Production-Ready AI Agents in Minutes.

:debuggable>compliant=reliable

Arc simulates 1000+ real-world scenarios to uncover risks and optimize costs before they reach production.

Building reliable AI agents is complex. We're making evaluation easier.

$ pip install arc-eval
✓ MIT Licensed📦 v0.2.8🐍 Python 3.9+

Start improving your agents today with our open-source, developer-first tool.

Generate compliance scenarios, identify inference bottlenecks, and get model recommendations with every run.

arc-eval-demo
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What makes an agent 'reliable'?

An AI agent impresses in demos. In production, it's often a different story. Unexpected failures emerge and inference costs spiral, making reliability and cost optimization critical for enterprise deployment.

AI Agent Agnostic

Works with LangChain, CrewAI, AutoGen, or any custom JSON output.

Enterprise Ready

400+ regulatory scenarios and cost profiling. Audit-ready reports with TCO analysis.

Instant Optimization

Reliability validation and cost recommendations in <60s. Zero config.

See it in Action

Curious? Try Arc instantly with sample data. No local setup, no API keys needed. Just one command to see reliability issues and cost optimizations:

$ arc-eval compliance --domain finance --quick-start

How Arc Works

Arc combines academic research with real world testing scenarios and cost optimization.

1

AI Agent Analysis

Automatically detects your AI agent's input/output format and profiles inference costs and performance optimizations across different scenarios.

2

Scenario Generation & Cost Profiling

Generates 400+ compliance test cases while identifying bottlenecks and recommending optimal models for cost reduction.

3

Reliability + Cost Optimization

Delivers reliability scores, compliance reports, and specific recommendations to increase reliability and reduce inference costs.

4

Continuous Learning Loop

Create custom synthetic scenarios on-the-fly and retrain your agents in a continuous improvement loop for optimal performance.

$ arc-eval analyze --agent my_agent.py
🔍 Detected: LangChain ReAct Agent
📊 Generated: 47 test scenarios
⚡ Running evaluations...
📈 Results Summary:
✅ Safety Score: 94%
🎯 Accuracy: 87%
🛡️ Robustness: 82%
📋 Compliance: 91%

Ready to Build More Reliable AI Agents?

Explore our documentation, check out examples, or contribute on GitHub. Arc is open-source and built for the developer community. Let's make AI agents better, together.

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