archestra-ai/archestra
Archestra
Archestra is a platform for enterprise AI agent deployment and Model Context Protocol orchestration. It provides a centralized system for configuring specialized agents with specific system prompts and toolsets, and managing the deployment of Model Context Protocol servers that provide large language models with external tools and data sources.
The system features an AI agent gateway that exposes configured agents as networked services for external clients and integrated development environments. It incorporates a security suite that provides deterministic guardrails to prevent prompt injection and data exfiltration, alongside application sandboxing and network restrictions for protocol servers.
The platform includes a retrieval-augmented generation system with hybrid vector-text search for knowledge base management. It covers broader operational capabilities including role-based access control, enterprise identity integration, distributed telemetry export via OpenTelemetry, and operational cost management through budget limits.
The infrastructure is designed for self-hosting within private networks using Kubernetes, with deployment supported via Helm charts and Terraform providers.
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.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
