How do I install Secure Rag Multi Agent?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Secure Rag Multi Agent need an API key?

No required environment variable is listed on this record.

What transport does Secure Rag Multi Agent use?

Secure Rag Multi Agent runs locally over stdio.

MCP-сервер Secure Rag Multi Agent

Q: When was Secure Rag Multi Agent last checked?

Last checked 2026-10-28. Secure Retrieval-Augmented Generation (RAG) with role-based access control using Mastra AI orchestration, with JWT Secure, also Deep Research built with Cedar & Next.js. Gemini, OpenAI, Openrouter, & Gemini CLI usable. Also exposes tools, agents as MCP Server Also A2A Agent Cards

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Install from the publisher. Not a PlugMCP endpoint.

Запустить на PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Запустить на PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Запуск

Server facts

Local

Transport: stdio
License: NOASSERTION
Last checked: 2026-10-28

Overview

About Secure Rag Multi Agent

Secure Retrieval-Augmented Generation (RAG) with role-based access control using Mastra AI orchestration, with JWT Secure, also Deep Research built with Cedar & Next.js. Gemini, OpenAI, Openrouter, & Gemini CLI usable. Also exposes tools, agents as MCP Server Also A2A Agent Cards

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

🔐 Multi-Agent AI Orchestration System

Logo

A comprehensive multi-agent AI orchestration platform featuring secure Retrieval-Augmented Generation (RAG), advanced research capabilities, voice interactions, MCP integration, and sophisticated memory management using Mastra AI framework.

Overview

A comprehensive multi-agent AI orchestration platform that combines secure Retrieval-Augmented Generation (RAG) with advanced research capabilities, web scraping, content analysis, voice interactions, MCP integration, and sophisticated memory management. Built with enterprise-grade security featuring hierarchical RBAC, document classification, and multi-agent security pipelines to ensure users only access authorized corporate knowledge while enabling powerful AI-driven research and analysis workflows.

Key Features

Hierarchical RBAC: Roles inherit access (public → employee → dept viewer/admin → admin)
Document Classification: Public/internal/confidential with tag-based filtering
Multi-Agent Orchestration: 20+ specialized agents for research, analysis, content creation, and security
Advanced Research: Web scraping, multi-phase research workflows, and content evaluation
Voice Integration: Google Gemini Live voice capabilities for conversational AI
MCP Integration: Model Context Protocol support for enhanced agent capabilities
Secure RAG: Enterprise-grade retrieval with access controls and audit trails
Advanced Memory: Persistent memory with semantic recall and working memory templates
Content Analysis: Learning extraction, evaluation scoring, and quality assessment
Audit-Ready: Citations, logs, and compliance validation throughout

Architecture

flowchart TD
    %% Frontend Layer
    subgraph "🎨 Frontend Layer"
        A[Next.js App Router<br/>React 19 + TypeScript]
        B[Cedar OS Integration<br/>Product Roadmap UI]
        C[React Components<br/>Chat, Auth, Indexing]
        D[Shared Libraries<br/>JWT, MDX, Hooks]
    end
    
    %% Backend Layer  
    subgraph "⚙️ Backend Layer"
        E[Mastra Core<br/>Orchestration Engine]
        F[AI Agents<br/>27+ Specialized Agents<br/>5 Domains]
        G[Workflows<br/>10+ Multi-step Orchestration<br/>Complex Tasks]
        H[Tools & Services<br/>50+ Tools<br/>Business Logic]
        I[API Routes<br/>Chat & Indexing<br/>Streaming]
    end
    
    %% Data Layer
    subgraph "💾 Data Layer"
        J[(PostgreSQL + PgVector<br/>Vector Embeddings<br/>Document Storage)]
        K[(Content Corpus<br/>Markdown Files<br/>RAG Source)]
    end
    
    %% External Services
    subgraph "🔗 External Services"
        L[Google Gemini<br/>AI Models]
        M[OpenAI<br/>LLM Services]
        N[Data Sources<br/>Alpha Vantage, Finnhub<br/>SerpAPI, Polygon]
        O[Academic Sources<br/>ArXiv, Research APIs]
    end
    
    %% Agent Categories (detailed breakdown)
    subgraph "🤖 Agent Categories"
        P1[Governed RAG Agents<br/>identity, retrieve, rerank<br/>answerer, verifier]
        P2[Domain Agents<br/>research, copywriter<br/>cryptoAnalysis, compliance]
        P3[Specialized Agents<br/>productRoadmap, salesIntelligence<br/>stockAnalysis, voiceAgent]
        P4[Orchestration Agents<br/>a2aCoordinator, assistant<br/>editor, evaluation]
    end
    
