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LangGraph Integration

Real-Time State Protection for LangGraph Workflows

Scan each node's state for conversation history and intent, then assign a security classification that routes your graph's flow for real-time protection against multilingual, multimodal, and multi-turn attacks at sub-99ms latency, for safe and reliable multi-agent workflows.

How it works

Protect LangGraph Workflows with WonderFence in minutes

WonderFence integrates directly into LangGraph workflows, inspecting graph state, enforcing policy-based routing, and protecting multi-agent systems in production.

Inspect Graph State in Real Time
Route Risky Actions for Review
Protect Multi-Agent Workflows
Technical Documentation
langchain_integration.py
from wonderfence import Guardrails
from langchain.agents import Agent

guardrails = Guardrails(
policies=["prompt_injection", "pii_detection", "tool_misuse"],
mode="enforce",
log_violations=True
)

agent = Agent("customer-support-langchain")

response = guardrails.run(
agent=agent,
input=user_message,
context={"user_role": "customer", "channel": "web_chat"}
)

return response.safe_output
Stay on your toes

What WonderFence adds to LangGraph Framework

LangGraph Framework gives you the agent. WonderFence gives you the controls — dynamic guardrails, granular policies, and full runtime visibility on top of what LangGraph Framework ships by default.

Policies That Match Your Business

LangGraph Framework's default guardrails are generic. WonderFence lets you enforce policies that reflect your compliance rules, brand voice, and risk tolerance — preconfigured options out of the box, or write your own. Apply them organization-wide or per-agent.

Defense Against Prompt Injection and Tool Misuse

Stop jailbreaks, data exfiltration, and tool abuse before they hit your databases, APIs, or customers. WonderFence catches malicious patterns that get past LangGraph Framework's default guardrails.

Visibility Into What Agents Actually Do

LangGraph Framework shows you what you built. WonderFence shows you what your agents are doing in production — every prompt, tool call, and output, plus drift, policy violations, and unexpected behavior as it happens.
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Integration FAQ’s

Do our existing platform guardrails cover us for HIPAA compliance?

Platform guardrails don't generate HIPAA audit evidence or enforce institution-specific PHI controls. WonderFence logs every interaction and enforces customizable policies at the interaction layer — built for what regulators actually require.

What happens to our compliance posture when the model gets updated?

WonderCheck supports ongoing adversarial evaluations at scheduled intervals, catching behavioral drift and PHI exposure regressions as models change. Your compliance posture doesn't reset after every update.

How do we validate clinical AI before go-live without slowing down our deployment timeline?

WonderBuild integrates into existing CI/CD workflows and returns prioritized findings against your specific clinical use cases so your team knows what to fix before launch, not after.

Who owns AI governance when it sits across clinical, IT, and compliance teams?

WonderSuite surfaces prioritized clinical risk findings for product teams, evidentiary logs for compliance, and lifecycle-wide visibility for the CISO. So, governance doesn't have to live in one place to work.