Usage

Basic Example

from oju.agent import Agent

# Initialize and use an agent
response = Agent(
    agent_name="example_agent",  # Name of the agent (must correspond to a prompt file)
    model="gpt-4",              # Model to use
    provider="openai",           # Provider: 'openai', 'claude', or 'gemini'
    api_key="your-api-key-here", # API key for the provider
    prompt_input="Hello, how are you?"  # User input
)
print(response)

Using Different Providers

OJU supports multiple LLM providers, with plans to continuously add more in the future. Here’s how to use the currently supported providers:

# Using OpenAI
response = Agent(
    agent_name="example_agent",
    model="gpt-4",
    provider="openai",
    api_key="your-openai-key",
    prompt_input="Your prompt here"
)

# Using Anthropic Claude
response = Agent(
    agent_name="example_agent",
    model="claude-3-opus-20240229",
    provider="claude",
    api_key="your-anthropic-key",
    prompt_input="Your prompt here"
)

# Using Google Gemini
response = Agent(
    agent_name="example_agent",
    model="gemini-pro",
    provider="gemini",
    api_key="your-google-key",
    prompt_input="Your prompt here"
)

Note

The OJU team is continuously working to add support for more LLM providers. Check the latest documentation or release notes for updates on newly added providers.

Using Multiple Agents

You can run multiple agents in parallel for more complex workflows:

from concurrent.futures import ThreadPoolExecutor
from oju.agent import Agent

def run_agent(agent_config):
    return Agent(**agent_config)

# Define multiple agent configurations
agent_configs = [
    {
        'agent_name': 'research_agent',
        'model': 'gpt-4',
        'provider': 'openai',
        'prompt_input': 'Research the latest AI trends',
        'temperature': 0.7
    },
    {
        'agent_name': 'summarizer_agent',
        'model': 'claude-3-opus-20240229',
        'provider': 'claude',
        'prompt_input': 'Summarize this research',
        'temperature': 0.3
    },
    {
        'agent_name': 'critic_agent',
        'model': 'gemini-pro',
        'provider': 'gemini',
        'prompt_input': 'Provide constructive criticism',
        'temperature': 0.5
    }
]

# Run agents in parallel
with ThreadPoolExecutor() as executor:
    results = list(executor.map(run_agent, agent_configs))

# Process results
for i, result in enumerate(results):
    print(f"Agent {i+1} output:")
    print(result)
    print("-" * 50)

Environment Variables

You can set your API keys as environment variables:

# For OpenAI
export OPENAI_API_KEY='your-api-key-here'

# For Anthropic
export ANTHROPIC_API_KEY='your-api-key-here'

# For Google Gemini
export GOOGLE_API_KEY='your-api-key-here'

Or use a .env file in your project root:

OPENAI_API_KEY=your-api-key-here
ANTHROPIC_API_KEY=your-api-key-here
GOOGLE_API_KEY=your-api-key-here