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Archestra

Routes AI clients through MCP and LLM gateways with registry, orchestration, guardrails, identity, and observability.

Install Archestra

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Open setup steps on GitHub

Overview

Archestra MCP server connects AI clients to MCP tools and LLM providers through a centralized platform. It combines an MCP gateway, private registry, OAuth and on-behalf-of authentication, tool-call guardrails, and an orchestrator for deploying MCP services. Teams can also use its agent runtime, RAG connectors, identity controls, cost limits, and OpenTelemetry-based observability. Reach for Archestra when multiple users, agents, or developer clients need governed access to shared MCP and model infrastructure.

Use cases

  • Centralize access to internal MCP servers for AI clients
  • Route model requests across multiple LLM providers
  • Publish and promote private MCP services across environments
  • Enforce identity, tool-call, egress, and cost policies
  • Trigger governed agents from schedules, email, or webhooks

Key features

  • MCP gateway with OAuth and on-behalf-of authentication
  • LLM gateway with provider routing and cost limits
  • Private MCP registry and Kubernetes-based orchestration
  • Agent runtime with sandboxed code execution
  • Deterministic tool-call guardrails
  • OpenTelemetry traces and Prometheus metrics

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars4.4kStargazers on the source repository.
Last commitTodayMost recent push to the default branch.

How Archestra works

What Archestra MCP server does

Archestra provides a centralized control plane for AI applications, MCP services, LLM providers, and agent workflows. It gives users and developer tools one endpoint for accessing model providers and MCP servers while applying authentication, authorization, policy, and observability controls.

The platform serves both technical and non-technical users. Claude Code, Codex, and Cursor can use its developer portal and proxy, while internal users can interact through chat projects, MCP applications, Slack, Microsoft Teams, or email. Teams can publish internal MCP services through a private registry and promote them across environments.

How it works

The LLM gateway accepts requests for supported providers, including Anthropic, OpenAI, Azure, Bedrock, and DeepSeek. It can apply virtual API keys, cost limits, and dynamic model routing. The MCP gateway exposes tools with OAuth and on-behalf-of authentication, allowing requests to run under the user's identity instead of a shared service account.

Archestra also includes an MCP orchestrator with a Kubernetes operator and self-service promotion between environments. Its agent runtime supports scheduled, email, and webhook triggers, along with sub-agent delegation, reusable skills, sandboxed code execution, and a Kubernetes-native filesystem. Agent and tool activity can be monitored through OpenTelemetry traces, Prometheus metrics, logs, and per-team cost tracking.

Guardrails can evaluate tool calls deterministically and apply protections for the Lethal Trifecta. The platform adds SSO, RBAC with role mapping and team synchronization, secrets management, environment-specific egress policies, and environment-specific cost limits.

Setup and configuration

A local quickstart runs the platform as a Docker container:

Terminal
docker pull archestra/platform:latest

docker run \
  -p 127.0.0.1:9000:9000 -p 127.0.0.1:3000:3000 \
  -e ARCHESTRA_QUICKSTART=true \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -v archestra-postgres-data:/var/lib/postgresql/data \
  -v archestra-app-data:/app/data \
  archestra/platform:latest

After starting the container, open http://localhost:3000. The quickstart mounts Docker access, PostgreSQL data, and application data. Production deployments can use Docker, Helm, or Kubernetes; a Helm chart is recommended for production deployments. Pin an exact image or chart version for production because the latest Docker tag generally points to the newest release, which may be a release candidate.

SSO configuration supports OIDC, SAML, Okta, and Entra. Deployment, secrets, identity, access control, and environment behavior are configured through the platform's administration options and deployment settings.

Tools and capabilities

  • MCP gateway with OAuth and on-behalf-of authentication
  • LLM gateway for multiple model providers
  • Private MCP registry for team-owned services
  • MCP orchestration with Kubernetes operator support
  • Agent runtime with scheduled, email, and webhook triggers
  • Sub-agent delegation and reusable skills
  • Sandboxed code execution
  • RAG knowledge base connectors
  • Tool-call guardrails and Lethal Trifecta protections
  • SSO, RBAC, role mapping, team synchronization, and secrets management
  • Environment-level egress and cost controls
  • OpenTelemetry traces, Prometheus metrics, logs, and team cost tracking
  • Mini app builder for chat-based applications

Limitations and notes

Archestra is distributed under an AGPL 3.0 or Enterprise licensing model. The project maintains stable releases and release candidates; release candidates are built from the main branch and may contain upcoming features. For production use, select a pinned stable image or Helm chart version rather than relying on latest.

The platform requires container-based deployment for the documented quickstart and mounts Docker and persistent storage volumes in that setup. Its broad feature set is oriented toward organizations managing shared AI infrastructure, identity, environments, and policy rather than a minimal single-purpose MCP tool server.

Reviews

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Frequently Asked Questions about Archestra

Archestra is an MCP server that connects AI clients to MCP servers and LLM providers through MCP and LLM gateways. It provides a private MCP registry, tool-call guardrails, identity controls, and agent orchestration for clients such as Claude Code, Codex, and Cursor.

Install Archestra with the documented Docker quickstart, Helm, or Kubernetes deployment options. The quickstart pulls the platform image, exposes ports 9000 and 3000, enables quickstart mode, mounts persistent data, and then makes the interface available at http://localhost:3000.

Yes, Archestra is intended to provide a developer LLM and MCP portal for Cursor. Cursor can connect through the platform's centralized endpoint, where Archestra applies MCP gateway authentication and exposes configured services.

Yes, Archestra's LLM gateway supports providers including Anthropic, OpenAI, Azure, Bedrock, and DeepSeek. It also supports virtual API keys, cost limits, and dynamic model routing.

Yes, Archestra supports Kubernetes deployment through Helm and includes a Kubernetes operator for MCP orchestration. The project identifies Helm as the recommended deployment approach for production.

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