🐛 Bug Description
when runniing
rdagent fin_quant
where it comes to evaluate the generated python script .
It went wrong saying no torch module.
It doesn't make sense because the docker image is base on the pytorch distribution.
And I double checked it by running a standalone container using the generated local_qlib image.
It says the torch module is totally there and fine.
And, I search the whole filesystem and there is ONLY ONE python, so it dosn't seem to have a virtual environment conflict problem.
root@f1a92349fde1:/opt/conda/bin# python
Python 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
import torch
exit
Use exit() or Ctrl-D (i.e. EOF) to exit
root@f1a92349fde1:/opt/conda/bin# pip show torch
Name: torch
Version: 2.2.1
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Home-page: https://pytorch.org/
Author: PyTorch Team
Author-email: packages@pytorch.org
License: BSD-3
Location: /opt/conda/lib/python3.10/site-packages
Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions
Required-by: torchaudio, torchelastic, torchvision
To Reproduce
Steps to reproduce the behavior:
Expected Behavior
Screenshot
Role:system
Content: User is trying to implement some models in the following scenario:
None
User will provide you the information of the model.
Your job is to check whether user's code is align with the model information and the scenario.
The user will provide the source python code and the execution error message if execution failed.
The user might provide you the ground truth code for you to provide the critic. You should not leak the ground truth code to the user in any form but you can use it to provide the critic.
User has also compared the output generated by the user's code and the ground truth code. The user will provide you some analysis results comparing two output. You may find some error in the code which caused the difference between the two output.
If the ground truth code is provided, your critic should only consider checking whether the user's code is align with the ground truth code since the ground truth is definitely correct.
If the ground truth code is not provided, your critic should consider checking whether the user's code is reasonable and correct to the description and to the scenario.
Notice that your critics are not for user to debug the code. They are sent to the coding agent to correct the code. So don't give any following items for the user to check like "Please check the code line XXX".
You suggestion should not include any code, just some clear and short suggestions. Please point out very critical issues in your response, ignore non-important issues to avoid confusion. If no big issue found in the code, you can response "No critics found".
You should provide the suggestion to each of your critic to help the user improve the code. Please response the critic in the following format. Here is an example structure for the output:
critic 1: The critic message to critic 1
critic 2: The critic message to critic 2
Role:user
Content: --------------Model information:---------------
name: BidirectionalGRUAttentionModel
description: A bidirectional GRU model with attention mechanism and dropout regularization for time series forecasting in quantitative finance. This model is designed to capture temporal dependencies in market data while controlling model complexity through dropout layers. It processes sequential input to predict future returns by leveraging both past and future context through bidirectional GRU layers and focusing on important time steps via attention.
formulation: \hat{y} = \text{Linear}\left(\sum_{t=1}^{T} \alpha_t \cdot h_t\right) \quad \text{where} \quad \alpha_t = \frac{\exp(\text{score}(h_t))}{\sum_{t'=1}^{T} \exp(\text{score}(h_{t'}))}, \quad h_t = \text{BiGRU}(x_t, h_{t-1})
architecture: The model consists of: 1) Input layer accepting tensors of shape (batch_size, num_timesteps, num_features). 2) Dropout layer (p=0.2) for input regularization. 3) Two stacked bidirectional GRU layers with hidden size 64, returning sequences. 4) Attention mechanism that computes weights over time steps using a learnable context vector and tanh activation. 5) Dropout layer (p=0.3) after attention for further regularization. 6) Fully connected layer mapping the weighted sum to a single output value.
