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# Berkeley Function Calling Leaderboard (BFCL)
## Table of Contents
- [Berkeley Function Calling Leaderboard (BFCL)](#berkeley-function-calling-leaderboard-bfcl)
- [Table of Contents](#table-of-contents)
- [Introduction](#introduction)
- [Installation \& Setup](#installation--setup)
- [Basic Installation](#basic-installation)
- [Installing from PyPI](#installing-from-pypi)
- [Extra Dependencies for Self-Hosted Models](#extra-dependencies-for-self-hosted-models)
- [Configuring Project Root Directory](#configuring-project-root-directory)
- [Setting up Environment Variables](#setting-up-environment-variables)
- [Configuring SerpAPI for Web Search Category](#configuring-serpapi-for-web-search-category)
- [Running Evaluations](#running-evaluations)
- [Generating LLM Responses](#generating-llm-responses)
- [Selecting Models and Test Categories](#selecting-models-and-test-categories)
- [Selecting Specific Test Cases with `--run-ids`](#selecting-specific-test-cases-with---run-ids)
- [Output and Logging](#output-and-logging)
- [For API-based Models](#for-api-based-models)
- [For Locally-hosted OSS Models](#for-locally-hosted-oss-models)
- [For Pre-existing OpenAI-compatible Endpoints](#for-pre-existing-openai-compatible-endpoints)
- [(Alternate) Script Execution for Generation](#alternate-script-execution-for-generation)
- [Evaluating Generated Responses](#evaluating-generated-responses)
- [Output Structure](#output-structure)
- [(Optional) WandB Evaluation Logging](#optional-wandb-evaluation-logging)
- [(Alternate) Script Execution for Evaluation](#alternate-script-execution-for-evaluation)
- [Contributing \& How to Add New Models](#contributing--how-to-add-new-models)
- [Additional Resources](#additional-resources)
---
## Introduction
We introduce the Berkeley Function Calling Leaderboard (BFCL), the **first comprehensive and executable function call evaluation** dedicated to assessing Large Language Models' (LLMs) ability to invoke functions. Unlike previous evaluations, BFCL accounts for various forms of function calls, diverse scenarios, and executability.
💡 Read more in our blog posts:
- [BFCL v1: Simple, Parallel, and Multiple Function Call eval with AST](https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html)
- [BFCL v2: Enterprise and OSS-contributed Live Data](https://gorilla.cs.berkeley.edu/blogs/12_bfcl_v2_live.html)
- [BFCL v3: Multi-Turn & Multi-Step Function Call Evaluation](https://gorilla.cs.berkeley.edu/blogs/13_bfcl_v3_multi_turn.html)
- [BFCL V4 Part 1: Agentic Web Search](https://gorilla.cs.berkeley.edu/blogs/15_bfcl_v4_web_search.html)
- [BFCL V4 Part 2: Agentic Memory Management](https://gorilla.cs.berkeley.edu/blogs/16_bfcl_v4_memory.html)
- [BFCL V4 Part 3: Agentic Format Sensitivity](https://gorilla.cs.berkeley.edu/blogs/17_bfcl_v4_prompt_variation.html)
🦍 See the live leaderboard at [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html#leaderboard)
![Architecture Diagram](https://raw.githubusercontent.com/ShishirPatil/gorilla/main/berkeley-function-call-leaderboard/architecture_diagram.png)
---
## Installation & Setup
### Basic Installation
```bash
# Create a new Conda environment with Python 3.10
conda create -n BFCL python=3.10
conda activate BFCL
# Clone the Gorilla repository
git clone https://github.com/ShishirPatil/gorilla.git
# Change directory to the `berkeley-function-call-leaderboard`
cd gorilla/berkeley-function-call-leaderboard
# Install the package in editable mode
pip install -e .
```
### Installing from PyPI
If you simply want to run the evaluation without making code changes, you can
install the prebuilt wheel instead. **Be careful not to confuse our package with
the *unrelated* `bfcl` project on PyPI—make sure you install `bfcl-eval`:**
```bash
pip install bfcl-eval # Be careful not to confuse with the unrelated `bfcl` project on PyPI!
```
### Extra Dependencies for Self-Hosted Models
For locally hosted models, choose one of the following backends, ensuring you have the right GPU and OS setup:
`sglang` is *much faster* than `vllm` in our specific multi-turn use case, but it only supports newer GPUs with SM 80+ (Ampere etc).
