl. Describe a data job in one YAML file. agentl reads your files and APIs, runs LLM steps that return typed fields, checks the data and writes it where you need it.
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Built so LLM pipelines don't burn money: • per-record cache: run it again, you don't pay twice • budget caps stop a run before it overspends • cheap model first, strong model only when needed • resumes where a failed run stopped
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