variables: {'\hat{y}': 'The predicted output (future return) for the entire batch', 'h_t': 'Hidden state at time step t from the bidirectional GRU', '\alpha_t': 'Attention weight for time step t', 'x_t': 'Input features at time step t'}
hyperparameters: {'input_dropout': '0.2', 'gru_hidden_size': '64', 'num_gru_layers': '2', 'attention_hidden_size': '32', 'output_dropout': '0.3'}
training_hyperparameters: {'n_epochs': '100', 'lr': '0.001', 'early_stop': '10', 'batch_size': '256', 'weight_decay': '0.0001'}
model_type: TimeSeries
--------------Python code:---------------
File Path: model.py
import torch
import torch.nn as nn
import torch.nn.functional as F
class BidirectionalGRUAttentionModel(nn.Module):
def __init__(self, num_features, num_timesteps):
super(BidirectionalGRUAttentionModel, self).__init__()
# Hyperparameters
self.input_dropout = 0.2
self.gru_hidden_size = 64
self.num_gru_layers = 2
self.attention_hidden_size = 32
self.output_dropout = 0.3
# Input dropout for regularization
self.input_dropout_layer = nn.Dropout(self.input_dropout)
# Bidirectional GRU layers
self.gru = nn.GRU(
input_size=num_features,
hidden_size=self.gru_hidden_size,
num_layers=self.num_gru_layers,
batch_first=True,
bidirectional=True
)
# Attention mechanism
# GRU output has shape (batch_size, num_timesteps, 2 * gru_hidden_size) due to bidirectional
self.attention_linear = nn.Linear(2 * self.gru_hidden_size, self.attention_hidden_size)
self.attention_context = nn.Parameter(torch.randn(self.attention_hidden_size))
# Output dropout for regularization
self.output_dropout_layer = nn.Dropout(self.output_dropout)
# Final fully connected layer to produce single output
self.fc = nn.Linear(2 * self.gru_hidden_size, 1)
def forward(self, x):
# x shape: (batch_size, num_timesteps, num_features)
# Apply input dropout
x = self.input_dropout_layer(x)
# Pass through bidirectional GRU
# gru_out shape: (batch_size, num_timesteps, 2 * gru_hidden_size)
gru_out, _ = self.gru(x)
# Attention mechanism
# Compute attention scores
# First project to attention hidden space
attention_proj = torch.tanh(self.attention_linear(gru_out)) # shape: (batch_size, num_timesteps, attention_hidden_size)
# Compute attention weights using context vector
# attention_proj * self.attention_context and sum over last dimension
attention_scores = torch.matmul(attention_proj, self.attention_context) # shape: (batch_size, num_timesteps)
# Apply softmax to get attention weights
attention_weights = F.softmax(attention_scores, dim=1) # shape: (batch_size, num_timesteps)
# Apply attention weights to GRU outputs
# attention_weights.unsqueeze(-1) shape: (batch_size, num_timesteps, 1)
# Multiply and sum over time dimension
weighted_sum = torch.sum(gru_out * attention_weights.unsqueeze(-1), dim=1) # shape: (batch_size, 2 * gru_hidden_size)
# Apply output dropout
weighted_sum = self.output_dropout_layer(weighted_sum)
# Final fully connected layer
output = self.fc(weighted_sum) # shape: (batch_size, 1)
return output
model_cls = BidirectionalGRUAttentionModel
--------------Execution feedback:---------------
Execution error: Error in running the model code: Traceback (most recent call last):
File "/home/chillywhale/git_ignore_folder/RD-Agent_workspace/1487c99ad87142e580fed2f2edbba2fe/model_test.py", line 19, in
import torch
ModuleNotFoundError: No module named 'torch'
Traceback: Traceback (most recent call last):
File "/home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/site-packages/rdagent/components/coder/model_coder/model.py", line 149, in execute
raise RuntimeError(f"Error in running the model code: {log}")
RuntimeError: Error in running the model code: Traceback (most recent call last):
File "/home/chillywhale/git_ignore_folder/RD-Agent_workspace/1487c99ad87142e580fed2f2edbba2fe/model_test.py", line 19, in
import torch
ModuleNotFoundError: No module named 'torch'
--------------Model value feedback:---------------
No output generated from the model. No shape evaluation conducted.
No output generated from the model. Skip value evaluation
Environment
Note: Users can run rdagent collect_info to get system information and paste it directly here.
