> ## Documentation Index
> Fetch the complete documentation index at: https://docs.declaw.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Data analyst agent

> Agent loads a CSV, runs pandas queries, renders a chart, and writes a markdown report inside a declaw python sandbox.

## Use case

You have a dataset and want an agent to do the analysis — load,
summarize, visualize, report — without the analysis tooling ever
running on your host machine. The agent gets a fresh microVM with
pandas and matplotlib, a locked-down network (only the OpenAI API
plus one dataset host), and PII redaction on every outbound call.

## Template

`python` — ships with Python 3.11, pip, common scientific packages
on request. PII scanner and injection defense run at the sandbox's
edge proxy.

## Run it

```bash theme={null}
export DECLAW_API_KEY=dcl_...
export DECLAW_DOMAIN=api.declaw.ai
export OPENAI_API_KEY=sk-...

python cookbook/examples/openai-agents-data-analyst/main.py
```

## Security policy

```python theme={null}
SecurityPolicy(
    pii=PIIConfig(enabled=True, action="redact", rehydrate_response=True),
    injection_defense=InjectionDefenseConfig(enabled=True, sensitivity="high", domains=["api.openai.com"]),
    network=NetworkPolicy(
        allow_out=[
            "api.openai.com",
            "pypi.org",
            "files.pythonhosted.org",
            "raw.githubusercontent.com",
        ],
    ),
)
```

`rehydrate_response=True` matters here: the analyst's pandas output
may echo back PII that the scanner redacted on the way out. The
edge proxy restores the originals before the sandbox receives the
response, so the agent's code sees a normal API response, not a
pile of `REDACTED_*` tokens.

## What the agent does

1. `printenv` to confirm the sandbox-provided config variables.
2. `curl` the CSV into `/workspace/data.csv`.
3. `pip install pandas matplotlib`.
4. Generate a script that loads, summarizes, plots, and writes
   `/workspace/report.md`.
5. Return the report path. The Python driver then reads
   `/workspace/report.md` back through the sandbox API.

## Expected output

```
== agent output ==
/workspace/report.md

== /workspace/report.md ==
# COVID-19 time series summary
- Rows: ...
- Columns: ...
- Top 5 countries by latest confirmed case count:
  1. ...
(+ bar chart at /workspace/top5.png)
```

## Why filesystem isolation matters here

Every artifact (downloaded CSV, pip cache, plot, report) lives in a
fresh overlay that's discarded when `client.delete(session=...)`
runs. The next caller gets a clean VM with none of this caller's
state. You don't need to pre-provision scratch directories or clean
them up — the sandbox lifecycle handles it.

## Full source

See `cookbook/examples/openai-agents-data-analyst/main.py` in the repo.