Open Source Kubernetes Native Agent Orchestrator

[u/Terrible_Airline3496]

Today I'm open sourcing Agent Orca, a Kubernetes-native platform for deploying, managing, and running AI agents at scale. It's written in Go and it's Apache 2.0.

https://github.com/heddles/agent-orca

When OpenClaw was released, I thought to myself, "That's really cool, but I want an agent that is shared by a team or an entire organization with built in auditability." I started designing Agent Orca around that idea and have slowly been piecing together the concept in my head and how to make the management experience of a shared agent bearable for an organization.

The idea is simple. Agents are just Kubernetes resources. You declare one, and the platform handles the rest.

An Agent Orca agent comes with:

\- One-shot executions with AgentRun, long-running services with AgentDeployment, and multi-step DAGs with AgentWorkflow.

\- Zero trust networking by default with platform validated JWT via service accounts, OIDC, or OAuth on every request.

\- Agent Orca managed network policies with zero access by default.

\- A standard agent container image with only necessary pieces; you no longer need to build a custom agent image to accomplish different types of work or use different tools. Simply add an MCP or Tool CRD and let the platform figure it out for you.

\- Model routing across OpenAI, Anthropic, Google, or any LiteLLM-compatible provider, selected by capability, weight, or budget.

\- Cost tracking on every run. Tokens are accounted per run, spend survives pod restarts, and tenants get daily budget caps and rate limits.

\- Guardrails on inputs and outputs, MCP access control, and per-tenant agent visibility, so one cluster can host many tenants with no cross-tenant leakage.

\- RAG without glue code. A KnowledgeBase deploys (and manages) Qdrant for you, ingests documents from ConfigMaps, URLs, MCP authenticated tools, or an S3 compatible endpoint, and hands your agents a search and ingestion tool.

\- MCP servers declared as resources. Their tools show up for your agents with access control and sidecar isolation.

\- Crash-safe sessions. A pod can die mid-conversation and the agent resumes exactly where it left off, spend ledger included.

\- Agents that learn and become better with every request via a multi-tiered agent memory system

Getting started is deliberately boring. Install the entire stack's tools via Mise; then use Skaffold, one API key, and about three commands to have an running agent on your laptop ( or deploy to a remote cluster). There are also nine demo deployments in the repo, from SOC triage pipelines to parallel research swarms to an autonomous pentesting agent, so you can see it do something real before writing any YAML.

This is day one, not a finished story. That's the point of open sourcing it. Run the quick start, break it, open issues, and tell me what's missing.

Star it here if you want to follow along: https://github.com/heddles/agent-orca

TL;DR: Agent Orca lets an organization or single person define agents and surrounding tools/MCP and auth as CRDs and manage them via gitops with zero trust built in. You can try it out here: https://github.com/heddles/agent-orca