2026-02-27 13:41:03.110 | INFO | rdagent.app.utils.info:sys_info:22 - Name of current operating system: Linux
2026-02-27 13:41:03.112 | INFO | rdagent.app.utils.info:sys_info:22 - Processor architecture: x86_64
2026-02-27 13:41:03.112 | INFO | rdagent.app.utils.info:sys_info:22 - System, version, and hardware information: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39
2026-02-27 13:41:03.112 | INFO | rdagent.app.utils.info:sys_info:22 - Version number of the system: #1 SMP PREEMPT_DYNAMIC Thu Jun 5 18:30:46 UTC 2025
2026-02-27 13:41:03.113 | INFO | rdagent.app.utils.info:python_info:29 - Python version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0]
2026-02-27 13:41:05.310 | INFO | rdagent.app.utils.info:docker_info:39 - Container ID: f1a92349fde1ba04255bd8d8f4501df74c6fc07b14ca7d5c71845186799a5401
2026-02-27 13:41:05.311 | INFO | rdagent.app.utils.info:docker_info:40 - Container Name: condescending_pascal
2026-02-27 13:41:05.311 | INFO | rdagent.app.utils.info:docker_info:41 - Container Status: running
2026-02-27 13:41:05.531 | INFO | rdagent.app.utils.info:docker_info:42 - Image ID used by the container: sha256:8b96c899d63688a87b4b5e14f5758a066dd3dd577bd9e2c98dd7890ca92d0bc6
2026-02-27 13:41:05.649 | INFO | rdagent.app.utils.info:docker_info:43 - Image tag used by the container: ['local_qlib:latest']
2026-02-27 13:41:05.650 | INFO | rdagent.app.utils.info:docker_info:44 - Container port mapping: {}
2026-02-27 13:41:05.650 | INFO | rdagent.app.utils.info:docker_info:45 - Container Label: {'com.nvidia.volumes.needed': 'nvidia_driver', 'desktop.docker.io/wsl-distro': 'Ubuntu-24.04', 'org.opencontainers.image.ref.name': 'ubuntu', 'org.opencontainers.image.version': '22.04'}
2026-02-27 13:41:05.699 | INFO | rdagent.app.utils.info:docker_info:46 - Startup Commands: /bin/bash
2026-02-27 13:41:05.701 | INFO | rdagent.app.utils.info:rdagent_info:54 - RD-Agent version: 0.8.0
╭────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────────────────────────────────────────────────╮
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/site-packages/rdagent/app/utils/info.py:85 in collect_info │
│ │
│ 82 │ sys_info() │
│ 83 │ python_info() │
│ 84 │ docker_info() │
│ ❱ 85 │ rdagent_info() │
│ 86 │ return None │
│ 87 │
│ │
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/site-packages/rdagent/app/utils/info.py:74 in rdagent_info │
│ │
│ 71 │ for package in package_list: │
│ 72 │ │ if package == "typer[all]": │
│ 73 │ │ │ package = "typer" │
│ ❱ 74 │ │ version = importlib.metadata.version(package) │
│ 75 │ │ package_version_list.append(f"{package}=={version}") │
│ 76 │ logger.info(f"Package version: {package_version_list}") │
│ 77 │ return None │
│ │
│ ╭─────────────────────────────────────────────────────────────────────────────────────────────── locals ────────────────────────────────────────────────────────────────────────────────────────────────╮ │
│ │ all_file_contents = ['# Requirements for runtime.', 'pydantic-settings', '', 'python-Levenshtein', 'scikit-learn', 'filelock', 'loguru', 'fire', 'fuzzywuzzy', 'openai', ... +68] │ │
│ │ api_url = 'https://api.github.com/repos/microsoft/RD-Agent/contents/requirements.txt?ref=ma'+2 │ │
│ │ current_version = '0.8.0' │ │
│ │ file_response = <Response [200]> │ │
│ │ file_url = 'https://raw.githubusercontent.com/microsoft/RD-Agent/main/requirements.txt' │ │
│ │ files = { │ │
│ │ │ 'name': 'requirements.txt', │ │
│ │ │ 'path': 'requirements.txt', │ │
│ │ │ 'sha': '619b19fa81a6cd21ae3d3fdc42294ff742763171', │ │
│ │ │ 'size': 1127, │ │
│ │ │ 'url': 'https://api.github.com/repos/microsoft/RD-Agent/contents/requirements.txt?ref=ma'+2, │ │
│ │ │ 'html_url': 'https://github.com/microsoft/RD-Agent/blob/main/requirements.txt', │ │
│ │ │ 'git_url': 'https://api.github.com/repos/microsoft/RD-Agent/git/blobs/619b19fa81a6cd21ae3d3f'+18, │ │
│ │ │ 'download_url': 'https://raw.githubusercontent.com/microsoft/RD-Agent/main/requirements.txt', │ │
│ │ │ 'type': 'file', │ │
│ │ │ 'content': 'IyBSZXF1aXJlbWVudHMgZm9yIHJ1bnRpbWUuCnB5ZGFudGljLXNldHRpbmdz\nCgpweXRob24tTGV2ZW5'+1450, │ │
│ │ │ ... +2 │ │
│ │ } │ │
│ │ package = 'litellm>=1.73' │ │
│ │ package_list = ['pydantic-settings', 'python-Levenshtein', 'scikit-learn', 'filelock', 'loguru', 'fire', 'fuzzywuzzy', 'openai', 'litellm>=1.73', 'azure.identity', ... +41] │ │
│ │ package_version_list = ['pydantic-settings==2.12.0', 'python-Levenshtein==0.27.3', 'scikit-learn==1.7.2', 'filelock==3.20.3', 'loguru==0.7.3', 'fire==0.7.1', 'fuzzywuzzy==0.18.0', 'openai==2.16.0'] │ │
│ │ response = <Response [200]> │ │
│ │ version = '2.16.0' │ │
│ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
│ │
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/importlib/metadata/init.py:996 in version │
│ │
│ 993 │ :return: The version string for the package as defined in the package's ╭────────────── locals ───────────────╮ │
│ 994 │ │ "Version" metadata key. │ distribution_name = 'litellm>=1.73' │ │
│ 995 │ """ ╰─────────────────────────────────────╯ │
│ ❱ 996 │ return distribution(distribution_name).version │
│ 997 │
│ 998 │
│ 999 def entry_points(**params) -> Union[EntryPoints, SelectableGroups]: │
│ │
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/importlib/metadata/init.py:969 in distribution │
│ │
│ 966 │ :param distribution_name: The name of the distribution package as a string. ╭────────────── locals ───────────────╮ │
│ 967 │ :return: A Distribution instance (or subclass thereof). │ distribution_name = 'litellm>=1.73' │ │
│ 968 │ """ ╰─────────────────────────────────────╯ │
│ ❱ 969 │ return Distribution.from_name(distribution_name) │
│ 970 │
│ 971 │
│ 972 def distributions(**kwargs): │
│ │
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/importlib/metadata/init.py:548 in from_name │
│ │
│ 545 │ │ │ if dist is not None: │
│ 546 │ │ │ │ return dist │
│ 547 │ │ else: │
│ ❱ 548 │ │ │ raise PackageNotFoundError(name) │
│ 549 │ │
│ 550 │ @classmethod │
│ 551 │ def discover(cls, **kwargs): │
│ │
│ ╭────────────────────────────────────────────────────── locals ──────────────────────────────────────────────────────╮ │
│ │ dist = None │ │
│ │ dists = <map object at 0x79cb54af2fe0> │ │
│ │ name = 'litellm>=1.73' │ │
│ │ resolver = <bound method MetadataPathFinder.find_distributions of <class 'importlib_metadata.MetadataPathFinder'>> │ │
│ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
PackageNotFoundError: No package metadata was found for litellm>=1.73
Additional Notes
🐛 Bug Description
when runniing
rdagent fin_quant
where it comes to evaluate the generated python script .
It went wrong saying no torch module.
It doesn't make sense because the docker image is base on the pytorch distribution.
And I double checked it by running a standalone container using the generated local_qlib image.
It says the torch module is totally there and fine.
And, I search the whole filesystem and there is ONLY ONE python, so it dosn't seem to have a virtual environment conflict problem.
root@f1a92349fde1:/opt/conda/bin# python
Python 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
root@f1a92349fde1:/opt/conda/bin# pip show torch
Name: torch
Version: 2.2.1
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Home-page: https://pytorch.org/
Author: PyTorch Team
Author-email: packages@pytorch.org
License: BSD-3
Location: /opt/conda/lib/python3.10/site-packages
Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions
Required-by: torchaudio, torchelastic, torchvision
To Reproduce
Steps to reproduce the behavior:
Expected Behavior
Screenshot
Role:system
Content: User is trying to implement some models in the following scenario:
None
User will provide you the information of the model.
