Repository: modelscope/evalscope
Issue: TAU_Bench 和 BFCL_V3 报错
Root cause: Interface Contract Mismatch

## 自查清单

在提交 issue 之前，请确保您已完成以下步骤:
- [X] 我已仔细阅读了[相关使用说明文档](https://evalscope.readthedocs.io/zh-cn/latest/get_started/parameters.html)
- [X] 我已查看了[常见问题解答](https://evalscope.readthedocs.io/zh-cn/latest/get_started/faq.html)
- [X] 我已搜索并查看了现有的 issues，确认这不是一个重复的问题

## 问题描述

**（1）TAU-Bench 报错  Error in tau_bench prediction: 1 validation error for EnvResetResponse**
<img width="1833" height="621" alt="Image" src="https://github.com/user-attachments/assets/0dd2be6c-17b5-449f-a7fe-de0a9486e4d4" />

**（2）BFCL_V3 报错 Object of type ContentReasoning is not JSON serializabl**
<img width="1096" height="761" alt="Image" src="https://github.com/user-attachments/assets/1d7b2efa-dcd5-4842-a1e1-3a16f9fe9b01" />

## EvalScope 版本（必填）
v1.0.0

## 使用的工具
- [X] Native / 原生框架
- [ ] Opencompass backend
- [ ] VLMEvalKit backend
- [ ] RAGEval backend
- [ ] Perf / 模型推理压测工具
- [ ] Arena / 竞技场模式

## 执行的代码或指令

**TAU-Bench**
```python
from evalscope.constants import EvalType
from evalscope.run import run_task

task_cfg = {
    'eval_type': EvalType.SERVICE,
    'api_url': 'http://127.0.0.1:8000/v1/',
    'api_key': '',
    'stream': True,
    'model': 'GLM-4.5-Air-AWQ-FP16Mix',
    'limit': 5,
    'datasets': ['tau_bench'],
    'dataset_dir': './dataset',
    'dataset_args': {
        'tau_bench': {
            'subset_list': ['airline', 'retail'],  # 选择评测领域
            'extra_params': {
                'user_model': 'GLM-4.5-Air-AWQ-FP16Mix',
                'api_key': '',
                'api_base': 'http://127.0.0.1:8000/v1/',
                'generation_config': {
                    'temperature': 0.7,
                    'max_new_tokens': 1024
                }
            }
        }
    },
    'eval_batch_size': 5,
    'generation_config': {
        'temperature': 0,
        'max_tokens': 65536,
        'parallel_tool_calls': True,  # 启用并行函数调用
    }
}

run_task(task_cfg=task_cfg)
```

**BFCL_V3**
```python
from evalscope.constants import EvalType
from evalscope.run import run_task

task_cfg = {
    'eval_type': EvalType.SERVICE,
    'api_url': 'http://127.0.0.1:8000/v1/',
    'api_key': '',
    'model': 'GLM-4.5-Air-AWQ-FP16Mix',
    'datasets': ['bfcl_v3'],
    'dataset_dir': 'D:/AI/test/dataset',
    'dataset_args': {
        'bfcl_v3': {
            'extra_params':{
                # 模型在函数名称中拒绝使用点号（`.`）；设置此项，以便在评估期间自动将点号转换为下划线。
                'underscore_to_dot': True,
                # 模式是否为函数调用模型（Function Calling Model），如果是则会启用函数调用相关的配置；否则会使用prompt绕过函数调用。
                'is_fc_model': True,
            }
        }
    },
    'eval_batch_size': 10,
    'generation_config': {
        'temperature': 0,
        'max_tokens': 32000,
        'parallel_tool_calls': True,  # 启用并行函数调用
    },
    'limit': 10
}

run_task(task_cfg=task_cfg)
```


