The VinciGit00/Scrapegraph-ai project currently has 8 open issues, which include both feature requests and bug reports. These issues are critical as they highlight the community's needs and the potential areas for improvement and expansion of the project.
insufficient_quota
error with suggestions to add parameters for controlling API access rates or integrating alternative hosted LLMs.The recent closure of several issues, including #167, #166, and #165, along with others related to model support and API key fixes (#162 through #157), indicates active development and responsiveness to community feedback.
scrapegraphai/graphs/smart_scraper_graph.py
SmartScraperGraph
class.AbstractGraph
and utilizes various nodes for processing web content.scrapegraphai/nodes/fetch_node.py
FetchNode
class for fetching HTML content.AsyncChromiumLoader
; however, handling different source types could be streamlined.scrapegraphai/models/openai.py
ChatOpenAI
class.examples/azure/smart_scraper_azure_openai.py
SmartScraperGraph
with Azure OpenAI services.scrapegraphai/utils/token_calculator.py
Recent commits indicate active development primarily led by Marco Vinciguerra (VinciGit00), with significant contributions from other team members like Cem Uzunoglu (cemkod). The rapid merging of PRs such as #168 and continuous enhancements across various branches like '88-blockscraper-implementation' demonstrate a dynamic development environment focused on expanding functionalities and maintaining responsiveness to user needs.
The ScrapeGraphAI project is in a healthy state with active maintenance, frequent updates, and responsiveness to both user feedback and technological advancements. The development team's efforts are well-coordinated, focusing on both expanding the project's capabilities and enhancing user experience through detailed documentation and robust code management practices.
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
Marco Vinciguerra | 5 | 33/32/1 | 73 | 114 | 6261 | |
Marco Perini | 3 | 8/7/1 | 31 | 78 | 6045 | |
Semantic Release Bot | 3 | 0/0/0 | 40 | 2 | 547 | |
Eric Page | 2 | 1/1/0 | 10 | 20 | 311 | |
Shubham Kamboj | 2 | 4/3/1 | 4 | 5 | 218 | |
Simone Pulcini | 1 | 1/1/0 | 2 | 2 | 159 | |
Lorenzo Padoan | 2 | 1/1/0 | 4 | 15 | 157 | |
Cem Uzunoglu | 1 | 1/1/0 | 2 | 5 | 133 | |
S4mpl3r | 1 | 1/1/0 | 1 | 3 | 116 | |
Federico Aguzzi | 1 | 1/1/0 | 1 | 1 | 31 | |
Tamas Darvas | 1 | 2/2/0 | 2 | 2 | 9 | |
Ikko Eltociear Ashimine | 1 | 1/1/0 | 1 | 1 | 4 | |
dependabot[bot] | 1 | 2/2/0 | 2 | 2 | 4 | |
Shixian Sheng | 1 | 1/1/0 | 1 | 1 | 2 | |
None (gioCarBo) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
ScrapeGraphAI is a Python library designed for advanced web scraping using AI technologies, specifically leveraging Large Language Models (LLMs) and graph-based logic. The project is hosted on GitHub under the repository VinciGit00/Scrapegraph-ai and has demonstrated substantial community engagement with 2160 stars and 172 forks. The library aims to simplify the scraping process by allowing users to specify desired information for extraction, which the library then autonomously handles.
The development team is actively enhancing the library's functionality, focusing on integrating new AI models, refining existing features, and improving documentation. Recent activities indicate a robust pace of development and a strong responsiveness to community feedback and contributions.
The integration of AI technologies in web scraping tools like ScrapeGraphAI positions the project at a promising intersection of AI and data extraction markets. The ability to integrate with platforms such as AWS Bedrock and support for various AI models from Hugging Face and Anthropic Claude 3 enhances its appeal to a broader range of users, from data scientists to enterprise clients needing sophisticated data extraction solutions.
The project exhibits a healthy development lifecycle characterized by:
Recent activities by the development team include:
The source code analysis reveals:
ScrapeGraphAI stands out as an innovative project with significant potential in the rapidly evolving landscape of web scraping and AI integration. The project is well-managed with an active development team that is responsive to community input and committed to continuous improvement. Strategic enhancements in documentation, error handling, and user interface could further elevate its market position and user adoption.
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
Marco Vinciguerra | 5 | 33/32/1 | 73 | 114 | 6261 | |
Marco Perini | 3 | 8/7/1 | 31 | 78 | 6045 | |
Semantic Release Bot | 3 | 0/0/0 | 40 | 2 | 547 | |
Eric Page | 2 | 1/1/0 | 10 | 20 | 311 | |
Shubham Kamboj | 2 | 4/3/1 | 4 | 5 | 218 | |
Simone Pulcini | 1 | 1/1/0 | 2 | 2 | 159 | |
Lorenzo Padoan | 2 | 1/1/0 | 4 | 15 | 157 | |
Cem Uzunoglu | 1 | 1/1/0 | 2 | 5 | 133 | |
S4mpl3r | 1 | 1/1/0 | 1 | 3 | 116 | |
Federico Aguzzi | 1 | 1/1/0 | 1 | 1 | 31 | |
Tamas Darvas | 1 | 2/2/0 | 2 | 2 | 9 | |
Ikko Eltociear Ashimine | 1 | 1/1/0 | 1 | 1 | 4 | |
dependabot[bot] | 1 | 2/2/0 | 2 | 2 | 4 | |
Shixian Sheng | 1 | 1/1/0 | 1 | 1 | 2 | |
None (gioCarBo) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
Marco Vinciguerra | 5 | 33/32/1 | 73 | 114 | 6261 | |
Marco Perini | 3 | 8/7/1 | 31 | 78 | 6045 | |
Semantic Release Bot | 3 | 0/0/0 | 40 | 2 | 547 | |
Eric Page | 2 | 1/1/0 | 10 | 20 | 311 | |
Shubham Kamboj | 2 | 4/3/1 | 4 | 5 | 218 | |
Simone Pulcini | 1 | 1/1/0 | 2 | 2 | 159 | |
Lorenzo Padoan | 2 | 1/1/0 | 4 | 15 | 157 | |
Cem Uzunoglu | 1 | 1/1/0 | 2 | 5 | 133 | |
S4mpl3r | 1 | 1/1/0 | 1 | 3 | 116 | |
Federico Aguzzi | 1 | 1/1/0 | 1 | 1 | 31 | |
Tamas Darvas | 1 | 2/2/0 | 2 | 2 | 9 | |
Ikko Eltociear Ashimine | 1 | 1/1/0 | 1 | 1 | 4 | |
dependabot[bot] | 1 | 2/2/0 | 2 | 2 | 4 | |
Shixian Sheng | 1 | 1/1/0 | 1 | 1 | 2 | |
None (gioCarBo) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
There are 8 open issues in the repository, ranging from feature requests to bug reports. Notable issues include requests for new features such as node structure returns, integration with AWS Bedrock, proxy rotation function changes, and scraping enhancements. Some issues also touch on quota limitations and the desire for more detailed documentation or examples.
Several issues have been closed very recently, indicating active development and responsiveness to user feedback.
The VinciGit00/Scrapegraph-ai project is actively developed with several notable open issues that suggest both opportunities for significant feature enhancements and areas where clearer documentation could improve user experience. The recent activity in closing issues indicates good project health and responsiveness to community input.
PR #168: Asdt - This PR was created and closed on the same day, indicating a fast merge cycle. It includes a significant amount of changes with multiple commits from Marco Perini (PeriniM) and a final merge by Marco Vinciguerra (VinciGit00). The PR seems to introduce a new feature or implementation called "asdt" with various additions to the codebase, including examples and new modules.
PR #167: Update README.md - A small but important change to the README file, changing a relative path to an absolute path for PyPI compatibility. This was also created and closed on the same day, showing quick responsiveness to documentation fixes.
PR #165: docs: update README.md - Another quick documentation fix that corrects a spelling mistake in the README. Also merged rapidly.
PR #162 & PR #161 - These PRs added new models to the project and were included in version 0.10.0-beta.1
. They were merged quickly, indicating an active development cycle.
PR #159 & PR #158 - These PRs fixed issues related to the gemini API key and embedding configurations. They were also included in version 0.10.0-beta.1
.
