archestra-ai/archestra

Features

• MCP Server Management - Coordinates the deployment and management of multiple Model Context Protocol servers to scale tool access across an organization.
• AI Agent Deployments - Provides a platform for packaging and deploying specialized AI agents to Kubernetes clusters using Helm charts.
• Agent Gateways - Exposes autonomous agent workflows as secure, production-ready API endpoints through a unified gateway.
• Agent System Prompts - Provides structured definitions and instructions to establish the role and behavioral constraints for specialized AI agents.
• Cluster Deployments - Allows running Model Context Protocol servers in a cluster to provide external tools and data sources for LLMs.
• AI Guardrails - Implements security layers that inspect and filter AI requests to prevent prompt injection and data exfiltration.
• Retrieval-Augmented Generation - Implements a retrieval-augmented generation pipeline to ground language model responses using relevant documents from a search index.
• MCP Lifecycle Orchestration - Coordinates the deployment and lifecycle of Model Context Protocol servers to provide a scalable toolset.
• Model Context Protocol Servers - Implements standardized interfaces to connect AI models to external tools and data sources via Model Context Protocol servers.
• Orchestrators - Provides a platform to deploy and manage a cluster of Model Context Protocol servers for LLM tool access.
• Enterprise AI Security - Ships an enterprise security suite for managing access control, audit logging, and prompt injection guardrails for AI deployments.
• Role-Based Access Control - Provides role-based permissions and identity provider integration to control access to agents and infrastructure.
• Prompt-Based Data Exfiltration Prevention - Implements deterministic guardrails to prevent data exfiltration and prompt injection in AI agent workflows.
• Security Guardrails - Intercepts tool invocations and prompt flows to apply security rules that prevent data exfiltration and prompt injection.
• Knowledge Base Retrieval - Implements a knowledge base system to store and retrieve enterprise data for context-aware AI responses.
• AI Monitoring - Provides observability tools to track language model calls, performance, and costs of AI agents in a cluster.
• Network Restrictions - Blocks outbound connections from server pods to private IP ranges to prevent request forgery attacks.
• Private AI Infrastructure - Provides infrastructure for hosting and managing agentic workflows on private, self-hosted Kubernetes clusters.
• Private Server Registries - Maintains a controlled, private catalog of approved protocol servers for internal organizational use.
• Hybrid Vector and Full-Text Search - Combines vector similarity search with full-text indexing to improve the accuracy of knowledge retrieval.
• Knowledge Base Management - Provides a full RAG stack for ingesting, chunking, embedding, and searching documents within a semantic knowledge base.
• Hybrid Retrieval - Combines dense vector similarity search with traditional full-text indexing to improve knowledge base retrieval accuracy.
• ChatOps Platforms - Integrates AI agents with messaging platforms and email via secure tunnels to enable chat-based interactions.
• Kubernetes Deployments - Installs as a self-hosted instance within private networks using Kubernetes, Helm, and Terraform.
• Kubernetes Application Deployments - Uses Helm charts and Terraform providers to deploy and scale system components as pods in Kubernetes.
• Application Sandboxing - Provides isolated execution environments for protocol servers to prevent unauthorized system resource access.
• Enterprise Identity Providers - Integrates with external identity providers and role-based access systems to control user permissions.
• AI Cost Monitoring - Sets budget limits per team and uses dynamic model selection to track and reduce operational AI spending.
• AI Agent Behavior Monitors - Tracks AI agent performance and decision-making using metrics, traces, and dashboards for behavior visibility.
• Telemetry Exporters - Forwards distributed traces and performance metrics to external monitoring endpoints via OpenTelemetry.

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