MCP-native Secure AI Platform

Simplify AI usage in your company, providing user-friendly MCP toolbox, observability and control built on a strong security foundation.

For Platform teams:

• Mitigate MCP chaos, move MCP servers from individual machines to a centralized orchestrator
• Manage how MCPs access data and use credentials
• Mitigate data exfiltration risks
• Manage AI costs
• AI Observability

For Developers:

• Deploy your MCP servers org-wide
• Build and deploy agents without worrying about security

For Management:

• Bring 1-click MCP adoption to the whole organization for technical and non-technical users
• Reduce AI costs up to 96%
• Get full visibility on AI adoption, usage and data access

🚀 Quickstart with Docker

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;

Full Quickstart Guide →

🧳 Migrating an existing AI pilot

Already have a Claude Code-style project, MCP config, local tools, hooks, or another hand-rolled agentic PoC? Use the migration kit to turn it into an Archestra pilot.

Migration Kit →

👩‍💻 ChatGPT-like chat with MCPs

🎁 with private company-wide prompt registry!

⏰ Agent triggers

Run agents on a schedule or invoke them over HTTP with the A2A protocol.

Schedule → | Webhook & A2A →

📋 Private MCP registry with governance

Add MCPs to your private registry to share them with your team: self-hosted and remote, self-built and third-party.

Learn more about Private MCP Registry →

☁️ Kubernetes-native MCP orchestrator

Run MCP servers in Kubernetes, managing their state, API keys, OAuth.

Learn more about MCP Orchestrator →

📚 RAG Knowledge Base

Built-in retrieval-augmented Knowledge Base — no external vector database or separate retrieval service required.

Learn more about Knowledge Base →

🤖 Security sub-agents

Isolating dangerous tool responses from the main agent to prevent prompt injections.

Learn more about Dual LLM →

🚫 Non-probabilistic security to prevent data exfiltration

Models could consume prompt injections via MCP uncontrollably (read your inbox, read your GitHub issues, read your customers' inquiries) and follow them resulting in data exfiltration.

Learn more about Tool Guardrails → | The Lethal Trifecta →

Live demo of Archestra security engine preventing data leak from the private GitHub repo to the public repo:

Read more: Simon Willison, The Economist

Examples of hacks: ChatGPT (April 2023), ChatGPT Plugins (May 2023), Google Bard (November 2023), Writer.com (December 2023), Amazon Q (January 2024), Google NotebookLM (April 2024), GitHub Copilot Chat (June 2024), Google AI Studio (August 2024), Microsoft Copilot (August 2024), Slack (August 2024), Mistral Le Chat (October 2024), xAI's Grok (December 2024), Anthropic's Claude iOS app (December 2024), ChatGPT Operator (February 2025), Microsoft 365 Copilot (EchoLeak) (June 2025), Notion 3.0 (September 2025), Salesforce Agentforce (ForcedLeak) (September 2025), Microsoft Copilot Cowork (May 2026).

💰 Cost monitoring, limits and dynamic optimization

Per-team, per-agent or per-org cost monitoring and limits. Dynamic optimizer allows to reduce cost up to 96% by simply switching to cheaper models automatically for simpler tasks.

Learn more about Costs & Limits →

📊 Observability

Metrics, traces and logs allowing to come to a conclusion about per-org, per-agent and per-team token and tool usage, and performance.

Learn more about Observability →

👍 Ready for production

1. ✅ Lightning fast, 31ms at 95p: Performance & Latency benchmarks →
2. ✅ Terraform provider →
3. ✅ Helm Chart →

🤝 Contributing

We welcome contributions from the community!

• Contribution Guidelines →
• Developer Quickstart →
• Security & Bug Bounty →

Thank you for contributing and continuously making Archestra better, you're awesome 🫶