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🚀 AgentStack

Configuration

Development

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AgentStack is a production-grade AI agent platform built on Mastra, delivering 57 enterprise tools, 25+ specialized agents, 10+ workflows, 12+ supervisor networks, 105 UI components (50+ AI Elements + 55+ base), and A2A/MCP orchestration for scalable AI systems. Features supervisor networks with delegation hooks, workspace management (AgentFS/Daytona/Local), TanStack Query integration, and LibSQL-backed persistence for agents, workspaces, supervisor networks, and auth. Focuses on financial intelligence, RAG pipelines, enterprise observability, secure governance, and AI chat interfaces.

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🎯 Why AgentStack?

AgentStack bridges the gap between basic AI chatbots and enterprise-grade multi-agent orchestration. While other AI agent platforms offer simple automation, AgentStack delivers the observability, security, and scalability required for production deployment.

| Feature | AgentStack | Fleece AI | Botpress | Vellum AI | | ----------------------------- | --------------------------------------------------------------- | ----------- | ---------- | ----------- | | Production Observability | ✅ Real-time traces via TanStack + Langfuse ready | ⚠️ Basic | ⚠️ Basic | ✅ Partial | | Dataset Management | ✅ Full dataset/eval/experiment API with versioning | ❌ None | ❌ None | ⚠️ Basic | | Supervisor Networks | ✅ 12+ coordinator agents with delegation hooks | ❌ None | ❌ None | ❌ None | | Financial Intelligence | ✅ Polygon/Finnhub/AlphaVantage (30+ endpoints) | ❌ None | ❌ None | ❌ None | | RAG Pipeline | ✅ LibSQL HNSW + rerank + graphRAG | ⚠️ Basic | ⚠️ Basic | ✅ External | | Multi-Agent Orchestration | ✅ A2A MCP + supervisor networks (25+ agents) | ✅ Advanced | ✅ Basic | ✅ Partial | | Live Browser Automation | ✅ Local Chrome/CDP browser agent + shared runtime | ⚠️ Basic | ⚠️ Partial | ⚠️ Partial | | Workspaces / Sandboxes | ✅ AgentFS + Daytona + local sandboxes + persistence | ⚠️ Basic | ❌ None | ⚠️ Partial | | Enterprise Security | ✅ Better Auth + RBAC + path traversal protection + HTML sanitization | ⚠️ Partial | ⚠️ Partial | ✅ Partial | | Type Safety | ✅ Zod schemas everywhere (57 tools) | ⚠️ Limited | ⚠️ Limited | ✅ Partial | | UI Components | ✅ 105 components (AI Elements + shadcn/ui) | ✅ 30+ | ✅ 50+ | ✅ 30+ | | Testing | ✅ Vitest + 97% coverage + comprehensive mocks | ⚠️ Partial | ⚠️ Partial | ✅ Partial |

🚀 Production-Ready from Day One

While other AI agent platforms offer basic chatbot functionality, AgentStack provides enterprise-grade multi-agent orchestration:

• Zero-config RAG: LibSQL with 3072D embeddings works out of the box
• Supervisor Networks: 12+ coordinator agents with delegation hooks and scoring
• Workspace Management: AgentFS, Daytona, and local sandboxes with LSP support and LibSQL-backed persistence
• Financial Intelligence: Polygon, Finnhub, AlphaVantage with 30+ endpoints
• Full Observability: Every agent call, tool execution, and workflow step is traced
• Enterprise Security: Better Auth, RBAC, path validation, HTML sanitization, LibSQL session storage

✨ Core Capabilities

• 💰 Financial Intelligence: 30+ tools (Polygon quotes/aggs/fundamentals, Finnhub analysis, AlphaVantage indicators)
• 🔍 Semantic RAG: LibSQL (3072D embeddings) + MDocument chunking + rerank + graph traversal
• 📊 Dataset Management: Full dataset API with versioning, experiments, and evaluation
• 🤖 25+ Agents: Individual specialized agents (research, stock analysis, copywriting, etc.)
• 📋 10+ Workflows: Multi-step orchestrated processes (weather analysis, content creation, financial reports)
• 🌐 12+ Supervisor Networks: Coordinator agents that route tasks to specialized agents using delegation hooks (primary router, coding team, financial intelligence, content creation, etc.)
• 🧭 Live Browser Automation: Shared Chrome/CDP browser runtime for local verification, screenshots, and interaction testing
• 🧩 Workspaces & Sandboxes: AgentFS, Daytona, and local sandbox support with persistent LibSQL-backed state
• 🔌 A2A/MCP: MCP server coordinates parallel agents (research+stock→report), A2A coordinator for cross-agent communication
• 🎨 105 UI Components: AI Elements (50 chat/reasoning/canvas components) + shadcn/ui (55 base primitives)
• 📊 Enterprise Observability: Default tracing + Langfuse integration + 10+ custom scorers + middleware logging
• 🛡️ Enterprise Security: JWT auth, RBAC, path validation, HTML sanitization, secrets masking, middleware protection
• ⚡ Extensible: Model registry (Gemini/OpenAI/Anthropic/OpenRouter), Zod schemas everywhere, MastraClient SDK integration

⚛️ TanStack Query Integration

Production-grade data fetching with comprehensive React hooks:

// lib/hooks/use-mastra-query.ts - 1590+ lines of typed hooks
import { useAgentsQuery } from '@/lib/hooks/use-mastra-query'

export function AgentsDashboard() {
    const { data: agents, isLoading, error } = useAgentsQuery()

    // 15+ specialized hooks for agents, workflows, tools, memory, vectors
    // Automatic caching, background refetching, optimistic updates
    // Type-safe with Zod schemas throughout
}

Key Features:

• 1590+ Lines: Comprehensive hook coverage for all Mastra APIs
• Type Safety: Full TypeScript with Zod schema validation
• Caching: Intelligent cache management with React Query
• Real-time: Automatic background updates and invalidation
• DevTools: Integrated with @tanstack/react-query-devtools

📊 Dataset Management & Evaluation

Complete dataset and evaluation pipeline with versioning and experiments:

// lib/hooks/use-mastra-query.ts - Full dataset API
const { data: datasets } = useDatasets()
const { data: experiments } = useDatasetExperiments(datasetId)

// Dataset operations
const createDataset = useCreateDatasetMutation()
const addItems = useAddDatasetItemsMutation()
const runExperiment = useTriggerDatasetExperimentMutation()

Features:

• Dataset Versioning: Full history tracking and rollback capabilities
• Experiment Management: Compare model performance across datasets
• Evaluation Scorers: Custom scoring functions for quality assessment
• Batch Operations: Efficient bulk data operations
• Type Safety: Full TypeScript support with Zod validation

🔍 Observability & Monitoring

Enterprise-grade observability with easy Langfuse integration:

// src/mastra/index.ts - Default observability setup
observability: new Observability({
  configs: {
    default: {
      sampling: { type: SamplingStrategyType.RATIO, probability: 0.75 },
      spanOutputProcessors: [new SensitiveDataFilter({...})],
      exporters: [new DefaultExporter({...})],
      // Easy Langfuse integration: uncomment and configure
      // exporters: [new LangfuseExporter({...})],
    }
  }
})

Features:

• Default Tracing: Built-in observability with no setup required
• Real-time Trace Viewing: View traces in real-time via TanStack Query hooks
• Langfuse Ready: Drop-in integration for advanced analytics and persistence
• Custom Scorers: 10+ evaluation metrics for agent performance
• Sensitive Data Protection: Automatic redaction of credentials
• Performance Monitoring: Latency, token usage, error tracking

Real-time Trace Monitoring:

// View traces in real-time with TanStack hooks
const { data: traces } = useTraces({ limit: 10 })
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