    %% Workflow Categories
    subgraph "🔄 Workflow Categories"
        Q1[Chat Workflows<br/>Multiple Versions<br/>Real-time Conversation]
        Q2[Analysis Workflows<br/>Financial Analysis V3<br/>Research Workflow]
        Q3[Content Workflows<br/>Content Generation<br/>Report Generation]
        Q4[RAG Workflows<br/>Governed RAG Answer<br/>Governed RAG Index]
    end
    
    %% Tool Categories
    subgraph "🛠️ Tool Categories"
        R1[Data Tools<br/>Alpha Vantage, Finnhub<br/>Polygon, SerpAPI]
        R2[Processing Tools<br/>Document Chunking<br/>PDF Conversion, Web Scraper]
        R3[Analysis Tools<br/>Competitive Intelligence<br/>Compliance Check, Sales Analysis]
        R4[Specialized Tools<br/>JWT Auth, Vector Query<br/>Graph RAG, Roadmap Tool]
    end

    %% Connections
    A --> I
    B --> I
    C --> I
    D --> I
    
    I --> E
    E --> F
    F --> G
    G --> H
    
    H --> J
    H --> K
    J --> H
    K --> H
    
    F --> L
    F --> M
    G --> L
    G --> M
    H --> N
    H --> O
    
    F --> P1
    F --> P2
    F --> P3
    F --> P4
    
    G --> Q1
    G --> Q2
    G --> Q3
    G --> Q4
    
    H --> R1
    H --> R2
    H --> R3
    H --> R4

    %% Styling for Dark Mode
    classDef darkMode fill:#1e1e1e,stroke:#ffffff,stroke-width:2px,color:#ffffff
    class A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R darkMode
    class P1,P2,P3,P4 darkMode
    class Q1,Q2,Q3,Q4 darkMode
    class R1,R2,R3,R4 darkMode
    class sys,web,api,db darkMode
    class sys,web,api,db,auth,sec darkMode
    class auth,policy,retrieve,rerank,answerer,verifier,research,report,copywriter,evaluation,learning,product,editor,self,assistant,starter,voice,mcp,template,network,rag,content darkMode

Research & Analysis Pipeline:

flowchart TD
    %% Research Input
    A[Research Query] --> B[Research Agent<br/>Query Decomposition<br/>Multi-phase Planning]

    %% Initial Research Phase
    B --> C[Web Scraping Tools<br/>Site Mapping, Content Extraction<br/>Batch Processing]

    %% Analysis Phase
    C --> D[Content Analysis<br/>Learning Extraction<br/>Evaluation Scoring]

    %% Follow-up Research
    D --> E[Iterative Research<br/>Follow-up Queries<br/>Depth Analysis]

    %% Synthesis Phase
    E --> F[Content Synthesis<br/>Report Generation<br/>Validation]

    %% Output
    F --> G[Research Report<br/>With Citations & Sources]

    %% Supporting Tools
    subgraph H["Research Tools"]
        I[Web Scraper<br/>Content Extraction]
        J[Evaluator<br/>Quality Assessment]
        K[Learning Extractor<br/>Insight Mining]
        L[Graph RAG<br/>Knowledge Graph]
    end

    %% Tool Integration
    B -.-> I
    C -.-> J
    D -.-> K
    E -.-> L

    %% Styling for Dark Mode
    classDef darkMode fill:#1e1e1e,stroke:#ffffff,stroke-width:2px,color:#ffffff
    class A,B,C,D,E,F,G,H darkMode

Secure RAG Pipeline:

flowchart TD
    %% Input Layer
    A[User Query + JWT] --> B[Identity Agent<br/>JWT Validation & User Context]

    %% Security Layer
    B --> C[Policy Agent<br/>Role-Based Access Control<br/>Document Classification Filtering]

    %% Retrieval Layer
    C --> D[Retrieve Agent<br/>PgVector Similarity Search<br/>Security-Filtered Results]

    %% Processing Layer
    D --> E[Rerank Agent<br/>Relevance Scoring<br/>Context Ranking]

    %% Generation Layer
    E --> F[Answerer Agent<br/>Secure Response Generation<br/>Citation Assembly]

