v0.1.0 Public Beta

Validate Agents at Scale.
Ship with Confidence.

The Loop-in-the-Loop AI Development Platform. Simulate, Evaluate, and Trust Your AI Agents.

fluxloop-cli — -zsh — 80x24
agent.py
# 1. Define your agent
@fluxloop.agent()
deftravel_agent(msg):
response = llm.chat(msg)
returnresponse

# 2. Define evaluation
@fluxloop.eval("politeness")
defcheck_tone(output):
...
Terminal
$ fluxloop run experiment
Running 50 traces...
[====>......]45%

Results:
✔ 42 Passed
✘ 8 Failed
Report saved to ./experiments/run_01.json

Deploy with Confidence

Everything you need to build reliable agents

FluxLoop provides the tooling infrastructure to take your AI agents from prototype to production with confidence.

Simulate at Scale

Run thousands of realistic multi-turn scenarios in parallel. Find edge cases before production.

Align to Your Standards

Capture your implicit decision criteria. Turn intuition into automated evaluation.

Act on Insights

Reports that show what to fix and how. Analysis that drives action.

Offline-First

Run experiments on your machine without cloud dependencies. Full control over your data.

Decorator-Based

Instrument existing agent code with minimal changes—just add @fluxloop.agent().

Version Control

Track every experiment run, configuration, and result. Reproducibility built-in.

How it works

Three simple steps to production-grade agents.

01

Instrument

Add a single decorator to your agent function. No complex setup required.

@fluxloop.agent()
02

Simulate

Define scenarios and run them offline. Test edge cases and happy paths alike.

$ fluxloop run experiment
03

Evaluate

View structured reports, analyze failures, and iterate with confidence.

Pass Rate: 98% | Latency: 200ms