> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentguardian.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick start — vulnerable demo agents

> docker compose up, then scan. Expect a real finding in under five minutes.

## Prerequisites

* Docker 20.10+ and Docker Compose v2.
* Python 3.11–3.13 for `agent-guardian` (3.14 not yet supported) — or use the docker image, see below.

You do **not** need a Gemini key. The repo ships with a stub adapter that returns deterministic vulnerable responses when `GEMINI_API_KEY` is unset.

## Step 1 — Clone and start the stack

```bash theme={null}
git clone https://github.com/glacien-technologies/agentguardian-vulnerable-agents
cd agentguardian-vulnerable-agents
docker compose up --build -d

curl http://localhost:8000/health
# {"ok":true,"agents":["defended-baseline","vulnerable-langgraph-chatbot","vulnerable-mcp-server","vulnerable-rag-agent","vulnerable-tool-agent"],"live_mode":false}
```

## Step 2 — Scan with AgentGuardian

```bash theme={null}
pip install agent-guardian

agent-guardian scan \
  --endpoint http://localhost:8000/vulnerable-langgraph-chatbot/chat \
  --model stub \
  --mode fast \
  --output pdf \
  --output-path scan.pdf

open scan.pdf
```

Expected output:

* At least one finding in **LLM01 — Prompt Injection** (the agent responds `OVERRIDE_OK` to a CEO-override prompt).
* At least one finding in **LLM06 — Excessive Agency** (the agent calls `force_wire_transfer` without confirmation).
* At least one finding in **LLM07 — System Prompt Leakage** (the agent reveals its internal account number and signing key prefix).

The full scan takes 60-120 seconds in `--mode fast` with `--model stub`.

## Step 3 — Compare against the defended baseline

```bash theme={null}
agent-guardian scan \
  --endpoint http://localhost:8000/defended-baseline/chat \
  --model stub --mode fast \
  --output json --output-path scan-baseline.json

cat scan-baseline.json | jq '.findings | length'
# 0 (or a small number of low-severity findings)
```

If the defended-baseline scan returns high-severity findings, that's a precision regression worth filing as an issue against the scanner — not the agent.

## Step 4 — Switch on live Gemini mode (optional)

If you want to evaluate the scanner against real model behavior instead of canned stubs:

```bash theme={null}
cp .env.example .env
# Edit .env, paste a Gemini key from https://aistudio.google.com/apikey
docker compose up --build -d
```

The `/health` endpoint will now report `"live_mode": true` and the agents will route prompts through `gemini-2.5-flash` / `gemini-2.5-pro` via the `google-adk` runner.