clrswarm

ClrSwarm 🐝

The open-source "bee colony" that assembles MCP Gateways, Kubernetes Operators, and next-gen AI automations—one modular cell at a time.

ClrSwarm is part of the ClrSlate platform and represents our contribution to the community. These are the battle-tested tools that have helped us build and scale our infrastructure, and we hope they'll help others in the community as well.

We will be gradually making our internal tooling public over the next couple of months, starting with ClrSwarm's core components.

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What is ClrSwarm?

ClrSwarm is the developer toolkit of the ClrSlate application suite.
It gives platform engineers a fast, opinionated way to generate, test, and ship:

Area	Built-in Power
🛣️ MCP Gateway	Combines multiple MCP servers into a single HTTP streamable gateway. Solves tool sprawl by letting you pick and choose which tools to expose. Includes observability, tracing, authentication pipelines. Works with any MCP server, including stdio transport.
⚙️ Kubernetes Operator	Automates MCP orchestration at scale on Kubernetes. Manages the full lifecycle of MCP server deployments, scaling, and configuration through declarative CRDs and reconciliation loops.
🤖 Agentic Extensions	AI-powered automation capabilities that can consume any MCP server. Available in A2A (Agent-to-Agent) format for seamless integration with existing workflows and decision-making pipelines.
🧩 Add-On Ecosystem	Clear interface contracts so teams can drop in auth, observability, or policy drivers without fork-lifting.
🚀 DevX Toolbelt	CLI, schematics, and VS Code snippets that let you prototype in minutes and release with confidence.

Why "Swarm"?

Like a hive of cooperative bees, ClrSwarm's modules work in concert:

• Swarm, don't sprawl – keep logic in focused, reusable units.
• Self-healing – operators reconcile desired state just as bees rebuild a damaged comb.
• Collective intelligence – agentic helpers learn and optimise pipelines over time.

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Core Components

🛣️ MCP Gateway

The MCP Gateway solves the critical problem of tool sprawl when working with multiple Model Context Protocol servers. Instead of managing dozens of individual MCP servers, the gateway:

• Consolidates multiple MCP servers into a single HTTP streamable endpoint
• Selective tool exposure - pick and choose which tools from each server to expose publicly
• Universal compatibility - works with any MCP server, including stdio transport-based servers
• Built-in pipeline for observability, distributed tracing, authentication, and other cross-cutting concerns
• Container-ready - can be hosted on any container runtime for maximum deployment flexibility

Use case: Turn 15 different MCP servers into one unified API that your applications can consume, while maintaining fine-grained control over which capabilities are exposed.

⚙️ Kubernetes Operator

The Kubernetes Operator brings enterprise-grade automation to MCP orchestration at scale:

• Declarative MCP management through Custom Resource Definitions (CRDs)
• Full lifecycle automation - deployment, scaling, configuration, and updates of MCP servers
• Reconciliation loops that ensure your desired MCP topology matches reality
• Day-2 operations - automated backup, monitoring, and disaster recovery
• Multi-tenant support with namespace isolation and resource quotas

Use case: Deploy and manage hundreds of MCP servers across multiple environments with GitOps workflows, automatic scaling, and zero-downtime updates.

🤖 Agentic Extensions

The Agentic Extensions layer adds AI-powered automation that can consume any MCP server:

• Universal MCP consumption - agents can work with any MCP server in your ecosystem
• A2A (Agent-to-Agent) protocol for seamless integration between automated systems
• Workflow automation - intelligent decision-making based on real-time data from MCP tools
• Learning pipelines - agents improve over time by analyzing successful automation patterns
• Event-driven architecture - reactive automation that responds to changes in your infrastructure

Use case: Create intelligent automation that can read logs via one MCP server, analyze trends via another, and automatically scale resources through a third - all coordinated through agent-to-agent communication.

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