langchain-enterprise-rbac

Implement role-based access control for LangChain applications with multi-tenant isolation, model access control, and usage quotas. Trigger: "langchain RBAC", "langchain permissions", "langchain access control", "langchain multi-tenant", "enterprise LLM auth".

claude-codecodexopenclaw
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langchain-pack Plugin
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langchain-pack

Claude Code skill pack for LangChain (24 skills)

saas packs v1.0.0
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Installation

This skill is included in the langchain-pack plugin:

/plugin install langchain-pack@claude-code-plugins-plus

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Instructions

LangChain Enterprise RBAC

Overview

Role-based access control for multi-tenant LangChain applications: permission models, model access gating, tenant-scoped vector stores, usage quotas, and FastAPI middleware integration.

Permission Model


// permissions.ts
enum Permission {
  CHAIN_INVOKE = "chain:invoke",
  CHAIN_STREAM = "chain:stream",
  MODEL_GPT4O = "model:gpt-4o",
  MODEL_GPT4O_MINI = "model:gpt-4o-mini",
  MODEL_CLAUDE = "model:claude-sonnet",
  TOOLS_EXECUTE = "tools:execute",
  ADMIN_CONFIG = "admin:config",
  ADMIN_USERS = "admin:users",
}

interface Role {
  name: string;
  permissions: Permission[];
}

const ROLES: Record<string, Role> = {
  viewer: {
    name: "viewer",
    permissions: [Permission.CHAIN_INVOKE, Permission.MODEL_GPT4O_MINI],
  },
  user: {
    name: "user",
    permissions: [
      Permission.CHAIN_INVOKE,
      Permission.CHAIN_STREAM,
      Permission.MODEL_GPT4O_MINI,
      Permission.TOOLS_EXECUTE,
    ],
  },
  power_user: {
    name: "power_user",
    permissions: [
      Permission.CHAIN_INVOKE,
      Permission.CHAIN_STREAM,
      Permission.MODEL_GPT4O_MINI,
      Permission.MODEL_GPT4O,
      Permission.MODEL_CLAUDE,
      Permission.TOOLS_EXECUTE,
    ],
  },
  admin: {
    name: "admin",
    permissions: Object.values(Permission),
  },
};

User and Tenant Models


interface Tenant {
  id: string;
  name: string;
  monthlyTokenLimit: number;
  tokensUsed: number;
  allowedModels: string[];
}

interface User {
  id: string;
  tenantId: string;
  role: string;
  email: string;
}

function hasPermission(user: User, permission: Permission): boolean {
  const role = ROLES[user.role];
  if (!role) return false;
  return role.permissions.includes(permission);
}

function canUseModel(user: User, tenant: Tenant, model: string): boolean {
  const modelPermission = `model:${model}` as Permission;
  return (
    hasPermission(user, modelPermission) &&
    tenant.allowedModels.includes(model)
  );
}

Middleware: Permission Enforcement


// Express middleware
import { Request, Response, NextFunction } from "express";

function requirePermission(permission: Permission) {
  return (req: Request, res: Response, next: NextFunction) => {
    const user = req.user as User;  // set by auth middleware
    if (!user) {
      return res.status(401).json({ error: "Authentication required" });
    }
    if (!hasPermission(user, permission)) {
      return res.status(403).json({
        error: `Missing permission: ${permission}`,
        role: user.role,
      });
    }
    next();
  };
}

// Usage
app.post("/api/chat",
  requirePermission(Permission.CHAIN_INVOKE),
  async (req, res) => {
    const result = await chain.invoke({ input: req.body.input });
    res.json({ result });
  }
);

app.post("/api/chat/stream",
  requirePermission(Permission.CHAIN_STREAM),
  async (req, res) => {
    // streaming endpoint...
  }
);