## 错误日志

（1）TAU-Bench
```
2025-08-26 13:43:09 - evalscope - INFO: Args: Task config is provided with dictionary type.
2025-08-26 13:43:09 - evalscope - WARNING: Deprecated: The `stream` parameter is deprecated and will be removed in v1.1.0. Use `generation_config.stream` instead.
2025-08-26 13:43:09 - evalscope - INFO: Creating model GLM-4.5-Air-AWQ-FP16Mix with eval_type=openai_api base_url=http://172.16.234.130:8000/v1/, api_key=, config=max_retries=None timeout=None batch_size=5 stream=True system_message=None max_tokens=65536 top_p=None temperature=0.0 stop_seqs=None best_of=None frequency_penalty=None presence_penalty=None logit_bias=None seed=None do_sample=None top_k=None n=None logprobs=None top_logprobs=None parallel_tool_calls=True internal_tools=None max_tool_output=None cache_prompt=None reasoning_effort=None reasoning_tokens=None reasoning_summary=None reasoning_history=None response_schema=None extra_body=None height=None width=None num_inference_steps=None guidance_scale=None, model_args={}
2025-08-26 13:43:16 - evalscope - INFO: Dump task config to ./outputs\20250826_134309\configs\task_config_edba79.yaml
2025-08-26 13:43:16 - evalscope - INFO: {
    "model": "GLM-4.5-Air-AWQ-FP16Mix",
    "model_id": "GLM-4.5-Air-AWQ-FP16Mix",
    "model_args": {},
    "model_task": "text_generation",
    "chat_template": null,
    "datasets": [
        "tau_bench"
    ],
    "dataset_args": {
        "tau_bench": {
            "name": "tau_bench",
            "dataset_id": "https://github.com/sierra-research/tau-bench",
            "output_types": [
                "generation"
            ],
            "subset_list": [
                "airline",
                "retail"
            ],
            "default_subset": "default",
            "few_shot_num": 0,
            "few_shot_random": false,
            "train_split": null,
            "eval_split": "test",
            "prompt_template": null,
            "few_shot_prompt_template": null,
            "system_prompt": null,
            "query_template": null,
            "pretty_name": "τ-bench",
            "description": "A benchmark emulating dynamic conversations between a user (simulated by language models) and a language agent provided with domain-specific API tools and policy guidelines. Please install it with `pip install git+https://github.com/sierra-research/tau-bench` before evaluating and set a user model. [Usage Example](https://evalscope.readthedocs.io/zh-cn/latest/third_party/tau_bench.html)",
            "tags": [
                "FunctionCalling",
                "Reasoning"
            ],
            "filters": null,
            "metric_list": [
                "Pass^1"
            ],
            "aggregation": "mean",
            "extra_params": {
                "user_model": "GLM-4.5-Air-AWQ-FP16Mix",
                "api_key": "",
                "api_base": "http://127.0.0.1:8000/v1/",
                "generation_config": {
                    "temperature": 0.7,
                    "max_new_tokens": 1024
                }
            }
        }
    },
    "dataset_dir": "D:/AI/test/dataset",
    "dataset_hub": "modelscope",
    "repeats": 1,
    "generation_config": {
        "batch_size": 5,
        "stream": true,
        "max_tokens": 65536,
        "temperature": 0.0,
        "parallel_tool_calls": true
    },
    "eval_type": "openai_api",
    "eval_backend": "Native",
    "eval_config": null,
    "limit": 5,
    "eval_batch_size": 5,
    "use_cache": null,
    "rerun_review": false,
    "work_dir": "./outputs\\20250826_134309",
    "ignore_errors": false,
    "debug": false,
    "dry_run": false,
    "seed": 42,
    "api_url": "http://172.16.234.130:8000/v1/",
    "timeout": null,
    "stream": true,
    "judge_strategy": "auto",
    "judge_worker_num": 1,
    "judge_model_args": {},
    "analysis_report": false
}
2025-08-26 13:43:16 - evalscope - INFO: Loading TauBench environment: airline
2025-08-26 13:43:16 - evalscope - INFO: Creating model GLM-4.5-Air-AWQ-FP16Mix with eval_type=openai_api base_url=http://127.0.0.1:8000/v1/, api_key=, config=max_retries=None timeout=None bat
ch_size=None stream=None system_message=None max_tokens=None top_p=None temperature=0.7 stop_seqs=None best_of=None frequency_penalty=None presence_penalty=None logit_bias=None seed=None do_sample=None top_k=None n=None logprobs
=None top_logprobs=None parallel_tool_calls=None internal_tools=None max_tool_output=None cache_prompt=None reasoning_effort=None reasoning_tokens=None reasoning_summary=None reasoning_history=None response_schema=None extra_body=None height=None width=None num_inference_steps=None guidance_scale=None, model_args={}
Processing records: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 5011.12it/s]
2025-08-26 13:43:18 - evalscope - INFO: Loading TauBench environment: retail
Processing records: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<?, ?it/s]
