Quantify issues

Recent GitHub Issues Activity

_{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.}

Rate pull requests

PR#4131 - WIP - OpenAI Assistants Agentopen

2_/5

Leonardo Pinheiro (lspinheiro)Created: 2024-11-11

This pull request is an early draft and lacks completeness. The author has raised several unresolved questions about the implementation, indicating that the design is not yet fully thought out. Additionally, there are no tests or documentation updates included, which are crucial for assessing the quality and impact of the changes. The PR introduces a significant amount of new code but without clear direction or resolution of key issues, it remains notably flawed at this stage.

[+] Read More

PR#4102 - Readme Edits | Just cleanup editsopen

3_/5

Will Dembinski (wi-ski)Created: 2024-11-08

This pull request focuses on correcting minor English and grammar errors across several documentation files. While it contributes to improving the readability and professionalism of the documentation, the changes are not substantial or complex enough to warrant a higher rating. The edits are straightforward and do not introduce any significant new content or features. Therefore, it is considered average and unremarkable, fitting well within the criteria for a rating of 3.

[+] Read More

PR#4123 - Agent name terminationopen

3_/5

Thai Nguyen (thainduy)Created: 2024-11-10

The pull request introduces a new class, `AgentNameTermination`, which adds a termination condition for conversations based on the last agent's name. The implementation appears straightforward and includes tests to verify functionality. However, the PR lacks documentation updates and has not passed all auto checks, which are essential for maintaining code quality and consistency. Additionally, there are review comments suggesting potential improvements or changes to the naming convention, indicating room for refinement. Overall, this PR is functional but unremarkable, with some nontrivial flaws that need addressing.

[+] Read More

PR#4141 - Update model-client docs to include external LLM support via OpenAI c…open

3_/5

Vaibhav Balloli (vballoli)Created: 2024-11-11

The pull request primarily updates documentation to include support for external LLMs via the OpenAI client, which is a useful addition but not particularly groundbreaking. It addresses a related issue and includes necessary documentation changes, but lacks tests and has not passed all auto checks. The changes are functional and clear, but the PR does not introduce significant new features or improvements beyond documentation updates, making it average in impact and execution.

[+] Read More

PR#4130 - feat: watsonx supportopen

3_/5

tsingggggCreated: 2024-11-11

The pull request introduces support for watsonx.ai models in the AutoGen framework, which is a useful addition for users needing non-OpenAI model support. However, the changes are primarily documentation updates and a new markdown file, with minimal code alterations. The significance of the change is moderate, as it extends functionality but doesn't involve complex coding or significant architectural changes. The PR addresses reviewer comments and includes tests, but overall, the contribution is more about integration than innovation, making it average in impact.

[+] Read More

PR#4149 - task: added warning when none is called in intervention handleropen

3_/5

Pramod Goyal (goyalpramod)Created: 2024-11-12

The pull request introduces a utility function to warn users when an intervention handler returns None, which is a useful addition to prevent potential errors. However, the change is relatively minor and lacks comprehensive testing or documentation updates, as indicated by the unchecked boxes in the PR description. Additionally, there are unresolved issues with existing tests, which could be related to these changes. Overall, the PR is functional but not particularly significant or thorough, warranting an average rating.

[+] Read More

PR#4146 - Swarm Tutorialopen

3_/5

Gerardo Moreno (gziz)Created: 2024-11-12

The pull request introduces a tutorial for the Swarm pattern, which is a useful addition to the documentation. It provides a clear explanation of the Swarm pattern and includes code examples that demonstrate its implementation. However, it lacks thorough testing and documentation checks, as indicated by the unchecked boxes in the checklist. Additionally, while the tutorial is informative, it could benefit from more detailed examples or scenarios to enhance its educational value. Overall, it is an average contribution with room for improvement in completeness and detail.

[+] Read More

PR#4094 - feat: watsonx supportopen

4_/5

tsingggggCreated: 2024-11-08

The pull request introduces significant new functionality by adding support for IBM's Watsonx models to the AutoGen project. It includes comprehensive changes across multiple files, including new tests and documentation, which indicates thoroughness. The PR is well-structured and passes all checks, showing good coding practices. However, it lacks a direct link to a related issue or a detailed discussion on potential impacts on existing functionalities, which prevents it from being rated as exemplary.

