toolgrad 0.1.1

Official Repo for ToolGrad: Efficient Tool-use Dataset Generation with Textual “Gradients”

ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients"

TODOs:

This is an official repo for <ToolGrad: Efficient Tool-use Dataset Generation with Textual “Gradients”>.

Get Started: A Quick Demo

Step 0: Install packages

git clone https://github.com/zhongyi-zhou/toolgrad.git
cd toolgrad
conda env create -f environment.yml
conda activate toolgrad

Step 1: launch your first ToolGrad framework on a MCP service

export PYTHONPATH=./
python examples/mcp_filesystem.py

Reproduction of Dataset Generation

Step 0: ToolBench API Key

You need to first obtain a ToolBench API key by following their instruction:

• https://github.com/OpenBMB/ToolBench

Note: The API key is necessary for the following procedures.

Step 1: ToolBench Setups

export TOOLBENCH_KEY=YOURTOOLBENCHKEY

You also need to setup the ToolBench API database:

• Unzip tools.zip (Google Drive) and it will show a tools/ folder.
• add this path to the environ as follow

export TOOLBENCH_LIBRARY_ROOT=YOUR_PATH/TO/TOOLS

Step 2: Generate your first ToolGrad sample on the ToolBench API database

export PYTHONPATH=./
python examples/toolbench.py

You will then find a new json file under examples/outputs/. examples/example_outputs/seed=123__iter=5__num_apis=50.json is an example that we generated.

ToolGrad-5K is composed of 5k data generation sessions with different seed. It takes ~250 USD to generate the full 5K dataset, using gpt-4.1-mini.

Evaluation

First download the dataset from Google Drive and unzip it. You should be able see a folder structure as follows:

ToolGrad-5k  
├── data  
├── metadata  
├── prediction  
└── sft_data  

The prediction folder stores the prediction of three ToolGrad models on the test set. You can run the following command to perform evaluation with LLM judges:

python src/eval.py --pred_model toolgrad-1b --dataset ~/YOUR_DATASET_STORAGE_DIR/ToolGrad-5k/

You should see the following messages in CMD.

judge model: gpt-4.1
100%|████████████████████████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 1384.60it/s]
               Recall  Success Rate     QoR
Model                                      
toolgrad-1b  0.987917      0.955482  93.702

This is an exact reproduction of our results.

If you wish to run the LLM judge again, run the following command (note this introduces costs on your OpenAI API):

python src/eval.py --pred_model toolgrad-1b \
  --dataset ~/YOUR_DATASET_STORAGE_DIR/ToolGrad-5k/ \
  --overwrite \
  --num_process 16 

You should be able to see a new result with similar values of ours. Note that you can adjust the num_process dependent on your OpenAI API RPM.