Developers spend a significant portion of time not on writing code but on routine tasks: creating an issue, updating a label, checking a pull request, running a pipeline, compiling release notes. In 2026, this no longer has to be done manually. The ASI Biont AI agent can connect to your GitHub directly via API and perform these operations itself—you just need to write a command in the chat.
In this article, I'll show how to set up a GitHub connection to ASI Biont in 15 minutes, what tasks this integration actually covers, and why you'll no longer have to wait for someone to write a "plugin" for the service you need.
What is ASI Biont and why connect GitHub to it
ASI Biont is an AI agent that works in the chat at asibiont.com. It doesn't just answer questions: it can execute actions in external services using their APIs. And an API is a standard interface through which programs communicate with services. GitHub has one of the most advanced APIs in the industry: REST, GraphQL, as well as APIs for Issues, Pull Requests, Actions, Releases, and Dependabot Alerts.
Connecting GitHub to ASI Biont gives the agent "hands" inside your repositories. You can ask it to:
- create an issue based on a bug description;
- review and comment on a pull request;
- run and track a workflow in GitHub Actions;
- get a report on dependency vulnerabilities;
- prepare and publish a release.
At the same time, the integration is created not through a control panel with an "Add integration" button, but through an ordinary dialogue with the AI. More on that below.
How the connection works: an API key instead of "buttons"
Traditional integrations in most services look like this: you go to settings, find the desired application, click "Connect", go through OAuth confirmation, and wait for developers to add support. ASI Biont works differently.
You simply pass an API key in the chat:
"Connect my GitHub. Here's the token: ghp_xxxxxxxx"
The AI agent independently studies the GitHub API documentation, writes the integration code right in your workspace, and connects to your repositories. No control panels, no buttons, and no waiting for a new platform release.
This is a key architectural advantage of ASI Biont: it connects to any service that has an API, and it does so through dialogue. GitHub is not an exception, but merely a convenient example. You can use the same scheme to connect GitLab, Jira, Slack, Google Drive, Notion—anything that provides an API.
What tasks does this integration automate
1. Creating and managing issues
You receive a bug report from a user, paste it into the ASI Biont chat, and write: "Create an issue in the asibiont/docs repository." The AI will create the issue, add the "bug" label, assign someone responsible, and attach a link to the dialogue. If a similar issue already exists, the AI will find it and suggest not creating a duplicate.
2. Pull request review
Reviewing is one of the most time-consuming tasks. Through the GitHub API, the AI gets the diff of changes, analyzes new files, and leaves comments in the pull request: what errors might arise, where the style is violated, what tests should be added. It doesn't replace a human reviewer, but it handles the first wave of checks.
3. Running and monitoring CI/CD pipelines
GitHub Actions is the standard tool for CI/CD on GitHub. With the integration, you can say: "Run the release workflow on the main branch"—and the AI will run the workflow via the API, then return the status: success, failure, cancelled. In case of failure, the AI will analyze the step logs and explain where it crashed.
4. Vulnerability analysis
GitHub collects information about vulnerabilities in dependencies via Dependabot Alerts. The AI can use the API to get a list of alerts, group them by severity, and produce a text report. You can ask: "Which dependencies need updating first?"—and the AI will answer based on data from your repository.
5. Release management
Preparing a release involves several operations: collecting a list of all merged pull requests, generating release notes, creating a git tag, and publishing the release on GitHub. The integration with ASI Biont lets you do this with a single command: "Prepare release v2.3.0." The AI will create the tag, fill in the description of changes, and publish the release.
Examples of specific usage scenarios
Scenario 1. Supporting an open-source project
Imagine you maintain a popular open-source project. Every day similar issues come in: "doesn't work on Windows," "add a PDF export feature." Previously, you manually categorized them, looked for duplicates, and set labels. Now you simply forward the text of the request to the ASI Biont chat: "Create an issue and mark it as a feature request." This takes 2–3 seconds, and the AI independently analyzes similar issues and creates the ticket.
Scenario 2. Automatic review before merging
Your team has a rule: every PR must be reviewed. Small PRs often wait for hours. You set up ASI Biont to check PRs against a checklist: presence of tests, absence of console.log, correctness of error handling. The AI leaves comments, and the developer fixes the issues immediately before the PR reaches a human.
Scenario 3. Nightly security analysis
Every morning you ask ASI Biont: "Check Dependabot alerts from last night." The AI collects notifications, filters those that appeared in the last 24 hours, and sends a summary to the chat: which libraries are vulnerable, how critical the update is. You save at least 30 minutes a day just by not reading alerts manually.
Scenario 4. Release coordinator
Before a release, you usually need to manually review all merged PRs and compile a changelog. ASI Biont does this instantly: it requests the list of merged PRs for a selected period via the API, groups them by type (feat, fix, docs), and creates a release with ready-made release notes. Errors related to a missed PR are practically eliminated.
Scenario 5. Receiving new issues from external channels
If your project receives bugs from chats, email, or a ticketing system, you can
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