# Command line

> Check the connection, download the benchmark, and run it from the shell or CI.

The `generalcontext` command comes with the SDK. It reads `GENERALCONTEXT_API_KEY` like the SDK does, prints one plain line per result, and exits 0 when everything worked, 1 when a check failed, and 2 for a usage or setup problem, so a coding agent or a CI job can act on it.

Python:

```bash
pip install generalcontext
generalcontext verify
```

Node.js:

```bash
npx generalcontext verify
```

## Check the connection

`verify` shows which workspace the key belongs to, what the key can do, and when traffic last arrived and from where. With `--session`, it checks that one conversation arrived, with its number of calls and models. It never prints conversation content. Add `--json` for the raw answer.

```bash
generalcontext verify --session ticket-4821
# Conversation ticket-4821: arrived, 6 calls (gpt-6.1-terra)
```

## Download the benchmark

`benchmark pull` writes your ready tasks to a file and prints how many are in each split. It needs a key that can run the benchmark: on **API keys**, create one with **Run the benchmark** checked. Keys that only send traffic are refused, so a leaked production key cannot read your tasks.

```bash
generalcontext benchmark pull --out benchmark.json
```

## Run it and upload the results

`evals run` runs every task with your agent or a reference model, as described in [Tasks and environments](https://generalcontextlabs.com/docs/tasks#run-it-as-an-eval). `--upload` sends the results to your workspace, where **Tasks** shows how each agent scores. Running needs Python; in Node, `verify` and `benchmark pull` work and the rest point you to Python.

```bash
generalcontext evals run benchmark.json --agent my_agent:handle --trials 3 --upload
```