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Agent Security for n8n workflows and AI Monitoring for enterprise AI usage, deployed in our cloud or fully on-prem.

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CrewAIComing soon

Protect your CrewAI

multi-agent pipelines

Privent will hook into CrewAI at the Agent and Task level as a middleware layer. It intercepts agent context, task memory, tool call arguments, and inter-task payloads, scoring risk before data crosses outside your infrastructure. API preview below. n8n is available today.

Get startedAgent Security overview
How it works

How Privent integrates with CrewAI

  1. 01

    Agent executes task

    A CrewAI agent receives a task, queries tools, and generates responses. Data flows between agents (analyst passes output to executor, researcher feeds into writer), with no default visibility into what crosses each boundary.

  2. 02

    Privent middleware intercepts

    The middleware hook fires before each agent's external call. It reads agent context, task memory, tool call arguments, and inter-task payloads. ACARS scores the payload, accounting for what the entire crew has processed.

  3. 03

    Data protected, crew continues

    APE applies the appropriate transformation stage. The downstream agent or tool receives a safe, semantically coherent payload. Task completion is unaffected. Audit trail is recorded.

Integration

Drop-in integration

Add Privent to your existing CrewAI pipeline without changing your architecture. One addition. Full graph-state visibility.

No proxy
No sidecar
Zero orchestration changes
crewai_pipeline.py
from privent import privent_middleware
from crewai import Agent, Task, Crew
 
@privent_middleware(policy="finance") # ← wrap your agent
class DataAnalystAgent(Agent):
role = "Senior Data Analyst"
goal = "Analyze financial reports"
backstory = "Expert in financial data analysis"
 
crew = Crew(
agents=[DataAnalystAgent()],
tasks=[analysis_task],
)
Runtime visibility

What Privent reads at runtime

External gateways receive only the final prompt string. Privent reads everything in context, in real time.

01
Agent contextThe full context passed to each agent in the crew at execution time
02
Task memoryAccumulated memory from previous tasks fed into the current agent
03
Tool call argumentsArguments passed to every tool invoked by any agent in the crew
04
Inter-task payloadsData passed between agents: analyst → executor, researcher → writer
05
Agent role contextRole-specific context that shapes each agent's behavior and outputs
Use cases

Built for CrewAI teams

01

Cross-agent data propagation

Prevents sensitive data discovered by one agent from propagating to the next agent in the crew chain, without breaking task continuity.

02

Tool output contamination

Detects when tool responses (database results, API responses) contain sensitive fields that would be passed into the next agent's context.

03

Strategic content in analyst output

Flags proprietary business logic, M&A parameters, or pricing strategy embedded in analyst agent outputs before they reach external LLMs.

Get started

Add Privent to your CrewAI pipeline

We integrate in under 30 minutes. No orchestration changes required. Your pipelines keep running. Privent keeps watching.

Get startedRead the docs