Turn scattered AI experiments into production systems
by making context infrastructure a first-class layer.

MCP Mesh · MCP Studio · Docs · Discord

Your teams are building impressive AI demos in Lovable, Replit, and n8n. But they never reach production. The challenge is operating agents reliably as usage spreads across teams, environments, and tool surfaces.

Why?

• Governance gaps — no consistent place to enforce SSO/RBAC, approvals, or audit trails
• Integration sprawl - every client wires every tool with its own config, retries, and auth
• Operational blind spots - debugging is slow, costs are hard to attribute, and failures are hard to trace end-to-end
• Context Obesity — Bloated, expensive prompts delivering confused answers

When those problems aren’t solved, teams either stall (over-centralize) or sprawl (ship shadow AI). Agents hallucinate because they lack unified access to enterprise data. IT locks down access because they lack visibility into what AI is doing.

deco CMS is an open-source Context Management System for platform teams.

It gives you:

• a production control plane for MCP traffic (connect, govern, observe)
• a path to package durable, reusable capabilities (so teams stop reinventing the same “agent”)
• a way to distribute what works safely across teams over time

We're built on the Model Context Protocol because enterprises need interoperability at the tool layer, and a way to operate that tool access with the same rigor as any other production surface.

MCP gives you a standard interface. deco CMS provides the control plane around it:

• Portable — Self-host on Kubernetes, deploy on-prem, or use our cloud (coming soon)
• Composable — swap models, swap MCP servers, adopt community tools, build your own
• Auditable — centralized policies, logs, and traces across tool + model calls
• No lock-in — Your context, your infrastructure. Audit the code, fork if needed

npx @decocms/mesh init

→ One command spins up a local MCP Mesh so you can connect MCP servers and test with MCP clients

Most AI efforts optimize prompts. AI-native companies optimize context.

Better context produces better outputs — and outcomes create new context (feedback, policies, routing decisions, and reusable components).

deco CMS is built for that loop: instrument every call, learn what works, and improve routing, retrieval, execution over time.

_{Made with ❤️ by the deco community}