llm-ci-runner 1.2.2

A simple CI/CD utility for running LLM tasks with Semantic Kernel

AI-First Toolkit: LLM-Powered Automation

🚀 The Future of DevOps is AI-First
This toolkit represents a step toward AI-First DevOps - where intelligent automation handles the entire development lifecycle. Built for teams ready to embrace the exponential productivity gains of AI-powered development. Please read the blog post for more details on the motivation.

TLDR: What This Tool Does

Purpose: Zero-friction LLM integration for pipelines with 100% guaranteed schema compliance. This is your foundation for AI-first integration practices.

Perfect For:

• 🤖 AI-Generated Code Reviews: Automated PR analysis with structured findings
• 📝 Intelligent Documentation: Generate changelogs, release notes, and docs automatically
• 🔍 Security Analysis: AI-powered vulnerability detection with structured reports
• 🎯 Quality Gates: Enforce standards through AI-driven validation
• 🚀 Autonomous Development: Enable AI agents to make decisions in your pipelines
• 🎯 JIRA Ticket Updates: Update JIRA tickets based on LLM output
• 🔗 Unlimited Integration Possibilities: Chain it multiple times and use as glue code in your tool stack

---

Simple structured output example

# Install and use immediately
pip install llm-ci-runner
llm-ci-runner --input-file examples/02-devops/pr-description/input.json --schema-file examples/02-devops/pr-description/schema.json

The AI-First Development Revolution

This toolkit embodies the principles outlined in Building AI-First DevOps:

Traditional DevOps	AI-First DevOps (This Tool)
Manual code reviews	🤖 AI-powered reviews with structured findings
Human-written documentation	📝 AI-generated docs with guaranteed consistency
Reactive security scanning	🔍 Proactive AI security analysis
Manual quality gates	🎯 AI-driven validation with schema enforcement
Linear productivity	📈 Exponential gains through intelligent automation

Features

• 🎯 100% Schema Enforcement: Your pipeline never gets invalid data. Token-level schema enforcement with guaranteed compliance
• 🔄 Resilient execution: Retries with exponential back-off and jitter plus a clear exception hierarchy keep transient cloud faults from breaking your CI.
• 🚀 Zero-Friction CLI: Single script, minimal configuration for pipeline integration and automation
• 🔐 Enterprise Security: Azure RBAC via DefaultAzureCredential with fallback to API Key
• 📦 CI-friendly CLI: Stateless command that reads JSON/YAML, writes JSON/YAML, and exits with proper codes
• 🎨 Beautiful Logging: Rich console output with timestamps and colors
• 📁 File-based I/O: CI/CD friendly with JSON/YAML input/output
• 📋 Template-Driven Workflows: Handlebars and Jinja2 templates with YAML variables for dynamic prompt generation
• 📄 YAML Support: Use YAML for schemas, input files, and output files - more readable than JSON
• 🔧 Simple & Extensible: Easy to understand and modify for your specific needs
• 🤖 Semantic Kernel foundation: async, service-oriented design ready for skills, memories, orchestration, and future model upgrades
• 📚 Documentation: Comprehensive documentation for all features and usage examples. Use your semantic kernel skills to extend the functionality.
• 🧑‍⚖️ Acceptance Tests: pytest framework with the LLM-as-Judge pattern for quality gates. Test your scripts before you run them in production.
• 💰 Coming soon: token usage and cost estimation appended to each result for budgeting and optimisation

Installation

pip install llm-ci-runner

That's it! No complex setup, no dependency management - just install and use. Perfect for CI/CD pipelines and local development.

Quick Start

1. Install from PyPI

pip install llm-ci-runner

2. Set Environment Variables

export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_MODEL="gpt-4.1-nano"  # or any other GPT deployment name
export AZURE_OPENAI_API_VERSION="2024-12-01-preview"  # Optional

Authentication Options:

• RBAC (Recommended): Uses DefaultAzureCredential for Azure RBAC authentication - no API key needed! See Microsoft Docs for setup.
• API Key: Set AZURE_OPENAI_API_KEY environment variable if not using RBAC.

