The repository Shubhamsaboo/awesome-llm-apps is a curated collection of applications utilizing Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) and AI agents. Created by Shubham Saboo, it features integrations with models from OpenAI, Anthropic, Google, and open-source options like LLaMA. The project is actively maintained and serves as a resource for developers exploring LLM applications across various domains.
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 0 | 0 | 0 | 0 | 0 |
| 30 Days | 1 | 1 | 4 | 1 | 1 |
| 90 Days | 3 | 2 | 10 | 3 | 1 |
| All Time | 21 | 20 | - | - | - |
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.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Madhu Shantan |  |
1 | 9/9/1 | 36 | 67 | 13360 |
| Shubham Saboo |  |
1 | 0/0/0 | 13 | 23 | 254 |
| gargigupta97 |  |
1 | 1/1/0 | 2 | 1 | 9 |
| Camille-Maxime Thea (MaxRev) (MaxRev-Dev) | 0 | 1/0/0 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
| Risk | Level (1-5) | Rationale |
|---|---|---|
| Delivery | 4 | The project faces significant delivery risks due to several factors. The low level of recent issue activity suggests potential stagnation or lack of active development, which could hinder the project's ability to meet evolving requirements. Additionally, the presence of open pull requests with incomplete submissions, such as PR #29 lacking essential components, indicates gaps in the review process that could delay delivery. The recurring issues with missing dependencies and configuration errors further exacerbate these risks, as they could prevent successful deployment and operation of the project. |
| Velocity | 3 | The project's velocity is at moderate risk due to uneven contribution levels among team members and potential bottlenecks in code review processes. While there is significant activity from contributors like Madhu Shantan, the disparity between opened and merged pull requests suggests delays in integration. The minimal engagement from other contributors could also impact overall progress. Furthermore, the lack of new issues and minimal comments over recent periods may imply insufficient testing or feedback, which could slow down the development pace. |
| Dependency | 4 | Dependency risks are high due to the project's reliance on external libraries such as Streamlit and OpenAI's API. Any changes or issues with these dependencies could significantly impact functionality. The recurring problems with missing dependencies in issues and pull requests highlight ongoing challenges in managing these dependencies effectively. Additionally, the recent PRs addressing dependency additions indicate a reactive rather than proactive approach to dependency management, which could lead to future disruptions. |
| Team | 3 | The team faces moderate risks related to uneven workload distribution and potential burnout. With Madhu Shantan driving much of the development effort, there is a risk of over-reliance on a single contributor, which could affect team dynamics and sustainability. The minimal contributions from other team members suggest possible engagement or capacity issues that need addressing to ensure balanced workload distribution and effective collaboration. |
| Code Quality | 4 | Code quality risks are high due to recurring issues with incomplete pull requests and validation errors. PRs like #29 lacking essential components highlight significant gaps in quality assurance processes. Additionally, initial problems with nested Git repositories and validation errors in other PRs suggest challenges in maintaining consistent code standards. The absence of formal review processes for some contributors further exacerbates these risks by potentially allowing low-quality code into the main branch. |
| Technical Debt | 4 | The project is at high risk of accumulating technical debt due to insufficient testing coverage and error handling practices. The reliance on direct commits without formal reviews increases the likelihood of introducing bugs and complexity into the codebase. Additionally, recurring configuration errors and dependency issues suggest underlying weaknesses that could contribute to long-term maintenance challenges if not addressed systematically. |
| Test Coverage | 4 | Test coverage risks are high as indicated by the need for further testing mentioned in several pull requests. The absence of explicit test coverage in critical files raises concerns about the system's robustness under various scenarios. This lack of thorough testing could lead to undetected bugs and regressions, impacting delivery timelines and system reliability. |
| Error Handling | 3 | Error handling practices pose moderate risks due to limited exception handling observed in key files. Broadly caught exceptions without specific logging can result in uninformative error messages, complicating troubleshooting efforts. While some improvements have been made in addressing initial validation errors in PRs, ongoing challenges with API rate limits and authentication errors suggest that error handling mechanisms need strengthening to ensure reliable operation. |
Recent GitHub issue activity in the repository indicates a low volume of open issues, with only one currently open issue (#28) and a history of 20 closed issues. The open issue is a proposal for a new application, suggesting active contribution and interest from the community. The closed issues reveal a pattern of technical challenges related to dependencies, configuration errors, and integration with external APIs. Notably, several issues involve troubleshooting installation and runtime errors, indicating potential areas for improvement in documentation or setup scripts.
Dependency and Configuration Issues: Many closed issues (#31, #23, #22) highlight problems with missing dependencies or incorrect configurations, such as missing installations for OpenAI or configuration validation errors. These recurring themes suggest that users might benefit from more robust setup instructions or automated dependency checks.
API Rate Limits and Authentication: Issues like #7 and #6 point to challenges with API rate limits and authentication errors. This is significant for applications relying heavily on external APIs, suggesting a need for better handling of API usage limits or alternative solutions like local models.