If you are using an older GPU (T4/V100), you should use `vllm` instead as it supports a much wider range of GPUs.
**Using `vllm`:**
```bash
pip install -e .[oss_eval_vllm]
```
**Using `sglang`:**
```bash
pip install -e .[oss_eval_sglang]
```
*Optional:* If using `sglang`, we recommend installing `flashinfer` for speedups. Find instructions [here](https://docs.flashinfer.ai/installation.html).
### Configuring Project Root Directory
**Important:** If you installed the package from PyPI (using `pip install bfcl-eval`), you **must** set the `BFCL_PROJECT_ROOT` environment variable to specify where the evaluation results and score files should be stored.
Otherwise, you'll need to navigate deep into the Python package's source code folder to access the evaluation results and configuration files.
For editable installations (using `pip install -e .`), setting `BFCL_PROJECT_ROOT` is *optional*--it defaults to the `berkeley-function-call-leaderboard` directory.
Set `BFCL_PROJECT_ROOT` as an environment variable in your shell environment:
```bash
# In your shell environment
export BFCL_PROJECT_ROOT=/path/to/your/desired/project/directory
```
When `BFCL_PROJECT_ROOT` is set:
- The `result/` folder (containing model responses) will be created at `$BFCL_PROJECT_ROOT/result/`
- The `score/` folder (containing evaluation results) will be created at `$BFCL_PROJECT_ROOT/score/`
- The library will look for the `.env` configuration file at `$BFCL_PROJECT_ROOT/.env` (see [Setting up Environment Variables](#setting-up-environment-variables))
### Setting up Environment Variables
We store API keys and other configuration variables (separate from the `BFCL_PROJECT_ROOT` variable mentioned above) in a `.env` file. A sample `.env.example` file is distributed with the package.
**For editable installations:**
```bash
cp bfcl_eval/.env.example .env
# Fill in necessary values in `.env`
```
**For PyPI installations (using `pip install bfcl-eval`):**
```bash
cp $(python -c "import bfcl_eval; print(bfcl_eval.__path__[0])")/.env.example $BFCL_PROJECT_ROOT/.env
# Fill in necessary values in `.env`
```
If you are running any proprietary models, make sure the model API keys are included in your `.env` file. Models like GPT, Claude, Mistral, Gemini, Nova, will require them.
The library looks for the `.env` file in the project root, i.e. `$BFCL_PROJECT_ROOT/.env`.
#### Configuring SerpAPI for Web Search Category
For the `web_search` test category, we use the [SerpAPI](https://serpapi.com/) service to perform web search. You need to sign up for an API key and add it to your `.env` file. You can also switch to other web search APIs by changing the `search_engine_query` function in `bfcl_eval/eval_checker/multi_turn_eval/func_source_code/web_search.py`.
---
## Running Evaluations
### Generating LLM Responses
#### Selecting Models and Test Categories
- `MODEL_NAME`: For available models, please refer to [SUPPORTED_MODELS.md](./SUPPORTED_MODELS.md). If not specified, the default model `gorilla-openfunctions-v2` is used.
- `TEST_CATEGORY`: For available test categories, please refer to [TEST_CATEGORIES.md](./TEST_CATEGORIES.md). If not specified, all categories are included by default.
You can provide multiple models or test categories by separating them with commas. For example:
```bash
bfcl generate --model claude-3-5-sonnet-20241022-FC,gpt-4o-2024-11-20-FC --test-category simple_python,parallel,live_multiple,multi_turn
```
#### Selecting Specific Test Cases with `--run-ids`
Sometimes you may only need to regenerate a handful of test entries—for instance when iterating on a new model or after fixing an inference bug. Passing the `--run-ids` flag lets you target **exact test IDs** rather than an entire category:
```bash
bfcl generate --model MODEL_NAME --run-ids # --test-category will be ignored
```
When this flag is set the generation pipeline reads a JSON file named
`test_case_ids_to_generate.json` located in the *project root* (the same
place where `.env` lives). The file should map each test category to a list of
IDs to run:
```json
{
"simple_python": ["simple_python_102", "simple_python_103"],
"multi_turn_base": ["multi_turn_base_15"]
}
```
> Note: When using `--run-ids`, the `--test-category` flag is ignored.