3a. Basic Usage

# Simple chat example
llm-ci-runner --input-file examples/01-basic/simple-chat/input.json

# With structured output schema
llm-ci-runner \
  --input-file examples/01-basic/sentiment-analysis/input.json \
  --schema-file examples/01-basic/sentiment-analysis/schema.json

# Custom output file
llm-ci-runner \
  --input-file examples/02-devops/pr-description/input.json \
  --schema-file examples/02-devops/pr-description/schema.json \
  --output-file pr-analysis.json

# YAML input files (alternative to JSON)
llm-ci-runner \
  --input-file config.yaml \
  --schema-file schema.yaml \
  --output-file result.yaml

3b. Template-Based Workflows

Dynamic prompt generation with YAML, Handlebars or Jinja2 templates:

# Handlebars template example
llm-ci-runner \
  --template-file examples/05-templates/handlebars-template/template.hbs \
  --template-vars examples/05-templates/handlebars-template/template-vars.yaml \
  --schema-file examples/05-templates/handlebars-template/schema.yaml \
  --output-file handlebars-result.yaml
  
# Or using Jinja2 templates
llm-ci-runner \
  --template-file examples/05-templates/jinja2-template/template.j2 \
  --template-vars examples/05-templates/jinja2-template/template-vars.yaml \
  --schema-file examples/05-templates/jinja2-template/schema.yaml \
  --output-file jinja2-result.yaml

For more examples see the examples directory.

Benefits of Template Approach:

• 🎯 Reusable Templates: Create once, use across multiple scenarios
• 📝 YAML Configuration: More readable than JSON for complex setups
• 🔄 Dynamic Content: Variables and conditional rendering
• 🚀 CI/CD Ready: Perfect for parameterized pipeline workflows

4. Development Setup (Optional)

For contributors or advanced users who want to modify the source:

# Install UV if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and install for development
git clone https://github.com/Nantero1/ai-first-devops-toolkit.git
cd ai-first-devops-toolkit
uv sync

# Run from source
uv run llm-ci-runner --input-file examples/01-basic/simple-chat/input.json

Real-World Examples

You can explore the examples directory for a complete collection of self-contained examples organized by category.

For comprehensive real-world CI/CD scenarios, see examples/uv-usage-example.md which includes:

• 🔄 Automated PR Description Updates: Generate comprehensive PR descriptions from commit messages and code changes
• 🔒 Security Analysis with LLM-as-Judge: Analyze code changes for vulnerabilities with guaranteed schema compliance
• 📋 Automated Changelog Generation: Create structured changelogs from commit history
• 🤖 Code Review Automation: Automated reviews with structured findings and quality gates
• 🔗 Multi-Stage AI Pipelines: Chain multiple AI operations for complex workflows

Input Formats

Traditional JSON Input

{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user", 
      "content": "Your task description here"
    }
  ],
  "context": {
    "session_id": "optional-session-id",
    "metadata": {
      "any": "additional context"
    }
  }
}

YAML Input

messages:
  - role: system
    content: "You are a helpful assistant."
  - role: user
    content: "Your task description here"
context:
  session_id: "optional-session-id"
  metadata:
    any: "additional context"

Template-Based Input

Handlebars Template (template.hbs):

<message role="system">
You are an expert {{expertise.domain}} engineer.
Focus on {{expertise.focus_areas}}.
</message>

<message role="user">
Analyze this {{task.type}}:

{{#each task.items}}
- {{this}}
{{/each}}

Requirements: {{task.requirements}}
</message>

Jinja2 Template (template.j2):

<message role="system">
You are an expert {{expertise.domain}} engineer.
Focus on {{expertise.focus_areas}}.
</message>

<message role="user">
Analyze this {{task.type}}:

{% for item in task.items %}
- {{item}}
{% endfor %}

Requirements: {{task.requirements}}
</message>

Template Variables (vars.yaml):

expertise:
  domain: "DevOps"
  focus_areas: "security, performance, maintainability"
task:
  type: "pull request"
  items:
    - "Changed authentication logic"
    - "Updated database queries"
    - "Added input validation"
  requirements: "Focus on security vulnerabilities"

Structured Outputs with 100% Schema Enforcement

When you provide a --schema-file, the runner guarantees perfect schema compliance:

llm-ci-runner \
  --input-file examples/01-basic/sentiment-analysis/input.json \
  --schema-file examples/01-basic/sentiment-analysis/schema.json

Note: Output defaults to result.json. Use --output-file custom-name.json for custom output files.