PR #157: add lava integration for ollama - This PR was merged within a day and included in two beta versions (0.9.0-beta.8
and 0.10.0-beta.1
), suggesting it's an important addition.
PR #155: GraphIteratorNode and MergeAnswersNode - Introduced two new nodes for creating multiple instances of a graph and merging answers, which could be a significant feature for users needing to aggregate data from multiple sources.
PR #154: Pass common params to nodes in graph - This PR refactored how common parameters are passed to nodes within graphs, which could improve usability and reduce redundancy in code.
PR #153: feat: add gemini embeddings - Added embeddings for Gemini, which could enhance the model's capabilities.
PR #149: new version - This seems to be a release-related PR that was merged quickly, indicating active version management.
PR #148: Enable end users to pass model instances of HuggingFaceHub - Allows end users more flexibility when using models from HuggingFaceHub, which is a significant enhancement.
PR #146 & PR #144: Dependency updates for tqdm
. These are routine maintenance updates but are important for keeping dependencies secure and up-to-date.
There are no open pull requests at the time of this analysis.
The repository shows a very active development cycle with rapid merges of pull requests, including both feature additions and bug fixes. The maintainers seem responsive to contributions from multiple developers, as seen by the variety of contributors involved in recent pull requests.
There are no open pull requests at the moment, which suggests that the maintainers are keeping up well with incoming changes. However, it's essential to ensure that this rapid cycle does not compromise code quality or thorough review processes.
The use of bots for versioning and dependency review indicates an automated CI/CD pipeline that helps maintain software quality and security standards.
Overall, the activity on the repository suggests a healthy project with active contributions and maintenance.
ScrapeGraphAI is a Python library designed for web scraping using AI technologies. It leverages Large Language Models (LLMs) and direct graph logic to create scraping pipelines for websites, documents, and XML files. The project is managed on GitHub under the repository VinciGit00/Scrapegraph-ai and has gained significant attention with 2160 stars and 172 forks. The library simplifies the scraping process by allowing users to specify the information they want to extract, and the library handles the rest. It is recommended to install ScrapeGraphAI in a virtual environment to avoid conflicts with other libraries.
The project's documentation is comprehensive and available on Read the Docs and Render. The library is licensed under the MIT License, ensuring open-source availability.
The development team has been active in maintaining and enhancing the library, with recent commits focusing on adding new features, fixing bugs, improving documentation, and integrating new AI models. The team has also been working on implementing asynchronous support and refining search functionalities.
pyproject.toml
.The team has been actively managing pull requests with a majority being merged successfully. There are ongoing pull requests that are under review or have been recently merged.
The ScrapeGraphAI development team is highly active and collaborative. They are focused on expanding the library's capabilities by integrating various AI models and improving existing functionalities. The project's trajectory shows a commitment to maintaining high-quality standards while responding promptly to issues and feature requests from the community. The use of automated tools like semantic-release-bot indicates a streamlined workflow for releases. Overall, the project appears to be in a healthy state with an engaged development team driving its progress.
ScrapeGraphAI is a Python library designed for web scraping using natural language models and direct graph logic. The repository is well-maintained with continuous integration setups like pylint and CodeQL, indicating a focus on code quality and security.
scrapegraphai/graphs/smart_scraper_graph.py
SmartScraperGraph
class, which orchestrates the scraping process using a graph of nodes.AbstractGraph
.FetchNode
, ParseNode
, RAGNode
, and GenerateAnswerNode
to process web content.scrapegraphai/nodes/fetch_node.py
FetchNode
class responsible for fetching HTML content from URLs or local directories.BaseNode
.AsyncChromiumLoader
for fetching web content, supporting both headless and verbose operations.scrapegraphai/models/openai.py
ChatOpenAI
class from langchain_openai
.ChatOpenAI
class.examples/azure/smart_scraper_azure_openai.py
SmartScraperGraph
with Azure OpenAI services.scrapegraphai/utils/token_calculator.py
max_tokens = models_tokens[model] - 500
) seems arbitrary without context; needs clarification or configuration option.Overall, ScrapeGraphAI exhibits a solid foundation with well-structured code that adheres to good software engineering principles. However, attention to detailed error management and configuration could further enhance its robustness.