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https://github.com/langwatch/langwatch
🤖 Build AI applications with confidence ✅ DSPy Visualizer ✅ Understand how your users are using your LLM-app ✅ Get a full picture of the quality performance of your LLM-app ✅ Collaborate with your stakeholders in ONE platform ✅ Iterate towards the most valuable & reliable LLM-app.
https://github.com/langwatch/langwatch
ai analytics datasets evaluation gpt llm observability openai prompt-engineering
Last synced: about 2 months ago
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🤖 Build AI applications with confidence ✅ DSPy Visualizer ✅ Understand how your users are using your LLM-app ✅ Get a full picture of the quality performance of your LLM-app ✅ Collaborate with your stakeholders in ONE platform ✅ Iterate towards the most valuable & reliable LLM-app.
- Host: GitHub
- URL: https://github.com/langwatch/langwatch
- Owner: langwatch
- License: other
- Created: 2023-09-09T11:33:18.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-29T10:08:43.000Z (3 months ago)
- Last Synced: 2024-10-29T12:13:06.127Z (3 months ago)
- Topics: ai, analytics, datasets, evaluation, gpt, llm, observability, openai, prompt-engineering
- Language: TypeScript
- Homepage: https://langwatch.ai
- Size: 16.4 MB
- Stars: 327
- Watchers: 6
- Forks: 18
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
Awesome Lists containing this project
- StarryDivineSky - langwatch/langwatch
- awesome-ai-repositories - LangWatch
- awesome-ai-repositories - LangWatch
- jimsghstars - langwatch/langwatch - Source available LLM Ops platform and LLM Optimization Studio powered by DSPy. (TypeScript)
- awesome-llmops - LangWatch - square) | (Optimizations / Profiling)
- awesome-ChatGPT-repositories - langwatch - The ultimate LLM Ops platform - Monitoring, Analytics, Evaluations, Datasets and Prompt Optimization ✨ (Prompts)
README
[](https://discord.gg/kT4PhDS2gH)
[](https://pypi.org/project/langwatch/)
[](https://www.npmjs.com/package/langwatch)
# LangWatch - LLM Optimization Studio
LangWatch is a visual interface for [DSPy](https://github.com/stanfordnlp/dspy) and a complete LLM Ops platform for experimenting, measuring and improving LLM pipelines, with a [fair-code](https://faircode.io/) distribution model.
## Demo
[📺 Short video (3 min)](https://www.youtube.com/watch?v=dZG44oRTz84) for a sneak peak of LangWatch and a brief introduction to the concepts.
## Features
### 🎯 Optimization Studio
- 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
### 📊 Quality Assurance
- 30+ off-the-shelf evaluators
- Custom evaluation builder
- Full dataset management
- Compliance and safety checks
- [**DSPy Visualizer**](https://docs.langwatch.ai/dspy-visualization/quickstart)
### 📈 Monitoring & Analytics
- Cost and performance tracking
- Real-time debugging and tracing details
- User analytics and custom business metrics
- Custom dashboards and alerts
## LangWatch Cloud
Sign-up for a free account on [LangWatch Cloud](https://app.langwatch.ai) as the easiest way to get started.
- [📚 Learn how the platform works](https://docs.langwatch.ai/)
- [🚀 Start creating your optimization workflows](https://app.langwatch.ai/)
- [📈 Integrate Monitoring with Python or TypeScript](https://docs.langwatch.ai/integration/overview)
## Getting Started (local setup)
You need to have docker installed in your local environment to be able to run LangWatch locally.
Get started with:
```bash
git clone https://github.com/langwatch/langwatch.git
cp langwatch/.env.example langwatch/.env
docker compose up --build
```
Then, open LangWatch at http://localhost:3000
## Development
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:
```bash
docker compose up redis postgres opensearch
```
Then, on another terminal, install the dependencies and start LangWatch:
```bash
make install
make start
```
## On-Prem (Self-Hosting)
LangWatch also offers commercial support for self-hosting on your own infrastructure. For more information, please refer to the [Self-Hosting](https://docs.langwatch.ai/self-hosting) section of the documentation.
## Contributing
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](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests.
## Support
If you have questions or need help, join our community:
- [Discord Community](https://discord.gg/kT4PhDS2gH)
- [Documentation](https://docs.langwatch.ai)
- [Email Support](mailto:[email protected])