    %% Verification Layer
    F --> G[Verifier Agent<br/>Compliance Validation<br/>PII Detection<br/>Policy Enforcement]

    %% Output Layer
    G --> H[Secure Response<br/>With Citations & Metadata]

    %% Multi-Agent Enhancement
    I[Research Content Network<br/>Multi-Source Analysis] -.-> F
    J[Governed RAG Network<br/>Cross-Agent Coordination] -.-> G

    %% Supporting Components
    subgraph K["Available Tools (12 total)"]
        L[Vector Query Tool<br/>Secure Similarity Search]
        M[Document Chunking Tool<br/>Intelligent Text Splitting]
        N[Web Scraper Tool<br/>Content Extraction]
        O[JWT Auth Tool<br/>Token Validation]
        P[Content Tools<br/>Copywriter, Editor, Evaluation]
        Q[Data Tools<br/>Weather, Roadmap, File Manager]
    end

    %% Tool Usage
    D -.-> L
    D -.-> M
    F -.-> P
    I -.-> N
    I -.-> Q
    B -.-> O

    %% Styling for Dark Mode
    classDef darkMode fill:#1e1e1e,stroke:#ffffff,stroke-width:2px,color:#ffffff
    class A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q darkMode

Getting Started

Prerequisites

Node.js >=20.9.0
Docker and Docker Compose
Git
OpenAI API key

Quick Setup

Clone and install:

git clone https://github.com/ssdeanx/governed-rag-ai.git
cd mastra-governed-rag
npm install

Configure environment:

cp .env.example .env
# Edit .env with your API keys and other settings:
# - OPENAI_API_KEY or GOOGLE_GENERATIVE_AI_API_KEY (required for AI models)
# - SERPAPI_API_KEY (optional, for web search tools - get from https://serpapi.com/manage-api-key)
# - SUPABASE credentials (required for authentication)
# - DATABASE_URL (required for PostgreSQL)

Set up authentication (Supabase):

This application uses Supabase for user authentication. You need to:

a. Create a Supabase project at https://supabase.com/dashboard

b. Get your credentials from Project Settings:
- SUPABASE_URL - Your project URL (e.g., https://xxxxx.supabase.co)
- SUPABASE_ANON_KEY - Your anon/public key
c. Update your .env file with Supabase credentials:
```
SUPABASE_URL="https://your-project.supabase.co"
SUPABASE_ANON_KEY="your_anon_key_here"
NEXT_PUBLIC_SUPABASE_URL="https://your-project.supabase.co"
NEXT_PUBLIC_SUPABASE_ANON_KEY="your_anon_key_here"
```
d. Configure GitHub OAuth (optional):
- In Supabase Dashboard: Authentication > Providers > GitHub
- Add your GitHub OAuth app credentials
- Callback URL: https://your-project.supabase.co/auth/v1/callback
- Update .env with GITHUB_CLIENT_ID and GITHUB_CLIENT_SECRET
📖 See docs/AUTH_ARCHITECTURE.md for detailed authentication documentation.

Start database (optional):

# Only needed if using local PostgreSQL
docker-compose up -d db

Index documents:
```
npm run cli index
```
Start development:
```
npm run dev  # http://localhost:3000
```

AI & Research Capabilities

Multi-Agent Orchestration: 20+ specialized agents for research, analysis, content creation, and security
Advanced Research Workflows: Multi-phase research with web scraping, content evaluation, and learning extraction
Secure Vector Search: Filtered retrieval based on user permissions with PgVector
Content Analysis: Automated learning extraction, quality evaluation, and insight mining
Web Search & Data Collection: SerpAPI-powered Google Search, News, Trends, Shopping (Amazon, Walmart, eBay, Home Depot), Academic (Google Scholar), Finance, and Local Business (Yelp) searches
Web Scraping Tools: Comprehensive web content extraction and processing
Contextual Reranking: Relevance scoring with security constraints
Citation Generation: Source attribution with access verification
Voice Integration: Google Gemini Live voice capabilities for conversational AI
MCP Integration: Model Context Protocol support for enhanced agent tools
Advanced Memory: Persistent memory with semantic recall and working memory templates
Quality Assurance: Automated evaluation scoring and compliance validation
Streaming Responses: Real-time answer generation with SSE

AI Agents

This system employs a comprehensive multi-agent architecture with 20+ specialized agents:

