cleanpredict-devguard 0.4.2

Autonomous AI security agent for your codebase

DevGuard

Autonomous AI security agent for your codebase. Runs offensive and defensive analysis — SAST, secrets detection, dependency audit, dynamic testing, auth review — and delivers a structured report with findings, CVSS scores, and ready-to-apply remediations.

Install

pip install cleanpredict-devguard

For GCP Vertex AI support:

pip install cleanpredict-devguard[vertex]

Configuration

DevGuard needs two things: a license key and an LLM provider key.

1. License key

export DEVGUARD_API_KEY=your-api-key    # get yours at https://cleanpredict.com

2. LLM provider (choose one)

DevGuard auto-selects the best available model per provider and falls back to cheaper alternatives if unavailable.

Anthropic (recommended)

export ANTHROPIC_API_KEY=sk-ant-...
# Models: claude-sonnet-4 -> claude-3.5-sonnet -> claude-3-haiku

OpenAI

export OPENAI_API_KEY=sk-...
# Models: gpt-4.1 -> gpt-4o -> gpt-4o-mini

Azure OpenAI

export AZURE_OPENAI_API_KEY=your-key
export AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
# Optional:
export AZURE_OPENAI_DEPLOYMENT=gpt-4o     # your deployment name
export AZURE_OPENAI_API_VERSION=2023-05-15
# Models: gpt-4.1 -> gpt-4o -> gpt-4o-mini (or your deployment)

GCP Vertex AI

export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
export VERTEX_PROJECT=my-gcp-project
# Optional:
export VERTEX_LOCATION=us-central1
export VERTEX_MODEL=gemini-2.5-pro
# Models: gemini-2.5-pro -> gemini-2.5-flash -> gemini-2.0-flash

Groq (cheapest)

export GROQ_API_KEY=gsk_...
# Models: llama-3.1-70b -> llama-3.1-8b

Usage

devguard security ./my-project          # full security analysis
devguard security .                     # current directory
devguard security . --model gpt-4o     # force specific model
devguard security . --no-save           # don't save report file
devguard history ./my-project           # view analysis history
devguard version

What it does

DevGuard runs 5 phases autonomously:

Phase	What runs	Tools used
1. Recon	Detect stack, deps, configs, secrets, git history	list_dir, read_file, find, git log
2. SAST	Static analysis, secrets scan, dependency audit	gitleaks, semgrep, pip-audit, npm audit, trivy
3. Dynamic	Port scan, header analysis, vuln scanning	nmap, OWASP ZAP, nuclei, http requests
4. Auth	JWT, cookies, OAuth, RBAC review	Code reading + analysis
5. Report	Structured markdown with CVSS, CWE, remediations	write_file

Tools are auto-detected. If not installed locally, DevGuard tries Docker. If neither is available, it documents the skipped check.

Output

Generates devguard-report.md in the project root:

# DevGuard Security Report
**Project:** my-app | **Date:** 2025-05-18 | **Stack:** Python + Docker

## Executive summary
The project has 2 critical and 3 medium vulnerabilities...

## Critical findings — CVSS >= 7.0
### [CRITICAL] SQL Injection in /api/users
**CVSS:** 9.8 | **CWE:** CWE-89 | **Tool:** semgrep
**Location:** src/routes/users.py:42
**Remediation:** <ready-to-copy fix>

## Medium findings — CVSS 4.0-6.9
...

Memory between runs

DevGuard remembers findings across analyses. On the second run:

• Shows what was fixed since last analysis
• Shows what's still open (and for how many days)
• Highlights new findings

History is stored in .devguard/devguard.db (add .devguard/ to your .gitignore).

Model fallback

If the best model isn't available on your account, DevGuard automatically tries the next one:

anthropic:  claude-sonnet-4 → claude-3.5-sonnet → claude-3-haiku
openai:     gpt-4.1 → gpt-4o → gpt-4o-mini
azure:      your-deployment → gpt-4.1 → gpt-4o → gpt-4o-mini
vertex:     gemini-2.5-pro → gemini-2.5-flash → gemini-2.0-flash
groq:       llama-3.1-70b → llama-3.1-8b

Override with --model:

devguard security . --model claude-3-haiku-20240307