Model Access Control


import { ChatOpenAI } from "@langchain/openai";
import { ChatAnthropic } from "@langchain/anthropic";

class ModelGateway {
  private models: Record<string, any> = {};

  constructor() {
    this.models["gpt-4o-mini"] = new ChatOpenAI({ model: "gpt-4o-mini" });
    this.models["gpt-4o"] = new ChatOpenAI({ model: "gpt-4o" });
    this.models["claude-sonnet"] = new ChatAnthropic({ model: "claude-sonnet-4-20250514" });
  }

  getModel(modelName: string, user: User, tenant: Tenant) {
    if (!canUseModel(user, tenant, modelName)) {
      throw new Error(
        `User ${user.email} (role: ${user.role}) cannot access model: ${modelName}`
      );
    }

    if (tenant.tokensUsed >= tenant.monthlyTokenLimit) {
      throw new Error(
        `Tenant ${tenant.name} exceeded monthly token limit (${tenant.monthlyTokenLimit})`
      );
    }

    return this.models[modelName];
  }
}

Tenant-Scoped Vector Store


// Isolate each tenant's documents in the vector store
import { PineconeStore } from "@langchain/pinecone";

class TenantScopedRetriever {
  constructor(
    private vectorStore: PineconeStore,
    private tenantId: string,
  ) {}

  async retrieve(query: string, k = 4) {
    // Filter by tenant ID — prevents cross-tenant data leakage
    return this.vectorStore.similaritySearch(query, k, {
      tenantId: { $eq: this.tenantId },
    });
  }

  asRetriever(k = 4) {
    return this.vectorStore.asRetriever({
      k,
      filter: { tenantId: { $eq: this.tenantId } },
    });
  }
}

// When ingesting documents, always tag with tenant ID
async function ingestForTenant(tenantId: string, docs: any[]) {
  const tagged = docs.map((doc) => ({
    ...doc,
    metadata: { ...doc.metadata, tenantId },
  }));
  await vectorStore.addDocuments(tagged);
}

Usage Quota Tracking


class UsageQuotaManager {
  private usage = new Map<string, number>();

  async trackUsage(tenantId: string, tokens: number) {
    const current = this.usage.get(tenantId) ?? 0;
    this.usage.set(tenantId, current + tokens);
  }

  async checkQuota(tenant: Tenant): Promise<boolean> {
    const used = this.usage.get(tenant.id) ?? 0;
    return used < tenant.monthlyTokenLimit;
  }

  async getUsageReport(tenantId: string) {
    return {
      tenantId,
      tokensUsed: this.usage.get(tenantId) ?? 0,
      period: new Date().toISOString().slice(0, 7), // YYYY-MM
    };
  }
}

// Integrate with callback
class QuotaCallback extends BaseCallbackHandler {
  name = "QuotaCallback";

  constructor(
    private quotaManager: UsageQuotaManager,
    private tenantId: string,
  ) { super(); }

  async handleLLMEnd(output: any) {
    const tokens = output.llmOutput?.tokenUsage?.totalTokens ?? 0;
    await this.quotaManager.trackUsage(this.tenantId, tokens);
  }
}

Python FastAPI Equivalent


from fastapi import Depends, HTTPException

async def get_current_user(token: str = Depends(oauth2_scheme)):
    user = decode_jwt(token)
    if not user:
        raise HTTPException(status_code=401)
    return user

def require_permission(permission: str):
    async def checker(user = Depends(get_current_user)):
        if permission not in ROLES[user.role]["permissions"]:
            raise HTTPException(status_code=403, detail=f"Missing: {permission}")
        return user
    return checker

@app.post("/api/chat")
async def chat(input: dict, user = Depends(require_permission("chain:invoke"))):
    return await chain.ainvoke({"input": input["text"]})

Error Handling

Issue Cause Fix
401 Unauthorized Missing or invalid token Check auth middleware, token format
403 Forbidden Insufficient permissions Upgrade user role or add permission
Tenant data leak Missing filter on vector store Always filter by tenantId
Quota exceeded High usage Increase tenant limit or optimize

Resources

Next Steps

Use langchain-data-handling for tenant-scoped RAG pipelines.

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