Python ai-observability Projects
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openlit
OpenLIT is an open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics in a single application š„ š„ . Open source GenAI and LLM Application Performance Monitoring (APM) & Observability tool
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
Last week we showcased our open-source project, OpenLIT (https://github.com/openlit/openlit), here, and thanks to this incredible community, we hit 300 stars in just a couple of days!
One of my mentors, a core lead on OpenTelemetry, suggested we consider adding a Contributor License Agreement (CLA) to our project, similar to what has been done with OpenTelemetry.
I understand the potential legal benefits a CLA offers, such as ensuring contributions can be freely used and distributed, which could be crucial for the project's long-term viability and to avoid legal complications.
However, Iām equally concerned about the potential downsides, especially regarding community contributions. I worry that a CLA might stop new contributors who prefer to avoid legal hurdles or are reluctant to sign documents. Since OpenLIT aims to be truly open-source and community-driven, keeping the contribution process as straightforward as possible is essential to me.
So, Iām turning to you, HN community, for guidance:
- Have you implemented a CLA for your project? What impact did it have on contributions?
This is really cool!
I've been using this auditor tool that some friends at Fiddler created: https://github.com/fiddler-labs/fiddler-auditor
They went with a langchain interface for custom Evals which I really like. I am curious to hear if anyone has tried both of these. What's been your key take away for these?
Python ai-observability related posts
Index
Project | Stars | |
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1 | openlit | 351 |
2 | fiddler-auditor | 143 |
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