{"schemaVersion":1,"profileUrl":"https://nowen.ai/agents/kagent-dev","markdownUrl":"https://nowen.ai/agents/kagent-dev.md","jsonUrl":"https://nowen.ai/agents/kagent-dev.json","name":"kagent","website":"https://kagent.dev","industry":"Developer Platforms","status":{"trust":"unclaimed","text":"Unclaimed: compiled by Nowen from public sources, not reviewed by the company"},"updatedAt":"2026-09-06T23:34:32.317Z","summary":"Kagent is an open-source CNCF sandbox project by Solo.io for building and running AI-driven agents on Kubernetes.","description":"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":"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.","directory":{"industryName":"Developer Platforms","agentCount":39,"indexable":true,"hubUrl":"https://nowen.ai/industry/developer-platforms"},"aiReaderFamilies":["Anthropic","Amazon"],"products":[{"name":"kagent","type":"Platform","url":"https://kagent.dev/","summary":"Build and orchestrate AI agents in Kubernetes with efficient resource management and full observability.","description":"Kubernetes-native agent runtime for running AI agents inside Kubernetes. kagent defines agents as Kubernetes CRDs, provides a production-grade runtime, observability, and governance for agent workloads. It supports multiple runtimes (Go and Python ADK), brings your own frameworks, NVIDIA NemoClaw guardrails, and a suite of MCP tooling to integrate with MCP servers. The platform emphasizes declarative operations, GitOps-driven deployment, and full observability via OpenTelemetry and Prometheus. It targets developers building agent-based automation, site reliability engineers, and organizations seeking scalable, secure orchestration of autonomous capabilities in production.","pricing":{},"features":[{"name":"Agent Substrate Runtime","value":"Ensures Fast Startups","valueDescription":"Agents benefit from rapid initialization through snapshot restoration rather than cold booting, leading to improved responsiveness."},{"name":"Full Observability","value":"Ensures Transparency","valueDescription":"Provides comprehensive monitoring features that allow users to analyze and understand agent behavior and performance."},{"name":"Enterprise Distributions for kagent","value":"Delivers Context-Aware Distribution","valueDescription":"This feature enables enterprises to run and manage AI agents efficiently, ensuring they can handle production demands securely and effectively."},{"name":"Production-Ready Support","value":"Ensures Reliable Operations","valueDescription":"With comprehensive production support, businesses can trust that their kagent configurations are backed by expert guidance, reducing downtime and improving performance."},{"name":"Agent-to-Agent Workflows","value":"Facilitates Collaboration","valueDescription":"Enable agents to interact seamlessly, enhancing functionalities by allowing coordinated tasks and workflows."},{"name":"CRD-Based Agent Lifecycle","value":"Streamlines Management","valueDescription":"Facilitates DevOps workflows with familiar tools, making it straightforward to manage agent lifecycles."},{"name":"NVIDIA NemoClaw Integration","value":"Enhances Security","valueDescription":"Ensures safe agent operations with robust security measures to govern interactions and deployments."},{"name":"Advanced Management Features","value":"Streamlines Agent Management","valueDescription":"This feature empowers users with tools that enhance visibility and control over their deployed agents, allowing for more efficient troubleshooting and maintenance."},{"name":"Multi-Cluster Federation Support","value":"Facilitates Scalable Operations","valueDescription":"This support allows enterprises to run and manage agents consistently across diverse environments, adding flexibility and efficiency to agent deployments."},{"name":"Bring Your Own Frameworks","value":"Supports Diverse Frameworks","valueDescription":"Flexibility to integrate various agent frameworks allows users to leverage existing tools and technologies."},{"name":"Go and Python ADK Runtimes","value":"Enhances Development Choices","valueDescription":"Developers can select the optimal language for their use case, fostering rapid innovation and adaptability."},{"name":"Postgres Storage","value":"Provides Reliable Storage","valueDescription":"Utilizes familiar technologies for data storage to facilitate ease of access, management, and integration."},{"name":"Training and Technical Labs","value":"Facilitates Hands-On Learning","valueDescription":"Access to training labs allows users to become proficient in kagent and MCP tools, ensuring effective implementation and utilization within their environments."},{"name":"Context Compaction","value":"Reduces Token Usage","valueDescription":"Enhances memory usage by summarizing conversations, preventing excessive token consumption while maintaining conversation quality."},{"name":"Long-Term Memory","value":"Improves User Experience","valueDescription":"Allows agents to retain context across sessions, enhancing continuity and user interactions."},{"name":"Prompt Templates","value":"Promotes Efficiency","valueDescription":"Encourages the use of standardized prompts across agents, streamlining interaction design and development."}]},{"name":"kmcp","type":"Product","url":"https://github.com/kagent-dev/kmcp","summary":"Accelerate MCP service development and deployment with kmcp's streamlined workflows.","description":"KMCP is a lightweight toolkit to take MCP servers from prototype to production. It provides end-to-end workflow support to scaffold, containerize, and deploy MCP services in Kubernetes, using CRDs and standard tooling. It also offers built-in integration with agentgateway for secure tool communications and supports multiple transports, all designed to simplify production-grade MCP development.","pricing":{},"features":[{"name":"Agentgateway Integration","value":"Secure Agent Communications","valueDescription":"Integrate with agentgateway to ensure secure communication between agents and tools while maintaining observability and governance."},{"name":"Kubernetes-native Deployment with kmcp Deploy","value":"Streamlined Deployment Process","valueDescription":"Leverage kmcp deploy to easily rollout your MCP services in Kubernetes, customizing transport and configuration options."},{"name":"Container Image Packaging with kmcp Build","value":"Simplify Image Management","valueDescription":"Effortlessly create and manage container images for your MCP services, enabling simpler deployments across environments."},{"name":"MCP Services as CRDs","value":"Kubernetes Native Management","valueDescription":"Utilize Custom Resource Definitions to manage the lifecycle of your MCP services within the Kubernetes ecosystem."},{"name":"Project Scaffolding with kmcp Init","value":"Accelerated Project Setup","valueDescription":"Quickly initialize new MCP projects using best practice templates, streamlining your development process."},{"name":"Multiple Transports Support","value":"Flexible Transport Options","valueDescription":"Switch between different transport methods based on your architecture needs, allowing greater flexibility in service integration."}]}],"audiences":[],"segments":[{"name":"Autonomous Agent Orchestration","description":"Capabilities to compose, coordinate, and monitor multi-agent workflows, including memory and context management for autonomous AI agents.","size":6.27,"cagr":35.32,"color":1,"productNames":["kagent"],"methodology":"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.","citations":[{"url":"https://www.mordorintelligence.com/industry-reports/agentic-artificial-intelligence-orchestration-and-memory-systems-market","text":"Market Size (2025) USD 6.27 Billion; Growth Rate (2025-2030) 35.32% CAGR"},{"url":"https://www.marketsandmarkets.com/Market-Reports/ai-agents-market-15761548.html","text":"AI Agents market projected from USD 7.84 billion in 2025 to USD 52.62 billion by 2030; CAGR 46.3%."},{"url":"https://marketintelo.com/report/ai-powered-multi-agent-orchestration-market","text":"Global AI-powered multi-agent orchestration market valued at $4.2 billion in 2025; expected CAGR 38.5% (2026–2034)."},{"url":"https://www.gminsights.com/industry-analysis/ai-orchestration-market","text":"Global AI orchestration market size estimated at USD 12.8 billion in 2025; CAGR 18.5% (2026–2034)."}]},{"name":"Model Context Protocol deployment","description":"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.","size":0.9,"cagr":35,"color":2,"productNames":["kmcp","kagent"],"methodology":"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.","citations":[{"url":"https://guptadeepak.com/the-complete-guide-to-model-context-protocol-mcp-enterprise-adoption-market-trends-and-implementation-strategies/","text":"The MCP ecosystem is projected to grow from $1.2 billion (2022) to $4.5 billion (2025)"},{"url":"https://arxiv.org/html/2509.25292v1","text":"MCPCrawler aggregated 17,630 raw entries; 8,401 valid projects (8,060 servers and 341 clients) were analyzed"},{"url":"https://www.digitalapplied.com/blog/mcp-adoption-statistics-2026-model-context-protocol","text":"Anthropic's December 2025 update cites 10K+ active public servers; 97M+ monthly SDK downloads"}]},{"name":"AI governance and agent monitoring","description":"Monitors autonomous AI agents and models for policy compliance, performance, and risk; provides enforcement, audit capabilities, and analytics to govern AI-driven actions.","size":0.43,"cagr":34.3,"color":3,"productNames":["kagent","kmcp"],"methodology":"Multiple market reports in the provided results place the AI governance market (which includes agent monitoring) in the low hundreds of millions USD in the mid-2020s, with strong multi‑year growth forecasts. Reported mid‑2020s market values range ~USD 0.31–0.62B; CAGR forecasts vary from ~28% to 51% depending on scope. I selected ~USD 0.43B as a midpoint estimate for current market size and a CAGR of ~34.3% reflecting the central tendency of specialist governance forecasts (Mordor, Precedence, Persistence) while acknowledging higher agent/agentic‑AI growth