AI Agents Configuration Library

A robust Python library for managing AI agent configurations with Pydantic validation, environment variable substitution, and internal reference resolution. Perfect for building scalable AI applications with complex tool integrations and deployment flexibility.

Hire Me

Please send email if you consider hiring me.

Give a Star! ⭐

If you like or are using this project to learn or start your solution, please give it a star. Thanks!

🚀 Quick Start

Installation

# Install with uv (recommended)
uv add agents-config

# Or with pip
pip install agents-config

# Install development version from GitHub
pip install git+https://github.com/kdcllc/agents_config.git

# For development work
git clone https://github.com/kdcllc/agents_config.git
cd agents_config
uv sync --all-extras

Basic Usage

from app.agents_config.config_loader import ConfigLoader

# Load configuration from YAML file
config = ConfigLoader.load_from_file("ai-config.yaml")

# Access models, agents, and tools
model = config.get_model("gpt-4")
agent = config.get_agent("search-agent")
print(f"Agent: {agent.description}")

Run the Demo

# See the library in action
uv run main.py

# Or with python
python main.py

🎯 Key Features

• 🔧 Pydantic V2 Validation: Type-safe configuration with automatic validation
• 🌍 Environment Variables: ${env:VAR_NAME} substitution for secrets and deployment configs
• 🔗 Internal References: ${ref:path.to.value} resolution for DRY configurations
• 🛠️ Multi-Tool Support: OpenAPI tools, Azure AI Foundry agents, custom integrations
• 📦 Flexible Models: Support for Azure OpenAI, OpenAI, Ollama, and custom providers
• ✅ Cross-Reference Validation: Ensures agents reference valid models and tools
• 🔄 Programmatic Configuration: Create configs in code or load from YAML

📖 Configuration Structure

YAML Configuration Example

version: '1.0'

# Models define AI providers and their configurations
models:
  gpt-4:
    provider: azure_openai
    id: gpt-4-turbo
    version: '1.0'
    config:
      api_key: ${env:AZURE_OPENAI_KEY}
      endpoint: ${env:AZURE_OPENAI_ENDPOINT}
      deployment: my-deployment
    params:
      temperature: 0.7
      max_tokens: 4096

# Tools define external integrations
tools:
  ai_foundry:
    default_project_endpoint: ${env:AZURE_AI_FOUNDRY_ENDPOINT}
    tools:
      search_tool:
        version: '1.0'
        type: bing
        description: 'Web search via Azure AI Foundry'
        connection_ids:
          - ${env:BING_CONNECTION_ID}
        config:
          project_endpoint: ${ref:tools.ai_foundry.default_project_endpoint}

# Agents combine models and tools for specific tasks
agents:
  search-agent:
    version: '1.0'
    name: 'Search Agent'
    description: 'AI agent for web searches'
    model:
      name: gpt-4
      temperature: 0.5
    tools:
      - ${ref:tools.ai_foundry.tools.search_tool}
    platform: azure_openai
    system_prompt:
      version: '1.0'
      path: prompts/search-agent.md

🔧 Core Components

1. ConfigLoader

The main entry point for loading and validating configurations.

from app.agents_config.config_loader import ConfigLoader

# Load from YAML file
config = ConfigLoader.load_from_file("config.yaml")

# Load from dictionary
config_dict = {"version": "1.0", "models": {...}}
config = ConfigLoader.load_from_dict(config_dict)

# Validate environment variables
missing_vars = ConfigLoader.validate_environment_variables(config)
if missing_vars:
    print(f"Missing environment variables: {missing_vars}")

# Create example configuration
ConfigLoader.save_example_config("example-config.yaml")

2. Environment Variable Substitution

Automatically resolves ${env:VAR_NAME} patterns in your configuration:

models:
  gpt-4:
    config:
      api_key: ${env:AZURE_OPENAI_KEY} # → Resolves to actual API key
      endpoint: ${env:AZURE_OPENAI_ENDPOINT} # → Resolves to actual endpoint

import os
os.environ["AZURE_OPENAI_KEY"] = "your-api-key"
os.environ["AZURE_OPENAI_ENDPOINT"] = "https://your-endpoint.com"

config = ConfigLoader.load_from_file("config.yaml")
# Environment variables are automatically resolved

