
Refined GitHub
GitZip
Enhanced GitHub
Board for Github
GitHub Folder Downloader
DownGit
Octo Mate
GitHub Hovercard provides neat hovercards for GitHub.

Langfuse
Helicone AI
LangChain
Portkey
Braintrust
Humanloop
Braintrust.dev
Build and deploy LLM applications with confidence

Which is more popular?
Based on our record, GitHub Hovercard seems to be more popular. It has been mentioned 1 time since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | justineo.github.io | langchain.com |
| Pricing | — | |
| Listed in |
What each product offers, as listed by its team.


Possible disadvantages
Possible disadvantages
An editorial look at what each product does well and who it suits.


No analysis of GitHub Hovercard yet.
Overall verdict
Why this product is good
Recommended for
LangSmith is recommended for AI developers, machine learning engineers, and businesses aiming to build, test, and optimize applications based on language models. It is particularly useful for teams that require robust evaluation tools and a streamlined process for managing and deploying language-driven applications.
Walkthroughs and reviews on video.
🦜🛠️ Getting started with LangSmith - Integrating with LANGCHAIN powered Web Applications & Chatbots
How often each product is chosen within a category, 0–100% relative to the other.


Share your experience with using GitHub Hovercard and LangSmith. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


GitHub Hovercard brings you instant access to quick information about the user, repo, issues, commits, and pull requests information from anywhere on GitHub. - Source: dev.to / over 4 years ago
Tracking LangSmith since Jul 2023.
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Download or create a download link for a GitHub project folder/sub-folder or file.
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Open-source LLM Observability for Developers
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:rocket: Chrome extension to display size of each file, download link and copy file contents directly to clipboard - softvar/enhanced-github
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Framework for building applications with LLMs through composability
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