본문으로 건너뛰기

Quick Start

Get up and running with FluxLoop in 5 minutes.

Step 1: Create a Project​

Initialize a new FluxLoop project:

fluxloop init project --name my-agent
cd fluxloop/my-agent

This creates:

fluxloop/my-agent/
├── configs/
│   ├── project.yaml          # Project metadata
│   ├── input.yaml            # Input generation settings
│   ├── simulation.yaml       # Experiment configuration
│   └── evaluation.yaml       # Evaluation rules
├── .env                      # Environment variables
├── examples/
│   └── simple_agent.py       # Sample agent
├── experiments/              # Results output
├── inputs/                   # Generated inputs
└── recordings/               # Recorded arguments

Step 2: Instrument Your Agent​

Edit your agent code and add the @fluxloop.agent() decorator:

# examples/my_agent.py
import fluxloop

@fluxloop.agent()
def run(input_text: str) -> str:
    """Your agent logic"""
    # Process the input
    result = process_input(input_text)
    return result

Step 3: Configure LLM Provider (Optional)​

If you want to generate inputs using LLM:

# Set OpenAI API key
fluxloop config set-llm openai sk-your-api-key --model gpt-4o-mini

# Or set in .env file
echo "OPENAI_API_KEY=sk-your-api-key" >> .env

Edit configs/input.yaml to define personas and base inputs:

personas:
  - name: novice_user
    description: A user new to the system
    
base_inputs:
  - input: "How do I get started?"
    expected_intent: help

Step 4: Generate Inputs​

Create input variations:

fluxloop generate inputs --limit 50

This produces inputs/generated.yaml with 50 variations.

Step 5: Run Experiment​

Execute your agent with all generated inputs:

fluxloop run experiment

Results are saved to experiments/my_agent_experiment_YYYYMMDD_HHMMSS/.

Step 6: View Results​

Parse the results into human-readable format:

fluxloop parse experiment experiments/my_agent_experiment_*/

This creates Markdown timelines under per_trace_analysis/.

What You Get​

After running an experiment, you'll have:

  • summary.json - Aggregate statistics
  • trace_summary.jsonl - Per-trace summaries
  • traces.jsonl - Detailed execution traces
  • observations.jsonl - Observation stream
  • per_trace_analysis/ - Human-readable timelines

Next Steps​

Congratulations! 🎉 You've run your first FluxLoop experiment.