[+] Read More

PR#4143 - Max turn parameter for group chatsopen

4_/5

Eric Zhu (ekzhu)Created: 2024-11-11

The pull request introduces a useful feature by adding a 'max_turns' parameter to control the number of turns in group chats, which enhances user interaction in chatbot scenarios. The implementation is thorough, with changes across multiple files and comprehensive test coverage. The documentation is updated to reflect the new functionality, ensuring users understand how to utilize it. However, the significance of the change is moderate, as it adds a specific functionality rather than a groundbreaking feature. Overall, it's a well-executed and valuable addition.

[+] Read More

PR#4148 - Add visualization of Agent Message Transitions State in AGS open

4_/5

Victor Dibia (victordibia)Created: 2024-11-12

The pull request introduces a significant feature by adding visualization for agent message transitions, which enhances the usability and understanding of the AGS system. The implementation includes substantial code additions and modifications across multiple files, indicating a thorough and detailed effort. However, the PR lacks documentation updates and test cases, which are essential for maintaining code quality and ensuring functionality. These omissions prevent it from achieving an exemplary rating.

[+] Read More

Quantify commits

Quantified Commit Activity Over 14 Days

Developer	Avatar	Branches	PRs	Commits	Files	Changes
Victor Dibia		3	5/3/1	8	130	36738
Kosta Petan (kostapetan)		1	1/0/0	1	85	13194
Eric Zhu		3	31/30/1	31	68	8376
Xiaoyun Zhang		1	2/3/0	3	110	2581
Ryan Sweet		3	2/2/1	27	45	1533
Hussein Mozannar		1	2/2/0	2	17	960
Mohammad Mazraeh		1	3/2/0	2	20	529
Anthony Uphof		1	0/1/0	1	4	382
Reuben Bond		1	4/4/0	4	25	373
Gerardo Moreno		1	3/3/0	3	7	291
Diego Colombo		1	2/1/0	1	21	214
afourney		1	2/2/0	2	1	161
peterychang		1	1/1/0	1	1	34
David Luong		1	2/2/0	2	5	15
Rohan Thacker		1	1/1/0	1	4	8
Mark Sze		1	1/1/0	1	1	6
Joel Klaverkamp (kampernet)		1	1/1/0	1	1	2
Mahesh Subramanian		1	1/1/0	1	1	2
Eduardo Salinas (lalo)		0	1/0/0	0	0	0
Will Dembinski (wi-ski)		0	1/0/0	0	0	0
Tomek Słoma (Hedrekao)		0	1/0/0	0	0	0
Md Azfar Alam (mdazfar2)		0	1/0/1	0	0	0
Thai Nguyen (thainduy)		0	2/0/1	0	0	0
Vaibhav Balloli (vballoli)		0	1/0/0	0	0	0
Benoit Moussaud (bmoussaud)		0	1/0/0	0	0	0
None (tsinggggg)		0	2/0/0	0	0	0
None (frances720)		0	1/0/1	0	0	0
Leonardo Pinheiro (lspinheiro)		0	1/0/0	0	0	0
Pramod Goyal (goyalpramod)		0	1/0/0	0	0	0
None (BrennanOwYong)		0	1/0/0	0	0	0

_{PRs: created by that dev and opened/merged/closed-unmerged during the period}

Quantify risks

Project Risk Ratings

Report On: Fetch issues

Recent Activity Analysis

Recent activity in the "microsoft/autogen" GitHub repository shows a significant number of issues being addressed, with a focus on enhancing the framework's capabilities and resolving bugs. The project is actively maintained, with issues being created and closed frequently, indicating a responsive development team. Key themes include improving documentation, expanding support for different LLMs (Large Language Models), and refining the framework's architecture for better performance and usability.

Notable Issues

• #4144: This issue highlights the need for better documentation of nested chat examples using ChatAgent. The request includes API additions to improve initialization processes, reflecting a focus on enhancing developer experience.

• #4136: A critical bug was reported where an example failed due to an undefined component (MultimodalWebSurfer). This indicates potential gaps in testing or documentation that need addressing to ensure smooth user experiences.

• #4117: The introduction of constants and documentation for model clients through external APIs suggests ongoing efforts to expand the framework's compatibility with various LLMs, enhancing its versatility.