3. Internal Reference Resolution

Use ${ref:path.to.value} to reference other parts of your configuration:

shared_config:
  database_url: 'postgresql://localhost:5432/mydb'
  timeout: 30

models:
  gpt-4:
    config:
      database_url: ${ref:shared_config.database_url} # → References shared value
      timeout: ${ref:shared_config.timeout} # → References shared value

4. Working with Models

# Get all available models
model_names = config.list_models()

# Get specific model
model = config.get_model("gpt-4")
if model:
    print(f"Provider: {model.provider}")
    print(f"ID: {model.id}")
    print(f"Config: {model.config}")
    print(f"Parameters: {model.params}")

5. Working with Agents

# Get all available agents
agent_names = config.list_agents()

# Get specific agent
agent = config.get_agent("search-agent")
if agent:
    print(f"Name: {agent.name}")
    print(f"Description: {agent.description}")
    print(f"Model: {agent.model.name}")
    print(f"Tools: {agent.tools}")
    print(f"System Prompt: {agent.system_prompt.path}")

6. Tool Configuration

OpenAPI Tools

tools:
  openapi:
    weather_api:
      schema_path: 'tools/openapi/weather.json'
      version: '1.0'
      headers:
        Authorization: ${env:WEATHER_API_KEY}
        Content-Type: 'application/json'

Azure AI Foundry Tools

tools:
  ai_foundry:
    default_project_endpoint: ${env:AZURE_AI_FOUNDRY_ENDPOINT}
    tools:
      bing_search:
        version: '1.0'
        type: bing
        description: 'Bing search integration'
        connection_ids:
          - ${env:BING_CONNECTION_ID}
        config:
          project_endpoint: ${ref:tools.ai_foundry.default_project_endpoint}

🏗️ Programmatic Configuration

Create configurations in code for dynamic setups:

from app.agents_config.ai_config import AIConfig
from app.agents_config.model_config import ModelConfig
from app.agents_config.agent_config import AgentConfig

# Create model configuration
model = ModelConfig(
    provider="azure_openai",
    id="gpt-4",
    version="1.0",
    config={
        "api_key": "your-api-key",
        "endpoint": "https://your-endpoint.com",
        "deployment": "gpt-4-deployment"
    },
    params={
        "temperature": 0.7,
        "max_tokens": 4000
    }
)

# Create agent configuration
agent = AgentConfig(
    version="1.0",
    name="My Agent",
    description="A programmatically created agent",
    model={"name": "gpt-4", "temperature": 0.5},
    tools=[],
    platform="azure_openai",
    system_prompt={
        "version": "1.0",
        "path": "prompts/my-agent.md"
    }
)

# Create full configuration
config = AIConfig(
    version="1.0",
    models={"gpt-4": model},
    tools={},
    agents={"my-agent": agent}
)

🔍 Validation and Error Handling

The library provides comprehensive validation with clear error messages:

try:
    config = ConfigLoader.load_from_file("config.yaml")
except ValueError as e:
    print(f"Configuration validation failed: {e}")
    # Shows detailed validation errors:
    # - Missing required fields
    # - Invalid data types
    # - Cross-reference validation failures

Example validation error output:

Configuration validation failed: 6 validation errors for AIConfig
models.gpt-4.version
  Field required [type=missing, input_value={'provider': 'azure_openai'...}, input_type=dict]
models.gpt-4.config
  Input should be a valid dictionary [type=dict_type, input_value='not_a_dict', input_type=str]
agents.my-agent.system_prompt
  Field required [type=missing, input_value={'name': 'My Agent'...}, input_type=dict]

🌍 Environment Variables

Required Environment Variables

Set these environment variables for the example configuration:

# Azure OpenAI
export AZURE_OPENAI_KEY="your-azure-openai-key"
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"

# Azure AI Foundry
export AZURE_AI_FOUNDRY_PROJECT_ENDPOINT="https://your-project.azure.ai/"

# Tool Connections
export BING_SEARCH_CONNECTION_ID="your-bing-connection-id"
export OPOINT_API_CONNECTION_ID="your-opoint-connection-id"
export OPENAPI_OPOINT_API_KEY="your-opoint-api-key"

Environment Variable Validation

# Check for missing environment variables before deployment
missing_vars = ConfigLoader.validate_environment_variables(config)
if missing_vars:
    raise EnvironmentError(f"Missing required environment variables: {missing_vars}")