A sample file is provided at `bfcl_eval/test_case_ids_to_generate.json.example`; **copy it to your project root** so the CLI can pick it up regardless of your working directory:
**For editable installations:**
```bash
cp bfcl_eval/test_case_ids_to_generate.json.example ./test_case_ids_to_generate.json
```
**For PyPI installations:**
```bash
cp $(python -c "import bfcl_eval, pathlib; print(pathlib.Path(bfcl_eval.__path__[0]) / 'test_case_ids_to_generate.json.example')") $BFCL_PROJECT_ROOT/test_case_ids_to_generate.json
```
Once `--run-ids` is provided only the IDs listed in the JSON will be evaluated.
#### Output and Logging
- By default, generated model responses are stored in a `result/` folder under the project root (which defaults to the package directory): `result/MODEL_NAME/BFCL_v3_TEST_CATEGORY_result.json`.
- You can customise the location by setting the `BFCL_PROJECT_ROOT` environment variable or passing the `--result-dir` option.
An inference log is included with the model responses to help analyze/debug the model's performance, and to better understand the model behavior. For more verbose logging, use the `--include-input-log` flag. Refer to [LOG_GUIDE.md](./LOG_GUIDE.md) for details on how to interpret the inference logs.
#### For API-based Models
```bash
bfcl generate --model MODEL_NAME --test-category TEST_CATEGORY --num-threads 1
```
- Use `--num-threads` to control the level of parallel inference. The default (`1`) means no parallelization.
- The maximum allowable threads depends on your API's rate limits.
#### For Locally-hosted OSS Models
```bash
bfcl generate \
--model MODEL_NAME \
--test-category TEST_CATEGORY \
--backend {sglang|vllm} \
--num-gpus 1 \
--gpu-memory-utilization 0.9 \
--local-model-path /path/to/base/model \
--enable-lora \
--max-lora-rank 128 \
--lora-modules module1="/path/to/lora/adapter1" module2="/path/to/lora/adapter2" # ← optional
```
- Choose your backend using `--backend sglang` or `--backend vllm`. The default backend is `vllm`.
- Control GPU usage by adjusting `--num-gpus` (default `1`, relevant for multi-GPU tensor parallelism) and `--gpu-memory-utilization` (default `0.9`), which can help avoid out-of-memory errors.
- `--local-model-path` (optional): Point this flag at a directory that already contains the model's files (`config.json`, tokenizer, weights, etc.). Use it only when you've pre-downloaded the model and the weights live somewhere other than the default `$HF_HOME` cache.
- `--enable-lora` (optional): Enable LoRA for the vLLM backend. This flag is required to use LoRA modules. This only works when backend is `vllm`.
- `--max-lora-rank` (optional): Specify the maximum LoRA rank for the vLLM backend. This is an integer value. This only works when backend is `vllm` and `--enable-lora` flag is set.
- `--lora-modules` (optional): Specify the path to the LoRA modules for the vLLM backend in `name="path"` format. This allows evaluation of fine-tuned models with LoRA adapters. You can specify multiple LoRA modules by repeating this argument. This only works when backend is `vllm` and `--enable-lora` flag is set.
##### For Pre-existing OpenAI-compatible Endpoints
If you have a server already running (e.g., vLLM in a SLURM cluster), you can bypass the vLLM/sglang setup phase and directly generate responses by using the `--skip-server-setup` flag:
```bash
bfcl generate --model MODEL_NAME --test-category TEST_CATEGORY --skip-server-setup
```
In addition, you should specify the endpoint and port used by the local server. By default, the endpoint is `localhost` and the port is `1053`. These can be overridden by the `LOCAL_SERVER_ENDPOINT` and `LOCAL_SERVER_PORT` environment variables in the `.env` file:
```bash
LOCAL_SERVER_ENDPOINT=localhost
LOCAL_SERVER_PORT=1053
```
For remote deployments (e.g., via RunPod, ngrok, or enterprise gateways) that require custom authentication or use non-standard base URLs, you can specify a full base URL and API key:
```bash
REMOTE_OPENAI_BASE_URL=https://your-vllm-server.com/v1
REMOTE_OPENAI_API_KEY=your-api-key-here
REMOTE_OPENAI_TOKENIZER_PATH=/path/to/local/tokenizer # Optional: specify local tokenizer for local/remote endpoints
```
#### (Alternate) Script Execution for Generation
For those who prefer using script execution instead of the CLI, you can run the following command:
```bash
python -m bfcl_eval.openfunctions_evaluation --model MODEL_NAME --test-category TEST_CATEGORY
```
When specifying multiple models or test categories, separate them with **spaces**, not commas. All other flags mentioned earlier are compatible with the script execution method as well.