• #4109: A proposal to make certain methods asynchronous reflects a push towards optimizing performance and scalability, crucial for handling complex AI workflows.

• #4089: A discussion on renaming components for clarity indicates active efforts to refine the framework's architecture and improve code readability.

Themes and Commonalities

1. Documentation Enhancements: Several issues focus on improving documentation (#4144, #4117), highlighting a commitment to making the framework more accessible to developers.

2. Bug Fixes and Stability: Issues like #4136 emphasize the importance of addressing bugs promptly to maintain user trust and ensure reliable performance.

3. Feature Expansion: The addition of new features such as support for external LLMs (#4117) and asynchronous methods (#4109) demonstrates ongoing efforts to keep the framework competitive and versatile.

4. Architectural Refinements: Discussions around method naming (#4089) and async implementations reflect a continuous effort to refine the framework's architecture for better usability and performance.

Issue Details

Most Recently Created Issues

• #4144: Created 1 day ago; Priority: High; Status: Open

  • Focuses on documenting nested chat examples using ChatAgent.

• #4140: Created 1 day ago; Priority: Medium; Status: Open

  • Proposes using Simple RPC for certain operations in Distributed Agent Runtime.

Most Recently Updated Issues

• #4136: Updated 0 days ago; Priority: High; Status: Open

  • Reports a failure in an example due to an undefined component (MultimodalWebSurfer).

• #4117: Updated 1 day ago; Priority: Medium; Status: Open

  • Discusses adding constants and documentation for model clients through external APIs.

Overall, the recent activity in the "microsoft/autogen" repository reflects a dynamic development environment focused on enhancing functionality, improving documentation, and ensuring stability. The team's responsiveness to issues suggests a strong commitment to maintaining the framework's relevance and usability in the evolving landscape of AI development.

Report On: Fetch pull requests

Analysis of Pull Requests for microsoft/autogen

Open Pull Requests

Notable Open PRs:

1. #4149: task: added warning when none is called in intervention handler

   • State: Open
   • Created by: Pramod Goyal
   • Summary: This PR introduces a warning mechanism when None is returned in the intervention handler, addressing issue #3621. The implementation uses Python's built-in warnings library to alert users.
   • Concerns: The author encountered test issues with autogen-core\tests\execution\test_user_defined_functions.py and is unsure if they are related to the changes.
   • Action Needed: Review and address test failures, ensure documentation and tests are updated.

2. #4148: Add visualization of Agent Message Transitions State in AGS

   • State: Open
   • Created by: Victor Dibia
   • Summary: This PR adds visualization capabilities for agent message transitions, enhancing the AGS interface.
   • Concerns: The PR includes significant UI changes, and the author has not specified requested reviewers.
   • Action Needed: Ensure thorough testing of UI changes and solicit feedback from UI/UX experts.

3. #4146: Swarm Tutorial

   • State: Open
   • Created by: Gerardo Moreno
   • Summary: Provides a tutorial for the Swarm pattern, including examples for chatbot scenarios and autonomous content generation.
   • Concerns: Awaiting completion of related PR #4143 for max_turn functionality.
   • Action Needed: Coordinate with related PRs to ensure consistency and completeness.

4. #4143: Max turn parameter for group chats

   • State: Open
   • Created by: Eric Zhu
   • Summary: Introduces a max_turns parameter to control user engagement in group chats, useful for chatbots.
   • Concerns: Needs integration with related tutorials and examples.
   • Action Needed: Ensure documentation reflects new functionality, coordinate with related PRs.

5. #4131: WIP - OpenAI Assistants Agent

   • State: Open
   • Created by: Leonardo Pinheiro
   • Summary: Early draft for integrating OpenAI Assistants into the framework.
   • Concerns: The author has several open questions regarding model client integration and file handling.
   • Action Needed: Address open questions and refine design based on feedback.

Recently Closed Pull Requests

Notable Closed PRs:

1. #4147: [Fix] MultimodalWebSurfer initialization issue in page_script.js

   • State: Closed without merge
   • Summary: Attempted to fix initialization issues with MultimodalWebSurfer.
   • Concerns: Closed without merging; may indicate unresolved issues or alternative solutions found.

