archestra
archestra-ai · archestra-ai/archestra
Archestra is an enterprise AI platform that provides an MCP registry and gateway, enabling secure and observable management of LLMs and agent interactions for developers and non-technical users.
Enterprise AI Platform with guardrails, MCP registry, gateway & orchestrator
Preview
What you can do with it
- Set up a secure, centralized LLM gateway for all your internal AI agents and applications.
- Provide a user-friendly chat interface for non-technical employees to interact with custom MCP apps.
- Monitor and manage costs and usage of various LLM providers across different agent teams.
Safety check
Usable, but read the notes first.
- ●No clear license: check with the author before using it in a commercial product.
Automated scan of the repo files. Always review code you run.
From the README
The all-in-one open-source enterprise AI platform.
Built on a strong security and observability foundation: SSO and RBAC, sandboxed code execution, Dual-LLM and Lethal-Trifecta guardrails, OpenTelemetry traces, and Prometheus metrics — first-class, not bolted on.
Quickstart · Docs · Releases · Slack
https://github.com/user-attachments/assets/912b85e2-ef14-4e83-a807-5e2a81457a88
What it does
Point your users — or your agents, or Claude / Codex / Cursor — at one URL. Archestra handles the rest:
- 💬 Chat for non-technical users. Internal AI assistant with projects, MCP apps, and Slack, MS Teams, and email front-ends.
- 🛠️ Developer LLM & MCP portal. One token for Claude Code, Codex, Cursor — see the proxy.
- 🚪 LLM gateway for any provider (Anthropic, OpenAI, Azure, Bedrock, DeepSeek, …) with cost limits, virtual API keys, and dynamic model routing.
- 🔌 MCP gateway with OAuth + On-Behalf-Of so tools run as the user, not a shared service account.
- 🤝 A2A gateway for agent-to-agent triggers.
- 📦 Private MCP registry so teams ship their own tools — see registry docs.
- 🎼 MCP orchestrator with a Kubernetes operator and self-serve promotion.
- 🤖 Agent runtime with scheduled / email / webhook triggers, sub-agent delegation, reusable skills, sandboxed code execution, and a K8s-native filesystem.
- 📚 RAG knowledge base plumbed via connectors to your existing stack.
- 🧩 Mini app builder — see apps.
- 🛡️ Deterministic guardrails for tool calls, Dual-LLM verification, and Lethal Trifecta protections.
- 🪪 Identity & access with SSO (OIDC, SAML, Okta, Entra), RBAC with role mapping & team sync, and secrets management.
- 🌎 Environments with per-env egress policies and per-env cost limits.
- 🔭 Observability out of the box: OpenTelemetry traces, Prometheus metrics, logs, per-team cost tracking.
Already running dangerous single-tenant agents like Claude Cowork, OpenClaw, or Hermes in your enterprise? Migration Kit →
Quickstart
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
Open http://localhost:3000. Full Docker / Helm / Kubernetes instructions live in the quickstart docs.
Release channels
Archestra maintains two active release tracks:
- Stable releases (e.g.
1.3.51): Qualified, tested releases for production deployments. Archestra maintains one active stable release line at a time with security patches and bug fixes. - Release candidates (e.g.
1.4.0-rc.22): Previews of upcoming features built directly from themainbranch. They let you test new capabilities and provide feedback before they land in a stable release. The Docker tagarchestra/platform:latestpoints to the newest release, so it usually runs a release candidate.
New features and bug fixes land on main first and ship in rolling beta releases. Fixes are then selectively backported to the supported stable branch. For production environments, pin an exact version tag or Helm chart version rather than tracking latest. See our Release guide for full release lifecycle details.
Ready for production
- ✅ $13.5M total funding
- ✅ Three Fortune-50 deployments
- ✅ 31 ms at p95 — performance benchmarks →
- ✅ Terraform provider →
- ✅ Helm chart →
Deeper docs
- 📖 Platform overview — what's in the box, how the pieces fit together.
- 📐 Deployment — Docker, Helm, Kubernetes