The Helicone project is an open-source observability platform for large language models (LLMs), designed to facilitate monitoring, evaluation, and experimentation with AI models. Developed by Helicone and incubated in Y Combinator's Winter 2023 batch, the platform integrates with AI services like OpenAI and Anthropic, offering features such as agent tracing and prompt management. The project is in a state of active development with a focus on enhancing user experience and expanding functionality.
Recent activities indicate a strong focus on both feature development and content updates, with ongoing efforts to address bugs and improve documentation.
Overall, Helicone is progressing well with active development and community involvement but must address documentation and deployment challenges to ensure broader adoption and smoother user experiences.
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 4 | 0 | 0 | 0 | 1 |
| 30 Days | 9 | 1 | 0 | 1 | 1 |
| 90 Days | 29 | 4 | 53 | 5 | 1 |
| 1 Year | 66 | 61 | 104 | 31 | 1 |
| All Time | 168 | 126 | - | - | - |
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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| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Kavin Valli |  |
4 | 18/17/1 | 20 | 142 | 134065 |
| Justin Torre |  |
5 | 19/18/1 | 50 | 378 | 113260 |
| colegottdank |  |
2 | 10/10/0 | 11 | 71 | 104271 |
| LinaLam | |
2 | 11/11/0 | 14 | 139 | 4260 |
| Nathan Baschez |  |
1 | 1/1/0 | 1 | 1 | 20 |
| koshyviv |  |
1 | 0/1/0 | 1 | 1 | 3 |
| use-tusk[bot] |  |
1 | 2/1/1 | 1 | 1 | 2 |
| None (zm1355) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Shoaib Akhtar (STAR-173) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Ikko Eltociear Ashimine (eltociear) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Rupali Kavale (coderquill) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (josh-sophon) | 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 a backlog of unresolved issues and a slow resolution rate. Over the last 90 days, 29 issues were opened with only 4 closed, indicating a growing backlog. The lack of structured issue management, as evidenced by minimal labeling and milestone setting, further exacerbates this risk. High-priority bugs like #3082 and #3081 require immediate attention to prevent further degradation of user experience. |
| Velocity | 4 | The project's velocity is at risk due to a disparity in contributions among developers and potential bottlenecks in the review process. While some developers like Justin Torre are highly productive, others show minimal activity, indicating uneven workload distribution. The high volume of changes without corresponding merges suggests potential delays in integrating new features, impacting overall project velocity. |
| Dependency | 3 | Dependency risks are moderate due to integration challenges with external systems like Azure OpenAI and Anthropic. Issues related to Docker deployment and self-hosting also indicate technical debt accumulation. While there are efforts to maintain comprehensive API documentation, the reliance on external systems requires careful management to prevent integration failures. |
| Team | 3 | The team faces moderate risks related to workload distribution and communication gaps. The disparity in commit activity among team members suggests potential engagement issues or uneven workload distribution. Additionally, the presence of incomplete or unclear pull requests indicates possible inefficiencies in the review process or communication gaps within the team. |
| Code Quality | 3 | Code quality risks are moderate due to a mix of well-executed security improvements and trivial updates with minimal impact. Significant changes like moving authorization checks server-side improve security but lack comprehensive documentation or tests, which could affect maintainability. The presence of trivial pull requests indicates a focus on minor corrections rather than substantial development progress. |
| Technical Debt | 4 | Technical debt is accumulating due to unresolved Docker-related errors and frequent changes without corresponding updates in test coverage. The high volume of changes by a few developers without timely integration increases the risk of technical debt if these changes are not managed effectively. Documentation gaps also contribute to this risk, as seen in issues like #3081 and #3079. |
| Test Coverage | 4 | Test coverage is insufficient across several areas, as highlighted by the absence of additional tests for significant security improvements and new features. The lack of detailed testing information for major pull requests like #3092 raises concerns about the robustness of new functionalities. This gap poses risks for catching bugs and regressions effectively. |
| Error Handling | 4 | Error handling is inadequate, with several issues highlighting poor error messages or handling mechanisms. Bugs such as improperly formatted API error responses (#3057) suggest insufficient error handling practices. The absence of dynamic content handling in files like 'evaluate.tsx' further underscores this risk. |
Recent GitHub issue activity for the Helicone project reveals a focus on addressing bugs, enhancing features, and improving documentation. Notably, several issues pertain to integration challenges with various AI platforms, such as Azure OpenAI and Anthropic, indicating ongoing efforts to streamline these processes. There are also multiple reports of discrepancies in token counting and API responses, suggesting a need for more robust error handling and validation mechanisms.
A significant anomaly is the recurring theme of documentation inconsistencies, particularly concerning integration instructions and feature usage. This has led to user confusion and implementation errors, highlighting the importance of maintaining up-to-date and clear documentation. Additionally, issues related to Docker deployment and self-hosting indicate potential barriers for users attempting to deploy Helicone independently.
#3082: [Bug]: Bad UX when someone accesses sessions with a wrong session id
#3081: [Bug]: Enumerate all required components
#3080: [Bug]: Docker compose, running locally
#3079: [Bug]: Pre-reqs - Really?
#3057: [Bug]: Improperly Formatted Error from API Call
#2977: [Bug]: Docker account creation error in supabase
#2463: [Bug]: Helicone Helm Chart Repository Returns 404
The most recent issues primarily focus on bugs affecting user experience and deployment processes. Critical issues like #3082 highlight significant user interface problems that could hinder usability. Issues such as #3081 and #3080 reflect ongoing challenges with local deployment and component enumeration, which are crucial for seamless integration and operation. The persistence of these issues suggests a need for enhanced testing and documentation efforts to ensure smoother user experiences.
These PRs were closed within the last few days, indicating active development and maintenance. Notably:
Unmerged Closed PRs (#3052):
Older Open PRs (#3016, etc.):
Overall, the Helicone project is actively maintained with a focus on both feature development and content creation, which is essential for community engagement and platform growth.
Summary
Quantified Reports
Detailed Reportsbifrost/app/blog/blogs/openai-o3/metadata.jsontitle, title1, title2) seems redundant and could be streamlined to a single field unless there is a specific reason for this duplication.bifrost/app/pi/total-cost/page.tsxreact-query, recharts) to fetch and display cost data over time in a bar chart. It handles API key validation and redirects if the API key is invalid.docs/swagger.jsonvalhalla/jawn/src/controllers/public/piController.tsweb/components/templates/users/UserMetrics.tsxChart). State management using hooks is appropriate. Consider optimizing the rendering logic by memoizing components where necessary to improve performance.bifrost/app/community/communityPage.tsxclsx for conditional class names enhances readability.bifrost/lib/clients/jawnTypes/private.tsvalhalla/jawn/src/managers/UserManager.tsbifrost/app/changelog/changes/20241219-user-metrics/src.mdxbifrost/packages/cost/providers/openai/fine-tuned-models.tsOverall, the source code files are well-organized with clear separation of concerns. There are opportunities for minor improvements in code maintainability through refactoring redundant elements or optimizing rendering logic in React components.
LinaLam
Justin Torre (chitalian)
Kavin Valli (kavinvalli)
Nathan Baschez (nbashaw)
Colegott Dank (colegottdank)
Use-tusk[bot]
Koshyviv