---LangChain & LlamaIndex MCP Firewall | Panovista
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LangChain & LlamaIndex MCP Firewall

When building agentic workflows with orchestrators like LangChain or LlamaIndex, your application often constructs dynamic runtime graphs. These frameworks allow agents to autonomously choose which tools to execute based on conversational context.

If an agent decides to pull a user profile or a localized file from an MCP tool, it passes that data directly into the LLM context pool. Panovista provides a native, inline L7 firewall that hooks directly into these framework lifecycles, ensuring no unredacted data slips through your loops.

1. Intercepting LLM Completions (Base URL Routing)

To scan and redact standard chat prompts and completions, developers can point the orchestrator’s core LLM client directly to the Panovista sidecar egress port instead of the public vendor API.

LangChain LLM Override (Python)

from langchain_openai import ChatOpenAI

# Point the base URL directly to the Panovista proxy sidecar
model = ChatOpenAI(
    model="gpt-4o",
    openai_api_base="[http://127.0.0.1:4321/v1](http://127.0.0.1:4321/v1)",
    openai_api_key="your-api-key" # Relayed securely by Panovista
)

# Your existing chain execution remains completely unchanged
response = model.invoke("Analyze customer record 90812.")

LlamaIndex LLM Override (TypeScript)

import { OpenAI, Settings } from "llamaindex";

const panovistaLLM = new OpenAI({
  model: "gpt-4o",
  additionalSessionOptions: {
    baseURL: "[http://127.0.0.1:4321/v1](http://127.0.0.1:4321/v1)" // Reroutes chat traffic to the sidecar
  }
});

// Set as the global default for your LlamaIndex application
Settings.llm = panovistaLLM;

2. Intercepting MCP Tool Calls (Tool Guardrails)

In addition to base LLM traffic, Panovista acts as an inline Layer 7 firewall for Model Context Protocol (MCP) tool execution streams. By connecting your framework’s tool adapters to Panovista, outbound data payloads are tokenized before they reach the model.

How-To: LangChain MCP Implementation Guide

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI

async def main():
    # 1. Connect to Panovista's firewall port instead of the raw tool server
    client = MultiServerMCPClient({
        "secure-database": {
            "url": "http://localhost:4321/v1/mcp", 
            "transport": "http"
        }
    })
    
    # 2. Load the proxy-filtered tools
    tools = await client.get_tools()
    
    # 3. Initialize the LangChain agent pointing to the Panovista LLM proxy
    model = ChatOpenAI(model="gpt-4o", openai_api_base="[http://127.0.0.1:4321/v1](http://127.0.0.1:4321/v1)")
    agent = create_agent(model, tools)
    
    # 4. Panovista intercepts and redacts the tool output automatically
    response = await agent.ainvoke({"messages": "Fetch the user profile for ID 9942"})
    print(response)

asyncio.run(main())

Enforced Security Guardrails

By injecting Panovista into your framework pipelines, you instantly gain three layers of enterprise defense: