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kagent.dev

Kagent is an open-source CNCF sandbox project by Solo.io for building and running AI-driven agents on Kubernetes.

kagent is an open-source, CNCF sandbox project originated by Solo.io. It provides a cloud-native framework for building, deploying, and operating AI-driven agents within Kubernetes. The project includes a Kubernetes controller that manages agent lifecycles, an engine that runs the agent runtime, a dashboard and CLI for management, and a suite of MCP tooling to support Model Context Protocol servers and agent-enabled workflows. kagent supports running agents and MCP tools in Kubernetes, emphasizes safe, observable operations, and offers documentation to install, start, and extend its capabilities. The target audience includes developers building agent-based automation, site reliability engineers, and organizations seeking scalable, secure orchestration of autonomous capabilities in production. Its core concepts cover architecture components, runtimes, observability, and provider integrations.

Mission statement

To empower developers and operations teams to create, deploy, and manage autonomous agents within Kubernetes, enabling scalable, secure, and observable agent-based automation through MCP servers, tooling, and clear guidance.

Products & Services

kagent Platform

Build and orchestrate AI agents in Kubernetes with efficient resource management and full observability.

kagent.dev
  • ✓ Agent Substrate Runtime — Ensures Fast Startups
  • ✓ Full Observability — Ensures Transparency
  • ✓ Enterprise Distributions for kagent — Delivers Context-Aware Distribution
  • ✓ Production-Ready Support — Ensures Reliable Operations
  • ✓ Agent-to-Agent Workflows — Facilitates Collaboration
  • ✓ CRD-Based Agent Lifecycle — Streamlines Management
  • ✓ NVIDIA NemoClaw Integration — Enhances Security
  • ✓ Advanced Management Features — Streamlines Agent Management
  • ✓ Multi-Cluster Federation Support — Facilitates Scalable Operations
  • ✓ Bring Your Own Frameworks — Supports Diverse Frameworks
  • ✓ Go and Python ADK Runtimes — Enhances Development Choices
  • ✓ Postgres Storage — Provides Reliable Storage
  • ✓ Training and Technical Labs — Facilitates Hands-On Learning
  • ✓ Context Compaction — Reduces Token Usage
  • ✓ Long-Term Memory — Improves User Experience
  • ✓ Prompt Templates — Promotes Efficiency

kmcp Product

Accelerate MCP service development and deployment with kmcp's streamlined workflows.

github.com/kagent-dev/kmcp
  • ✓ Agentgateway Integration — Secure Agent Communications
  • ✓ Kubernetes-native Deployment with kmcp Deploy — Streamlined Deployment Process
  • ✓ Container Image Packaging with kmcp Build — Simplify Image Management
  • ✓ MCP Services as CRDs — Kubernetes Native Management
  • ✓ Project Scaffolding with kmcp Init — Accelerated Project Setup
  • ✓ Multiple Transports Support — Flexible Transport Options

Market Segments

Billion USD 0 1 2 3 4 5 6 7 Autonomous Agen… Model Context P… AI governance a… Federated and s… Market Size (Billion USD)
0% 3% 6% 9% 12% 15% 18% 21% 24% 27% 30% 33% 36% 39% 42% 45% CAGR Growth Potential

Autonomous Agent Orchestration

Capabilities to compose, coordinate, and monitor multi-agent workflows, including memory and context management for autonomous AI agents.

Market size: $6.3B CAGR: 35.32%
Primary estimate uses Mordor Intelligence’s Agentic AI Orchestration and Memory Systems report, which specifically targets orchestration plus memory layers and reports a 2025 market size of USD 6.27B and a 2025–2030 CAGR of 35.32%. Comparable industry reports for adjacent definitions (AI agents / multi-agent orchestration) show 2025 market sizes spanning roughly USD 4.2B–12.8B and CAGRs from ~18% to ~46%, indicating consensus around a multi‑billion dollar market with high (mid‑to‑high double-digit) growth potential.
MA AI GL GL 4 references

Model Context Protocol deployment

Tooling and deployment workflows for Model Context Protocol (MCP) services, including project scaffolding, container image packaging, CRD-based service management, support for stdio/HTTP transports, and agentgateway integration for secure tool communications.

Market size: $900M CAGR: 35%
Estimated segment size uses published MCP ecosystem projections and measured adoption signals from the provided results. Gupta's market projection ($1.2B → $4.5B) was used as a total‑ecosystem anchor; MCPCrawler and Anthropic/registry statistics (server counts, SDK downloads, and enterprise production adoption) indicate strong developer and enterprise uptake. Assuming deployment/tooling (project scaffolding, packaging, CRDs, transports, agent‑gateway integration) represents roughly 15–20% of the broader MCP ecosystem yields ~0.9B. High recent adoption and registry growth imply rapid continued adoption; conservatively estimate a 35% CAGR for deployment tooling over the near term.
TH MC AN 3 references

AI governance and agent monitoring

Monitors autonomous AI agents and models for policy compliance, performance, and risk; provides enforcement, audit capabilities, and analytics to govern AI-driven actions.

Market size: $430M CAGR: 34.3%
Multiple market reports in the provided results place the AI governance market (which includes agent monitoring) in the low hundreds of millions USD in the mid-2020s, with strong multi‑year growth forecasts. Reported mid‑2020s market values range ~USD 0.31–0.62B; CAGR forecasts vary from ~28% to 51% depending on scope. I selected ~USD 0.43B as a midpoint estimate for current market size and a CAGR of ~34.3% reflecting the central tendency of specialist governance forecasts (Mordor, Precedence, Persistence) while acknowledging higher agent/agentic‑AI growth projections for adjacent agent markets.
MA MA MA GL AI 5 references

Federated and scalable runtime management

Management of federated, multi-cluster Kubernetes deployments for AI runtimes, enabling multi-cluster federation, centralized control of agent fleets, production-grade Postgres-backed storage, and operational scalability.

Market size: $100M CAGR: 27.3%
Estimate anchored to a focused ‘‘federated learning platforms’’ market dataset that directly covers orchestration, agent fleets, and coordinator services (components matching federated, multi-cluster runtime management). Marketgenics values the federated learning platforms market at USD 0.1B in 2025 and projects a 27.3% CAGR to 2035; broader federated-learning reports (PrecedenceResearch, IMARC) show larger but variable totals for the overall federated learning market, supporting a view of rapid growth and sizeable upside for platform and runtime orchestration subsegments. Chosen figures use Marketgenics as the primary, closely aligned source and the other reports to indicate addressable-market context and variance in published estimates.
GL TH TH 3 references

Related Organizations

Organizations in the same market segments as kagent.

GoMeta, Inc.

GoMeta, Inc. provides a platform to build, deploy, and manage AI-powered agents for enterprise automation.
Teramind

Teramind provides AI-powered security, governance, and workforce analytics to protect data and boost organizational productivity.

Common Questions

What does kagent do?
kagent is an open-source, CNCF sandbox project originated by Solo.io. It provides a cloud-native framework for building, deploying, and operating AI-driven agents within Kubernetes. The project includes a Kubernetes controller that manages agent lifecycles, an engine that runs the agent runtime, a dashboard and CLI for management, and a suite of MCP tooling to support Model Context Protocol servers and agent-enabled workflows. kagent supports running agents and MCP tools in Kubernetes, emphasizes safe, observable operations, and offers documentation to install, start, and extend its capabilities. The target audience includes developers building agent-based automation, site reliability engineers, and organizations seeking scalable, secure orchestration of autonomous capabilities in production. Its core concepts cover architecture components, runtimes, observability, and provider integrations.
What is kagent's role in the Autonomous Agent Orchestration market?
Capabilities to compose, coordinate, and monitor multi-agent workflows, including memory and context management for autonomous AI agents.
What is kagent's role in the Model Context Protocol deployment market?
Tooling and deployment workflows for Model Context Protocol (MCP) services, including project scaffolding, container image packaging, CRD-based service management, support for stdio/HTTP transports, and agentgateway integration for secure tool communications.
How was the Autonomous Agent Orchestration market size estimate for kagent calculated?
Primary estimate uses Mordor Intelligence’s Agentic AI Orchestration and Memory Systems report, which specifically targets orchestration plus memory layers and reports a 2025 market size of USD 6.27B and a 2025–2030 CAGR of 35.32%. Comparable industry reports for adjacent definitions (AI agents / multi-agent orchestration) show 2025 market sizes spanning roughly USD 4.2B–12.8B and CAGRs from ~18% to ~46%, indicating consensus around a multi‑billion dollar market with high (mid‑to‑high double-digit) growth potential.
How was the Model Context Protocol deployment market size estimate for kagent calculated?
Estimated segment size uses published MCP ecosystem projections and measured adoption signals from the provided results. Gupta's market projection ($1.2B → $4.5B) was used as a total‑ecosystem anchor; MCPCrawler and Anthropic/registry statistics (server counts, SDK downloads, and enterprise production adoption) indicate strong developer and enterprise uptake. Assuming deployment/tooling (project scaffolding, packaging, CRDs, transports, agent‑gateway integration) represents roughly 15–20% of the broader MCP ecosystem yields ~0.9B. High recent adoption and registry growth imply rapid continued adoption; conservatively estimate a 35% CAGR for deployment tooling over the near term.
kagent — company overview

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