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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.}

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PR#7061 - Update model json to add gemini-exp-1121open

3_/5

Prabhakar Chaganti (pchaganti)Created: 2024-12-06

The pull request updates a JSON file to include a new experimental model, gemini-exp-1121, with detailed configuration settings. While it appears to be a necessary update for maintaining current model offerings, the change is straightforward and lacks complexity or significant innovation. It does not introduce any apparent bugs or security risks, but it also doesn't demonstrate exceptional work or impact. Therefore, it merits an average rating.

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PR#7058 - Added a guide for users who want to use LiteLLM with AI/ML API.open

3_/5

waterstarkCreated: 2024-12-06

The pull request adds a new guide for integrating LiteLLM with AI/ML APIs, which is a useful addition to the documentation. It includes examples and usage instructions, enhancing user understanding. However, the changes are primarily documentation-focused with minor updates to the README and the addition of a new markdown file. While these changes are beneficial, they are not particularly significant or complex, thus warranting an average rating.

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PR#7079 - Allows overriding keep_alive time in ollamaopen

3_/5

Regis David Souza Mesquita (regismesquita)Created: 2024-12-07

This pull request introduces a new feature allowing the override of the keep_alive parameter for ollama completions, which aligns with an existing implementation for embeddings. The changes are minimal and straightforward, involving only a few lines of code across three files, including a documentation update. While the feature is useful, it is not particularly significant or complex, and the implementation appears to be correct without any obvious flaws. However, it lacks substantial impact or innovation, making it an average contribution.

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PR#7072 - Include error message if no error textopen

3_/5

மனோஜ்குமார் பழனிச்சாமி (SmartManoj)Created: 2024-12-07

The pull request addresses a specific issue by improving error messaging when exceptions occur, which enhances clarity for developers. The change is minor, involving only two lines of code, and it uses standard Python practices like `getattr` to handle exceptions. While it improves usability, the change is not particularly significant or complex, thus warranting an average rating. It does not introduce new features or major improvements, but it is a useful fix.

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PR#7093 - Code QOL improvement - remove unused imports, attempt #2 open

3_/5

Ishaan Jaff (ishaan-jaff)Created: 2024-12-08

The pull request focuses on removing unused imports across multiple files, which is a standard refactoring task aimed at improving code quality and maintainability. While this is beneficial for reducing clutter and potential technical debt, the changes are not particularly significant or complex. The PR does not introduce new features, fix critical bugs, or enhance performance in a noticeable way. It lacks testing evidence or documentation updates, which are typically essential for a higher rating. Therefore, it is rated as average (3) due to its routine nature and limited impact.

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PR#7062 - Add AgentOps Integration Documentationopen

4_/5

teocnsCreated: 2024-12-06

The pull request provides comprehensive documentation for integrating AgentOps with LiteLLM, including a feature overview, installation instructions, code examples, and support resources. The documentation is thorough and well-organized, enhancing the usability of the integration. However, as it is a documentation-only change, it lacks the impact of code changes or feature additions. This limits its significance to a 4 rather than a 5, which would be reserved for more substantial contributions.

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PR#7057 - refactor: add type annotations and overloads to completion functionsopen

4_/5

Zeeland (Undertone0809)Created: 2024-12-06

The pull request effectively enhances the codebase by adding type annotations and overloads to key functions, improving type safety and code readability. This refactoring is beneficial for maintaining robust code and providing better IDE support. However, it is a refactoring task that, while valuable, does not introduce new features or significant changes to the functionality of the software. The PR is well-executed but lacks the impact or innovation that would warrant a higher rating.

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PR#7055 - feat,docs: instructions for using a runtime debugger with liteLLMopen

4_/5

Luiz Rennó Costa (luizrennocosta)Created: 2024-12-06

This pull request introduces a useful feature by adding runtime debugging capabilities to the liteLLM proxy CLI, which is significant for developers who need to troubleshoot and debug their applications. The addition of a `__main__.py` file allows for easier attachment of debuggers, and the documentation updates provide clear instructions for using VSCode and debugpy. The changes are well-documented and include necessary updates to configuration files like `poetry.lock` and `pyproject.toml`. However, the PR could be improved by including a demonstration or test cases to validate the new debugging functionality.

