LangWatch is a visual interface for DSPy and a complete LLM Ops platform for monitoring, experimenting, measuring and improving LLM pipelines, with a fair-code distribution model.
📺 Short video (3 min) for a sneak peak of LangWatch and a brief introduction to the concepts.
- Drag-and-drop interface for LLM pipeline optimization
- Built on Stanford's DSPy framework
- Automatic prompt and few-shot examples generation
- Visual experiment tracking and version control
- 30+ off-the-shelf evaluators
- Custom evaluation builder
- Full dataset management
- Compliance and safety checks
- DSPy Visualizer
- Cost and performance tracking
- Real-time debugging and tracing details
- User analytics and custom business metrics
- Custom dashboards and alerts
Sign-up for a free account on LangWatch Cloud as the easiest way to get started.
- 📚 Learn how the platform works
- 🚀 Start creating your optimization workflows
- 📈 Integrate Monitoring with Python or TypeScript
You need to have docker installed in your local environment to be able to run LangWatch locally.
Get started with:
git clone https://github.com/langwatch/langwatch.git
cp langwatch/.env.example langwatch/.env
docker compose up --buildThen, open LangWatch at http://localhost:5560
You can also run LangWatch locally without docker to develop and help contribute to the project.
Start just the databases using docker and leave it running:
docker compose up redis postgres opensearchThen, on another terminal, install the dependencies and start LangWatch:
make install
make startLangWatch also offers commercial support for self-hosting on your own infrastructure. For more information, please refer to the Self-Hosting section of the documentation.
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
Please read our Contribution Guidelines for details on our code of conduct, and the process for submitting pull requests.
If you have questions or need help, join our community: