How to Use the Cerbos (Access Control) MCP in LangChain

Q: Can LangChain run batch authorization checks using Cerbos (Access Control)?

Yes. Your agent uses the authzenevaluations tool to verify permissions for multiple resources in a single call. This prevents network overhead from making separate requests for every item.

Q: Can I trace Cerbos policy checks in LangSmith?

Absolutely. Every tool execution, including checkresources and listpolicies, generates a standard trace in LangSmith. You see the exact payload sent to Cerbos and the latency of the check.

Run multi-step access control checks and policy updates directly inside your LangChain reasoning loops.

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Connect Cerbos (Access Control) MCP to LangChain

Create your Vinkius account to connect Cerbos (Access Control) to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cerbos (Access Control) with LangChain

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Chain live policy updates in LangChain

The `add_policy` tool lets your agent modify access control rules on the fly during a run. When a developer requests a new role or permission change, LangChain chains this tool with `add_schema` to validate the structure before applying the policy to your Cerbos instance. You get full visibility into this chain through LangSmith. Every call to `update_policy` or `enable_policy` gets logged with exact inputs and latency, letting you debug why a specific access rule changed during an agentic run.

Multi-step permission evaluations

The `check_resources` tool evaluates whether a user can perform specific actions on your application resources. Your LangChain agent handles this by feeding the output directly into subsequent steps, letting it block or allow downstream tool execution based on the authorization result. If a bulk action is requested, the agent switches to `authzen_evaluations` to run batch checks. This keeps your decision logic centralized in Cerbos while your agent manages the execution flow.

Audit policies using this Cerbos MCP Server

The `list_audit_logs` tool exposes the historical record of access decisions directly to your agent. When debugging a failed request, the LangChain agent calls this tool to inspect raw access logs and pinpoint exactly which policy caused the denial. It combines this with `get_policy` to retrieve the active ruleset for comparison. Having these tools in a single MCP Server means your debugging chain doesn't need custom API wrapper code to inspect system state.

Setup guide

Set up Cerbos (Access Control) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Cerbos (Access Control) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "cerbos-access-control-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Cerbos (Access Control) transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cerbos. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cerbos (Access Control) MCP in LangChain

Install the adapter package using pip, then initialize the client with your Vinkius server URL. Call get_tools on the client and pass them directly to your LangChain agent constructor.

Yes. Your agent uses the `authzen_evaluations` tool to verify permissions for multiple resources in a single call. This prevents network overhead from making separate requests for every item.

The agent receives validation errors directly from tools like `add_schema` or `update_policy`. It then uses its reasoning loop to correct the payload structure and retry the call.

Absolutely. Every tool execution, including `check_resources` and `list_policies`, generates a standard trace in LangSmith. You see the exact payload sent to Cerbos and the latency of the check.

They stay inside your isolated V8 sandbox on Vinkius. This MCP Server processes your Cerbos policies, schemas, and audit logs locally, never sending your raw access control rules to third-party servers.

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