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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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LangGraphComing soon

Secure your

LangGraph pipelines

PriventSecurityNode will embed directly into your StateGraph between tool-call nodes. It reads the full graph state (messages, tool calls, accumulated context) and applies ACARS + APE before data reaches any external model. One node. Zero pipeline changes. API preview below. n8n is available today.

Get startedAgent Security overview
How it works

How Privent integrates with LangGraph

  1. 01

    Agent executes, tool calls fire

    Your LangGraph workflow runs. Tool calls happen. The agent accumulates context across multiple reasoning steps, pulling from internal systems, chaining LLM calls, composing prompts autonomously.

  2. 02

    PriventSecurityNode intercepts

    Inserted between tool-call nodes and the external model, PriventSecurityNode reads state["messages"], state["tool_calls"], and full graph state. ACARS computes a 6-signal risk score against the agent's entire runtime context.

  3. 03

    APE transforms, graph continues

    Based on risk level, APE applies tokenization, semantic substitution, or structural decomposition. The external model receives a coherent, safe version. The graph continues. No state corruption. No workflow disruption.

Integration

Drop-in integration

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

No proxy
No sidecar
Zero orchestration changes
langgraph_pipeline.py
from privent import PriventSecurityNode
from langgraph.graph import StateGraph, END
 
graph = StateGraph(AgentState)
graph.add_node("agent", agent_node)
graph.add_node("tools", tool_node)
graph.add_node("privent", PriventSecurityNode()) # ← one line
 
graph.add_edge("agent", "tools")
graph.add_edge("tools", "privent")
graph.add_edge("privent", END)
Runtime visibility

What Privent reads at runtime

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

01
state["messages"]Full message history across all reasoning steps and LLM calls
02
state["tool_calls"]Every tool invocation with arguments before execution
03
state["context"]Accumulated runtime context from all prior graph nodes
04
Full graph stateComplete StateGraph state object, not just the current node
05
Decision historyAgent reasoning chain across multi-step orchestration
Use cases

Built for LangGraph teams

01

PII leakage through tool arguments

Detects when customer names, emails, or national IDs are passed to external LLM calls via tool arguments, and masks them before transmission.

02

Credential exposure in prompts

Catches API keys, connection strings, and auth tokens that accumulate in agent context during multi-step workflows.

03

Cross-node context aggregation risk

Monitors the behavioral velocity signal and flags when individually low-risk payloads aggregate to a high-risk pattern across graph nodes.

Get started

Add Privent to your LangGraph pipeline

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

Get startedRead the docs