Local Quickstart

You get Community Edition on your own laptop cluster. Bring the agent you already wrote; it is sandboxed — it can't break out, reach unauthorized data or networks, its prompts are verified, budget controlled, and it is under observation; tenants stay isolated (see the tenant hop). This tutorial installs that runtime on kind, proves a model call, and opens the matching trace — then points you at the CLI for your own agent.

Community Edition is the self-hosted runtime in this repo. Pro adds SSO, team controls (budgets and approvals), and production HA / GitOps. Enterprise adds isolation and compliance on Pro (hardware sandbox, mTLS, retained audit, BAA). You do not need Pro or Enterprise to finish this page.

Prerequisites

Install and leave running:

• Docker Desktop, Docker Engine, or OrbStack
• kind
• helm v3.10+
• kubectl v1.28+

You also need one LLM provider credential (or a local Ollama). The install refuses to start without it. Upstream keys go into the cluster secret; client calls use a local consumer token (dev-key on this tutorial).

Pick one:

Provider	Env when you install	Default model the install picks
OpenAI	OPENAI_API_KEY	openai/gpt-4o-mini
Ollama Cloud	OLLAMA_API_KEY	gpt-oss:20b
Ollama on the host	OLLAMA_LOCAL_HOST=http://host.docker.internal:11434	ollama/llama3.2
Anthropic	ANTHROPIC_API_KEY	anthropic/claude-3-5-sonnet
Gemini	GEMINI_API_KEY	gemini/gemini-2.0-flash
vLLM	VLLM_BACKEND_URL	vllm/default

You can set more than one provider; the first match in the table above wins for DEFAULT_LLM_MODEL unless you override it.

Install Community Edition

From a clone of this repository:

git clone https://github.com/devopssquaddev/zelkor-platform.git
cd zelkor-platform
OPENAI_API_KEY=sk-... ./install.sh

Other providers use the same script — substitute the env var from the table. Example with Ollama Cloud:

OLLAMA_API_KEY=... ./install.sh

What ./install.sh does (you do not run these by hand):

1. Checks Docker, kind, helm, and kubectl
2. Creates kind cluster zelkor with host port 8088
3. Installs the gVisor runtime on the kind node for sandboxed code
4. Deploys Envoy Gateway, Envoy AI Gateway, and the Zelkor platform Helm chart (appVersion / image tag 2.1.1)
5. Deploys the optional FinServe example agents by default
6. Prints service URLs and a ready curl when everything is healthy

First create is typically under five minutes when Docker is already warm and image pulls are not bandwidth-bound. Re-runs skip work that is already ready.

Success: the script ends with Done. Zelkor Platform deployed on kind cluster: zelkor and a footer of localhost URLs on port 8088.

Call a model

Use the consumer token dev-key. The model must match the provider you configured at install.

OpenAI install:

curl -X POST http://ai-gateway.localhost:8088/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer dev-key" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello from Zelkor"}]
  }'

Ollama Cloud install — change the model to gpt-oss:20b. Ollama on the host — use ollama/llama3.2. Anthropic — anthropic/claude-3-5-sonnet. Gemini — gemini/gemini-2.0-flash.

Success: HTTP 200 and a completion in the JSON body. If you see no healthy upstream, the model id does not match the provider you installed with.

Open a trace

1. Open http://langfuse.localhost:8088
2. Sign in with the credentials the install footer printed (local defaults: admin@zelkor.local / Zelkor-dev1!)
3. Open project Zelkor Platform → Traces
4. Find the gateway call from the curl above

Success: a new trace appears for that completion. Later agent runs land in the same project as one waterfall per run.

Deploy your own agent (next)

You already write LangGraph or Deep Agents graphs. After this install, the platform path is:

pip install -e cli/
zelkor env add local --kube-context kind-zelkor --namespace default
zelkor env use local

Then from your agent project directory: zelkor init (if needed), zelkor deploy (or zelkor dev on kind), zelkor run, and refresh Langfuse Traces.

Command reference: cli/README.md. Keep provider keys in the platform; your agent talks to the gateway and tools the cluster already exposes.

Optional: try the FinServe sample

./install.sh also deploys a wealth-management sample under examples/finserve/. Use it when you want a ready-made agent — not as the way you learn how to ship your own. The install footer prints sample curl commands and how to mint a tenant token with zelkor token mint (that token is the tenant, see Tenant Isolation).

Tear down

helm --kube-context kind-zelkor uninstall finserve --ignore-not-found
helm --kube-context kind-zelkor uninstall zelkor-platform --ignore-not-found
kind delete cluster --name zelkor

Where to go next

Goal	Page
Ship your agent on this cluster	cli/README.md
Read the FinServe sample	examples/finserve/README.md
Docs map	Documentation index
Vertex gemini-* 500 unknown backend	KB

Read Install on an Existing Cluster and Production Install to move off kind. The objects you set here — model, tool, agent — are the same ones you keep.