Sumanth (@Sumanth_077) “I built a self-directed data analyst! 100% Open Source The agent investigates a” — TopicDigg

I built a self-directed data analyst! 100% Open Source The agent investigates a given dataset on its own without requiring you to guide it through every step. Give it a dataset and an objective like “Why did revenue decline?”, and it decides what to inspect, which analyses to run, what hypotheses to test, and what to investigate next based on the evidence it finds. It can inspect the data, run SQL, use Python for deeper analysis, create charts, form hypotheses, test them against the dataset, and keep following new evidence until it has enough to explain what happened. There is no fixed sequence telling it what analysis to run next. Each result becomes context for the next decision. If a query fails, the agent can use that error to adjust its approach. If it starts repeating similar analyses without making progress, the harness can detect that and push it to reassess the investigation. It also keeps explicit state across the run, including findings, hypotheses, tool history, and usage, instead of relying only on raw conversation history. For the model layer, I used Liner Mark 1.0. This works well for this kind of setup because one investigation can involve many model calls, but every step does not need the same level of reasoning. Liner Mark 1.0 routes each request to an appropriate underlying model while exposing a single model interface to the agent. So the same loop can move between dataset inspection, query planning, hypothesis evaluation, error recovery, and final synthesis without manually choosing a different model for every step. The workflow looks like this: Dataset + Objective → Investigate → Run SQL/Python → Observe → Update Hypotheses → Decide Next Step → Repeat → Final Report At the end, the agent returns the root cause, supporting findings, hypotheses it tested, relevant charts, recommended next steps, and a usage breakdown. GitHub repo: What I like about this setup is that the analysis path is not predetermined. You start with a goal, inspect the evidence, form a theory, test it, and let the results decide what to investigate next.