2. #4145 & #4142 & #4139 & #4137 & #4129 & #4128 & #4126 & #4124 & #4121 & #4118 & #4110 & #4104 & #4097 & #4096 & #4093 & #4092 & #4091 & #4090 & #4088

   • These PRs were closed after being merged, indicating successful integration of features or fixes such as handling reset methods, adding inner message flags, various documentation improvements, and new functionalities like SocietyOfMind agent for nested teams.

General Observations

• The project is actively maintained with frequent updates and enhancements, reflecting a dynamic development environment.
• Several PRs focus on improving documentation and tutorials, highlighting an emphasis on user experience and onboarding.
• Integration of new features like visualization tools and advanced agent functionalities indicates ongoing expansion of the framework's capabilities.
• Coordination between related PRs (e.g., tutorials depending on new features) is crucial for maintaining consistency across the project.

Recommendations

• Prioritize resolving test failures in open PRs to maintain code quality and reliability.
• Encourage more detailed documentation updates alongside feature additions to aid user understanding and adoption.
• Continue fostering community engagement through clear communication of roadmap updates and soliciting feedback on new features.

Report On: Fetch Files For Assessment

Source Code Assessment

File 1: _multimodal_web_surfer.py

Overview

This file defines the MultimodalWebSurfer class, a multimodal agent that can perform web searches, visit web pages, and interact with content. It extends the BaseChatAgent class from the autogen_agentchat package.

Key Points

• Imports: The file imports various modules and components, including Playwright for browser automation, PIL for image processing, and several custom modules for handling events, tools, and types.
• Class Definition: The MultimodalWebSurfer class is initialized with several parameters such as name, model_client, description, and browser-related settings.
• Functionality:
  • The agent can perform actions like visiting URLs, scrolling pages, clicking elements, and inputting text.
  • It uses Playwright to automate browser interactions and can take screenshots.
  • The agent maintains a chat history and can generate replies based on messages received.
  • It includes error handling mechanisms for web surfing errors.
• Code Quality:
  • The code is well-organized with clear method definitions and docstrings.
  • Use of type hints enhances readability and maintainability.
  • Exception handling is present but could be more granular in some areas.

Concerns

• Security: Handling of URLs and user inputs should be scrutinized to prevent injection attacks or unintended browsing behavior.
• Performance: Frequent use of screenshots and OCR might impact performance; consider optimizing these operations if necessary.

File 2: _reply_chat_completion_client.py

Overview

This file implements the ReplayChatCompletionClient class, a mock client that replays predefined chat responses.

Key Points

• Purpose: The class simulates a chat completion client by replaying a list of predefined responses. It supports both single completion and streaming responses.
• Functionality:
  • Provides methods to create chat completions (create) and stream completions (create_stream).
  • Tracks token usage for prompt and completion tokens.
  • Includes methods to reset the client state and calculate token counts.
• Code Quality:
  • Clear documentation with examples provided in docstrings.
  • Use of type hints is consistent throughout the code.
  • Logging is integrated for tracking events.

Concerns

• Scalability: As a mock client, it may not scale well for large datasets or complex scenarios without modifications.
• Error Handling: Limited error handling; consider adding more robust checks for invalid inputs or states.

File 3: _terminations.py

Overview

This file defines several termination conditions for conversations in the agent chat system.

Key Points

• Classes Defined:
  • StopMessageTermination: Terminates conversation upon receiving a stop message.
  • MaxMessageTermination: Limits conversation to a maximum number of messages.
  • TextMentionTermination: Ends conversation if specific text is mentioned.
  • TokenUsageTermination: Terminates based on token usage limits.
  • HandoffTermination: Ends conversation upon receiving a handoff message with a specific target.
• Functionality:
  • Each class implements an asynchronous call method to evaluate termination conditions based on incoming messages.
• Code Quality:
  • The code is concise with clear separation of concerns across different termination conditions.
  • Use of type hints improves clarity.

Concerns

• Extensibility: While current conditions are well-defined, adding new termination conditions might require significant changes if not designed with extensibility in mind.

File 4: teams.ipynb

Overview

This Jupyter Notebook serves as a tutorial for using teams in the AgentChat framework.

