VisionAgent is a Python library developed by Landing AI that leverages agent frameworks and large language models (LLMs) to facilitate computer vision tasks. It supports multiple LLM providers, offers a web app for testing, and includes comprehensive documentation. The project is actively maintained, with a strong community presence and extensive toolset for vision-related tasks.
Asia Cao (AsiaCao)
Summary
Quantified Reports
Detailed Reports[skip ci] chore(release) commits.pyproject.toml.Dillon Laird (dillonalaird)
Hernan Payrumani (hrnn)
Camilo Zapata (camiloaz)
Camilo Iral (CamiloInx)
Other Contributors
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 2 | 5 | 6 | 2 | 1 |
| 30 Days | 16 | 11 | 37 | 16 | 1 |
| 90 Days | 19 | 17 | 40 | 19 | 1 |
| All Time | 33 | 28 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
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| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Dillon Laird |  |
3 | 2/4/0 | 9 | 11 | 332 |
| Asia |  |
1 | 0/0/0 | 2 | 1 | 4 |
| Hernan Payrumani (hrnn) | 0 | 0/0/1 | 0 | 0 | 0 | |
| Vishal Kumar (vishalk1995) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
| Risk | Level (1-5) | Rationale |
|---|---|---|
| Delivery | 3 | The project shows a moderate delivery risk due to a backlog of unresolved issues and dependency challenges. Issues like #381 and #376 highlight integration and compatibility problems that could delay delivery if not addressed promptly. The team's active engagement in resolving issues is positive, but the recurring nature of some problems suggests potential gaps in testing and error handling processes. |
| Velocity | 3 | Velocity is stable but not exceptional, with a focus on maintenance over innovation. The concentration of commit activity from a single developer, Dillon Laird, poses a risk if he becomes unavailable. The limited involvement from other team members suggests potential bottlenecks in development speed. |
| Dependency | 2 | The project maintains up-to-date dependencies through automated tools like Dependabot, reducing dependency risks. However, issues like #376 indicate some compatibility challenges that need addressing to prevent broader integration problems. |
| Team | 3 | The team faces potential risks related to workload distribution and dependency on key individuals like Dillon Laird. Limited contributions from other team members could affect delivery and velocity if not addressed. |
| Code Quality | 2 | Code quality is generally well-maintained with a focus on documentation and minor enhancements. However, the reliance on automated updates necessitates robust testing to ensure stability post-update. |
| Technical Debt | 3 | Technical debt is managed through regular dependency updates, but the backlog of unresolved issues and delayed pull requests indicates potential accumulation of technical debt that needs addressing. |
| Test Coverage | 3 | Test coverage is thorough for specific utility functions and integration tests, but there may be gaps in coverage for other critical components. Ensuring comprehensive test coverage across all modules is essential to minimize technical debt. |
| Error Handling | 3 | Error handling mechanisms show room for improvement, as indicated by recurring issues related to model configurations and execution environments. Enhancing these processes could reduce technical debt and improve delivery outcomes. |
Recent GitHub issue activity for the VisionAgent project shows a mix of technical inquiries, feature requests, and bug reports. Notably, there are several issues related to integrating and configuring different models and libraries, such as #381 regarding local model deployment and #376 about pydantic version compatibility. There are also multiple discussions around improving functionality, like #377's request for multi-class detection support. A recurring theme is troubleshooting errors and debugging, as seen in issues #375 and #357. The project appears to be actively maintained with responsive interactions from contributors.
#381: This issue highlights complications with using a locally deployed Qwen-VL model instead of OpenAI's model. The user faces errors when integrating their local setup with VisionAgent, indicating potential gaps in documentation or configuration flexibility.
#377: The request for multi-class detection in a single call points to a limitation in the current implementation that could impact efficiency and resource utilization.
#376: Compatibility issues with the pydantic library version suggest potential integration challenges with other dependencies, which could affect users relying on different versions.
#375: Reports of a non-responsive frontend during testing indicate usability issues that could hinder user experience.
#357: An error related to agentic object detection suggests possible bugs or misconfigurations in the demo environment.
The issues reflect common themes of integration challenges, feature enhancement requests, and debugging needs. Users frequently encounter difficulties with configuring external models and libraries, suggesting a need for clearer guidance or more flexible configurations. There is also a demand for enhanced functionality, such as multi-object detection capabilities. Debugging and error resolution are recurrent topics, indicating areas where the project's robustness could be improved.