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PR#7095 - fix: add default credential for azureopen

4_/5

you-n-gCreated: 2024-12-08

The pull request addresses a specific issue (#4417) by enhancing the Azure authentication mechanism with a default credential fallback, improving robustness. The changes are concise, adding only 8 lines and modifying 4, indicating a focused and efficient solution. The use of verbose logging for exceptions enhances debugging capabilities. However, the PR lacks detailed testing evidence or documentation updates, which slightly detracts from its completeness. Overall, it's a quite good improvement but could benefit from more comprehensive testing documentation.

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PR#7088 - Add Support for Amazon nova top k (#7087)open

4_/5

ryanh-aiCreated: 2024-12-08

The pull request introduces a new feature by adding support for Amazon Nova's topK functionality, which is a significant enhancement. It includes both the implementation and associated tests, demonstrating thoroughness. The code changes are well-contained and adhere to the existing code structure. However, while the feature is useful, it is not exceptionally groundbreaking or complex, which prevents it from receiving the highest rating.

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Quantify commits

Quantified Commit Activity Over 14 Days

Developer	Avatar	Branches	PRs	Commits	Files	Changes
Ishaan Jaff		52	48/43/5	337	477	28401
Krish Dholakia		15	17/16/0	153	283	24480
yujonglee		1	1/1/0	1	2	87
ZeroPath		2	0/0/0	5	1	66
None (dependabot[bot])		2	2/0/1	2	8	61
Sara Han		1	1/1/0	1	1	49
Emerson Gomes		1	1/1/1	1	1	36
Paul Maunders		1	1/1/0	1	1	27
ali sayyah		2	1/1/0	2	1	22
Steven Crake		1	0/1/0	1	1	14
fengjiajie		1	1/1/0	1	1	6
superpoussin22		2	1/1/0	2	1	4
None (ershang-dou)		1	1/1/0	1	1	3
Yuki Watanabe		2	1/1/0	2	1	2
paul-gauthier		1	3/1/1	1	1	2
None (h4n0)		0	1/1/0	0	0	0
Engel Nyst (enyst)		0	1/0/0	0	0	0
Takashi Iwamoto (iwamot)		0	1/0/1	0	0	0
Graham Neubig (neubig)		0	2/0/2	0	0	0
teocns (teocns)		0	1/0/0	0	0	0
yehonathan moshkovitz (YMoshko)		0	1/0/1	0	0	0
None (bahtman)		0	1/0/0	0	0	0
Hammad Saeed (hsaeed3)		0	1/0/0	0	0	0
Cameron (wallies)		0	1/0/0	0	0	0
None (you-n-g)		0	1/0/0	0	0	0
Corey Zumar (dbczumar)		0	2/0/1	0	0	0
Josh Morrow (jcmorrow)		0	1/0/0	0	0	0
Rashmi Pawar (raspawar)		0	1/0/0	0	0	0
None (ryanh-ai)		0	1/0/0	0	0	0
None (hgulersen)		0	1/1/0	0	0	0
Prabhakar Chaganti (pchaganti)		0	1/0/0	0	0	0
மனோஜ்குமார் பழனிச்சாமி (SmartManoj)		0	2/0/1	0	0	0
None (lloydchang)		0	1/1/0	0	0	0
Owais Aamir (owaisaamir)		0	0/0/1	0	0	0
None (waterstark)		0	1/0/0	0	0	0
Doron Kopit (doronkopit5)		0	1/1/0	0	0	0
David DeCaprio (DaveDeCaprio)		0	0/0/1	0	0	0
Dan Siwiec (danielsiwiec)		0	1/0/0	0	0	0
None (karter-liner)		0	1/0/0	0	0	0
Zeeland (Undertone0809)		0	1/0/0	0	0	0
Igor Martinelli (martinelligor)		0	2/0/1	0	0	0
Regis David Souza Mesquita (regismesquita)		0	1/0/0	0	0	0
Luiz Rennó Costa (luizrennocosta)		0	1/0/0	0	0	0
None (delve-auditor[bot])		0	2/0/2	0	0	0

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

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Project Risk Ratings

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Recent Activity Analysis

Recent GitHub issue activity for the LiteLLM project indicates a high level of engagement and ongoing development, with numerous issues being reported and addressed. The project currently has 698 open issues, reflecting both the complexity of the software and the active involvement of its user community in identifying and resolving problems.

Notable Issues

• Issue #7094: This issue highlights a bug related to stream + tools functionality with Ollama, which is critical as it affects the usability of the chat interface. The presence of a TypeError in the logs suggests a need for type handling improvements.

• Issue #7091: Reports an infinite loop when using the Python SDK for completion calls, indicating potential flaws in error handling or retry logic that could lead to resource exhaustion.