Predicting[tau_bench@airline]:   0%|                                                                                                                                                                         | 0/5 [00:00<?, ?it/s]2025-08-26 13:43:24 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r...ou help me with that?"}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@airline]:  20%|████████████████████████████████▏                                                                                                                                | 1/5 [00:04<00:19,  4.85s/it]2025-08-26 13:43:25 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r...e some changes to it."}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@airline]:  40%|████████████████████████████████████████████████████████████████▍                                                                                                | 2/5 [00:06<00:08,  2.69s/it]2025-08-26 13:43:26 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r... that's not possible."}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@airline]:  60%|████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                | 3/5 [00:06<00:03,  1.56s/it]2025-08-26 13:43:27 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r... Seattle on May 20th."}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@airline]:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                | 4/5 [00:07<00:01,  1.45s/it]2025-08-26 13:43:28 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r...ou help me with that?"}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@airline]: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:08<00:00,  1.68s/it] 
Reviewing[tau_bench@airline]: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1661.64it/s] 
Predicting[tau_bench@retail]:   0%|                                                                                                                                                                          | 0/5 [00:00<?, ?it/s]2025-08-26 13:43:32 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r...on your online store."}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@retail]:  20%|████████████████████████████████▍                                                                                                                                 | 1/5 [00:03<00:14,  3.70s/it]2025-08-26 13:43:32 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r...or some of the items."}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@retail]:  40%|████████████████████████████████████████████████████████████████▊                                                                                                 | 2/5 [00:04<00:05,  1.70s/it]2025-08-26 13:43:32 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r...for some items in it."}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@retail]:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                | 3/5 [00:04<00:02,  1.03s/it]2025-08-26 13:43:32 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r... smart watch options."}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@retail]:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                | 4/5 [00:04<00:00,  1.37it/s]2025-08-26 13:43:33 - evalscope - ERROR: Error in tau_bench prediction: 1 validation error for EnvResetResponse
observation
  Input should be a valid string [type=string_type, input_value=[{'type': 'reasoning', 'r...ently have available?"}], input_type=list]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
Predicting[tau_bench@retail]: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:04<00:00,  1.09it/s] 
Reviewing[tau_bench@retail]: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 999.50it/s] 
2025-08-26 13:43:33 - evalscope - INFO: 
tau_bench report table:
+-------------------------+-----------+-------------+----------+-------+---------+---------+
| Model                   | Dataset   | Metric      | Subset   |   Num |   Score | Cat.0   |
+=========================+===========+=============+==========+=======+=========+=========+
| GLM-4.5-Air-AWQ-FP16Mix | tau_bench | mean_Pass^1 | airline  |     5 |       0 | default |
+-------------------------+-----------+-------------+----------+-------+---------+---------+
| GLM-4.5-Air-AWQ-FP16Mix | tau_bench | mean_Pass^1 | retail   |     5 |       0 | default |
+-------------------------+-----------+-------------+----------+-------+---------+---------+
| GLM-4.5-Air-AWQ-FP16Mix | tau_bench | mean_Pass^1 | OVERALL  |    10 |       0 | -       |
+-------------------------+-----------+-------------+----------+-------+---------+---------+

2025-08-26 13:43:33 - evalscope - INFO: Skipping report analysis (`analysis_report=False`).
2025-08-26 13:43:33 - evalscope - INFO: Dump report to: ./outputs\20250826_134309\reports\GLM-4.5-Air-AWQ-FP16Mix\tau_bench.json

2025-08-26 13:43:33 - evalscope - INFO: Overall report table:
+-------------------------+-----------+-------------+----------+-------+---------+---------+
| Model                   | Dataset   | Metric      | Subset   |   Num |   Score | Cat.0   |
+=========================+===========+=============+==========+=======+=========+=========+
| GLM-4.5-Air-AWQ-FP16Mix | tau_bench | mean_Pass^1 | airline  |     5 |       0 | default |
+-------------------------+-----------+-------------+----------+-------+---------+---------+
| GLM-4.5-Air-AWQ-FP16Mix | tau_bench | mean_Pass^1 | retail   |     5 |       0 | default |
+-------------------------+-----------+-------------+----------+-------+---------+---------+
| GLM-4.5-Air-AWQ-FP16Mix | tau_bench | mean_Pass^1 | OVERALL  |    10 |       0 | -       |
+-------------------------+-----------+-------------+----------+-------+---------+---------+

2025-08-26 13:43:33 - evalscope - INFO: Finished evaluation for GLM-4.5-Air-AWQ-FP16Mix on ['tau_bench']
2025-08-26 13:43:33 - evalscope - INFO: Output directory: ./outputs\20250826_13430
```

