VLAgent #250
VLAgent #250
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Review by Korbit AI
Korbit automatically attempts to detect when you fix issues in new commits.
| Category | Issue | Status |
|---|---|---|
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Missing return type hint ▹ view | |
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Mixed Responsibilities in VLAgentArgs ▹ view | |
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Unclear Action Format Specification ▹ view | |
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Unspecified Observation Preprocessing Requirements ▹ view | |
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Unsafe image data processing without validation ▹ view | |
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Inflexible Image Format Handling ▹ view | |
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Unsafe Numpy Array Conversion ▹ view | |
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Silent Exception Masking ▹ view | |
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Missing API Key Validation ▹ view | |
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Unbounded Exponential Backoff ▹ view |
Files scanned
| File Path | Reviewed |
|---|---|
| src/agentlab/agents/vl_agent/vl_model/base.py | ✅ |
| src/agentlab/agents/vl_agent/vl_prompt/base.py | ✅ |
| src/agentlab/agents/vl_agent/vl_agent/base.py | ✅ |
| src/agentlab/agents/vl_agent/main.py | ✅ |
| src/agentlab/agents/vl_agent/utils.py | ✅ |
| src/agentlab/agents/vl_agent/config.py | ✅ |
| src/agentlab/agents/vl_agent/vl_model/openrouter_api_model.py | ✅ |
| src/agentlab/agents/vl_agent/vl_model/llama_model.py | ✅ |
| src/agentlab/agents/vl_agent/vl_agent/ui_agent.py | ✅ |
| src/agentlab/agents/vl_agent/vl_prompt/ui_prompt.py | ✅ |
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| import numpy as np | ||
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| def image_to_image_url(image: Union[Image.Image, np.ndarray]): |
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Missing return type hint 
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What is the issue?
Return type hint is missing for the function.
Why this matters
Missing return type hints make it harder for developers to understand the expected output type without reading the implementation details.
Suggested change ∙ Feature Preview
def image_to_image_url(image: Union[Image.Image, np.ndarray]) -> str:Provide feedback to improve future suggestions
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| @dataclass | ||
| class VLAgentArgs(ABC): | ||
| @property | ||
| @abstractmethod | ||
| def agent_name(self) -> str: | ||
| raise NotImplementedError | ||
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| @abstractmethod | ||
| def make_agent(self) -> VLAgent: | ||
| raise NotImplementedError |
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Mixed Responsibilities in VLAgentArgs 
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What is the issue?
VLAgentArgs is mixing the Factory pattern (make_agent) with configuration (agent_name) and lifecycle management (prepare, close) responsibilities, violating the Single Responsibility Principle.
Why this matters
This design makes the class harder to maintain and test, while reducing reusability. Lifecycle management, configuration, and object creation should be handled by separate components.
Suggested change ∙ Feature Preview
Split the class into separate components:
@dataclass
class VLAgentConfig:
agent_name: str
# other configuration parameters
class VLAgentFactory(ABC):
@abstractmethod
def make_agent(self, config: VLAgentConfig) -> VLAgent:
pass
class VLAgentLifecycle(ABC):
@abstractmethod
def prepare(self):
pass
@abstractmethod
def close(self):
passProvide feedback to improve future suggestions
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| class VLAgent(ABC): | ||
| @abstractmethod | ||
| def get_action(self, obs: dict) -> tuple[str, dict]: |
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Unclear Action Format Specification 
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What is the issue?
The return type tuple[str, dict] lacks documentation about what the string and dictionary represent in the action tuple, which could lead to incorrect implementations by derived classes.
Why this matters
Without clear documentation of the expected format and content of the action tuple, implementations may return incompatible or incorrect action formats that could cause runtime errors or unexpected behavior.
Suggested change ∙ Feature Preview
Add a docstring explaining the expected format of the action tuple:
@abstractmethod
def get_action(self, obs: dict) -> tuple[str, dict]:
"""Get the next action based on the observation.
Args:
obs (dict): The observation from the environment
Returns:
tuple[str, dict]: A tuple containing:
- action_type (str): The type of action to perform (e.g., 'click', 'type')
- action_params (dict): Parameters specific to the action type
"""
raise NotImplementedErrorProvide feedback to improve future suggestions
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| raise NotImplementedError | ||
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| @abstractmethod | ||
| def obs_preprocessor(self, obs: dict) -> dict: |
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Unspecified Observation Preprocessing Requirements
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What is the issue?
The obs_preprocessor method lacks specification about what preprocessing should be performed and what the output format should be.
Why this matters
Implementations might process observations incorrectly or inconsistently, leading to unexpected agent behavior when processing environment observations.
Suggested change ∙ Feature Preview
Add a docstring specifying the preprocessing requirements:
@abstractmethod
def obs_preprocessor(self, obs: dict) -> dict:
"""Preprocess the raw observation from the environment.
Args:
obs (dict): Raw observation from the environment
Returns:
dict: Processed observation with standardized format:
- Required fields should be documented here
- Any transformations that must be applied
"""
raise NotImplementedErrorProvide feedback to improve future suggestions
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| def image_url_to_image(image_url: str) -> Image.Image: | ||
| image_base64 = image_url.replace("data:image/jpeg;base64,", "") | ||
| image_data = base64.b64decode(image_base64.encode()) | ||
| buffer = io.BytesIO(image_data) | ||
| image = Image.open(buffer) |
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Unsafe image data processing without validation 
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What is the issue?