📁 Project Structure

agents-config/ ├── app/ │ ├── agents_config/ # Main library package │ │ ├── init.py │ │ ├── ai_config.py # Main configuration class │ │ ├── agent_config.py # Agent configuration models │ │ ├── base.py # Base mixins for env/ref resolution │ │ ├── config_loader.py # Configuration loading utilities │ │ ├── config_models.py # Common configuration models │ │ ├── model_config.py # AI model configuration │ │ └── tool_config.py # Tool configuration models │ └── ai-config/ # Example configuration │ ├── ai-config.yaml # Main configuration file │ ├── prompts/ # System prompt templates │ └── tools/ # Tool schema definitions ├── main.py # Demo script ├── pyproject.toml # Project configuration └── README.md # This file

## 🛠️ Advanced Usage

### Custom Tool Integration

```python
from app.agents_config.tool_config import ToolsConfig

# Define custom tools
custom_tools = ToolsConfig(
    openapi={
        "my_api": {
            "schema_path": "tools/my_api.json",
            "version": "1.0",
            "headers": {
                "Authorization": "${env:MY_API_KEY}"
            }
        }
    }
)

Multi-Environment Configuration

# config/production.yaml
version: '1.0'
models:
  gpt-4:
    config:
      endpoint: ${env:PROD_OPENAI_ENDPOINT}
      api_key: ${env:PROD_OPENAI_KEY}

# config/development.yaml
version: '1.0'
models:
  gpt-4:
    config:
      endpoint: ${env:DEV_OPENAI_ENDPOINT}
      api_key: ${env:DEV_OPENAI_KEY}

import os

# Load environment-specific configuration
env = os.getenv("ENVIRONMENT", "development")
config_path = f"config/{env}.yaml"
config = ConfigLoader.load_from_file(config_path)

Configuration Composition

# Base configuration
base_config = ConfigLoader.load_from_file("base-config.yaml")

# Environment-specific overrides
env_config = ConfigLoader.load_from_file(f"config/{env}.yaml")

# Merge configurations (implement your merge logic)
final_config = merge_configurations(base_config, env_config)

🧪 Testing

import pytest
from app.agents_config.config_loader import ConfigLoader

def test_configuration_loading():
    """Test that configuration loads successfully."""
    config = ConfigLoader.load_from_file("test-config.yaml")
    assert config.version == "1.0"
    assert "gpt-4" in config.models
    assert len(config.agents) > 0

def test_environment_variable_substitution():
    """Test environment variable resolution."""
    import os
    os.environ["TEST_API_KEY"] = "test-key-123"

    config_dict = {
        "version": "1.0",
        "models": {
            "test-model": {
                "provider": "azure_openai",
                "config": {"api_key": "${env:TEST_API_KEY}"}
            }
        }
    }

    config = ConfigLoader.load_from_dict(config_dict)
    model = config.get_model("test-model")
    assert model.config["api_key"] == "test-key-123"

🚀 Deployment

Docker Integration

FROM python:3.12-slim

# Install dependencies
COPY pyproject.toml .
RUN pip install -e .

# Copy configuration
COPY config/ /app/config/
COPY prompts/ /app/prompts/

# Set environment variables
ENV ENVIRONMENT=production
ENV AZURE_OPENAI_ENDPOINT=https://your-endpoint.com

# Run your application
CMD ["python", "your_app.py"]

Kubernetes Configuration

apiVersion: v1
kind: ConfigMap
metadata:
  name: ai-config
data:
  ai-config.yaml: |
    version: '1.0'
    models:
      gpt-4:
        provider: azure_openai
        config:
          endpoint: ${env:AZURE_OPENAI_ENDPOINT}
          api_key: ${env:AZURE_OPENAI_KEY}
---
apiVersion: v1
kind: Secret
metadata:
  name: ai-secrets
data:
  AZURE_OPENAI_KEY: <base64-encoded-key>
  AZURE_OPENAI_ENDPOINT: <base64-encoded-endpoint>

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Run the test suite: pytest
5. Commit your changes: git commit -m 'Add amazing feature'
6. Push to the branch: git push origin feature/amazing-feature
7. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙋‍♂️ Support

• Issues: GitHub Issues
• Documentation: This README and inline code documentation
• Examples: See main.py for comprehensive usage examples

🔄 Changelog

v0.1.0

• Initial release
• Pydantic V2 configuration models
• Environment variable substitution
• Internal reference resolution
• OpenAPI and Azure AI Foundry tool support
• Comprehensive validation and error handling

---

Built with ❤️ for the AI development community