Key Points

• Content:
  • Explains how teams of agents collaborate to address tasks within the AgentChat framework.
  • Demonstrates creating teams using different design patterns such as Round-Robin Group Chat and Reflection Pattern.
  • Includes code examples for setting up agents, defining termination conditions, and running tasks.
• Code Quality:
  • The notebook is well-documented with markdown cells explaining each step of the process.
  • Code snippets are clear and executable within the notebook environment.

Concerns

• Completeness: Ensure that all necessary imports and setup steps are included for users to run the examples without issues.

File 5: HelloAIAgent.cs

Overview

This C# file defines a sample AI agent named HelloAIAgent.

Key Points

• Functionality:
  • Implements an agent that handles new messages by generating a limerick greeting using a chat client.
  • Publishes messages asynchronously after processing them.
• Code Quality:
  • The code is succinct but lacks detailed comments or documentation explaining its purpose or usage context.

Concerns

• Error Handling: There is minimal error handling; consider adding try-catch blocks to handle potential exceptions during asynchronous operations.

Report On: Fetch commits

Repo Commits Analysis

Development Team and Recent Activity

Team Members and Their Recent Activities:

1. afourney:

   • Worked on the microsoft/autogen repository.
   • Recent activities include changes to the WebSurfer component with lazy initialization and fixing mypy errors.
   • Collaborated with Hussein Mozannar.

2. Mohammad Mazraeh (MohMaz):

   • Added a reply chat completion client and worked on distributed group chat samples.
   • Collaborated with Eric Zhu and others.

3. Gerardo Moreno (gziz):

   • Made various documentation improvements.
   • Removed isinstance check from FunctionTool.
   • Collaborated with Eric Zhu.

4. Peter Y Chang (peterychang):

   • Implemented protobuf serialization in Python.

5. Eric Zhu (ekzhu):

   • Very active with multiple contributions across various components including AgentChat, documentation updates, and version updates.
   • Worked on features like handoff termination, team introduction, auto-reset termination, and more.
   • Collaborated extensively with multiple team members.

6. Victor Dibia (victordibia):

   • Worked on AutoGen Studio, including UI refactoring and backend API updates.
   • Developed interactive visualizations for agent states.
   • Collaborated with Eric Zhu and others.

7. Diego Colombo (colombod):

   • Simplified publish events in agents and added task types for multi-modal tasks.
   • Collaborated with Eric Zhu.

8. Mark Sze (marklysze):

   • Fixed typos in README.md.

9. Mahesh Subramanian (maheshpec):

   • Fixed a typo in the agent identity document.

10. David Luong (DavidLuong98):

    • Enabled package vulnerability checks for .NET packages.
    • Updated versions of Microsoft.Extensions.AI & System.Text.Json.

11. Hussein Mozannar (husseinmozannar):

    • Updated README.md for Magentic-One and added benchmarks to agbench.
    • Worked on Magentic-One Log Viewer + preview API.

12. Reuben Bond (ReubenBond):

    • Cleaned up Hello sample, supported Aspire 9.0, added CancellationToken parameters to API surface.
    • Committed missing files related to Properties or appsettings.json.

13. Xiaoyun Zhang (LittleLittleCloud):

    • Created tools from M.E.A.I AIFunctionFactory.
    • Updated .NET packages and fixed test issues.

14. Rohan Thacker (rohanthacker):

    • Corrected typos in model information files.

15. Ryan Sweet (rysweet):

    • Made significant contributions to .NET components, including agent state persistence and refactoring.
    • Worked on interface inheritance examples and agent runtime improvements.

16. Anthony Uphof (auphof):

    • Fixed token usage counts in create_stream function.

17. Kosta Petan (kostapetan):

    • Ported marketing sample to AutoGen framework.

Patterns, Themes, and Conclusions:

• The development team is actively working on both Python and .NET components of the project, indicating a focus on cross-language support.
• There is a strong emphasis on improving documentation, refactoring existing code for better maintainability, and adding new features like multi-modal tasks and interactive visualizations.
• Collaboration among team members is evident through co-authored commits and shared contributions to complex features like AgentChat enhancements and AutoGen Studio development.
• The project is undergoing significant architectural changes as it transitions from version 0.2 to 0.4, with efforts focused on scalability, usability, and integration capabilities.
• The team is also addressing technical debt by fixing bugs, updating dependencies, and enhancing security measures across different parts of the codebase.