BFCL-v4

Overview

BFCL-v4 (Berkeley Function-Calling Leaderboard V4) is a comprehensive benchmark for evaluating agentic function-calling capabilities of LLMs. It tests web search, memory operations, and format sensitivity as building blocks for agentic applications.

Task Description

Task Type: Agentic Function Calling Evaluation
Input: Scenarios requiring function calls for web search, memory, etc.
Output: Correct function calls with proper arguments
Capabilities: Web search, memory read/write, format handling

Key Features

Holistic agentic evaluation framework
Tests building blocks for LLM agents (search, memory, formatting)
Multiple scoring categories for detailed analysis
Supports both FC models and non-FC models
Optional web search with SerpAPI integration

Evaluation Notes

Installation Required: pip install bfcl-eval==2025.10.27.1
Primary metric: Accuracy per category
Configure is_fc_model based on model capabilities
Optional: Set SERPAPI_API_KEY for web search tasks
Usage Example

Properties

Property	Value
Benchmark Name	`bfcl_v4`
Dataset ID	berkeley-function-call-leaderboard
Paper	N/A
Tags	`Agent`, `FunctionCalling`
Metrics	`acc`
Default Shots	0-shot
Evaluation Split	`train`

Data Statistics

Metric	Value
Total Samples	5,106
Prompt Length (Mean)	350.4 chars
Prompt Length (Min/Max)	36 / 10805 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`irrelevance`	240	85.98	55	203
`live_irrelevance`	884	198.72	36	10805
`live_multiple`	1,053	149.16	42	4828
`live_parallel`	16	163.88	88	365
`live_parallel_multiple`	24	217.67	72	650
`live_relevance`	16	385.94	71	1830
`live_simple`	258	171.91	47	1247
`memory_kv`	155	646.46	585	826
`memory_rec_sum`	155	646.46	585	826
`memory_vector`	155	646.46	585	826
`multi_turn_base`	200	808.43	106	1756
`multi_turn_long_context`	200	808.41	106	1756
`multi_turn_miss_func`	200	811.88	110	1760
`multi_turn_miss_param`	200	863.88	167	1791
`multiple`	200	120.75	65	229
`parallel`	200	297.93	69	781
`parallel_multiple`	200	432.17	88	1249
`simple_java`	100	228.93	138	531
`simple_javascript`	50	222.34	118	637
`simple_python`	400	119.66	57	303
`web_search_base`	100	831.01	748	963
`web_search_no_snippet`	100	831.01	748	963

Sample Example

Subset: irrelevance

{
  "input": [
    {
      "id": "4e05e910",
      "content": "[[{\"role\": \"user\", \"content\": \"Calculate the area of a triangle given the base is 10 meters and height is 5 meters.\"}]]"
    }
  ],
  "target": "{}",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "id": "irrelevance_0",
    "question": [
      [
        {
          "role": "user",
          "content": "Calculate the area of a triangle given the base is 10 meters and height is 5 meters."
        }
      ]
    ],
    "function": [
      {
        "name": "determine_body_mass_index",
        "description": "Calculate body mass index given weight and height.",
        "parameters": {
          "type": "dict",
          "properties": {
            "weight": {
              "type": "float",
              "description": "Weight of the individual in kilograms."
            },
            "height": {
              "type": "float",
              "description": "Height of the individual in meters."
            }
          },
          "required": [
            "weight",
            "height"
          ]
        }
      }
    ],
    "category": "irrelevance",
    "ground_truth": {}
  }
}

Prompt Template

No prompt template defined.

Extra Parameters

Parameter	Type	Default	Description
`underscore_to_dot`	`bool`	`True`	Convert underscores to dots in function names for evaluation.
`is_fc_model`	`bool`	`True`	Indicates the evaluated model natively supports function calling.
`SERPAPI_API_KEY`	`str	null`	`None`

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets bfcl_v4 \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['bfcl_v4'],
    dataset_args={
        'bfcl_v4': {
            # subset_list: ['irrelevance', 'live_irrelevance', 'live_multiple']  # optional, evaluate specific subsets
            # extra_params: {}  # uses default extra parameters
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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