💻 programming

OpenLIT

OpenLIT is an open source platform for observability in GenAI and LLM applications.

#AI
#Open source
#Privacy protection
#Performance optimization
#Abnormal monitoring
OpenLIT

Product Details

OpenLIT is an open source AI engineering platform focused on observability for generative AI and large language model (LLM) applications. It helps developers simplify the AI ​​development process and improve development efficiency and application performance by providing code transparency, privacy protection, performance visualization and other functions. As an open source project, users are free to view the code or host it themselves, ensuring data security and privacy. Its main advantages include easy integration, support for OpenTelemetry native integration, and provision of fine-grained usage insights. OpenLIT is aimed at AI developers, data scientists and enterprises, aiming to help them better build, optimize and manage AI applications. The specific price is not yet clear, but judging from the open source features, it may provide free use of basic functions.

Main Features

1
Privacy first: The code is transparent, supports self-hosting, and simplifies the AI ​​development process, especially generative AI and LLM.
2
Visual tracking: Provides application and request tracking, supports OpenTelemetry, automatically tracks AI applications, and monitors response times and costs.
3
Exception monitoring: Automatically monitor exceptions through Python and TypeScript SDK, provide detailed stack trace information, and integrate with tracking data.
4
Openground testing: Test and compare key indicators such as performance and cost of different LLMs, supporting side-by-side comparisons, cost analysis and comprehensive reports.
5
Prompt management: Provides a centralized prompt repository that supports prompt creation, editing, version management and variable replacement.
6
Secure key management: Provides a secure key storage and management method, supporting key creation, editing, monitoring and environment integration.

How to Use

1
1. Visit the OpenLIT official website to learn about product functions and documentation.
2
2. Clone the OpenLIT project on GitHub, or use the `docker-compose up -d` command to start the container.
3
3. Add the `openlit.init()` code to the AI ​​application to start collecting data.
4
4. Use the SDK provided by OpenLIT, such as Python or TypeScript SDK, for exception monitoring and key management.
5
5. Use the Openground test function to compare the performance and cost of different LLMs.
6
6. Create, edit and version control AI prompts through the prompt management function.
7
7. View visual tracking data, analyze application performance and behavior, and optimize AI applications.

Target Users

The target audience is mainly AI developers, data scientists and enterprises. For AI developers, OpenLIT provides tools to simplify the development process and optimize performance; for data scientists, it helps to better understand and analyze the behavior of AI models; for enterprises, OpenLIT can improve the observability and management efficiency of AI applications and ensure data security and privacy.

Examples

The AI ​​development team uses OpenLIT's prompt management function to efficiently manage and version control a large number of AI prompts, improving development efficiency.

Data scientists use the Openground test function to compare the performance of different LLMs on specific tasks and provide data support for model selection.

Enterprises use OpenLIT's security key management function to ensure the safe storage and use of sensitive information such as API keys and protect enterprise data assets.

Quick Access

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Categories

💻 programming
› Development and Tools
› Model training and deployment

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