**（2）BFCL_V3**
```
2025-08-26 13:47:58 - evalscope - INFO: Args: Task config is provided with dictionary type.
2025-08-26 13:47:58 - evalscope - INFO: Creating model GLM-4.5-Air-AWQ-FP16Mix with eval_type=openai_api base_url=http://172.16.234.130:8000/v1/, api_key=x6bY8DR4S7y1ra9Iu7UymTeL2KUPvddn, config=max_retries=None timeout=None batch_size=10 stream=None system_message=None max_tokens=32000 top_p=None temperature=0.0 stop_seqs=None best_of=None frequency_penalty=None presence_penalty=None logit_bias=None seed=None do_sample=None top_k=None n=None logprobs=None top_logprobs=None parallel_tool_calls=True internal_tools=None max_tool_output=None cache_prompt=None reasoning_effort=None reasoning_tokens=None reasoning_summary=None reasoning_history=None response_schema=None extra_body=None height=None width=None num_inference_steps=None guidance_scale=None, model_args={}
2025-08-26 13:47:59 - evalscope - INFO: Dump task config to ./outputs\20250826_134758\configs\task_config_8c2416.yaml
2025-08-26 13:47:59 - evalscope - INFO: {
    "model": "GLM-4.5-Air-AWQ-FP16Mix",
    "model_id": "GLM-4.5-Air-AWQ-FP16Mix",
    "model_args": {},
    "model_task": "text_generation",
    "chat_template": null,
    "datasets": [
        "bfcl_v3"
    ],
    "dataset_args": {
        "bfcl_v3": {
            "name": "bfcl_v3",
            "dataset_id": "AI-ModelScope/bfcl_v3",
            "output_types": [
                "generation"
            ],
            "subset_list": [
                "simple",
                "multiple",
                "parallel",
                "parallel_multiple",
                "java",
                "javascript",
                "live_simple",
                "live_multiple",
                "live_parallel",
                "live_parallel_multiple",
                "irrelevance",
                "live_relevance",
                "live_irrelevance",
                "multi_turn_base",
                "multi_turn_miss_func",
                "multi_turn_miss_param",
                "multi_turn_long_context"
            ],
            "default_subset": "default",
            "few_shot_num": 0,
            "few_shot_random": false,
            "train_split": null,
            "eval_split": "train",
            "prompt_template": null,
            "few_shot_prompt_template": null,
            "system_prompt": null,
            "query_template": null,
            "pretty_name": "BFCL-v3",
            "description": "Berkeley Function Calling Leaderboard (BFCL), the **first comprehensive and executable function call evaluation** dedicated to assessing Large Language Models' (LLMs) ability to invoke functions. Unli
ke previous evaluations, BFCL accounts for various forms of function calls, diverse scenarios, and executability. Need to run `pip install bfcl-eval==2025.6.16` before evaluating. [Usage Example](https://evalscope.readthedocs.io/zh-cn/latest/third_party/bfcl_v3.html)",
            "tags": [
                "FunctionCalling"
            ],
            "filters": null,
            "metric_list": [
                "acc"
            ],
            "aggregation": "mean",
            "extra_params": {
                "underscore_to_dot": true,
                "is_fc_model": true
            }
        }
    },
    "dataset_dir": "D:/AI/test/dataset",
    "dataset_hub": "modelscope",
    "repeats": 1,
    "generation_config": {
        "batch_size": 10,
        "max_tokens": 32000,
        "temperature": 0.0,
        "parallel_tool_calls": true
    },
    "eval_type": "openai_api",
    "eval_backend": "Native",
    "eval_config": null,
    "limit": 10,
    "eval_batch_size": 10,
    "use_cache": null,
    "rerun_review": false,
    "work_dir": "./outputs\\20250826_134758",
    "ignore_errors": false,
    "debug": false,
    "dry_run": false,
    "seed": 42,
    "api_url": "http://172.16.234.130:8000/v1/",
    "timeout": null,
    "stream": null,
    "judge_strategy": "auto",
    "judge_worker_num": 1,
    "judge_model_args": {},
    "analysis_report": false
}
2025-08-26 13:47:59 - evalscope - INFO: Loading dataset AI-ModelScope/bfcl_v3 from modelscope > subset: default > split: train ...
2025-08-26 13:47:59,763 - modelscope - WARNING - Use trust_remote_code=True. Will invoke codes from bfcl_v3. Please make sure that you can trust the external codes.
2025-08-26 13:48:00,842 - modelscope - WARNING - Use trust_remote_code=True. Will invoke codes from AI-ModelScope/bfcl_v3. Please make sure that you can trust the external codes.
2025-08-26 13:48:00,842 - modelscope - WARNING - Use trust_remote_code=True. Will invoke codes from AI-ModelScope/bfcl_v3. Please make sure that you can trust the external codes.
2025-08-26 13:48:00,842 - modelscope - WARNING - Use trust_remote_code=True. Will invoke codes from AI-ModelScope/bfcl_v3. Please make sure that you can trust the external codes.
Saving the dataset (1/1 shards): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4441/4441 [00:00<00:00, 33382.86 examples/s]
Processing records: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4441/4441 [00:01<00:00, 2467.02it/s]
Predicting[bfcl_v3@simple]: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:05<00:00,  1.75it/s]
Reviewing[bfcl_v3@simple]: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:05<00:00,  1.73it/s] 
Predicting[bfcl_v3@multiple]: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00,  1.57it/s]
Reviewing[bfcl_v3@multiple]: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 1428.19it/s] 
Predicting[bfcl_v3@parallel]: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:07<00:00,  1.36it/s]
Reviewing[bfcl_v3@parallel]: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 1427.80it/s] 
Predicting[bfcl_v3@parallel_multiple]: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:08<00:00,  1.19it/s]
Reviewing[bfcl_v3@parallel_multiple]: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 1666.66it/s] 
Predicting[bfcl_v3@java]: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:10<00:00,  1.02s/it]
Reviewing[bfcl_v3@java]: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 1428.53it/s] 
Predicting[bfcl_v3@javascript]:  60%|███████████████████████████████████████████████████████████████████████████████████████████████▍                                                               | 6/10 [00:07<00:03,  1.14it/s]2025-08-26 13:49:08 - evalscope - ERROR: {
  "input": [
    {
      "id": "9cddbb4e",
      "content": "",
      "source": null,
      "metadata": null,
      "internal": null,
      "role": "user",
      "tool_call_id": null
    }
  ],
  "choices": null,
  "target": "",
  "id": 2,
  "group_id": 2,
  "tools": null,
  "category": null,
  "subset_key": "javascript",
  "metadata": {
    "id": "javascript_2",
    "multi_turn": false,
    "functions": [
      {
        "name": "extractLastTransactionId",
        "description": "This function scans a database log file for lines indicating transaction completion or failure, extracting the last transaction ID that matches the criteria. It uses a processing function `processFunction` to further handle the extracted transaction ID.",
        "parameters": {
          "type": "dict",
          "properties": {
            "filepath": {
              "type": "String",
              "description": "The path to the database log file to be examined."
            },
            "status": {
              "type": "array",
              "items": {
                "type": "String"
              },
              "description": "An array of statuses to search for within the log file, indicating the end of a transaction."
            },
            "encoding": {
              "type": "String",
              "description": "The encoding of the log file."
            },
            "processFunction": {
              "type": "any",
              "description": "A function that processes the extracted transaction ID."
            }
          },
          "required": [
            "filepath",
            "status",
            "encoding",
            "processFunction"
          ]
        }
      }
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "extractLastTransactionId",
          "description": "This function scans a database log file for lines indicating transaction completion or failure, extracting the last transaction ID that matches the criteria. It uses a processing function `processFunction` to further handle the extracted transaction ID. Note that the provided function is in JavaScript syntax.",
          "parameters": {
            "type": "object",
            "properties": {
              "filepath": {
                "type": "string",
                "description": "The path to the database log file to be examined. This is JavaScript String type parameter in string representation."
              },
              "status": {
                "type": "string",
                "description": "An array of statuses to search for within the log file, indicating the end of a transaction. This is JavaScript array type parameter in string representation. The list elements are of type String; they are not in string representation."
              },
              "encoding": {
                "type": "string",
                "description": "The encoding of the log file. This is JavaScript String type parameter in string representation."
              },
              "processFunction": {
                "type": "string",
                "description": "A function that processes the extracted transaction ID. This parameter can be of any type of JavaScript object in string representation."
              }
            },
            "required": [
              "filepath",
              "status",
              "encoding",
              "processFunction"
            ]
          }
        }
      }
    ],
    "missed_functions": "{}",
    "initial_config": {},
    "involved_classes": [],
    "turns": [
      [
        {
          "role": "user",
          "content": "Help me extract the last transaction ID that has a status of 'completed' or 'failed' from a database log located at '/var/log/db.log', using 'utf-8' encoding, and process the information with a processing function?"
        }
      ]
    ],
    "language": "JavaScript",
    "test_category": "javascript",
    "subset": "javascript",
    "ground_truth": [
      {
        "extractLastTransactionId": {
          "filepath": [
            "/var/log/db.log"
          ],
          "status": [
            [
              "completed",
              "failed"
            ]
          ],
          "encoding": [
            "utf-8"
          ],
          "processFunction": [
            "processFunction"
          ]
        }
      }
    ],
    "should_execute_tool_calls": false,
    "missing_functions": {},
    "is_fc_model": true,
    "generation": [
      [
        [
          {
            "internal": null,
            "type": "reasoning",
            "reasoning": "The user wants to extract the last transaction ID with status 'completed' or 'failed' from a database log file. Let me analyze what parameters I need:\n\n1. filepath: '/var/log/db.log' - provided by use
r\n2. status: ['completed', 'failed'] - user specified these two statuses\n3. encoding: 'utf-8' - provided by user\n4. processFunction: The user mentioned \"process the information with a processing function\" but didn't specify
 what processing function to use. This is a required parameter, so I need to ask the user for this.\n\nLooking at the function definition, processFunction is described as \"A function that processes the extracted transaction ID. This parameter can be of any type of JavaScript object in string representation.\"\n\nSince the user didn't specify what processing function they want to use, I need to ask them for this required parameter.",
            "signature": null,
            "redacted": false
          },
          {
            "internal": null,
            "type": "text",
            "text": "\nI can help you extract the last transaction ID with 'completed' or 'failed' status from the database log. However, I need one more piece of information from you.\n\nYou mentioned wanting to \"process the i
nformation with a processing function,\" but you didn't specify what processing function you'd like to use. The `processFunction` parameter is required and can be any JavaScript function that will handle the extracted transactio
n ID.\n\nCould you please provide the processing function you'd like to use? For example, it could be:\n- A simple function that just logs the transaction ID\n- A function that performs additional analysis\n- A function that saves the result to another file\n- Or any other custom processing logic\n\nOnce you provide the processing function, I'll be able to execute the extraction for you.",
            "refusal": null
          }
        ]
      ]
    ]
  },
  "sandbox": null,
  "files": null,
  "setup": null
} prediction failed: due to Object of type ContentReasoning is not JSON serializable
Predicting[bfcl_v3@javascript]:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                               | 7/10 [00:08<00:03,  1.18s/it] 
Traceback (most recent call last):
  File "D:\AI\benchmark\BFCL_V3.py", line 30, in <module>
    run_task(task_cfg=task_cfg)
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\run.py", line 28, in run_task
    return run_single_task(task_cfg, run_time)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\run.py", line 41, in run_single_task
    result = evaluate_model(task_cfg, outputs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\run.py", line 143, in evaluate_model
    res_dict = evaluator.eval()
               ^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\evaluator\evaluator.py", line 100, in eval
    subset_score = self.evaluate_subset(subset, dataset)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\evaluator\evaluator.py", line 124, in evaluate_subset
    task_states = self.get_answers(subset, dataset)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\evaluator\evaluator.py", line 191, in get_answers
    raise exc
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\evaluator\evaluator.py", line 177, in get_answers
    task_state = future.result()
                 ^^^^^^^^^^^^^^^
  File "D:\tools\miniconda3\Lib\concurrent\futures\_base.py", line 449, in result
    return self.__get_result()
           ^^^^^^^^^^^^^^^^^^^
  File "D:\tools\miniconda3\Lib\concurrent\futures\_base.py", line 401, in __get_result
    raise self._exception
  File "D:\tools\miniconda3\Lib\concurrent\futures\thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\evaluator\evaluator.py", line 211, in _predict_sample
    task_state = self.benchmark.run_inference(model=self.model, sample=sample, output_dir=model_prediction_dir)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\api\benchmark\adapters\default_data_adapter.py", line 436, in run_inference
    model_output = self._on_inference(model, sample)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\benchmarks\bfcl\bfcl_adapter.py", line 119, in _on_inference
    return predict(model, sample)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\benchmark\.venv\Lib\site-packages\evalscope\benchmarks\bfcl\generation.py", line 29, in predict
    choices=[ChatCompletionChoice.from_content(json.dumps(response, ensure_ascii=False, indent=2))],
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\tools\miniconda3\Lib\json\__init__.py", line 238, in dumps
    **kw).encode(obj)
          ^^^^^^^^^^^
  File "D:\tools\miniconda3\Lib\json\encoder.py", line 202, in encode
    chunks = list(chunks)
    chunks = list(chunks)
    chunks = list(chunks)
             ^^^^^^^^^^^^
  File "D:\tools\miniconda3\Lib\json\encoder.py", line 430, in _iterencode
    yield from _iterencode_list(o, _current_indent_level)
  File "D:\tools\miniconda3\Lib\json\encoder.py", line 326, in _iterencode_list
    yield from chunks
  File "D:\tools\miniconda3\Lib\json\encoder.py", line 326, in _iterencode_list
    yield from chunks
  File "D:\tools\miniconda3\Lib\json\encoder.py", line 326, in _iterencode_list
    yield from chunks
  File "D:\tools\miniconda3\Lib\json\encoder.py", line 439, in _iterencode
    o = _default(o)
        ^^^^^^^^^^^
  File "D:\tools\miniconda3\Lib\json\encoder.py", line 180, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type ContentReasoning is not JSON serializable
```

## 运行环境

- 操作系统：Windows 11
- Python版本：3.12.4

## 其他信息

- vLLM：0.10.1.1
- 模型：[QuantTrio/GLM-4.5-Air-AWQ-FP16Mix](https://huggingface.co/QuantTrio/GLM-4.5-Air-AWQ-FP16Mix)
如果有其他相关信息，请在此处提供。


看起来是reasoning部分处理有点问题，除了这两个数据集，测试其他数据集是否正常，例如gsm8k

> 看起来是reasoning部分处理有点问题，除了这两个数据集，测试其他数据集是否正常，例如gsm8k

正常的

已修复，使用main分支代码再试试

@Yunnglin 
感谢，这个问题已经没出现了。另外，还有 timeout 的问题，openai.APITimeoutError: Request timed out。出现这个报错能不停止进程，跳过这个请求，继续往下执行吗。

@Yunnglin 
大佬，還有個問題，TAU-Bench 分數一直是 0
<img width="990" height="589" alt="Image" src="https://github.com/user-attachments/assets/9501d46d-954b-4075-b56b-b35adb1b79b6" />

BFCL_V3 报错
```
parameters.properties.value.type
  Input should be 'string', 'integer', 'number', 'boolean', 'array', 'object' or 'null' [type=literal_error, input_value='float', input_type=str]
    For further information visit https://errors.pydantic.dev/2.11/v/literal_error
parameters.properties.base.type
  Input should be 'string', 'integer', 'number', 'boolean', 'array', 'object' or 'null' [type=literal_error, input_value='float', input_type=str]
    For further information visit https://errors.pydantic.dev/2.11/v/literal_error
Predicting[bfcl_v3@multi_turn_miss_func]:  11%|█         | 22/200 [03:33<28:45,  9.69s/it]
2025-08-28 10:29:27 - evalscope - ERROR: INFERENCE_ERROR: Exceeded max inference steps (20)
2025-08-28 10:29:49 - evalscope - ERROR: INFERENCE_ERROR: Exceeded max inference steps (20)
2025-08-28 10:30:23 - evalscope - ERROR: INFERENCE_ERROR: Exceeded max inference steps (20)
2025-08-28 10:30:27 - evalscope - ERROR: INFERENCE_ERROR: Exceeded max inference steps (20)
2025-08-28 10:31:31 - evalscope - ERROR: INFERENCE_ERROR: Exceeded max inference steps (20)
Traceback (most recent call last):
  File "/opt/evalscope/agentic.py", line 41, in <module>
    run_task(task_cfg=task_cfg)
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/run.py", line 28, in run_task
    return run_single_task(task_cfg, run_time)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/run.py", line 41, in run_single_task
    result = evaluate_model(task_cfg, outputs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/run.py", line 143, in evaluate_model
    res_dict = evaluator.eval()
               ^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/evaluator/evaluator.py", line 100, in eval
    subset_score = self.evaluate_subset(subset, dataset)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/evaluator/evaluator.py", line 124, in evaluate_subset
    task_states = self.get_answers(subset, dataset)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/evaluator/evaluator.py", line 191, in get_answers
    raise exc
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/evaluator/evaluator.py", line 177, in get_answers
    task_state = future.result()
                 ^^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/concurrent/futures/_base.py", line 449, in result
    return self.__get_result()
           ^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/usr/lib/python3.12/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/evaluator/evaluator.py", line 211, in _predict_sample
    task_state = self.benchmark.run_inference(model=self.model, sample=sample, output_dir=model_prediction_dir)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/api/benchmark/adapters/default_data_adapter.py", line 436, in run_inference
    model_output = self._on_inference(model, sample)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/benchmarks/bfcl/bfcl_adapter.py", line 119, in _on_inference
    return predict(model, sample)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/benchmarks/bfcl/generation.py", line 21, in predict
    response, model_usage = generate_turn_with_tools(model, row)
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/evalscope/benchmarks/bfcl/generation.py", line 167, in generate_turn_with_tools
    tools=[ToolInfo.model_validate(tool['function']) for tool in tools],
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/evalscope/.venv/lib/python3.12/site-packages/pydantic/main.py", line 705, in model_validate
    return cls.__pydantic_validator__.validate_python(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
pydantic_core._pydantic_core.ValidationError: 2 validation errors for ToolInfo
parameters.properties.value.type
  Input should be 'string', 'integer', 'number', 'boolean', 'array', 'object' or 'null' [type=literal_error, input_value='float', input_type=str]
    For further information visit https://errors.pydantic.dev/2.11/v/literal_error
parameters.properties.base.type
  Input should be 'string', 'integer', 'number', 'boolean', 'array', 'object' or 'null' [type=literal_error, input_value='float', input_type=str]
    For further information visit https://errors.pydantic.dev/2.11/v/literal_error
```

> [@Yunnglin](https://github.com/Yunnglin) 感谢，这个问题已经没出现了。另外，还有 timeout 的问题，openai.APITimeoutError: Request timed out。出现这个报错能不停止进程，跳过这个请求，继续往下执行吗。

1. 跳过出错样本，可以通过taskconifg加上`ignore_errors=True`参数来解决
2. 超时问题可以通过增加 generation config中的timeout参数来缓解

> [@Yunnglin](https://github.com/Yunnglin) 大佬，還有個問題，TAU-Bench 分數一直是 0 <img alt="Image" width="990" height="589" src="https://private-user-images.githubusercontent.com/44965565/482923556-9501d46d-954b-4075-b56b-b35adb1b79b6.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.m_rVf9tkXip5KeubOapdf9alYDfAj-jvBJePTkKrz8E">

“TAU-Bench 分數一直是 0” 可能是提供的 `'user_model': 'GLM-4.5-Air-AWQ-FP16Mix'`的问题，TAU官方用的是GPT4模拟user，这里建议你换成一个非reasoning模型，例如：GPT4-o，qwen-plus等，不同的user模型也会影响最终的打分。

> [@Yunnglin](https://github.com/Yunnglin) 大佬，還有個問題，TAU-Bench 分數一直是 0 <img alt="Image" width="990" height="589" src="https://private-user-images.githubusercontent.com/44965565/482923556-9501d46d-954b-4075-b56b-b35adb1b79b6.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.vfmgzJvr6r9nilJo7Sb3dgCwsIzOXHqP_DLItR-WkHw">

小哥，Tau-Bench 评分为 0 的问题最后解决了吗？

bfcl exceed steps issue still not been fix

> > [@Yunnglin](https://github.com/Yunnglin) 大佬，還有個問題，TAU-Bench 分數一直是 0 <img alt="Image" width="990" height="589" src="https://private-user-images.githubusercontent.com/44965565/482923556-9501d46d-954b-4075-b56b-b35adb1b79b6.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.vfmgzJvr6r9nilJo7Sb3dgCwsIzOXHqP_DLItR-WkHw">
> 
> 小哥，Tau-Bench 评分为 0 的问题最后解决了吗？

main分支已解决这个问题

> bfcl exceed steps issue still not been fix

@Hert4 bfcl评测过程中`Exceeded max inference steps`已修改为warning，不影响评测流程

Sorry for not clearly, but does it effect a quality of evaluate result ?