Supported Schema Features: ✅ String constraints (enum, minLength, maxLength, pattern)
✅ Numeric constraints (minimum, maximum, multipleOf)
✅ Array constraints (minItems, maxItems, items type)
✅ Required fields enforced at generation time
✅ Type validation (string, number, integer, boolean, array)

CI/CD Integration

GitHub Actions Example

- name: Setup Python
  uses: actions/setup-python@v5
  with:
    python-version: '3.12'

- name: Install LLM CI Runner
  run: pip install llm-ci-runner

- name: Generate PR Review with Templates
  run: |
    llm-ci-runner \
      --template-file .github/templates/pr-review.j2 \
      --template-vars pr-context.yaml \
      --schema-file .github/schemas/pr-review.yaml \
      --output-file pr-analysis.yaml
  env:
    AZURE_OPENAI_ENDPOINT: ${{ secrets.AZURE_OPENAI_ENDPOINT }}
    AZURE_OPENAI_MODEL: ${{ secrets.AZURE_OPENAI_MODEL }}

For complete CI/CD examples, see examples/uv-usage-example.md.

Authentication

Uses Azure's DefaultAzureCredential supporting:

• Environment variables (local development)
• Managed Identity (recommended for Azure CI/CD)
• Azure CLI (local development)
• Service Principal (non-Azure CI/CD)

Testing

We maintain comprehensive test coverage with 100% success rate:

# For package users - install test dependencies
pip install llm-ci-runner[dev]

# For development - install from source with test dependencies
uv sync --group dev

# Run specific test categories
pytest tests/unit/ -v          # 70 unit tests
pytest tests/integration/ -v   # End-to-end examples
pytest acceptance/ -v          # LLM-as-judge evaluation

# Or with uv for development
uv run pytest tests/unit/ -v
uv run pytest tests/integration/ -v
uv run pytest acceptance/ -v

Architecture

Built on Microsoft Semantic Kernel for:

• Enterprise-ready Azure OpenAI integration
• Future-proof model compatibility
• 100% Schema Enforcement: KernelBaseModel integration with token-level constraints
• Dynamic Model Creation: Runtime JSON schema → Pydantic model conversion
• RBAC: Azure RBAC via DefaultAzureCredential

The AI-First Development Journey

This toolkit is your first step toward AI-First DevOps. As you integrate AI into your development workflows, you'll experience:

1. 🚀 Exponential Productivity: AI handles routine tasks while you focus on architecture
2. 🎯 Guaranteed Quality: Schema enforcement eliminates validation errors
3. 🤖 Autonomous Operations: AI agents make decisions in your pipelines
4. 📈 Continuous Improvement: Every interaction improves your AI system

The future belongs to teams that master AI-first principles. This toolkit gives you the foundation to start that journey today.

License

MIT License - See LICENSE file for details. Copyright (c) 2025, Benjamin Linnik.

Support

🐛 Found a bug? 💡 Have a question? 📚 Need help?

GitHub is your primary destination for all support:

• 📋 Issues & Bug Reports: Create an issue
• 📖 Documentation: Browse examples
• 🔧 Source Code: View source

Before opening an issue, please:

1. ✅ Check the examples directory for solutions
2. ✅ Review the error logs (beautiful output with Rich!)
3. ✅ Validate your Azure authentication and permissions
4. ✅ Ensure your input JSON follows the required format
5. ✅ Search existing issues for similar problems

Quick Links:

• 🚀 Getting Started Guide
• 📚 Complete Examples
• 🔧 CI/CD Integration
• 🎯 Use Cases

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Ready to embrace the AI-First future? Start with this toolkit and build your path to exponential productivity. Learn more about the AI-First DevOps revolution in Building AI-First DevOps.