AI/LLM-Powered Pipeline Analysis

⚠️

This is a Technology Preview feature only. Technology Preview features are not currently supported and might not be functionally complete. We do not recommend using them in production. These features provide early access to upcoming Pipelines-as-Code features, enabling you to test functionality and provide feedback during the development process.

Pipelines-as-Code can analyze your CI/CD pipeline runs using Large Language Models (LLMs). An LLM is an AI service (such as OpenAI or Google Gemini) that reads pipeline logs and failure data, then posts human-readable analysis directly on your pull requests.

Use this feature when you want automated root-cause analysis of pipeline failures without manually reading through logs.

Overview

LLM-powered analysis lets you:

Analyze failed pipelines automatically and get root-cause analysis
Generate actionable recommendations for fixing issues
Post insights as PR comments so your team sees them immediately
Configure custom analysis scenarios using different prompts and triggers

Additional output destinations (check-run and annotation) and structured JSON output are planned for future releases.

Supported LLM Providers

Pipelines-as-Code supports two LLM providers:

OpenAI – Default model: gpt-5-mini
Google Gemini – Default model: gemini-2.5-flash-lite

You can specify any model your chosen provider supports. See Model Selection for guidance on choosing the right model.

Configuration

To enable LLM-powered analysis, configure the spec.settings.ai section in your Repository CR:

apiVersion: pipelinesascode.tekton.dev/v1alpha1
kind: Repository
metadata:
  name: my-repo
spec:
  url: "https://github.com/org/repo"
  settings:
    ai:
      enabled: true
      provider: "openai"
      timeout_seconds: 30
      max_tokens: 1000
      secret_ref:
        name: "openai-api-key"
        key: "token"
      roles:
        - name: "failure-analysis"
          model: "gpt-5-mini"  # Optional: specify model (uses provider default if omitted)
          prompt: |
            You are a DevOps expert. Analyze this failed pipeline and:
            1. Identify the root cause
            2. Suggest specific fixes
            3. Recommend preventive measures
          on_cel: 'body.pipelineRun.status.conditions[0].reason == "Failed"'
          context_items:
            error_content: true
            container_logs:
              enabled: true
              max_lines: 100
          output: "pr-comment"

Configuration Fields

The following tables describe every field available in the spec.settings.ai configuration block.

Top-Level Settings

Field	Type	Required	Description
`enabled`	boolean	Yes	Enable/disable LLM analysis
`provider`	string	Yes	LLM provider: `openai` or `gemini`
`api_url`	string	No	Custom API endpoint URL (overrides provider default)
`timeout_seconds`	integer	No	Request timeout (1-300, default: 30)
`max_tokens`	integer	No	Maximum response tokens (1-4000, default: 1000)
`secret_ref`	object	Yes	Reference to Kubernetes secret with API key
`roles`	array	Yes	List of analysis scenarios (minimum 1)

Analysis Roles

Each role defines a specific analysis scenario. You can configure multiple roles to handle different types of pipeline events (for example, one role for security failures and another for general build failures).

Field	Type	Required	Description
`name`	string	Yes	Unique identifier for this role
`prompt`	string	Yes	Prompt template for the LLM
`model`	string	No	Model name (consult provider documentation for available models). Uses provider default if not specified.
`on_cel`	string	No	CEL expression for conditional triggering. If not specified, the role will always run.
`output`	string	Yes	Output destination (currently only `pr-comment` is supported)
`context_items`	object	No	Configuration for context inclusion

Context Items

Context items control what information Pipelines-as-Code sends to the LLM provider. Choose carefully, because more context means higher token usage and cost.

Field	Type	Description
`commit_content`	boolean	Include commit information (see Commit Fields below)
`pr_content`	boolean	Include PR title, description, metadata
`error_content`	boolean	Include error messages and failures
`container_logs.enabled`	boolean	Include container/task logs
`container_logs.max_lines`	integer	Limit log lines (1-1000, default: 50). ⚠️ High values may impact performance

Commit Fields

When you set commit_content: true, Pipelines-as-Code includes the following fields in the data sent to the LLM provider:

Field	Type	Description	Example
`commit.sha`	string	Commit SHA hash	`"abc123def456..."`
`commit.message`	string	Commit title (first line/paragraph)	`"feat: add new feature"`
`commit.url`	string	Web URL to view the commit	`"https://github.com/org/repo/commit/abc123"`
`commit.full_message`	string	Complete commit message (if different from title)	`"feat: add new feature\n\nDetailed description..."`
`commit.author.name`	string	Author’s name	`"John Doe"`
`commit.author.date`	timestamp	When the commit was authored	`"2024-01-15T10:30:00Z"`
`commit.committer.name`	string	Committer’s name (may differ from author)	`"GitHub"`
`commit.committer.date`	timestamp	When the commit was committed	`"2024-01-15T10:31:00Z"`

Privacy and Security Notes:

Pipelines-as-Code intentionally excludes email addresses from the commit context to protect personally identifiable information (PII) when sending data to external LLM providers.
Fields appear only if your Git provider makes them available. Some providers supply limited information (for example, Bitbucket Cloud provides only the author name).
Author and committer may be the same person or different (for example, when using git commit --amend or rebasing).