The function accepts and processes base64 image data without proper input validation, which could lead to processing of malicious image data.
Why this matters
Malformed or malicious image data could lead to memory exhaustion, buffer overflows, or arbitrary code execution through image parsing vulnerabilities.
Suggested change ∙ Feature Preview
def image_url_to_image(image_url: str) -> Image.Image:
if not image_url.startswith("data:image/jpeg;base64,"):
raise ValueError("Invalid image URL format")
try:
image_base64 = image_url.replace("data:image/jpeg;base64,", "")
image_data = base64.b64decode(image_base64.encode())
buffer = io.BytesIO(image_data)
image = Image.open(buffer)
if image.format != "JPEG":
raise ValueError("Invalid image format")
return image
except (ValueError, IOError) as e:
raise ValueError("Invalid image data") from eProvide feedback to improve future suggestions
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| def image_url_to_image(image_url: str) -> Image.Image: | ||
| image_base64 = image_url.replace("data:image/jpeg;base64,", "") |
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Inflexible Image Format Handling 
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What is the issue?
The function assumes all image URLs will be JPEG format, but the input URL could contain other image formats (PNG, GIF, etc.).
Why this matters
This could cause conversion failures when processing non-JPEG images, leading to runtime errors in the virtual learning agent.
Suggested change ∙ Feature Preview
Modify the function to handle different image formats by extracting the format from the URL:
def image_url_to_image(image_url: str) -> Image.Image:
# Extract format from data URL
format_match = image_url.split(';')[0]
if not format_match.startswith('data:image/'):
raise ValueError('Invalid image data URL')
# Remove the data URL prefix
image_base64 = image_url.split(',')[1]
image_data = base64.b64decode(image_base64.encode())
buffer = io.BytesIO(image_data)
image = Image.open(buffer)
return imageProvide feedback to improve future suggestions
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| def image_to_image_url(image: Union[Image.Image, np.ndarray]): | ||
| if isinstance(image, np.ndarray): | ||
| image = Image.fromarray(image) |
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Unsafe Numpy Array Conversion
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What is the issue?
The function doesn't handle invalid numpy array inputs that can't be converted to PIL Images.
Why this matters
Invalid array shapes, data types, or value ranges could cause crashes when converting numpy arrays to PIL Images.
Suggested change ∙ Feature Preview
Add error handling for numpy array conversion:
def image_to_image_url(image: Union[Image.Image, np.ndarray]):
if isinstance(image, np.ndarray):
try:
image = Image.fromarray(image)
except TypeError as e:
raise ValueError(f"Invalid numpy array format: {e}")Provide feedback to improve future suggestions
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| try: | ||
| response = completion.choices[0].message.content | ||
| except: | ||
| response = "" |
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Silent Exception Masking
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What is the issue?
Bare except clause that silently catches all exceptions and returns an empty string
Why this matters
This could mask critical security-related exceptions like authentication failures or API key issues, making it harder to detect and respond to security problems
Suggested change ∙ Feature Preview
try:
response = completion.choices[0].message.content
except Exception as e:
# Log the error appropriately
raise # Re-raise the exception to handle it at a higher levelProvide feedback to improve future suggestions
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| max_tokens: int, | ||
| reproducibility_config: dict, | ||
| ): | ||
| self.client = OpenAI(base_url=base_url, api_key=os.getenv("OPENROUTER_API_KEY")) |
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Missing API Key Validation
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What is the issue?
No validation of required API key environment variable
Why this matters
If OPENROUTER_API_KEY is not set, it will be None, potentially causing unclear errors or unexpected behavior when making API calls
Suggested change ∙ Feature Preview
api_key = os.getenv("OPENROUTER_API_KEY")
if not api_key:
raise ValueError("OPENROUTER_API_KEY environment variable is required")
self.client = OpenAI(base_url=base_url, api_key=api_key)Provide feedback to improve future suggestions
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| @backoff.on_exception(backoff.expo, RateLimitError) | ||
| def get_response(messages, max_tokens, **kwargs): |
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Unbounded Exponential Backoff 
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What is the issue?
The backoff decorator's exponential retry strategy lacks maximum retry attempts and maximum delay parameters.
Why this matters
Without limits on retries and delays, the system could potentially keep retrying indefinitely or with extremely long delays, impacting system performance and resource utilization.
Suggested change ∙ Feature Preview
Add max_tries and max_time parameters to the backoff decorator:
@backoff.on_exception(
backoff.expo,
RateLimitError,
max_tries=5,
max_time=30
)Provide feedback to improve future suggestions
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Description by Korbit AI
What change is being made?
Introduce the VLAgent functionality, including model configurations, a UI agent implementation, utility functions for image-handling, and an extensible prompt system for browser-based interactions.
Why are these changes being made?
The VLAgent addition facilitates high-level web-based task automation by allowing the configuration of both primary and auxiliary models, leveraging visual inputs and historical context to choose appropriate browser actions. This modular approach, with abstract base classes and concrete implementations, supports flexibility and future extensions, though further validation on extensive real-world datasets would strengthen its general applicability.