Core RAG Agents

Identity Agent - Validates user authentication and permissions
Policy Agent - Enforces access control and security policies
Retrieve Agent - Performs intelligent document retrieval with security filtering
Rerank Agent - Ranks and scores retrieved documents for relevance
Answerer Agent - Generates secure responses with citations
Verifier Agent - Validates responses for compliance and accuracy

Specialized Agents

Research Agent - Conducts in-depth research and analysis
Report Agent - Generates structured reports and summaries
Copywriter Agent - Creates marketing and communication content
Evaluation Agent - Assesses content quality and relevance
Learning Extraction Agent - Identifies and extracts key learnings
Product Roadmap Agent - Analyzes product strategy and roadmaps
Editor Agent - Reviews and improves content quality
Self-Referencing Agent - Maintains context across conversations
Assistant Agent - Provides general AI assistance
Starter Agent - Handles initial query processing
Voice Agent - Manages voice-based interactions and audio processing
MCP Agent - Integrates with Model Context Protocol tools
Template Reviewer Agent - Reviews and validates templates and workflows

Network Agents

Research Content Network - Orchestrates multi-agent research workflows
Governed RAG Network - Manages secure RAG operations across agents

Each agent follows a single-tool-call policy, ensuring predictable and auditable AI behavior while maintaining security governance throughout the entire pipeline.

Research & Analysis Features

Beyond secure RAG, this system provides comprehensive research and analysis capabilities:

Multi-Phase Research Workflows

Query Decomposition: Breaking down complex research questions into focused search queries
Web Content Extraction: Advanced web scraping with site mapping and content cleaning
Content Evaluation: Automated assessment of information quality and relevance
Learning Extraction: AI-powered identification and extraction of key insights
Iterative Research: Follow-up research based on initial findings
Report Synthesis: Structured report generation with citations and sources

Advanced Analysis Tools

Graph RAG Queries: Complex knowledge graph-based retrieval and analysis
Content Quality Scoring: Automated evaluation using LLM-based scorers
Semantic Memory: Persistent context with working memory templates
Compliance Validation: PII detection and policy enforcement
Multi-Source Synthesis: Combining information from diverse sources

Research Agent Example

// Research agent with multi-phase workflow
const researchAgent = new Agent({
    name: 'Research Agent',
    instructions: `
        PHASE 1: Deconstruct topic into focused search queries
        PHASE 2: Web scraping and content extraction  
        PHASE 3: Content evaluation and learning extraction
        PHASE 4: Follow-up research and synthesis
    `,
    tools: {
        webScraperTool, evaluateResultTool, extractLearningsTool,
        graphRagQueryTool, mdocumentChunker
    },
    memory: pgMemory,
    scorers: {
        relevancy: createAnswerRelevancyScorer(),
        safety: createToxicityScorer()
    }
})

AI Tools

The system includes 12 specialized tools that agents can invoke to perform specific operations:

Core RAG Tools

Vector Query Tool - Performs secure vector similarity searches with access filtering
JWT Auth Tool - Validates and processes JWT tokens for user authentication
Document Chunking Tool - Intelligently splits documents into manageable chunks for indexing with metadata extraction
Graph RAG Query Tool - Executes complex graph-based retrieval augmented generation queries

Content & Analysis Tools

Copywriter Agent Tool - Generates marketing and communication content
Editor Agent Tool - Reviews and improves content quality and clarity
Evaluate Result Tool - Assesses the quality and relevance of generated content
Extract Learnings Tool - Identifies and extracts key insights and learnings
Starter Agent Tool - Handles initial query processing and routing

Research & Data Tools

Web Scraper Tool - Extracts and processes web content for research
Weather Tool - Provides weather data and forecasting capabilities
Roadmap Tool - Analyzes product strategy and roadmap information
Data File Manager - Manages file operations and data processing tasks

Core RAG Workflows

Governed RAG Answer Workflow - Main workflow for secure question answering with citations
Governed RAG Index Workflow - Handles document indexing with classification and security tagging
Chat Workflow - Manages conversational interactions with context preservation

Specialized Workflows

Research Workflow - Conducts comprehensive research operations across multiple sources
Content Generation Workflow - Creates various types of content using multiple agents
Generate Report Workflow - Produces structured reports and analytical summaries
Template Reviewer Workflow - Reviews and validates templates and workflows with claim extraction, planning, and scoring

Supporting Workflows

Chat Workflow Types - Type definitions and utilities for chat operations
Chat Workflow Shared Types - Common types and interfaces for workflow communication

All workflows implement comprehensive error handling, tracing, and security validation at each step, ensuring reliable and auditable AI operations.

