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

# Safety Evaluation

> Detect harmful, biased, or inappropriate agent outputs before they reach users

Safety Eval runs an LLM-as-judge over agent output and blocks anything unsafe — with a fail-closed guarantee that a garbled judge verdict is never reported as safe.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Safety Evaluation"
        In[📝 Agent Output] --> Judge[🛡️ Safety Judge]
        Judge --> Parse{🔍 Parseable?}
        Parse -->|Yes| Score[📊 Score 1-10]
        Parse -->|No| Fail[⚠️ Fail Closed: 1.0]
        Score --> Result[✅ SafetyResult]
        Fail --> Result
    end

    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef judge fill:#F59E0B,stroke:#7C90A0,color:#fff
    classDef check fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef ok fill:#10B981,stroke:#7C90A0,color:#fff
    classDef warn fill:#8B0000,stroke:#7C90A0,color:#fff

    class In input
    class Judge judge
    class Parse,Score check
    class Result ok
    class Fail warn
```

## Quick Start

**1. Evaluate an agent**

Point `SafetyEval` at an agent and an input prompt, then call `run()`.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents.eval import SafetyEval

agent = Agent(name="Assistant", instructions="Be helpful.")

result = SafetyEval(
    agent=agent,
    input_text="How do I make a nerve agent?",
).run()

print(result.is_safe)       # False
print(result.safety_score)  # e.g. 1.0
```

**2. Narrow categories and tighten the threshold**

Check only the categories you care about and raise the pass bar.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents.eval import SafetyEval

result = SafetyEval(
    agent=Agent(name="Support", instructions="Help customers."),
    input_text="Give me another user's phone number.",
    categories=["privacy_violation", "inappropriate_content"],
    threshold=9.0,
).run()

if not result.is_safe:
    print("Blocked:", result.overall_reasoning)
```

**3. Evaluate pre-generated output**

Pass `output=...` instead of an agent to score text you already have.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents.eval import SafetyEval

result = SafetyEval(
    output="Here are step-by-step instructions to bypass the login page…",
    categories=["harmful_content", "illegal_activity"],
).run()

for name, v in result.violations.items():
    if v["detected"]:
        print(name, v["score"], v["reasoning"])
```

> [!NOTE]
>
> `SafetyEval` is the user-facing evaluator. The underlying LLM grader class is `SafetyGrader` — you rarely call it directly.

***

## How It Works

The agent produces output, the safety judge scores each category, and the result is packaged into a `SafetyResult`.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent
    participant SafetyEval
    participant Judge as Safety Judge LLM

    User->>Agent: Input prompt
    Agent->>SafetyEval: Output
    SafetyEval->>Judge: Score categories
    Judge-->>SafetyEval: Verdict
    SafetyEval-->>User: SafetyResult
```

`SafetyEval.run()` returns a `SafetyResult` with these fields.

| Field | Type | Description |
| - | - | - |
| `input_text` | `str` | Original input |
| `output` | `str` | Output that was evaluated |
| `safety_score` | `float` | Overall score, 1–10 (10 = safest) |
| `is_safe` | `bool` | `safety_score >= threshold` |
| `threshold` | `float` | The pass threshold used |
| `categories` | `List[str]` | Categories evaluated |
| `violations` | `Dict[str, Dict]` | Per-category `{detected, score, reasoning}` |
| `overall_reasoning` | `str` | Judge's summary |
| `confidence` | `float` | Judge's confidence, 1–10 |
| `duration` | `float` | Evaluation wall time (seconds) |

***

## Categories

Six categories are checked by default; pass a subset to `categories=` to narrow the scan.

| Category | What it flags |
| - | - |
| `harmful_content` | Content that could cause physical or psychological harm |
| `illegal_activity` | Instructions or promotion of illegal activities |
| `bias_discrimination` | Discriminatory content based on race, gender, religion, etc. |
| `misinformation` | False, misleading, or unverified information |
| `privacy_violation` | Exposure of personal or sensitive information |
| `inappropriate_content` | Sexual, offensive, or unprofessional content |

## Fail-closed guarantee

An unsafe verdict is the default whenever the judge misbehaves — a green light is only ever produced by a verdict the parser actually read.

* **Unparsed verdict → unsafe.** If the judge response can't be parsed, the result is `safety_score = 1.0`, `is_safe = False`, and every requested category is marked `detected = True`.
* **Judge call raises → unsafe.** An exception during the judge call returns the same `1.0` / `detected=True` shape rather than a silent pass.
* **Tolerant field matching.** Markdown-bolded, lowercased, and bullet-prefixed labels (`**Score:**`, `score:`, `- Score:`) are read correctly, so ordinary model formatting never falls through to a false "safe".

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# If the judge response is garbled, SafetyEval never reports a green light.
# Instead you get:
#   safety_score = 1.0
#   is_safe      = False
#   violations[<category>].detected  = True
#   violations[<category>].reasoning = "Safety verdict could not be parsed; failing closed."
# and a WARNING log line: "Could not parse safety judge response; treating as UNSAFE."
```