#381: "Hi, hello. How do I skip api validation and go straight to my locally deployed Qwen2-VL model"
#377: "Support Multi-Class/Object Detection in a Single Call"
#376: "Allow different versions of pydantic"
#357: "Agentic Object Detection error"
#376 (Updated 6 days ago)
These issues highlight ongoing efforts to address integration challenges and enhance the library's capabilities while maintaining active community engagement through prompt updates and discussions.
next package version. It has been open for a significant amount of time (58 days), which might indicate potential issues with the update or a lack of priority.jinja2. The PR addresses security fixes, which are crucial for maintaining application security.nanoid package for minor bug fixes. It has been open for an extended period, suggesting it may not be critical or has encountered issues during testing.tornado package, addressing performance improvements and bug fixes.setuptools, which may include breaking changes or deprecations.Closed Without Merge
Details: Addressed an issue where an incorrect task name was used in an API call, potentially leading to incorrect results.
Merged
Details: Resolved parsing issues with GPT outputs that were causing unexpected markdown tags.
Merged
Details: Fixed a bug related to Ollama integration, improving stability and functionality.
The open pull requests highlight several dependency updates that have been pending for extended periods, indicating potential challenges or lower prioritization in addressing these updates. It's crucial to prioritize security-related updates like those for jinja2 while carefully testing major version bumps like setuptools.
Recently closed pull requests demonstrate ongoing efforts to resolve critical bugs and improve compatibility with external tools and libraries, such as GPT and Ollama integrations.
Overall, it's essential to maintain a balance between addressing pending updates and ensuring robust testing procedures are in place to prevent disruptions in functionality.
pyproject.tomlStructure and Quality:
>=3.9,<4.0, which is appropriate for leveraging modern Python features while maintaining backward compatibility.pytest, black, isort, and mypy for testing, formatting, sorting imports, and type checking, respectively. This indicates a strong emphasis on code quality and maintainability.Overall Assessment:
The pyproject.toml file is well-structured and comprehensive, reflecting best practices in dependency management and development tooling. It sets a strong foundation for the project's build system.
vision_agent/agent/vision_agent_coder_v2.pyStructure and Quality:
write_code, write_test, and debug_code. These functions are focused on specific tasks related to code generation and testing.VisionAgentCoderV2 class is well-documented with a clear constructor and methods that encapsulate the functionality of generating vision-related code.extract_tag improves readability by abstracting repetitive tasks.Overall Assessment: The file is well-organized with a clear separation of concerns. It demonstrates good coding practices with extensive use of docstrings and error handling.
vision_agent/agent/vision_agent_planner_v2.pyStructure and Quality:
ThreadPoolExecutor for running multiple planning trials concurrently, which can improve performance in generating plans.VisionAgentPlannerV2 class is designed to handle planning tasks with options for human-in-the-loop (HIL) interactions.Overall Assessment: This file exhibits strong design principles with a focus on concurrency, error handling, and adaptability. It effectively manages complex planning tasks with clear documentation.
vision_agent/utils/agent.pyStructure and Quality:
libcst library to manipulate Python code structures safely, indicating an advanced approach to code handling.Overall Assessment:
The utilities provided in this file are essential for the broader application logic. The use of advanced libraries like libcst demonstrates a sophisticated approach to code manipulation.
examples/custom_tools/run_custom_tool.pyStructure and Quality:
Overall Assessment: This file effectively illustrates how to extend VisionAgent's capabilities with custom tools. It serves as a useful reference for users looking to integrate their own functionalities into the framework.
In summary, the source files demonstrate high-quality coding practices with robust error handling, clear documentation, and thoughtful design patterns. The project appears well-maintained with a strong emphasis on modularity and extensibility.
[skip ci] chore(release) commits for version updates of the VisionAgent, indicating a role in maintaining the release cycle.pyproject.toml with minor line adjustments.fix-custom-tool and add-o3-mini.Active Maintenance: The project is under active development with regular commits focusing on bug fixes, feature enhancements, and tool updates. This indicates a well-maintained codebase with continuous improvements.
Collaborative Efforts: Multiple team members are involved in different aspects of the project, from release management to feature development and bug fixing. Collaboration is evident through co-authored commits and shared responsibilities across branches.
Focus on Tool Enhancement: A significant portion of recent activity revolves around enhancing existing tools and adding new functionalities, reflecting a focus on expanding the library's capabilities.
Diverse Contributions: While some team members have specific roles like release management, others are more involved in technical enhancements, showcasing a diverse set of contributions towards the project's goals.
Branch Management: The use of multiple branches for different features or fixes suggests an organized approach to development, allowing parallel progress on various aspects of the project.
Overall, the VisionAgent project demonstrates a dynamic development environment with active participation from multiple contributors, focusing on both stability through bug fixes and growth through new features.