projections for adjacent agent markets.","citations":[{"url":"https://www.mordorintelligence.com/industry-reports/ai-governance-market","text":"Market Size (2026) USD 0.44 Billion; Growth Rate (2026 - 2031) 28.15% CAGR"},{"url":"https://www.precedenceresearch.com/ai-governance-market","text":"Market valued at USD 309.01 Mn in 2025; forecast USD 5,883.90 Mn by 2035; CAGR 34.27% (2026-2035)"},{"url":"https://www.persistencemarketresearch.com/market-research/ai-governance-market.asp","text":"Market likely valued at US$ 429.8 million in 2026; projected to reach US$ 4,201.3 million by 2033; CAGR 38.5% (2026-2033)"},{"url":"https://aws.amazon.com/marketplace/pp/prodview-vqc2rbyqo4gbc","text":"Global AI Governance Market estimated at USD 620 million in 2024; USD 940 million by end of 2025; projected USD 7,380 million by 2030; CAGR 51% (2025-2030)"},{"url":"https://www.marketsandmarkets.com/Market-Reports/ai-agents-market-15761548.html","text":"AI Agents market projected from USD 7.84 billion in 2025 to USD 52.62 billion by 2030; CAGR 46.3% (2025-2030)"}]},{"name":"Federated and scalable runtime management","description":"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.","size":0.1,"cagr":27.3,"color":4,"productNames":["kagent"],"methodology":"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.","citations":[{"url":"https://marketgenics.co/reports/federated-learning-platforms-market-27174","text":"Global federated learning platforms market valued at USD 0.1 billion in 2025; projected to reach USD 1.6 billion by 2035, CAGR 27.3%."},{"url":"https://www.precedenceresearch.com/federated-learning-market","text":"The global federated learning market size accounted for USD 1,219.00 million in 2025; expanding at a CAGR of 30.50% from 2026 to 2035."},{"url":"https://www.imarcgroup.com/federated-learning-market","text":"The global federated learning market size was valued at USD 171.7 Million in 2025 and is estimated to reach USD 535.1 Million by 2034, CAGR 13.06%."}]}],"faq":[],"comparisons":[],"sources":[{"url":"https://kagent.dev","text":"kagent"},{"url":"https://kagent.dev/","text":"kagent"},{"url":"https://github.com/kagent-dev/kmcp","text":"kmcp"},{"url":"https://www.mordorintelligence.com/industry-reports/agentic-artificial-intelligence-orchestration-and-memory-systems-market","text":"Market Size (2025) USD 6.27 Billion; Growth Rate (2025-2030) 35.32% CAGR"},{"url":"https://www.marketsandmarkets.com/Market-Reports/ai-agents-market-15761548.html","text":"AI Agents market projected from USD 7.84 billion in 2025 to USD 52.62 billion by 2030; CAGR 46.3%."},{"url":"https://marketintelo.com/report/ai-powered-multi-agent-orchestration-market","text":"Global AI-powered multi-agent orchestration market valued at $4.2 billion in 2025; expected CAGR 38.5% (2026–2034)."},{"url":"https://www.gminsights.com/industry-analysis/ai-orchestration-market","text":"Global AI orchestration market size estimated at USD 12.8 billion in 2025; CAGR 18.5% (2026–2034)."},{"url":"https://guptadeepak.com/the-complete-guide-to-model-context-protocol-mcp-enterprise-adoption-market-trends-and-implementation-strategies/","text":"The MCP ecosystem is projected to grow from $1.2 billion (2022) to $4.5 billion (2025)"},{"url":"https://arxiv.org/html/2509.25292v1","text":"MCPCrawler aggregated 17,630 raw entries; 8,401 valid projects (8,060 servers and 341 clients) were analyzed"},{"url":"https://www.digitalapplied.com/blog/mcp-adoption-statistics-2026-model-context-protocol","text":"Anthropic's December 2025 update cites 10K+ active public servers; 97M+ monthly SDK downloads"},{"url":"https://www.mordorintelligence.com/industry-reports/ai-governance-market","text":"Market Size (2026) USD 0.44 Billion; Growth Rate (2026 - 2031) 28.15% CAGR"},{"url":"https://www.precedenceresearch.com/ai-governance-market","text":"Market valued at USD 309.01 Mn in 2025; forecast USD 5,883.90 Mn by 2035; CAGR 34.27% (2026-2035)"},{"url":"https://www.persistencemarketresearch.com/market-research/ai-governance-market.asp","text":"Market likely valued at US$ 429.8 million in 2026; projected to reach US$ 4,201.3 million by 2033; CAGR 38.5% (2026-2033)"},{"url":"https://aws.amazon.com/marketplace/pp/prodview-vqc2rbyqo4gbc","text":"Global AI Governance Market estimated at USD 620 million in 2024; USD 940 million by end of 2025; projected USD 7,380 million by 2030; CAGR 51% (2025-2030)"},{"url":"https://marketgenics.co/reports/federated-learning-platforms-market-27174","text":"Global federated learning platforms market valued at USD 0.1 billion in 2025; projected to reach USD 1.6 billion by 2035, CAGR 27.3%."},{"url":"https://www.precedenceresearch.com/federated-learning-market","text":"The global federated learning market size accounted for USD 1,219.00 million in 2025; expanding at a CAGR of 30.50% from 2026 to 2035."},{"url":"https://www.imarcgroup.com/federated-learning-market","text":"The global federated learning market size was valued at USD 171.7 Million in 2025 and is estimated to reach USD 535.1 Million by 2034, CAGR 13.06%."}]}