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1 change: 1 addition & 0 deletions docs/docs.json
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"edge/en/observability/opik",
"edge/en/observability/patronus-evaluation",
"edge/en/observability/portkey",
"edge/en/observability/highflame",
"edge/en/observability/weave",
"edge/en/observability/truefoundry"
]
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75 changes: 75 additions & 0 deletions docs/edge/en/observability/highflame.mdx
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---
title: "Highflame Integration"
description: "Add runtime AI security guardrails to your CrewAI agents with Highflame Shield."
icon: "shield-check"
mode: "wide"
---

# Highflame

[Highflame](https://highflame.ai) provides runtime AI security guardrails for agents —
prompt injection detection, sensitive-information / PII & DLP, content safety, and
agentic tool safety — organized around the [OWASP LLM Top 10](https://genai.owasp.org/llm-top-10/)
and enforced by its **Shield** engine.

The CrewAI integration registers Highflame on CrewAI's event bus (via
`BaseEventListener`), so every LLM call and tool call your crew makes is evaluated
against your Highflame application policy — no changes to your agents or tasks.

## Setup

<Steps>
<Step title="Install">
```bash
pip install 'highflame[crewai]'
```
</Step>
<Step title="Get a Highflame API key">
Create a service key (`hf_sk_...`) in the [Highflame console](https://studio.highflame.ai)
and configure which guardrails are active in your application policy.
</Step>
<Step title="Wrap your crew">
```python
from crewai import Crew
from highflame import Highflame
from highflame.integrations.crewai import HighflameCrewHooks

client = Highflame(api_key="hf_sk_...") # or set HIGHFLAME_API_KEY

crew = Crew(agents=[...], tasks=[...])

# Guards every LLM + tool call the crew makes.
with HighflameCrewHooks(client, mode="enforce"):
result = crew.kickoff()
```
</Step>
</Steps>

## Modes

`HighflameCrewHooks(client, mode=...)` accepts:

| Mode | Behavior |
|---|---|
| `enforce` | Block on a policy violation (default). |
| `monitor` | Allow and log — useful for tuning before enforcement. |
| `alert` | Allow and route to your alert pipeline. |
| `modify` | Redact sensitive content (e.g. PII) and continue. |

## What it catches

Which guardrails run is controlled by your Highflame **application policy**
(configured in the console), so coverage stays consistent across every place you
use Highflame. Capabilities map to the OWASP LLM Top 10:

- **LLM01 Prompt Injection** — jailbreaks and injection attempts in prompts and tool output.
- **LLM02 Sensitive Information Disclosure** — PII, secrets, and DLP.
- **LLM06 Excessive Agency** — risky tool calls, tool poisoning, command/SQL/path injection.
- **LLM09 Misinformation** — hallucination / groundedness.
- **Content safety** — toxicity and harmful-content moderation.

<Note>
Cross-turn context (cumulative risk, action sequences) is tracked per crew run via
session IDs automatically. See [docs.highflame.ai](https://docs.highflame.ai) for the
full guardrail catalog and policy configuration.
</Note>