Metadata-Version: 2.3
Name: aif-workflow-helper
Version: 0.1.1
Summary: Azure AI Foundry dev workflow helpers
Keywords: azure,ai,agents,workflow,automation
Author: Pete Roden
Author-email: Pete Roden <pete.roden@live.com>
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Dist: azure-ai-projects
Requires-Dist: azure-ai-agents
Requires-Dist: azure-identity
Requires-Dist: pyyaml
Requires-Dist: python-frontmatter
Requires-Dist: pytest>=7.0.0 ; extra == 'dev'
Requires-Dist: pytest-cov>=4.0.0 ; extra == 'dev'
Requires-Dist: pytest-mock>=3.10.0 ; extra == 'dev'
Requires-Python: >=3.10
Project-URL: Homepage, https://github.com/peteroden/aif-workflow-helper
Project-URL: Source, https://github.com/peteroden/aif-workflow-helper
Provides-Extra: dev
Description-Content-Type: text/markdown

# Azure AI Foundry Workflow Helpers

Utilities for exporting (downloading) and importing (creating/updating) Azure AI Foundry Agents along with dependency awareness, normalization, and consistent logging.

## 🚀 Quick Start

### 1. Set Environment Variables

```bash
export AZURE_TENANT_ID='your-tenant-id-here'
export PROJECT_ENDPOINT='your-ai-foundry-endpoint-here'
```

**Example:**

```bash
export AZURE_TENANT_ID='aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee'
export PROJECT_ENDPOINT='https://your-resource.services.ai.azure.com/api/projects/your-project'
```

> **Note:** You can also provide these values via CLI parameters (`--azure-tenant-id` and `--project-endpoint`) which will take precedence over environment variables.

### 2. Install the Package

For development (editable install):

```bash
pip install -e .
```

Or for production:

```bash
pip install .
```

This will install all required dependencies automatically.

### 3. Using the CLI (Recommended)

The CLI is available as a console script after installation.

```bash
aif-workflow-helper --download-all-agents --agents-dir agents
```

Common examples:

```bash
# Download all agents with optional prefix/suffix filtering
aif-workflow-helper --download-all-agents --prefix dev- --suffix -v1

# Download a single agent
aif-workflow-helper --download-agent my_agent

# Upload all agents from JSON definitions in a directory
aif-workflow-helper --upload-all-agents --agents-dir agents

# Upload a single agent definition file
aif-workflow-helper --upload-agent my_agent --agents-dir agents

# Download agents in different formats
aif-workflow-helper --download-all-agents --format json     # Default
aif-workflow-helper --download-all-agents --format yaml     # YAML format
aif-workflow-helper --download-all-agents --format md       # Markdown with frontmatter

# Upload agents from different formats
aif-workflow-helper --upload-all-agents --format yaml
aif-workflow-helper --upload-agent my_agent --format md

# Change log level
aif-workflow-helper --download-all-agents --log-level DEBUG

# Override environment variables with CLI parameters
aif-workflow-helper --download-all-agents \
  --azure-tenant-id "your-tenant-id" \
  --project-endpoint "https://your-endpoint.services.ai.azure.com/api/projects/your-project"

# Mix CLI parameters with environment variables (CLI takes precedence)
export AZURE_TENANT_ID="env-tenant-id"
aif-workflow-helper --download-all-agents --azure-tenant-id "cli-tenant-id"  # Uses CLI value
```

### 4. Direct Library Usage

You can import and compose the underlying functions directly:

```python
from aif_workflow_helpers import (
    configure_logging,
    download_agents,
    download_agent,
    create_or_update_agents,
    create_or_update_agent,
    create_or_update_agent_from_file,
    create_or_update_agents_from_files,
)
from azure.ai.agents import AgentsClient
from azure.identity import DefaultAzureCredential

configure_logging()

client = AgentsClient(
    credential=DefaultAzureCredential(
        exclude_interactive_browser_credential=False,
        interactive_tenant_id="your-tenant-id"
    ),
    endpoint="your-endpoint"
)

# Bulk download
download_agents(client, file_path="./agents", prefix="", suffix="", format="json")

# Create/update from a directory (dependency ordered)
create_or_update_agents_from_files(path="./agents", agent_client=client, prefix="", suffix="", format="json")
```