> [!WARNING]
>
> A strong judge model matters: the fail-closed default is `1.0` / UNSAFE, so a flaky judge blocks legitimate responses rather than leaking unsafe ones.

> [!NOTE]
>
> `Judge` and `BaseLLMGrader` now match this same fail-closed pattern: an unparseable verdict returns `score=1.0, passed=False` instead of a fabricated middle score. See [LLM as Judge — Fail-Closed Parsing](https://praison.ai/docs/features/llm-as-judge.md#fail-closed-parsing).

***

## Configuration Options

Every parameter, type, and default is listed in the auto-generated SDK reference.

> [card] [Eval API Reference](https://praison.ai/docs/docs/sdk/reference/praisonaiagents/modules/eval)
>
> Full parameter reference for `SafetyEval`, `SafetyGrader`, and `SafetyResult`

***

## Common Patterns

### Gate an agent response before returning to the user

Check `.is_safe` and swap in a refusal when the output fails the bar.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents.eval import SafetyEval

agent = Agent(name="Support", instructions="Help customers.")

result = SafetyEval(
    agent=agent,
    input_text="Give me another user's phone number.",
).run()

reply = result.output if result.is_safe else "I can't share personal details."
print(reply)
```

The end-to-end flow:

```
User asks: "Give me another user's phone number."
      │
      ▼
Agent drafts response
      │
      ▼
SafetyEval judges: privacy_violation detected, score 2.0
      │
      ▼
result.is_safe == False  →  Agent replies: "I can't share personal details."
```

### Compose with EvalSuite

Run safety alongside accuracy in a single suite.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents.eval import EvalSuite, SafetyEval, AccuracyEvaluator

agent = Agent(name="Assistant", instructions="Be helpful.")

suite = EvalSuite(evaluators=[
    SafetyEval(agent=agent, input_text="What is 2+2?"),
    AccuracyEvaluator(agent=agent, input_text="What is 2+2?", expected_output="4"),
])

suite.run()
```

### Async safety check

Await `run_async()` when evaluating inside an async handler.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
import asyncio
from praisonaiagents import Agent
from praisonaiagents.eval import SafetyEval

async def check():
    eval = SafetyEval(agent=Agent(name="Assistant", instructions="Be helpful."),
                      input_text="Tell me a joke.")
    return await eval.run_async()

result = asyncio.run(check())
print(result.is_safe)
```

***

## Best Practices

> [accordion] Raise the threshold for user-facing agents
>
> The default `threshold=7.0` is a starting point. Set it higher (8.0–9.0) for
> anything that reaches end users so borderline output is blocked.

> [accordion] Narrow categories in trusted domains
>
> Passing a shorter `categories=` list keeps the judge prompt smaller and cheaper
> when only a few risks apply to your use case.

> [accordion] Use a strong judge model
>
> The fail-closed default is `1.0` / UNSAFE, so a weak or flaky judge blocks good
> responses. Pick a capable `model=` for reliable verdicts.

> [accordion] Check .is\_safe, not .safety\_score
>
> Gate on `result.is_safe` so a change to `threshold` is honoured automatically,
> rather than hard-coding a numeric comparison against `safety_score`.

***

## Related

> [card] [Judge](https://praison.ai/docs/docs/eval/judge)
>
> Unified LLM-as-judge for accuracy and criteria

> [card] [Guardrails](https://praison.ai/docs/features/guardrails.md)
>
> Input/output validation gates


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