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AI-powered analysis finds misconfigurations, risky tool access, missing guardrails, and other issues. Automatically triages false positives so you see real problems, not noise.
Requirements: Python 3.13 or laterSupported frameworks: Google ADK, Google GenAI, Anthropic, OpenAI, OpenAI Agents SDK, LangGraph, CrewAI, AutoGen, HuggingFace Transformers, HuggingFace smolagents
1

Install `flintai-cli`

Using a virtual environment is recommended to avoid dependency conflicts:
Install Flint AI CLI:
2

Configure your LLM provider

flintai-cli uses AI to read your agent code contextually and filter false positives. Run the interactive setup and select your LLM:
You’ll be prompted to select a provider (Gemini, OpenAI, Anthropic, or LiteLLM), choose a model, and enter your API key. Your configuration is saved to ~/.flintai/.env.
Start free. Google Gemini offers a free tier with generous limits — test flintai-cli with no API costs.
3

Scan your Python agent code

Run the scan:
Example output:
flintai scan discovers agents in your codebase — you may find agents you didn’t know existed. Results are saved to scan_<timestamp>.json.
Integrate with CI/CD. Save scan_<timestamp>.json as a build artifact to prove validation before deployment. Learn how →

Next steps

Issues found?

Understand severity scores and what needs fixing before deployment

Clean scan?

Get a 0.0-1.0 reliability score for agent runtime behavior