## 📁 What the Tooling Does

1. Downloads existing agents to normalized files (JSON, YAML, or Markdown with frontmatter)
2. Normalizes (generalizes) definitions for portability (removes resource-specific fields)
3. Infers and resolves inter-agent dependencies (connected agent tools)
4. Creates or updates agents in dependency-safe order
5. Applies optional prefix/suffix for environment namespacing
6. Supports multiple file formats for flexible workflow integration

## 🔧 Core Functions

### Download Functions

- `download_agents(agent_client, file_path, prefix, suffix, format)` – Download and generalize all agents (optional prefix/suffix filters, format selection)
- `download_agent(agent_name, agent_client, file_path, prefix, suffix, format)` – Download and generalize a single agent
- `generalize_agent_dict(data, agent_client, prefix, suffix)` – Normalize agent JSON for portability

### Upload Functions

- `create_or_update_agent(agent_data, agent_client, existing_agents, prefix, suffix)` – Upsert a single agent object
- `create_or_update_agents(agents_data, agent_client, prefix, suffix)` – Upsert multiple agents with dependency ordering
- `create_or_update_agent_from_file(agent_name, path, agent_client, prefix, suffix, format)` – Upsert from a specific file
- `create_or_update_agents_from_files(path, agent_client, prefix, suffix, format)` – Bulk load and upsert directory

### Internal Helpers (Not all re-exported)

- `read_agent_file(path)` / `read_agent_files(path, format)` – Load definitions in any supported format (used internally by *from_files* wrappers)
- `extract_dependencies(agents_data)` – Build dependency graph
- `dependency_sort(agents_data)` – Topological sort of agents
- `get_agent_by_name(name, client)` – Lookup agent object
- `get_agent_name(agent_id, client)` – Reverse lookup by ID

## 🎯 CLI Reference

`aif-workflow-helper` arguments:

```text
--agents-dir DIR                Directory for agent definition files (default: agents)
--download-all-agents           Download all existing agents
--download-agent NAME           Download a single agent by name
--upload-all-agents             Create/update all agents from definition files
--upload-agent NAME             Create/update a single agent from definition file
--prefix TEXT                   Optional prefix applied during download/upload
--suffix TEXT                   Optional suffix applied during download/upload
--format FORMAT                 File format: json, yaml, or md (default: json)
--log-level LEVEL               Logging level (CRITICAL, ERROR, WARNING, INFO, DEBUG, NOTSET)
--azure-tenant-id TENANT_ID     Azure tenant ID (overrides AZURE_TENANT_ID environment variable)
--project-endpoint ENDPOINT     AI Foundry project endpoint URL (overrides PROJECT_ENDPOINT environment variable)
```

### Authentication Priority

1. **CLI Parameters** (highest priority): `--azure-tenant-id` and `--project-endpoint`
2. **Environment Variables** (fallback): `AZURE_TENANT_ID` and `PROJECT_ENDPOINT`

## 📄 Supported File Formats

The tool supports three file formats for agent definitions:

### JSON Format (Default)

Standard JSON format with all agent properties in a single object:

```json
{
  "name": "my-agent",
  "model": "gpt-4",
  "instructions": "You are a helpful AI assistant...",
  "tools": [],
  "temperature": 0.7,
  "top_p": 1.0
}
```

### YAML Format

Clean YAML format for better readability:

```yaml
name: my-agent
model: gpt-4
instructions: |
  You are a helpful AI assistant.
  Please provide clear and concise responses.
tools: []
temperature: 0.7
top_p: 1.0
```

### Markdown with Frontmatter

Markdown format where the `instructions` field becomes the content and all other properties go into YAML frontmatter:

```markdown
---
name: my-agent
model: gpt-4
tools: []
temperature: 0.7
top_p: 1.0
---
You are a helpful AI assistant.

Please provide clear and concise responses to user questions.
```

