Skyvern is a cutting-edge software project aimed at automating browser-based workflows through the integration of Large Language Models (LLMs) and Computer Vision technologies. This report provides a detailed analysis of the project's current state, including an examination of its source code, issues, pull requests, and team activities. The goal is to offer insights into the technical health, development pace, and potential areas for improvement within the project.
Summary
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Detailed Reportsskyvern/forge/sdk/services/bitwarden.pyget_secret_value_from_url to simplify and enhance maintainability.skyvern/forge/sdk/workflow/models/block.pyskyvern/webeye/browser_factory.pycheck_and_fix_state for better readability.skyvern/webeye/actions/handler.pyskyvern/forge/sdk/api/llm/config_registry.pyskyvern/forge/prompts/skyvern/extract-action.j2The source files demonstrate sophisticated use of various programming techniques, including object-oriented programming, asynchronous operations, and design patterns. While the overall quality is high, there are opportunities for improving documentation, reducing complexity, and enhancing testability.
The development team exhibits a healthy division of labor with clear specializations in backend logic, frontend development, documentation, and operational setups. Regular collaborations in PR reviews suggest a cohesive team environment. The frequency of commits across different areas indicates an active development phase with ongoing efforts to refine and expand the project's capabilities.
The closure of several setup-related issues suggests improvements in installation processes and configurations. Documentation enhancements indicate ongoing efforts to make the project more accessible to new users.
Skyvern is demonstrating robust growth and continuous improvement in automating browser-based workflows using advanced technologies. While there are areas requiring attention such as testing of new features across different environments and clarification on new feature integrations, the project's active maintenance and development indicate a promising trajectory. The team's structured approach to tackling both technical debt and new features is commendable. Continued focus on reducing complexity and enhancing documentation will further solidify Skyvern's position as a leading solution in its domain.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Kerem Yilmaz |  |
1 | 24/21/3 | 21 | 45 | 2993 |
| Salih Altun |  |
1 | 8/8/0 | 8 | 12 | 181 |
| Shuchang Zheng |  |
1 | 10/11/0 | 11 | 17 | 170 |
| LawyZheng |  |
2 | 7/6/0 | 7 | 6 | 136 |
| Suchintan |  |
1 | 2/2/0 | 2 | 1 | 4 |
| OB42 |  |
1 | 2/1/0 | 1 | 1 | 2 |
| Shixian Sheng |  |
1 | 1/1/0 | 1 | 1 | 2 |
| Ikko Eltociear Ashimine |  |
1 | 1/1/0 | 1 | 1 | 2 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
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Skyvern is a cutting-edge software project that leverages advanced technologies such as Large Language Models (LLMs) and Computer Vision to automate browser-based workflows. This approach allows Skyvern to adapt dynamically to changes in web page layouts, offering a robust alternative to traditional automation tools. The project is under active development by Skyvern-AI and is well-received in the open-source community, as evidenced by its GitHub activity and contributions.
The Skyvern project exhibits a healthy pace of development with regular updates and contributions from a diverse team. Recent activities suggest that the team is effectively addressing both new feature developments and maintenance issues. This balance is crucial for sustaining long-term growth and stability of the software.
By integrating LLMs and Computer Vision, Skyvern positions itself uniquely in the market of automation tools, potentially reducing the dependency on manual updates when web interfaces change. This capability could be particularly appealing to enterprises looking for stable, scalable automation solutions, thereby opening up significant market opportunities.
While the innovative approach provides a competitive edge, it also involves complexities related to maintaining such advanced technologies. The ongoing need to update models and manage sophisticated code can increase operational costs. However, these costs are justified by the high value and unique capabilities provided by Skyvern, which can lead to substantial market penetration and customer satisfaction.
The current team structure, with members specializing in different aspects such as backend logic, frontend development, and documentation, appears to be well-optimized for the project's needs. However, as the project scales, there might be a need to expand the team, particularly adding more expertise in areas like AI model integration and cloud infrastructure to handle increased load and complexity.
Skyvern is on a promising trajectory with its innovative use of technology in automating web interactions. The project demonstrates robust development activity and strategic management of resources. Addressing the key issues identified and considering the strategic recommendations will further enhance its position in the market and ensure sustainable growth.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Kerem Yilmaz | |
1 | 24/21/3 | 21 | 45 | 2993 |
| Salih Altun | |
1 | 8/8/0 | 8 | 12 | 181 |
| Shuchang Zheng | |
1 | 10/11/0 | 11 | 17 | 170 |
| LawyZheng | |
2 | 7/6/0 | 7 | 6 | 136 |
| Suchintan | |
1 | 2/2/0 | 2 | 1 | 4 |
| OB42 | |
1 | 2/1/0 | 1 | 1 | 2 |
| Shixian Sheng | |
1 | 1/1/0 | 1 | 1 | 2 |
| Ikko Eltociear Ashimine | |
1 | 1/1/0 | 1 | 1 | 2 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
parse_api_response function. This issue has been addressed by forcing the model to output JSON and changing the JSON extraction method. However, it has only been tested with Anthropic's Claude 3 Opus and not with OpenAI models due to a lack of credits.run_ui.bash, but the screenshot that could provide more context is missing. This issue is currently blocked due to insufficient information.The open issues indicate that Skyvern is actively being developed and maintained, with particular attention to model integration, setup processes, and user experience improvements. Critical issues such as JSON parsing reliability (#290) and task execution failures (#288) require immediate attention. Uncertainties around feature requests like Chrome extension interactions (#261) and local model integrations (#180) highlight the need for clearer communication or documentation. The recent closure of issues related to setup and configuration suggests that these areas are receiving timely updates, which is positive for user onboarding and overall project health.