AI Services

The system includes 13 specialized services that handle business logic and integrations:

Core Services

Authentication Service - Manages user authentication and session handling
Role Service - Handles role-based access control and permissions
Validation Service - Provides data validation and sanitization
Rate Limiting Service - Implements API rate limiting and throttling

Document & Content Services

Document Indexing Service - Manages document indexing and metadata
Document Processor Service - Processes and transforms documents
Chunking Service - Handles intelligent text chunking strategies
Embedding Service - Manages vector embeddings generation

Data & Storage Services

Vector Query Service - Provides vector similarity search capabilities
Vector Storage Service - Manages vector database operations
Tier Management Service - Handles subscription tiers and feature access

Workflow Services

Workflow Decorators - Provides decorators for workflow enhancement

Data Schemas

The system uses Zod schemas for comprehensive data validation:

Agent Schemas - Validation schemas for all agent inputs and outputs
Workflow Schemas - Type-safe workflow definitions and contracts
API Schemas - Request/response validation for all endpoints

Access Control Policy

Security policies are defined in src/mastra/policy/acl.yaml with hierarchical role-based access:

Role-Based Access Control

admin (100) > dept_admin (80) > dept_viewer (60) > employee (40) > public (10)

Document Access Control

docs:
    - path: './corpus/finance-policy.md'
      allow:
          roles: ['finance.viewer', 'finance.admin']
          tiers: ['pro', 'enterprise']
      tenant: 'acme'
      classification: 'internal'

    - path: './corpus/hr-confidential.md'
      allow:
          roles: ['hr.admin', 'admin']
          tiers: ['enterprise']
      tenant: 'acme'
      classification: 'confidential'

Feature Tiers

Free: Basic RAG capabilities, public docs access
Pro: Internal docs, advanced analytics, custom integrations
Enterprise: Confidential docs, white-label, on-premise deployment
Type-Safe Development: Full TypeScript with Zod schema validation
Hot Reload: Concurrent development for frontend and backend
Comprehensive Testing: Vitest framework with service and workflow tests
Docker Integration: Containerized development environment
Extensive Documentation: Complete API references and guides

Project Structure

mastra-governed-rag/
├── app/                    # Next.js routes and API endpoints
├── components/             # React UI components
├── cedar/                  # Cedar OS interactive components
├── corpus/                 # Sample documents for indexing
├── docs/                   # Comprehensive documentation
├── src/
│   ├── mastra/            # Core Mastra implementation
│   │   ├── agents/        # AI agents (20+ specialized agents for research, analysis, security)
│   │   ├── workflows/     # Orchestrated agent workflows (10 workflows for research, RAG, content)
│   │   ├── tools/         # Reusable agent tools (12 tools for web scraping, analysis, search)
│   │   ├── services/      # Business logic and integrations (13 services)
│   │   ├── networks/      # Multi-agent orchestration networks
│   │   ├── schemas/       # Data validation schemas (Zod)
│   │   ├── config/        # Configuration and external services
│   │   └── policy/        # Access control policies and ACL
│   └── cli/               # Command-line interface
├── lib/                    # Shared utilities and client libraries
└── docker-compose.yml     # Development services

Usage Examples

Research Query with Multi-Phase Analysis

// Advanced research with web scraping and analysis
const researchRequest = {
    question: "What are the latest developments in AI agent orchestration frameworks?",
    researchDepth: 3, // Multi-phase research
    includeWebSources: true,
    evaluationRequired: true
}

// Response includes:
// - Multi-source web research
// - Content evaluation scores
// - Extracted learnings and insights
// - Citations and source validation
// - Structured synthesis report

Secure RAG Query with Authentication

// Generate JWT for a finance viewer
const jwt = generateDemoJWT('finance')

// Query the system
const response = await fetch('/api/chat', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
        jwt,
        question: 'What is the expense reimbursement policy?',
    }),
})

const result = await response.json()
// Returns secure answer with citations

Role-Based Access Demonstration

# Test different user roles
npm run cli query "$(npm run jwt:finance)" "What are expense approval thresholds?"
npm run cli query "$(npm run jwt:hr)" "What is executive compensation policy?"
npm run cli query "$(npm run jwt:public)" "What is our company mission?"

[!NOTE] Each role sees different results based on their access level. See Demo Roles for complete examples.

