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This guide explains how to set up and configure AI agents for PyCatan.
## π Table of Contents
1. [Quick Start](#quick-start)
2. [API Key Setup](#api-key-setup)
3. [Configuration System](#configuration-system)
4. [Security Best Practices](#security-best-practices)
5. [Development Workflow](#development-workflow)
---
## π Quick Start
### Step 1: Get an API Key
You need an API key from an LLM provider. We recommend **Google Gemini** for development:
1. Go to [Google AI Studio](https://aistudio.google.com/app/apikey)
2. Click "Create API Key"
3. Copy your API key
**Other providers:**
- OpenAI: https://platform.openai.com/api-keys
- Anthropic: https://console.anthropic.com/settings/keys
### Step 2: Setup Environment Variables
```bash
# Copy the template file
cp .env.example .env
# Edit .env and paste your API key
# (Use any text editor)
```
Example `.env` file:
```bash
GEMINI_API_KEY=AIzaSyC_your_actual_api_key_here
```
### Step 3: Install Dependencies
```bash
pip install pyyaml
```
### Step 4: Test the Configuration
```bash
python examples/test_ai_config.py
```
You should see:
```
π All tests passed!
```
---
## π API Key Setup
### Where API Keys Are Stored
**API keys are stored in the `.env` file**, which is:
- β
**NOT** committed to Git (protected by `.gitignore`)
- β
Stored locally on your machine only
- β
Easy to update without changing code
### `.env` File Structure
```bash
# .env - YOUR PRIVATE FILE (never commit this!)
GEMINI_API_KEY=your_actual_key_here
OPENAI_API_KEY=your_openai_key_here # Optional
ANTHROPIC_API_KEY=your_anthropic_key # Optional
```
### `.env.example` File
The **`.env.example`** file is a template that:
- β
**IS** committed to Git
- β
Shows what variables are needed
- β
Contains NO actual secrets
- β
Helps other developers know what to set up
**Never put real API keys in `.env.example`!**
---
## βοΈ Configuration System
### Overview
PyCatan uses a **two-file configuration system**:
```
π Project Root
βββ .env # β NOT in Git - API keys
βββ .env.example # β
IN Git - Template
βββ pycatan/ai/
β βββ config_example.yaml # β
IN Git - Documentation
β βββ config_dev.yaml # β
IN Git - Dev defaults
β βββ my_agent_config.yaml # β NOT in Git - Your custom config
```
### File Types Explained
#### 1. `.env` - Environment Variables (API Keys)
- **Purpose:** Store sensitive API keys
- **Git:** β **NEVER** committed
- **Contains:** `GEMINI_API_KEY=actual_secret_key`
- **Who creates it:** Each developer on their machine
#### 2. `.env.example` - Environment Template
- **Purpose:** Template showing what variables are needed
- **Git:** β
Committed to Git
- **Contains:** `GEMINI_API_KEY=` (empty)
- **Who creates it:** Already created in the project
#### 3. `config_dev.yaml` - Development Defaults
- **Purpose:** Default configuration for development
- **Git:** β
Committed to Git
- **Contains:** All settings EXCEPT API keys
- **Who uses it:** Everyone, automatically
#### 4. `config_example.yaml` - Configuration Documentation
- **Purpose:** Complete documentation of all options
- **Git:** β
Committed to Git
- **Contains:** All possible settings with explanations
- **Who uses it:** Reference when creating custom configs
#### 5. Your Custom Config (e.g., `my_agent_config.yaml`)
- **Purpose:** Your personal agent configuration
- **Git:** β NOT committed (optional)
- **Contains:** Custom personality, strategies, etc.
- **Who uses it:** You, when you want special behavior
---
## π Security Best Practices
### β
DO:
- Keep your `.env` file local only
- Use different API keys for development and production
- Rotate your API keys regularly
- Review `.gitignore` to ensure `.env` is excluded
- Use `.env.example` as a template for team members
### β DON'T:
- Never commit `.env` to Git
- Never put API keys in configuration YAML files
- Never share your `.env` file
- Never hardcode API keys in Python code
- Never put API keys in commit messages or PR descriptions
### Checking Security
```bash
# Make sure .env is ignored by Git
git status
# .env should NOT appear in the list
# If it does, remove it:
git rm --cached .env
```
---
## π οΈ Development Workflow
### Using the Default Development Config
The simplest way - just use `config_dev.yaml`:
```python
from pycatan.ai.config import AIConfig
# Loads config_dev.yaml by default
config = AIConfig.from_file('pycatan/ai/config_dev.yaml')
# API key is automatically loaded from .env
api_key = config.get_api_key() # Reads GEMINI_API_KEY from .env
```
### Creating a Custom Agent
1. **Copy the example config:**
```bash
cp pycatan/ai/config_example.yaml my_aggressive_agent.yaml
```
2. **Edit your config:**
```yaml
agent_name: "Aggressive Trader"
agent:
personality: "aggressive"
risk_tolerance: 0.8
trade_willingness: 0.9
```
3. **Use it in your code:**
```python
config = AIConfig.from_file('my_aggressive_agent.yaml')
```
4. **The API key is still loaded from `.env`** - you don't need to specify it!