Overall, while there are some potential issues with open pull requests needing further testing or updates (such as PR #290), the project seems to be actively maintained with recent pull requests being addressed promptly.
- **Purpose**: Manages interactions with the Bitwarden CLI for secure credential management.
- **Structure**:
- Defines `BitwardenConstants` using `StrEnum` for environment variable keys.
- `BitwardenService` class provides methods to interact with the Bitwarden CLI, including login, unlock, list items, and logout functionalities.
- **Quality**:
- Good use of static methods and structured logging.
- Exception handling is robust, catching specific errors related to Bitwarden operations and raising custom exceptions.
- Uses subprocesses securely by sanitizing the environment variables and handling command outputs carefully.
- **Improvement Areas**:
- Could benefit from more detailed docstrings explaining parameters and return types.
- The method `get_secret_value_from_url` is quite long and handles multiple responsibilities; consider breaking it down into smaller functions for better maintainability.
- **Purpose**: This file likely contains critical logic for managing workflow blocks but was truncated in the provided content.
- **Assessment Based on Available Information**:
- Expected to handle complex logic given its significance in managing URL load timeouts and workflow operations.
- Without the full content, specific assessments on code quality and structure cannot be made.
- **Purpose**: Manages browser contexts and interactions using Playwright.
- **Structure**:
- Defines a protocol `BrowserContextCreator` and a factory class `BrowserContextFactory` for creating browser contexts with different configurations.
- Implements functionality to manage browser artifacts like videos and HAR files.
- **Quality**:
- Well-structured using design patterns like Factory for browser creation which enhances extensibility.
- Good use of async functions to handle browser operations efficiently.
- Includes detailed logging which aids in debugging and monitoring.
- **Improvement Areas**:
- Some methods are quite complex; for instance, `check_and_fix_state` could be refactored to improve readability and maintainability.
- **Purpose**: Handles various web actions like clicking, inputting text, uploading files, etc., within a browser context.
- **Structure**:
- Uses a class-based approach to register and execute actions based on type. Each action type like click, input text, etc., has a corresponding handler function.
- **Quality**:
- Demonstrates an advanced use of Python features like decorators and asynchronous programming.
- Robust error handling and logging provide clear insights into action executions and failures.
- **Improvement Areas**:
- The file is lengthy and handles multiple responsibilities; consider splitting into smaller modules based on action types or functionality.
- **Purpose**: Manages configurations for different LLM (Language Learning Models) providers.
- **Structure**:
- Contains a registry class that holds configurations for various LLM providers. Configurations are validated and registered dynamically based on the application settings.
- **Quality**:
- Uses a central registry for managing configurations which simplifies access across the application.
- Exception handling specific to configuration errors helps in identifying setup issues quickly.
- **Improvement Areas**:
- Dependency on global settings within the registry might make unit testing challenging; consider using dependency injection for settings.
- **Purpose**: Template for generating JSON structured actions based on webpage analysis to drive automated workflows.
- **Structure**:
- A Jinja2 template that outlines how actions should be structured in JSON format based on the analysis of web page elements and user goals.
- **Quality**:
- Provides a clear template structure that can be dynamically filled based on various conditions such as user goals, errors, etc., which enhances flexibility in automated decision-making processes.
- **Improvement Areas**:
- As a template, it's crucial to ensure that all possible edge cases are considered to prevent runtime errors during template rendering.
The reviewed files demonstrate a sophisticated use of Python programming techniques including OOP, asynchronous programming, design patterns like Factory, and effective error handling. However, there are areas where complexity could be reduced or documentation could be enhanced to improve maintainability and understandability.
Skyvern is an innovative software project designed to automate browser-based workflows through the use of Large Language Models (LLMs) and Computer Vision. It is developed and maintained by the organization Skyvern-AI. The project aims to provide a robust alternative to traditional automation solutions that often break due to website layout changes. By integrating computer vision and LLMs, Skyvern can interact with web elements in real-time, making it adaptable to new websites and resistant to changes in existing ones. The project is open-source, licensed under the GNU Affero General Public License v3.0, and its repository shows a healthy amount of activity with a significant number of stars (4379), forks (295), and watchers (31). The trajectory of the project appears positive with ongoing contributions and feature development.
skyvern/forge/sdk/services/bitwarden.py, poetry.lock, pyproject.toml.setup.sh.README.md.docs/quickstart.mdx.skyvern/webeye/scraper/domUtils.js and skyvern/forge/agent.py.skyvern/forge/sdk/db/client.py and skyvern/forge/api_app.py.skyvern-frontend/src/routes/tasks/list/TaskList.tsx.skyvern/webeye/browser_factory.py and related files.The development team behind Skyvern is actively working on improving the software's capabilities, with a clear division of labor among frontend, backend, documentation, and devops tasks. The team members collaborate through PR reviews, indicating a structured workflow. The frequent updates to configuration files and backend logic suggest that the project is still evolving rapidly, with ongoing efforts to enhance stability, performance, and user experience. The addition of new features such as workflow support indicates that Skyvern is expanding its scope to cater to more complex automation needs.