Your job is to check whether user's code is align with the model information and the scenario.
The user will provide the source python code and the execution error message if execution failed.
The user might provide you the ground truth code for you to provide the critic. You should not leak the ground truth code to the user in any form but you can use it to provide the critic.
User has also compared the output generated by the user's code and the ground truth code. The user will provide you some analysis results comparing two output. You may find some error in the code which caused the difference between the two output.
If the ground truth code is provided, your critic should only consider checking whether the user's code is align with the ground truth code since the ground truth is definitely correct.
If the ground truth code is not provided, your critic should consider checking whether the user's code is reasonable and correct to the description and to the scenario.
Notice that your critics are not for user to debug the code. They are sent to the coding agent to correct the code. So don't give any following items for the user to check like "Please check the code line XXX".
You suggestion should not include any code, just some clear and short suggestions. Please point out very critical issues in your response, ignore non-important issues to avoid confusion. If no big issue found in the code, you can response "No critics found".
You should provide the suggestion to each of your critic to help the user improve the code. Please response the critic in the following format. Here is an example structure for the output:
critic 1: The critic message to critic 1
critic 2: The critic message to critic 2
Role:user
Content: --------------Model information:---------------
name: BidirectionalGRUAttentionModel
description: A bidirectional GRU model with attention mechanism and dropout regularization for time series forecasting in quantitative finance. This model is designed to capture temporal dependencies in market data while controlling model complexity through dropout layers. It processes sequential input to predict future returns by leveraging both past and future context through bidirectional GRU layers and focusing on important time steps via attention.
formulation: \hat{y} = \text{Linear}\left(\sum_{t=1}^{T} \alpha_t \cdot h_t\right) \quad \text{where} \quad \alpha_t = \frac{\exp(\text{score}(h_t))}{\sum_{t'=1}^{T} \exp(\text{score}(h_{t'}))}, \quad h_t = \text{BiGRU}(x_t, h_{t-1})
architecture: The model consists of: 1) Input layer accepting tensors of shape (batch_size, num_timesteps, num_features). 2) Dropout layer (p=0.2) for input regularization. 3) Two stacked bidirectional GRU layers with hidden size 64, returning sequences. 4) Attention mechanism that computes weights over time steps using a learnable context vector and tanh activation. 5) Dropout layer (p=0.3) after attention for further regularization. 6) Fully connected layer mapping the weighted sum to a single output value.
variables: {'\hat{y}': 'The predicted output (future return) for the entire batch', 'h_t': 'Hidden state at time step t from the bidirectional GRU', '\alpha_t': 'Attention weight for time step t', 'x_t': 'Input features at time step t'}
hyperparameters: {'input_dropout': '0.2', 'gru_hidden_size': '64', 'num_gru_layers': '2', 'attention_hidden_size': '32', 'output_dropout': '0.3'}
training_hyperparameters: {'n_epochs': '100', 'lr': '0.001', 'early_stop': '10', 'batch_size': '256', 'weight_decay': '0.0001'}
model_type: TimeSeries
--------------Python code:---------------
File Path: model.py
--------------Execution feedback:---------------
Execution error: Error in running the model code: Traceback (most recent call last):
File "/home/chillywhale/git_ignore_folder/RD-Agent_workspace/1487c99ad87142e580fed2f2edbba2fe/model_test.py", line 19, in
import torch
ModuleNotFoundError: No module named 'torch'
Traceback: Traceback (most recent call last):
File "/home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/site-packages/rdagent/components/coder/model_coder/model.py", line 149, in execute
raise RuntimeError(f"Error in running the model code: {log}")
RuntimeError: Error in running the model code: Traceback (most recent call last):
File "/home/chillywhale/git_ignore_folder/RD-Agent_workspace/1487c99ad87142e580fed2f2edbba2fe/model_test.py", line 19, in
import torch
ModuleNotFoundError: No module named 'torch'
--------------Model value feedback:---------------
No output generated from the model. No shape evaluation conducted.