API Reference

Core Endpoints

POST /api/chat - Secure RAG queries with streaming responses
POST /api/index - Document indexing with classification
GET /api/auth/* - Authentication endpoints

Request/Response Format

// Chat request
{
  "jwt": "eyJhbGciOiJIUzI1NiIs...",
  "question": "What is the company policy on X?"
}

// Streaming response
data: {"content": "According to the policy..."}
data: {"done": true, "citations": [...]}

For complete API documentation, see API Reference.

Development

Local Development

# Start all services
npm run dev  # Frontend + Mastra backend

# Individual services
npm run dev:next   # Next.js only
npm run dev:mastra # Mastra only

Testing

# Run all tests
npm test

# CLI operations
npm run cli index    # Index documents
npm run cli query    # Test queries
npm run cli demo     # Interactive demo

Code Quality

# Linting and formatting
npm run lint
npm run pretty

Deployment

Docker Deployment

# Build and run
docker-compose up -d

Production Considerations

Configure production environment variables
Set up proper JWT secrets
Enable audit logging
Configure PostgreSQL + PgVector for production scale

Security Model

Role Hierarchy

admin (100) > dept_admin (80) > dept_viewer (60) > employee (40) > public (10)

Document Classifications

Public: General information accessible to all
Internal: Department-specific content for employees
Confidential: Highly sensitive data requiring elevated access

Access Control

Policies are defined in src/mastra/policy/acl.yaml with role-based and tier-based access control:

docs:
    - path: './corpus/finance-policy.md'
      allow:
          roles: ['finance.viewer', 'finance.admin']
          tiers: ['pro', 'enterprise']
      tenant: 'acme'
      classification: 'internal'

    - path: './corpus/hr-confidential.md'
      allow:
          roles: ['hr.admin', 'admin']
          tiers: ['enterprise']
      tenant: 'acme'
      classification: 'confidential'

Feature Access Tiers

Free: Basic RAG, public docs
Pro: Internal docs, advanced analytics, custom integrations
Enterprise: Confidential docs, white-label, on-premise deployment

Resources

Troubleshooting

Common Issues

PostgreSQL Connection Failed

# Check PostgreSQL status
docker-compose ps

# Check logs
docker-compose logs db

# Restart database
docker-compose down && docker-compose up -d

Authentication Errors

# Regenerate JWT tokens
npm run jwt:finance  # or other roles

No Search Results

Verify document indexing: npm run cli index
Check user role permissions
Review PgVector collection status

For detailed troubleshooting, see Troubleshooting Guide.

Roadmap

Multi-tenant support
Advanced reranking algorithms
Integration with additional LLM providers
Enhanced audit and compliance features
Performance optimizations for large document sets

Built with ❤️ using Mastra • Next.js • PostgreSQL

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Built with ❤️ by Mastra Community. Questions? Open an issue.

Добавить этот сервер

Host a copy with our keys and logs.

Server facts

About Secure Rag Multi Agent

Tools

Questions

How do I install Secure Rag Multi Agent?

Does Secure Rag Multi Agent need an API key?

What transport does Secure Rag Multi Agent use?

When was Secure Rag Multi Agent last checked?

Documentation

🔐 Multi-Agent AI Orchestration System

Overview

Key Features

Architecture

Getting Started

Prerequisites

Quick Setup

AI & Research Capabilities

AI Agents

Core RAG Agents

Specialized Agents

Network Agents

Research & Analysis Features

Multi-Phase Research Workflows

Advanced Analysis Tools

Research Agent Example

AI Tools

Core RAG Tools

Content & Analysis Tools

Research & Data Tools

Core RAG Workflows

Specialized Workflows

Supporting Workflows

AI Services

Core Services

Document & Content Services

Data & Storage Services

Workflow Services

Data Schemas

Access Control Policy

Role-Based Access Control

Document Access Control

Feature Tiers

Project Structure

Usage Examples

Research Query with Multi-Phase Analysis

Secure RAG Query with Authentication

Role-Based Access Demonstration

API Reference

Core Endpoints

Request/Response Format

Development

Local Development

Testing

Code Quality

Deployment

Docker Deployment

Production Considerations

Security Model

Role Hierarchy

Document Classifications

Access Control

Feature Access Tiers

Resources

Troubleshooting

Common Issues

PostgreSQL Connection Failed

Authentication Errors

No Search Results

Roadmap

Contributing

License