---
title: "GAIA Benchmark"
source: Microsoft AutoGen
source_url: https://github.com/microsoft/autogen
licence: CC-BY-4.0
domain: agentic-ai
subdomain: autogen-docs
date_added: 2026-04-25
---

# GAIA Benchmark

This scenario implements the [GAIA](https://arxiv.org/abs/2311.12983) agent benchmark. Before you begin, make sure you have followed instruction in `../README.md` to prepare your environment.

### Setup Environment Variables for AgBench

Navigate to GAIA

```bash
cd benchmarks/GAIA
```

Update `config.yaml` to point to your model host, as appropriate. The default configuration points to 'gpt-4o'.

Now initialize the tasks.

```bash
python Scripts/init_tasks.py
```

Note: This will attempt to download GAIA from Hugginface, but this requires authentication.

The resulting folder structure should look like this:

```
.
./Downloads
./Downloads/GAIA
./Downloads/GAIA/2023
./Downloads/GAIA/2023/test
./Downloads/GAIA/2023/validation
./Scripts
./Templates
./Templates/TeamOne
```

Then run `Scripts/init_tasks.py` again.

Once the script completes, you should now see a folder in your current directory called `Tasks` that contains one JSONL file per template in `Templates`.

### Running GAIA

Now to run a specific subset of GAIA use:

```bash
agbench run Tasks/gaia_validation_level_1__MagenticOne.jsonl
```

You should see the command line print the raw logs that shows the agents in action To see a summary of the results (e.g., task completion rates), in a new terminal run the following:

```bash
agbench tabulate Results/gaia_validation_level_1__MagenticOne/
```

## References

**GAIA: a benchmark for General AI Assistants** ``
Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, Thomas Scialom ``
[https://arxiv.org/abs/2311.12983](https://arxiv.org/abs/2311.12983)
