"Hey, test my agent for order cancellation with angry customers"
→ FluxLoop handles the rest: setup, CLI install, synthesis, execution, and analysis.

Your coding agent (Claude Code) orchestrates the entire testing flow. Just describe what you want to test—FluxLoop does the heavy lifting.

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

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

Install the plugin, then just talk.

/plugin install Fluxloop-AI/fluxloop-claude-plugin

That's it. Now say:

"test my agent for refund scenarios"

The Agent Test Skill handles everything:

• ✅ Installs FluxLoop CLI (if needed)
• ✅ Logs you in
• ✅ Creates project/scenario
• ✅ Synthesizes test inputs
• ✅ Runs simulations
• ✅ Analyzes results and suggests fixes

No commands to memorize. No manual setup. Just ask.

User: "Test my chatbot for refund scenarios with frustrated customers"

Agent: Let me set up FluxLoop and run tests...

       ✓ FluxLoop CLI installed
       ✓ Logged in
       ✓ Project created
       ✓ 10 test inputs synthesized (40% hard cases)
       ✓ Running simulation...
       
       📊 Results: 8/10 passed (80%)
       ⚠️ Failed on edge case: customer requesting partial refund
       
       💡 Suggested fix: Add handling for partial refund requests
       
       Would you like me to analyze the failures in detail?

📖 Documentation: docs.fluxloop.ai/claude-code

The primary way to use FluxLoop. Your coding agent orchestrates the entire testing workflow through natural conversation.

📖 Location: packages/fluxloop-plugin/
📖 Docs: docs.fluxloop.ai/claude-code

For power users and CI/CD pipelines. Direct command-line control when you need it.

pip install fluxloop-cli
fluxloop test --scenario my-test

📖 Docs: docs.fluxloop.ai/cli
📦 PyPI: fluxloop-cli

Core instrumentation library. Add @fluxloop.agent() decorator to trace agent execution.

import fluxloop

@fluxloop.agent()
def my_agent(input: str) -> str:
    # Your agent logic
    return response

📖 Docs: docs.fluxloop.ai/sdk
📦 PyPI: fluxloop

Just talk naturally:

"Test my order-bot for cancellation scenarios"
"Generate edge cases for payment failures"
"Why did the last test fail?"

The skill understands context and adapts to your state.

Works with any Python agent framework:

@fluxloop.agent()
def my_agent(input: str) -> str:
    # LangChain, LlamaIndex, custom—anything works
    return response

Define criteria, run reproducible experiments, get actionable insights.

Run experiments locally with full control. No cloud dependency for testing.

FluxLoop combines local execution with cloud intelligence for a powerful testing workflow.

When you say "generate edge cases", FluxLoop Web synthesizes realistic, diverse test data using advanced LLMs. This data is instantly synced to your local environment for testing.

Test results are automatically uploaded to alpha.app.fluxloop.ai for deep inspection:

• 🕵️ Trace Analysis: Step-by-step debugging of agent conversations
• 📈 Performance Metrics: Success rates, latency, token usage trends
• ⚖️ Comparison: Side-by-side view of how recent changes affected behavior

1. You: "Test my agent" (Claude Code)
2. Web: Generates test scenarios (Cloud)
3. CLI: Runs tests locally (Local)
4. Web: Analyzes results (Cloud)
5. You: Review summary in IDE & detailed report on Web

We're building the future of AI agent testing—where your coding agent tests your AI agents.

• Improve agentic workflows: Make the Claude Code skill smarter
• Build framework adapters: LangChain, LlamaIndex, CrewAI
• Enhance synthesis: Better intent-to-input generation
• Develop evaluation methods: Novel agent performance metrics

Check out our contribution guide and open issues.

• Issues: GitHub Issues
• Docs: docs.fluxloop.ai

FluxLoop is licensed under the Apache License 2.0.