### Evaluating Generated Responses
**Important:** You must have generated the model responses before running the evaluation.
Once you have the results, run:
```bash
bfcl evaluate --model MODEL_NAME --test-category TEST_CATEGORY
```
If you **only** generated a subset of benchmark entries (e.g. by using `--run-ids` during the generation step or by manually editing the result files) and you wish to evaluate *just* those entries, add the `--partial-eval` flag:
```bash
bfcl evaluate --model MODEL_NAME --test-category TEST_CATEGORY --partial-eval
```
When `--partial-eval` is set, the evaluator silently skips IDs that are not present in the model result file and computes accuracy on the remaining subset. Please note that the score may differ from a full-set evaluation and therefore might not match the official leaderboard numbers.
The `MODEL_NAME` and `TEST_CATEGORY` options are the same as those used in the [Generating LLM Responses](#generating-llm-responses) section. For details, refer to [SUPPORTED_MODELS.md](./SUPPORTED_MODELS.md) and [TEST_CATEGORIES.md](./TEST_CATEGORIES.md).
If in the previous step you stored the model responses in a custom directory, specify it using the `--result-dir` flag or set `BFCL_PROJECT_ROOT` so the evaluator can locate the files.
> Note: For unevaluated test categories, they will be marked as `N/A` in the evaluation result csv files.
> For summary columns (e.g., `Overall Acc`, `Non_Live Overall Acc`, `Live Overall Acc`, and `Multi Turn Overall Acc`), the score reported will treat all unevaluated categories as 0 during calculation.
#### Output Structure
Evaluation scores are stored in a `score/` directory under the project root (defaults to the package directory), mirroring the structure of `result/`: `score/MODEL_NAME/BFCL_v3_TEST_CATEGORY_score.json`.
- To use a custom directory for the score file, set the `BFCL_PROJECT_ROOT` environment variable or specify `--score-dir`.
Additionally, four CSV files are generated in `./score/`:
- `data_overall.csv` – Overall scores for each model. This is used for updating the leaderboard.
- `data_live.csv` – Detailed breakdown of scores for each Live (single-turn) test category.
- `data_non_live.csv` – Detailed breakdown of scores for each Non-Live (single-turn) test category.
- `data_multi_turn.csv` – Detailed breakdown of scores for each Multi-Turn test category.
#### (Optional) WandB Evaluation Logging
If you'd like to log evaluation results to WandB artifacts:
```bash
pip install -e.[wandb]
```
Mkae sure you also set `WANDB_BFCL_PROJECT=ENTITY:PROJECT` in `.env`.
#### (Alternate) Script Execution for Evaluation
For those who prefer using script execution instead of the CLI, you can run the following command:
```bash
python -m bfcl_eval.eval_checker.eval_runner --model MODEL_NAME --test-category TEST_CATEGORY
```
When specifying multiple models or test categories, separate them with **spaces**, not commas. All other flags mentioned earlier are compatible with the script execution method as well.
## Contributing & How to Add New Models
We welcome contributions! To add a new model:
1. Review `bfcl_eval/model_handler/base_handler.py` and/or `bfcl_eval/model_handler/local_inference/base_oss_handler.py` (if your model is hosted locally).
2. Implement a new handler class for your model.
3. Update `bfcl_eval/constants/model_config.py`.
4. Submit a Pull Request.
For detailed steps, please see the [Contributing Guide](./CONTRIBUTING.md).
---
## Additional Resources
- [Discord](https://discord.gg/grXXvj9Whz) (`#leaderboard` channel)
- [Project Website](https://gorilla.cs.berkeley.edu/leaderboard.html#leaderboard)
All the leaderboard statistics, and data used to train the models are released under Apache 2.0.
BFCL is an open source effort from UC Berkeley and we welcome contributors.
For any comments, criticisms, or questions, please feel free to raise an issue or a PR. You can also reach us via [email](mailto:huanzhimao@berkeley.edu).
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