### Multiple Agents with Different Personalities
```python
# Agent 1: Aggressive
config1 = AIConfig.from_file('configs/aggressive.yaml')
# Agent 2: Defensive
config2 = AIConfig.from_file('configs/defensive.yaml')
# Agent 3: Balanced (default)
config3 = AIConfig.from_file('pycatan/ai/config_dev.yaml')
# All three use the same API key from .env
```
---
## π Configuration Options
### LLM Settings
```yaml
llm:
provider: "gemini" # or "openai", "anthropic"
model_name: "gemini-2.0-flash-exp"
temperature: 0.7 # 0.0 = deterministic, 1.0 = creative
max_tokens: 4096
```
### Agent Personality
```yaml
agent:
personality: "balanced" # aggressive, defensive, balanced, trading
risk_tolerance: 0.5 # 0.0 = safe, 1.0 = risky
# Strategic focus (0.0 to 1.0)
focus_on_settlements: 0.6
focus_on_cities: 0.7
focus_on_roads: 0.5
focus_on_dev_cards: 0.6
# Trading behavior
trade_willingness: 0.5 # 0.0 = never, 1.0 = always
trade_fairness: 0.7 # How fair are trades
```
### Debug Settings
```yaml
debug:
debug_mode: true # Enable detailed logging
log_prompts: true # Log prompts sent to LLM
log_responses: true # Log LLM responses
save_game_states: true # Save states for analysis
```
---
## π§ͺ Testing Your Setup
### Test 1: Check API Key
```python
from pycatan.ai.config import AIConfig
config = AIConfig()
try:
api_key = config.get_api_key()
print("β API key loaded successfully")
except ValueError as e:
print(f"β Error: {e}")
```
### Test 2: Load Configuration
```python
config = AIConfig.from_file('pycatan/ai/config_dev.yaml')
print(f"β Loaded config for: {config.agent_name}")
print(f" Provider: {config.llm.provider}")
print(f" Model: {config.llm.model_name}")
```
### Test 3: Run Full Test Suite
```bash
python examples/test_ai_config.py
```
---
## π― Common Scenarios
### Scenario 1: "I just cloned the repo"
1. Create `.env`: `cp .env.example .env`
2. Add your API key to `.env`
3. Run tests: `python examples/test_ai_config.py`
### Scenario 2: "I want to test different personalities"
Use the existing configs:
- `config_dev.yaml` - Balanced agent
- Or create custom configs based on `config_example.yaml`
### Scenario 3: "I want to switch LLM providers"
Edit your config file:
```yaml
llm:
provider: "openai"
model_name: "gpt-4-turbo-preview"
api_key_env_var: "OPENAI_API_KEY"
```
Add the key to `.env`:
```bash
OPENAI_API_KEY=sk-your-key-here
```
### Scenario 4: "My API key changed"
Just edit `.env` - no code changes needed!
---
## π Next Steps
- Read [AI_ARCHITECTURE.md](AI_ARCHITECTURE.md) for system design
- Read [WORK_PLAN.md](WORK_PLAN.md) for development roadmap
- See [config_example.yaml](../pycatan/ai/config_example.yaml) for all options
---
## π Troubleshooting
### "ModuleNotFoundError: No module named 'yaml'"
```bash
pip install pyyaml
```
### "API key not found"
1. Check `.env` exists in project root
2. Check the API key variable name matches
3. Try: `cat .env` to verify content
### "Configuration file not found"
- Use absolute paths or run from project root
- Check file extension (`.yaml` not `.yml`)
### "Still stuck?"
- Check [GitHub Issues](your-repo-url/issues)
- Review the test output: `python examples/test_ai_config.py`
---
**Last Updated:** January 3, 2026
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