**File Extensions:**

- JSON: `.json`
- YAML: `.yaml` or `.yml`
- Markdown: `.md`

## 📋 File Structure

```text
├── pyproject.toml               # Package configuration and dependencies
├── requirements.txt             # Core runtime dependencies
├── README.md                    # Project documentation
├── agents/                      # Agent definition files
├── tests/                       # Test files
└── src/aif_workflow_helpers/    # Main package source code
    ├── __init__.py              # Public exports
    ├── cli/
    │   └── main.py              # CLI entrypoint
    ├── core/
    │   ├── upload.py            # Upload + dependency logic
    │   ├── download.py          # Download + generalization logic
    │   └── formats.py           # Format handling utilities
    └── utils/
        ├── logging.py           # Shared logging configuration
        └── validation.py        # Agent name validation
```

## ⚠️ Important Notes

1. **Authentication**: Uses `DefaultAzureCredential` (interactive fallback enabled)
2. **Dependency Ordering**: Creates/updates in safe order via topological sort
3. **Name Safety**: Validation ensures only alphanumerics + hyphens (prefix/suffix applied consistently)
4. **Logging**: Centralized configurable logger (`configure_logging`)
5. **Efficiency**: Minimizes duplicate lookups by caching existing agents during batch operations
6. **Format Flexibility**: Supports JSON, YAML, and Markdown with frontmatter for different workflow preferences

## 🔍 Troubleshooting

### Installation Issues

```bash
# Install in development mode for local changes
pip install -e .

# Or install for production use
pip install .
```

### Authentication Errors

```bash
# Check environment variables
echo $AZURE_TENANT_ID
echo $PROJECT_ENDPOINT

# Or use CLI parameters (recommended for CI/CD or when environment variables conflict)
aif-workflow-helper --download-all-agents \
  --azure-tenant-id "your-tenant-id" \
  --project-endpoint "your-endpoint"

# Try interactive login
az login --tenant $AZURE_TENANT_ID
```

### Command Not Found Error

If `aif-workflow-helper` is not found after installation:

```bash
# Make sure you installed the package
pip install -e .

# Check if it's in your PATH
which aif-workflow-helper

# Or run directly with Python
python -m aif_workflow_helpers.cli.main --help
```

## 🎉 Success Output

Typical successful run output (truncated example):

```text
🔌 Testing connection...
✅ Connected! Found X existing agents

📥 Downloading agents...
Saved agent 'agent-name' to agent-name.json

📂 Reading agent files...
Found X agents

🚀 Creating/updating agents...
Processing 1/X: agent-name
✅ Successfully processed agent-name
```

## 🔄 CI/CD Pipeline

This project includes a comprehensive CI/CD pipeline using GitHub Actions that ensures code quality and functionality.

### Pipeline Features

- **Multi-Python Version Testing**: Tests on Python 3.10, 3.11, and 3.12
- **Automated Testing**: Runs all pytest tests with coverage reporting
- **Code Quality**: Includes linting with flake8
- **Package Testing**: Verifies the package can be built and installed correctly
- **CLI Testing**: Ensures the command-line interface works after installation

### Branch Protection

The main branch is protected with the following requirements:

- ✅ **Pull Request Required**: Direct pushes to main are not allowed
- ✅ **Tests Must Pass**: All CI checks must pass before merging
- ✅ **Code Review**: At least 1 approval required
- ✅ **Up-to-date Branch**: Branches must be current with main

### Running Tests Locally

Before submitting a PR, run tests locally to ensure they pass:

```bash
# Activate virtual environment
source .venv/bin/activate

# Install with dev dependencies
pip install -e .[dev]

# Run tests
pytest tests/ -v --tb=short

# Run with coverage
pytest tests/ -v --cov=src --cov-report=term-missing

# Check CLI functionality
aif-workflow-helper --help
```

### Contributing

1. Create a feature branch from `main`
2. Make your changes
3. Ensure all tests pass locally
4. Submit a pull request
5. Wait for CI to pass and get code review approval
6. Merge when approved