• Issue #7087: Points out missing support for top_k parameters for Amazon Nova, which could limit functionality for users relying on this feature.

• Issue #7068: Requests local Stable Diffusion support, reflecting user demand for broader compatibility with local AI models.

Themes and Commonalities

Several issues revolve around integration challenges with various LLM providers, such as Ollama, Amazon Nova, and Watsonx. This suggests a theme of interoperability challenges as LiteLLM strives to maintain compatibility across a diverse set of APIs. Additionally, there are recurring mentions of bugs related to configuration handling and API parameter support, indicating areas where robustness could be improved.

Issue Details

Most Recently Created Issues

1. #7094: [Bug]: Stream + tools broken with Ollama

   • Priority: High (affects core functionality)
   • Status: Open
   • Created: 0 days ago

2. #7091: Completion call using Python SDK seems to be stuck in an infinite loop

   • Priority: High (potential resource drain)
   • Status: Open
   • Created: 0 days ago

3. #7087: [Bug]: LiteLLM Not Supporting top_k or topK input parameters for Amazon Nova

   • Priority: Medium (feature limitation)
   • Status: Open
   • Created: 1 day ago

Most Recently Updated Issues

1. #7080: [Bug]: Session handling causing fallback to default_user_id after UI login

   • Priority: Medium (user management issue)
   • Status: Closed
   • Updated: 0 days ago

2. #7079: [Feature]: Add support for new LLM provider XYZ

   • Priority: Low (new feature request)
   • Status: Open
   • Updated: 0 days ago

3. #7078: [Bug]: Incorrect cost calculation in budget tracking module

   • Priority: High (financial impact)
   • Status: Open
   • Updated: 0 days ago

These issues reflect ongoing efforts to enhance functionality and address user-reported bugs, underscoring the project's commitment to continuous improvement and responsiveness to community feedback.

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Analysis of Pull Requests for BerriAI/litellm

Open Pull Requests

1. #7095: fix: add default credential for azure

   • State: Open
   • Created: 0 days ago
   • Type: 🆕 New Feature, 🐛 Bug Fix, 🧹 Refactoring, 📖 Documentation, 🚄 Infrastructure, ✅ Test
   • Details: This PR addresses a bug related to Azure credentials by adding a default credential. It references issue #4417 and includes changes to the get_azure_ad_token_provider.py file. The PR is very recent and has not yet been reviewed or merged.

2. #7093: Code QOL improvement - remove unused imports, attempt #2

   • State: Open
   • Created: 0 days ago
   • Type: 🧹 Refactoring
   • Details: This PR focuses on improving code quality by removing unused imports across multiple files. It is part of ongoing refactoring efforts to enhance code maintainability.

3. #7088: Add Support for Amazon nova top k (#7087)

   • State: Open
   • Created: 1 day ago
   • Type: 🆕 New Feature, ✅ Test
   • Details: Introduces support for Amazon Nova Top K feature, including associated tests. This enhancement is linked to issue #7087.

4. #7079: Allows overriding keep_alive time in ollama

   • State: Open
   • Created: 1 day ago
   • Type: 🆕 New Feature
   • Details: Adds functionality to override the keep_alive time for Ollama completions, addressing a limitation previously only applicable to embeddings.

5. #7072: Include error message if no error text

   • State: Open
   • Created: 1 day ago
   • Type: 🐛 Bug Fix
   • Details: Enhances error handling by setting error_text to the exception's message attribute if not already set. This improves the clarity of error messages returned by the system.

6. #7062: Add AgentOps Integration Documentation

   • State: Open
   • Created: 2 days ago
   • Type: 📖 Documentation
   • Details: Adds comprehensive documentation for integrating AgentOps with LiteLLM, including setup instructions and code examples.

7. #7061: Update model json to add gemini-exp-1121

   • State: Open
   • Created: 2 days ago
   • Type: N/A (Update)
   • Details: Updates model pricing and context size configurations to include the latest Gemini experimental LLM.

8. #7058: Added a guide for users who want to use LiteLLM with AI/ML API

   • State: Open
   • Created: 2 days ago
   • Type: 📖 Documentation
   • Details: Provides a guide for integrating LiteLLM with AI/ML APIs, enhancing user understanding and accessibility.

9. #7057: refactor: add type annotations and overloads to completion functions

   • State: Open
   • Created: 2 days ago
   • Type: 🧹 Refactoring
   • Details: Improves type safety and code readability by adding type annotations and overloads to completion functions.

10. #7055: feat,docs: instructions for using a runtime debugger with liteLLM**

    • State: Open
    • Created: 2 days ago
    • Type: 🆕 New Feature, 📖 Documentation
    • Details: Introduces runtime debugging capabilities with instructions for using VSCode and local debugpy applications.

Notable Issues

• Several PRs focus on improving documentation (#7062, #7058), which is crucial for user adoption and understanding.
• The introduction of new features like Amazon Nova Top K support (#7088) and enhancements in error handling (#7072) indicate active development and responsiveness to user needs.
• The ongoing refactoring efforts (#7093, #7057) suggest a commitment to maintaining code quality and performance.
• The addition of new models and updates (#7061) reflects the project's adaptability to new developments in the LLM space.

Conclusion

The BerriAI/litellm project is actively maintained with a focus on expanding features, improving documentation, and enhancing code quality. The open pull requests highlight a balanced approach between introducing new functionalities and refining existing ones. There are no significant issues with closed PRs that were not merged; most open PRs are recent and under review or awaiting further development/testing.

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Source Code Assessment

File: litellm/main.py

Overview

• Purpose: This file appears to be the core of the LiteLLM application, handling various functionalities such as model completions, asynchronous operations, and integrations with different LLM providers.
• Length: 5,499 lines, indicating a large and complex file.

Structure and Quality

• Imports: The file imports a wide range of modules, suggesting it handles multiple responsibilities. This could be a sign of high coupling.
• Class Definitions:
  • LiteLLM, Chat, Completions, and AsyncCompletions are defined, encapsulating different aspects of the application's functionality.
  • The use of classes is appropriate for organizing related functionalities.
• Functions:
  • Functions like acompletion, completion, and mock_completion are central to the file's purpose.
  • The functions are well-documented with parameters and return types, enhancing readability.
• Error Handling:
  • There is extensive error handling using try-except blocks, which is crucial for maintaining robustness in asynchronous operations.
• Complexity:
  • The file is quite large, which might make it difficult to maintain. Consider breaking it into smaller modules focused on specific functionalities.

Recommendations

• Modularization: Break down the file into smaller modules to improve maintainability and readability.
• Documentation: Ensure all functions have comprehensive docstrings for better understanding and maintenance.
• Testing: Given the complexity, ensure there are extensive unit tests covering various scenarios.

File: litellm/router.py

Overview

• Purpose: Manages routing logic, including model fallback and prompt caching features.
• Length: 5,761 lines, another large file indicating complexity.

Structure and Quality

• Routing Logic:
  • Contains classes like Router that handle model selection and request routing based on various strategies.
  • Implements different routing strategies such as least-busy and cost-based routing.
• Caching:
  • Uses caching mechanisms like Redis to manage state across requests, which is crucial for performance in distributed systems.
• Error Handling:
  • Comprehensive error handling is present, which is essential for reliability in routing logic.

Recommendations

• Code Organization: Similar to main.py, consider splitting this file into smaller components focused on specific routing strategies or caching mechanisms.
• Performance Testing: Ensure that performance testing is conducted to validate the efficiency of routing strategies under load.

File: litellm/proxy/utils.py

Overview

• Purpose: Utility functions for proxy operations, including error handling and logging enhancements.
• Length: 2,908 lines.

Structure and Quality

• Utility Functions:
  • Provides various utility functions that support the main proxy operations.
  • Includes classes like InternalUsageCache for managing internal state efficiently.
• Logging and Error Handling:
  • Implements detailed logging mechanisms which are crucial for debugging and monitoring proxy operations.

Recommendations

• Refactoring: Evaluate if some utility functions can be moved to more specific modules to reduce the size of this file.
• Documentation: Ensure all utility functions are well-documented to aid developers in understanding their purpose.

File: litellm/llms/OpenAI/openai.py

Overview

• Purpose: Handles integration with OpenAI's API, supporting new parameters and retry logic.
• Length: 3,266 lines.

Structure and Quality

• Integration Logic:
  • Contains classes like OpenAIChatCompletion that encapsulate API interaction logic.
  • Supports both synchronous and asynchronous operations with OpenAI's API.
• Configuration Management:
  • Uses configuration classes like OpenAIConfig to manage API parameters effectively.

Recommendations

• Code Duplication: Check for any duplicated logic across different provider integrations that could be abstracted into common utilities.
• Testing: Ensure robust testing around API interaction logic to handle edge cases and API changes gracefully.

