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**Hosted version:** bambooai.org — the same analyst, nothing to install.
BambooAI is an LLM-driven data analyst that works in a persistent Python kernel, one cell at a time,
and reproduces its own results before it reports them. You give it a dataset and a question; it writes
and runs code, reads the output, decides the next step, and ends with a report whose cited numbers
have been re-executed in a fresh kernel. It runs on your machine with your own model keys, and the
code the model writes runs in a container, not under your account.
Table of Contents
- Overview
- Features
- How It Works
- Installation
- Quick Start
- Configuration
- Environment Variables
- Models and Seats
- Data and Integrations
- Documents
- Where the Code Runs
- Command Line
- Architecture
- Logging and Usage
- Development
- Contributing
- Contact
Overview
BambooAI is built for people who analyse data for a living and want the work done the way they would
do it: in a notebook, step by step, with every intermediate result visible and every final number
checked. It is not a chat interface over a code interpreter. The analyst has a fixed set of actions,
a one-page contract that is the same for every model, a budget it can see, and a report format it
must fill in. The web app streams the run as it happens, keeps the notebook, and lets you ask
follow-up questions against the same kernel state.
It came out of sports science — training and physiology data from Intervals.icu, Endura and
SweatStack load directly — and applies to any tabular dataset.
Features
- **One model, one kernel, one notebook.** The analysis is a sequence of Python cells in a
persistent kernel with a checkpoint per cell; a failed cell is rolled back and the namespace is
exactly as before the attempt.
- **A checked report.** Before the report is shown, the cells it cites are assembled into a script
and executed in a fresh kernel against the original data. The closing card states how many numbers
reproduced.
- **Three modes.** *Quick* (a lookup, a definition), *Deep* (a full analysis within a turn budget),
*Adaptive* (a long run, reviewed after every 8th turn by a separate reviewer on a stronger model that can redirect it or end it).
- **Follow-ups on a warm kernel.** A follow-up question continues with the variables the previous run
left; a new question starts fresh. Runs form chains you can step through. If the data loaded has
changed since the earlier chain (another window, a replaced file), the follow-up starts in a fresh
kernel on the data loaded now: the earlier chains stay in its ledger as context, marked as on other
data, and its replay and any routine made from it use its own cells. The same file uploaded again
(a new session, a favourite reopened) is the same data, and the follow-up continues as before.
- **Ask about a plot.** The chat button on a plot sends the picture with your question: the analyst's
model sees it when the config's model_properties says it takes images ("multimodal": "true");
otherwise the question goes alone and the pane says so.
- **Web search with sources.** With a Gemini key, the analyst searches for published figures to
compare against (Google AI grounding) and cites them in the report.
- **Memory.** Saving a run distils what was learned about the dataset — column quirks, approaches
that worked — into a memory pack the analyst reads in later runs on the same data.
- **Plain-language version.** A second, cheaper model rewrites the report for a non-technical reader;
both versions are kept.
- **Auxiliary datasets and integrations.** Several files per analysis; training data loaded from
Intervals.icu, Endura and SweatStack; loaded frames paged in the Data tab.
- **Documents beside the data.** Up to four papers, notes or transcripts a thread (PDF, Word, Markdown,
text), parsed into passages with locators; the analyst sees a map of each and reads the text with
code, so a dataset is analysed in the context of what was written about it.
- **Everything inspectable.** Every cell's code and output, every figure, every prompt that produced
a turn, the per-call tokens, time and cost.
- **Ten providers, per-seat configuration.** OpenRouter, OpenAI, Anthropic, Google, Groq, Mistral,
xAI, DeepSeek, Ollama and vLLM; each seat (analyst, reviewer, rewriter, …) has its own model and
reasoning effort. Local models keep everything on the machine.
- **Isolation by default.** The model-written code runs in the executor container, built from the