User Error and Misunderstandings: Several issues (#9, #2) were resolved by correcting user errors or misunderstandings, such as incorrect file naming or token placement. This indicates that clearer guidance or error messages could reduce such occurrences.
Community Engagement: The presence of detailed comments and resolutions in many issues shows active engagement from the repository owner and contributors. This is a positive sign of community involvement and support.
Issue #31: local_rag_agent/local_rag_agent.py
Issue #23: Nice, but this code is buggy
Issue #22: httpcore.ConnectError: [Errno 61] Connection refused
These issues reflect typical challenges faced in maintaining a complex software project involving multiple dependencies and integrations. The repository's responsiveness to these issues demonstrates active maintenance and community support.
Summary
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requirements.txt file for the local RAG agent, adding fastapi and uvicorn. This is crucial for ensuring that the application can be set up correctly in a fresh environment.PR #42: #Proj 5 - AI Legal Agent Team
PR #41 & #40: Fixed persistent issues in different modules.
PR #30 & #27: Not merged due to redundancy or lack of unique contribution.
PR #26 & #25: Not merged due to simplicity or lack of improvement over existing solutions.
The repository is actively maintained with several open pull requests addressing both enhancements and bug fixes. Notably, some open PRs require significant work before they can be merged, particularly those lacking complete implementations or documentation (#29). The closed PRs reflect a mix of successful integrations and rejections due to redundancy or insufficient complexity. Contributors should focus on ensuring their submissions are complete, well-documented, and add unique value to the repository to increase their chances of acceptance.
ai_agent_tutorials/ai_services_agency/agency.pyImports and Dependencies:
typing, pydantic, and streamlit. This indicates the use of type hints, data validation, and web app interface, respectively.agency_swarm are imported, suggesting reliance on external or custom libraries.Class Definitions:
AnalyzeProjectRequirements and CreateTechnicalSpecification, extend from BaseTool. These classes encapsulate functionality related to project analysis and technical specification creation.ToolConfig class for configuration, which is a good practice for encapsulating related settings.Methods:
run methods in both classes handle the core logic for their respective functionalities. They utilize shared state management to store and retrieve data, which is crucial for maintaining session consistency.Session Management:
init_session_state() initializes session variables using Streamlit's session state management, ensuring that necessary variables are set up before they are accessed.Main Functionality:
main() function orchestrates the application flow using Streamlit for UI rendering. It includes API key management, form handling, and agent initialization.Error Handling:
Code Quality:
Potential Improvements:
main() function.rag_tutorials/local_hybrid_search_rag/local_main.pyImports and Dependencies:
os, logging, streamlit, and custom modules like raglite and rerankers.Configuration Initialization:
initialize_config() sets up the RAGLite configuration using provided settings. It includes error handling to catch configuration issues.Document Processing:
process_document() handles document insertion into the system's database, with error logging for failures.Search Functionality:
perform_search() executes a hybrid search using the RAGLite system, retrieving and reranking document chunks based on relevance.Fallback Mechanism:
handle_fallback(), which uses a local LLM to generate responses when no relevant documents are found.Main Functionality:
main() function sets up the Streamlit page configuration and manages user interactions through a sidebar for configuration inputs and a main area for document uploads and queries.Session Management:
Code Quality:
Potential Improvements:
rag_tutorials/hybrid_search_rag/main.pyImports and Dependencies:
anthropic library for fallback responses.Configuration Initialization:
initialize_config() sets environment variables for API keys before creating a RAGLiteConfig object.Document Processing & Search:
process_document()) and performing searches (perform_search()) follow similar logic as in the local hybrid search file but include additional API key management.Fallback Mechanism:
Main Functionality:
Session Management & Error Handling:
Code Quality:
Potential Improvements:
Overall, these files demonstrate solid engineering practices with room for minor improvements in modularization and logging detail enhancement.
Recent Commits:
ai_legal_agent_team and ai_services_agency.ai_services_agency/agency.py.Collaboration:
Work in Progress:
Recent Commits:
ai_services_agency/agency.py.Collaboration:
Work in Progress:
Recent Commits:
Collaboration:
Active Development: The repository shows active development with frequent updates and new features being added. The focus is on enhancing existing projects like AI Legal Agent Team and AI Startup Org Agents.
Collaboration: There is significant collaboration between Shubham Saboo and Madhu Shantan, indicating a coordinated effort in project development and issue resolution.
Documentation Focus: Regular updates to README files suggest a strong emphasis on maintaining comprehensive documentation for ease of use and community engagement.
Community Contributions: While the majority of contributions come from the core team, there is some level of community involvement as seen with contributions from Gargi Gupta.
Project Diversity: The repository includes a wide range of applications leveraging LLMs across different domains, highlighting the versatility of the technology being developed.
Overall, the recent activities reflect a dynamic development environment with a focus on expanding functionality, improving documentation, and fostering collaboration within the team.