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Find misconfigurations, risky tool access, missing guardrails, and other issues in your Python agent code. An LLM does the analysis and triages false positives, so you see real problems, not noise.

Switch Trust CLI on GitHub

Source code, example agents, and issue tracking
Install our MCP server in Claude Code or your AI code assistant, then ask: “Help me set up Switch Trust Scan” to get live guidance, troubleshoot issues, and work through these steps together. Learn how →

Scan your Python agent code

1

Verify requirements

Check that Switch Trust CLI and OpenGrep are installed:
See OpenGrep installation for more options.
Using a virtual environment is recommended to avoid dependency conflicts:
Install Switch Trust CLI:
Full installation guide →
Bring your own LLM key. Switch Trust Scan uses AI reasoning to validate findings, so it needs an API key for your own LLM provider (Gemini, OpenAI, Anthropic, or LiteLLM). This is your provider key for the local CLI, not a Switch Trust API token. switch-trust init prompts for it and stores it locally in ~/.switch-trust/.env.
2

Scan your agent

Point to your agent directory and launch the scan:
No agent of your own yet? Clone switch-trust-cli and scan a bundled example from the repo root:
The repo ships two examples: bookstore_agent (OpenAI Agents SDK) and weather_agent (Google ADK).
Switch Trust Scan only analyzes Python files with supported framework imports. See supported frameworks →
Results are saved to scan_<timestamp>.json, or scan_<timestamp>.sarif if you pass --format sarif. See Scan results for details on understanding findings and severity scores.
Integrate with CI/CD. Save scan results as build artifacts to prove validation before deployment. See CI/CD integration guide →

Clean scan

Clean scan output The scan detected an OpenAI Agents SDK agent, analyzed 1 Python file, and found no security issues. Tools ran in sequence: static analyzers (bandit, opengrep, detect-secrets, pip-audit) followed by AI reasoning to validate results.

Scan with findings

Scan with findings output The scan detected an OpenAI Agents SDK agent and found 2 security issues:
  • High severity (CVSS 9.0): Missing authentication on agent endpoint
  • Medium severity (CVSS 6.9): Unbounded agent execution loop
After static analysis, the AI reasoning layer identified these issues, and triage confirmed them as real findings.

Next steps

Issues found?

Understand severity scores and what needs fixing before deployment

How scanning works

Learn how AI reasoning finds real issues and filters noise

Eval agent behavior

Get a 0.0-1.0 reliability score for runtime behavior