Wizard¶
A local-first autonomous data analysis agent. Ask a real question about your data; it investigates — looking, computing, revising its approach when the data disagrees with it — then verifies the result and explains it, streaming its reasoning as it goes.
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What it is¶
Wizard runs entirely on your machine. Your data never leaves it, and no API key is required by default — cloud providers are supported too, gated behind an explicit data mode.
You upload a file (or connect a database) and ask a question in plain language. A manager model works out what to do; a worker model writes the Python. The code is statically screened, then executed in its own subprocess under OS-native sandboxing by default — or in a Docker container scoped to your session if you opt into that instead. See Execution & Sandboxing.
The important part is what happens next. Rather than following a plan fixed before anything ran, the manager sees the real output and decides what to do next — examine a column, compute something else, consult an attached document, revise the plan outright, or stop and answer. It repeats until it has an answer or runs out of budget. Real analytical questions are rarely one step; you find out the join key is dirty, or that "active customer" means three different things in three tables, only once you have looked. See Architecture for how the loop works.
Why you might want it¶
- Local first. Two small Ollama models are enough to be useful. Nothing is sent anywhere unless you explicitly choose a cloud provider.
- You choose the models. Nothing is hardcoded — the app uses whatever your provider actually has, and you can assign a different model, on a different backend, to each role.
- It sizes itself to your hardware. A 1.5B model gets a short leash and
deterministic fallbacks; a large model gets a long investigation. Same app,
one setting, and
autoworks it out from the model itself. - It runs the code, it doesn't just suggest it. Results come from execution, not from a model claiming an answer.
- It checks its own work. The headline result is recomputed by a different route, and any figure in the answer that appears in no execution output is flagged rather than quietly presented.
- It tells you what it assumed. Dropped nulls, inner joins, top-N cuts — read back out of the code that actually ran, not out of the model's description of it.
- It corrects itself. Failures are fed back with the traceback, and successful repairs are remembered as negative examples for next time.
- Docker is optional, not a fallback. Without it, code runs in a subprocess per session — its own memory ceiling, a per-step timeout, a working Stop button, and variables that persist between steps.
- It is honest about degradation. No embedding model? Retrieval falls back to word overlap. Model unreachable? You get a clear message, not a hang. Nothing silently pretends — see Edge Cases & Gotchas for the full list.