No output generated from the model. Skip value evaluation
Environment
Note: Users can run
rdagent collect_infoto get system information and paste it directly here.2026-02-27 13:41:03.110 | INFO | rdagent.app.utils.info:sys_info:22 - Name of current operating system: Linux
2026-02-27 13:41:03.112 | INFO | rdagent.app.utils.info:sys_info:22 - Processor architecture: x86_64
2026-02-27 13:41:03.112 | INFO | rdagent.app.utils.info:sys_info:22 - System, version, and hardware information: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39
2026-02-27 13:41:03.112 | INFO | rdagent.app.utils.info:sys_info:22 - Version number of the system: #1 SMP PREEMPT_DYNAMIC Thu Jun 5 18:30:46 UTC 2025
2026-02-27 13:41:03.113 | INFO | rdagent.app.utils.info:python_info:29 - Python version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0]
2026-02-27 13:41:05.310 | INFO | rdagent.app.utils.info:docker_info:39 - Container ID: f1a92349fde1ba04255bd8d8f4501df74c6fc07b14ca7d5c71845186799a5401
2026-02-27 13:41:05.311 | INFO | rdagent.app.utils.info:docker_info:40 - Container Name: condescending_pascal
2026-02-27 13:41:05.311 | INFO | rdagent.app.utils.info:docker_info:41 - Container Status: running
2026-02-27 13:41:05.531 | INFO | rdagent.app.utils.info:docker_info:42 - Image ID used by the container: sha256:8b96c899d63688a87b4b5e14f5758a066dd3dd577bd9e2c98dd7890ca92d0bc6
2026-02-27 13:41:05.649 | INFO | rdagent.app.utils.info:docker_info:43 - Image tag used by the container: ['local_qlib:latest']
2026-02-27 13:41:05.650 | INFO | rdagent.app.utils.info:docker_info:44 - Container port mapping: {}
2026-02-27 13:41:05.650 | INFO | rdagent.app.utils.info:docker_info:45 - Container Label: {'com.nvidia.volumes.needed': 'nvidia_driver', 'desktop.docker.io/wsl-distro': 'Ubuntu-24.04', 'org.opencontainers.image.ref.name': 'ubuntu', 'org.opencontainers.image.version': '22.04'}
2026-02-27 13:41:05.699 | INFO | rdagent.app.utils.info:docker_info:46 - Startup Commands: /bin/bash
2026-02-27 13:41:05.701 | INFO | rdagent.app.utils.info:rdagent_info:54 - RD-Agent version: 0.8.0
╭────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────────────────────────────────────────────────╮
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/site-packages/rdagent/app/utils/info.py:85 in collect_info │
│ │
│ 82 │ sys_info() │
│ 83 │ python_info() │
│ 84 │ docker_info() │
│ ❱ 85 │ rdagent_info() │
│ 86 │ return None │
│ 87 │
│ │
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/site-packages/rdagent/app/utils/info.py:74 in rdagent_info │
│ │
│ 71 │ for package in package_list: │
│ 72 │ │ if package == "typer[all]": │
│ 73 │ │ │ package = "typer" │
│ ❱ 74 │ │ version = importlib.metadata.version(package) │
│ 75 │ │ package_version_list.append(f"{package}=={version}") │
│ 76 │ logger.info(f"Package version: {package_version_list}") │
│ 77 │ return None │
│ │
│ ╭─────────────────────────────────────────────────────────────────────────────────────────────── locals ────────────────────────────────────────────────────────────────────────────────────────────────╮ │
│ │ all_file_contents = ['# Requirements for runtime.', 'pydantic-settings', '', 'python-Levenshtein', 'scikit-learn', 'filelock', 'loguru', 'fire', 'fuzzywuzzy', 'openai', ... +68] │ │
│ │ api_url = 'https://api.github.com/repos/microsoft/RD-Agent/contents/requirements.txt?ref=ma'+2 │ │
