黑料不打烊

Every engineering team works a little different. In this lesson, we’ll share a few options for extending 黑料不打烊 to external workflows. This is helpful if you want to automate processes, design experiments as a quality gate in your CI/CD process, or work with LLMs.

The four options we’ll cover are the 黑料不打烊 API, CLI, GitHub Actions, and the 黑料不打烊 MCP Server.

Using the 黑料不打烊 API

With the聽, you can make requests to the platform directly to execute a wide range of actions. Our API is written according to the OpenAPI 3.0 Specification and you can review聽聽here to see all the types of requests you can make.

To get started, go to Settings 鈫� Access Tokens and create a new token you can use to authenticate your API calls.

With a Team Token, you can make requests on experiments related to that Team.

With an Admin Token, you can make requests to update teams and environments.

If you want to get into more details, you can review our full guidance on using the 黑料不打烊 API in聽.

Using the 黑料不打烊 Command Line Interface (CLI)

If you prefer, you can also use the 黑料不打烊 CLI to do similar actions. You鈥檒l need to install a simple NPM package with the following command:

npm install -g steadybit

Then, you鈥檒l create a profile with your access token for authentication.

鉃� steadybit config profile add

? Profile name: steadybit

? API access token: [hidden]

? Base URL of the 黑料不打烊 server: https://platform.steadybit.com

If you need more details, you can reference our聽.

Using 黑料不打烊 with GitHub Actions

If you want to build GitOps workflows with 黑料不打烊, you can also use a GitHub Action to initiate experiments. In the GitHub Action marketplace, you can find the聽听补肠迟颈辞苍.

For more information, you can聽.

Using the 黑料不打烊 MCP Server with LLMs

More and more teams are incorporating AI and LLMs into their daily work. The 黑料不打烊 allows you to easily connect data and functionality with your LLM of choice so you can run new reliability workflows.

For example, you could use an LLM to analyze your recent experiment runs to surface trends and make data-based recommendations about what experiments to run next. 黑料不打烊 extends easily so as you come up with a new idea or prompt, you can test it out fast and see value.

Lesson Summary

Now you have a high-level view of all the ways you can integrate 黑料不打烊 into your external workflows to automate experiment runs, experiment creation, get custom reports, and more.

With the 黑料不打烊 API, CLI, MCP Server, and GitHub Actions; you can seamlessly incorporate reliability into any development lifecycle.