MCP Servers · kagent-dev

kagent

Kagent is a Kubernetes-native framework written in Go for building, deploying, and managing AI agents at scale. It integrates multiple LLM providers (OpenAI, Anthropic, Google Vertex AI, Ollama) and MCP tools, with a declarative, YAML-driven approach to agent orchestration.

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Source: GitHub — github.com/kagent-dev/kagent

3.3k

GitHub stars

647

Forks

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	kagent-dev/kagent
Owner	kagent-dev
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	3.3k
Forks	647
Open issues	117
Latest release	v0.10.0-beta4 (2026-07-06)
Last updated	2026-07-08
Source	https://github.com/kagent-dev/kagent

What kagent is

Kagent uses Kubernetes custom resources (Agent, ModelConfig, ToolServer) to manage agentic AI workloads, backed by a Go-based controller, ADK engine, CLI, and web UI. It supports OpenTelemetry tracing, MCP server integration, and multi-provider LLM configuration for DevOps and cloud-native automation.

Quickstart

Get the kagent source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/kagent-dev/kagent.git2cd kagent3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Kubernetes Cluster Automation & DevOps

Deploy agents to automate infrastructure tasks using built-in Kubernetes, Istio, Helm, Argo, Prometheus, and Grafana tools. Agents can react to cluster events, perform remediation, and manage deployments declaratively.

Multi-LLM Orchestration in Kubernetes

Abstract LLM provider complexity behind Kubernetes resources. Swap providers (OpenAI to Anthropic to Ollama) without code changes, enable failover strategies, and manage model configurations centrally.

Cloud-Native AI Experimentation & Debugging

Leverage testability, observability (OpenTelemetry), and UI for interactive agent development. Use familiar kubectl workflows and YAML for version control, rollback, and audit trails.

Implementation considerations

Requires Kubernetes cluster (1.20+) and familiarity with kubectl, custom resources, and YAML. Plan for operator deployment, RBAC, and networking.
LLM provider authentication and rate limits must be managed via Kubernetes secrets and ModelConfig resources. Cost tracking and quota management are operator responsibilities.
MCP server integration relies on external tool definitions. Validate tool availability, error handling, and latency impact on agent performance before production.
Tracing and observability via OpenTelemetry require backend setup (e.g., Jaeger, Datadog). Without it, debugging agent behavior and tool calls will be harder.
State management, session persistence, and multi-agent coordination patterns are not explicitly detailed. Assess enterprise-readiness for stateful workloads.

When to avoid it — and what to weigh

Non-Kubernetes Environments — Kagent is purpose-built for Kubernetes. Non-containerized or edge deployments without K8s will incur significant overhead and architectural mismatch.
Simple Chatbot or Single-Agent Use Cases — The Kubernetes-native abstraction and orchestration layer add unnecessary complexity for single-agent, non-distributed scenarios. Lighter SDKs may be more pragmatic.
Closed or Offline Environments — Kagent requires external LLM provider connectivity (OpenAI, Anthropic, etc.) by design. Fully air-gapped setups can use Ollama, but lose cloud provider benefits and require additional on-prem infrastructure.
Production-Critical Systems Without Internal Expertise — Project is in beta (v0.10.0-beta4), 117 open issues, and active development. Requires Kubernetes operations skills and tolerance for rapid iteration.

License & commercial use

Kagent is licensed under Apache License 2.0, an OSI-approved, permissive open-source license. Full source is available on GitHub.

Apache 2.0 permits commercial use, redistribution, and modification without royalty, provided the license and copyright notices are retained and liability is disclaimed. No special commercial agreement, support contract, or warranty is stated in the repository. Users must assess production readiness (project is in beta) independently.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

Project has an OpenSSF Best Practices badge, signaling security awareness. No public security audit disclosed. Key concerns: (1) LLM provider API keys stored in Kubernetes secrets—RBAC and secret encryption at rest are operator responsibilities; (2) MCP tools grant agents capability to modify Kubernetes state, Istio configs, Helm releases—implement namespace isolation and pod security policies; (3) Beta status means potential unaudited code paths; (4) No stated vulnerability disclosure or response SLA. Requires threat modeling before production use.

Alternatives to consider

LangChain / LangGraph

Popular Python-based framework for agentic AI. Kubernetes-agnostic; better for dev-centric workflows. Lacks declarative K8s integration but simpler for single-machine or serverless deployment.

Anthropic Agents / Claude API

Anthropic's native agentic capabilities via Claude. Tightly coupled to Anthropic's LLM; no multi-provider abstraction. Easier onboarding for Anthropic-only shops.

OpenAI Assistants / Swarm (in Python)

OpenAI's managed agent framework. Less infrastructure overhead. Vendor lock-in to OpenAI; no Kubernetes-native orchestration or multi-provider failover.

Software development agency

Build on kagent with DEV.co software developers

Kagent is a mature, community-backed framework for production AI agent orchestration. Explore the quickstart guide, join the Discord community, or contact our team for an evaluation.

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kagent FAQ

Can I use Kagent without Kubernetes?

No. Kagent is purpose-built for Kubernetes. Running it outside K8s (e.g., Docker Compose, VMs) is not a supported deployment model.

Is Kagent production-ready?

Not fully. The project is in beta (v0.10.0-beta4) with 117 open issues. It is suitable for pilot projects and DevOps automation but requires careful evaluation, testing, and operational readiness for production systems.

What LLM providers are supported?

OpenAI, Azure OpenAI, Anthropic, Google Vertex AI, Ollama, and any custom provider accessible via AI gateways. Providers are configured declaratively via ModelConfig resources.

How are tools integrated?

Via MCP (Model Context Protocol) servers, defined as Kubernetes resources. Kagent provides built-in MCP tools for Kubernetes, Istio, Helm, Argo, Prometheus, Grafana, Cilium. Custom MCP servers can be registered.

Work with a software development agency

From first prototype to production, DEV.co delivers software development services around tools like kagent. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across mcp servers and beyond.

Ready to Deploy Agentic AI on Kubernetes?

Kagent is a mature, community-backed framework for production AI agent orchestration. Explore the quickstart guide, join the Discord community, or contact our team for an evaluation.

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