File: litellm/proxy/management_endpoints/team_endpoints.py

Overview

• Purpose: Manages team-related endpoints, supporting model alias updates.
• Length: 1,424 lines.

Structure and Quality

• Endpoint Management:
  • Defines FastAPI endpoints for team management operations such as creating, updating, and deleting teams.
  • Utilizes Pydantic models for request validation which enhances data integrity.

Recommendations

• Security Considerations: Ensure that appropriate authentication and authorization checks are implemented for all endpoints.
• Scalability Testing: Conduct scalability testing to ensure endpoints can handle increased load as more teams are managed through the system.

File: litellm/utils.py

Overview

• Purpose: Provides utility functions across the application with recent additions for prompt caching validation.
• Length: 6,282 lines.

Structure and Quality

• Utility Functions:
  • Contains a wide range of utility functions that support various parts of the application.
  • Includes functionality for token counting, configuration management, etc.

Recommendations

• Code Organization: Consider categorizing utilities into separate files based on their functionality (e.g., token utilities, configuration utilities).
• Documentation and Testing: Ensure comprehensive documentation and testing coverage given the critical nature of utility functions in supporting core application logic.

File: tests/local_testing/test_anthropic_prompt_caching.py

Overview

• Purpose: Test file for prompt caching functionality to ensure feature reliability.
• Length: 720 lines.

Structure and Quality

• Test Coverage:
  • Provides tests for prompt caching features using pytest fixtures and mocks.
  • Includes both synchronous and asynchronous test cases which is important for thorough testing of async features.

Recommendations

• Test Completeness: Ensure all edge cases related to prompt caching are covered by tests.
• Continuous Integration (CI): Integrate these tests into a CI pipeline to ensure prompt caching functionality remains reliable across code changes.

File: docs/my-website/docs/proxy/reliability.md

Overview

• Purpose: Documentation on proxy reliability updated to reflect new fallback mechanisms.
• Length: 825 lines.

Structure and Quality

• Content Clarity:
  • Provides detailed instructions on configuring load balancing, fallbacks, retries, timeouts, etc., within the proxy server setup.

Recommendations

• Examples and Use Cases: Ensure documentation includes practical examples or use cases demonstrating how reliability features can be configured in real-world scenarios.

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Development Team and Recent Activity

Team Members and Recent Activities

1. Krish Dholakia (krrishdholakia)

   • Worked on a variety of features and bug fixes including support for routing prompt caching, adding fallback models, updating team model aliases, and fixing issues related to max retries in Azure/OpenAI integrations.
   • Contributed to documentation updates and added new tests for various functionalities.

2. Ishaan Jaff (ishaan-jaff)

   • Focused on refactoring for consistent naming conventions, fixing database-related issues, and implementing structured outputs for Fireworks.AI.
   • Addressed logging improvements, including handling DB errors in SpendLogs and enhancing DataDog logging.
   • Worked on UI improvements and test coverage enhancements.

3. Yuki Watanabe (B-Step62)

   • Added MLflow to the sidebar documentation.

4. Ali Sayyah (AliSayyah)

   • Contributed to adding new models such as deepinfra/Meta-Llama-3.1-405B-Instruct.

5. Emerson Gomes (emerzon)

   • Corrected Vertex Embedding Model Data/Prices.

6. Paul Maunders (paulmaunders)

   • Added new model configurations like gemini-exp-1206 with 2M input tokens.

7. Steven Crake (stevencrake-nscale)

   • Worked on migration jobs for existing databases.

8. ZeroPath

   • Implemented security fixes to prevent RCE vulnerabilities by whitelisting safe module imports.

Patterns, Themes, and Conclusions

• Active Development: The team is actively working on multiple fronts including feature enhancements, bug fixes, refactoring, and documentation updates.
• Collaboration: There is significant collaboration among team members as seen in co-authored commits and shared tasks across different areas of the project.
• Focus on Stability and Security: Recent activities include addressing security vulnerabilities, improving error handling mechanisms, and ensuring consistent naming conventions across the codebase.
• Enhancements in Functionality: New features such as structured outputs support, prompt caching improvements, and additional model configurations indicate a focus on expanding the project's capabilities.
• Continuous Integration: The team is maintaining a robust CI/CD pipeline with frequent updates to testing frameworks and build processes.

Overall, the development team is engaged in comprehensive efforts to enhance the functionality, security, and usability of the LiteLLM project while maintaining an active collaboration environment.