│ │ current_version = '0.8.0' │ │
│ │ file_response = <Response [200]> │ │
│ │ file_url = 'https://raw.githubusercontent.com/microsoft/RD-Agent/main/requirements.txt' │ │
│ │ files = { │ │
│ │ │ 'name': 'requirements.txt', │ │
│ │ │ 'path': 'requirements.txt', │ │
│ │ │ 'sha': '619b19fa81a6cd21ae3d3fdc42294ff742763171', │ │
│ │ │ 'size': 1127, │ │
│ │ │ 'url': 'https://api.github.com/repos/microsoft/RD-Agent/contents/requirements.txt?ref=ma'+2, │ │
│ │ │ 'html_url': 'https://github.com/microsoft/RD-Agent/blob/main/requirements.txt', │ │
│ │ │ 'git_url': 'https://api.github.com/repos/microsoft/RD-Agent/git/blobs/619b19fa81a6cd21ae3d3f'+18, │ │
│ │ │ 'download_url': 'https://raw.githubusercontent.com/microsoft/RD-Agent/main/requirements.txt', │ │
│ │ │ 'type': 'file', │ │
│ │ │ 'content': 'IyBSZXF1aXJlbWVudHMgZm9yIHJ1bnRpbWUuCnB5ZGFudGljLXNldHRpbmdz\nCgpweXRob24tTGV2ZW5'+1450, │ │
│ │ │ ... +2 │ │
│ │ } │ │
│ │ package = 'litellm>=1.73' │ │
│ │ package_list = ['pydantic-settings', 'python-Levenshtein', 'scikit-learn', 'filelock', 'loguru', 'fire', 'fuzzywuzzy', 'openai', 'litellm>=1.73', 'azure.identity', ... +41] │ │
│ │ package_version_list = ['pydantic-settings==2.12.0', 'python-Levenshtein==0.27.3', 'scikit-learn==1.7.2', 'filelock==3.20.3', 'loguru==0.7.3', 'fire==0.7.1', 'fuzzywuzzy==0.18.0', 'openai==2.16.0'] │ │
│ │ response = <Response [200]> │ │
│ │ version = '2.16.0' │ │
│ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
│ │
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/importlib/metadata/init.py:996 in version │
│ │
│ 993 │ :return: The version string for the package as defined in the package's ╭────────────── locals ───────────────╮ │
│ 994 │ │ "Version" metadata key. │ distribution_name = 'litellm>=1.73' │ │
│ 995 │ """ ╰─────────────────────────────────────╯ │
│ ❱ 996 │ return distribution(distribution_name).version │
│ 997 │
│ 998 │
│ 999 def entry_points(**params) -> Union[EntryPoints, SelectableGroups]: │
│ │
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/importlib/metadata/init.py:969 in distribution │
│ │
│ 966 │ :param distribution_name: The name of the distribution package as a string. ╭────────────── locals ───────────────╮ │
│ 967 │ :return: A
Distributioninstance (or subclass thereof). │ distribution_name = 'litellm>=1.73' │ ││ 968 │ """ ╰─────────────────────────────────────╯ │
│ ❱ 969 │ return Distribution.from_name(distribution_name) │
│ 970 │
│ 971 │
│ 972 def distributions(**kwargs): │
│ │
│ /home/chillywhale/anaconda3/envs/rdagent/lib/python3.10/importlib/metadata/init.py:548 in from_name │
│ │
│ 545 │ │ │ if dist is not None: │
│ 546 │ │ │ │ return dist │
│ 547 │ │ else: │
│ ❱ 548 │ │ │ raise PackageNotFoundError(name) │
│ 549 │ │
│ 550 │ @classmethod │
│ 551 │ def discover(cls, **kwargs): │
│ │
│ ╭────────────────────────────────────────────────────── locals ──────────────────────────────────────────────────────╮ │
│ │ dist = None │ │
│ │ dists = <map object at 0x79cb54af2fe0> │ │
│ │ name = 'litellm>=1.73' │ │
│ │ resolver = <bound method MetadataPathFinder.find_distributions of <class 'importlib_metadata.MetadataPathFinder'>> │ │
│ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
PackageNotFoundError: No package metadata was found for litellm>=1.73
Additional Notes