📖 Add getting-started guide and sample CRs - #140
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This repository does not receive automatic reviews because it has fewer than 10 stars. ⚙️ Run configurationConfiguration used: defaults Review profile: CHILL Plan: Pro Plus Run ID: 📝 WalkthroughWalkthroughAdded a getting-started guide and README link. Added Gateway samples for four providers. Added migration Agent and AgentRun samples. Updated the AWS Bedrock lifecycle test for Claude Sonnet 4.5. ChangesDeployment and Agent usage
Estimated code review effort: 2 (Simple) | ~15 minutes Mergeability Score: 🟠 High · up to The guide and samples change how users deploy and configure the product, but several instructions can expose provider credentials, send AWS requests to mismatched regions, leave sample resources referencing an unavailable Gateway, fail on supported Kubernetes versions, or delete unrelated resources during cleanup. These concrete security, destructive-operation, and setup-correctness risks make the PR unsafe to merge until the documentation is corrected. Possibly related PRs
Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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✨ Finishing Touches🧪 Generate unit tests (beta)
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Inline comments:
In `@config/samples/gateway_anthropic.yaml`:
- Line 17: Update the Anthropic model name in the gateway sample configuration
from claude-sonnet-4-5-20250514 to the valid ID claude-sonnet-4-5-20250929,
preserving the existing configuration structure.
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📒 Files selected for processing (8)
README.mdconfig/samples/agent_example.yamlconfig/samples/agentrun_example.yamlconfig/samples/gateway_anthropic.yamlconfig/samples/gateway_aws_bedrock.yamlconfig/samples/gateway_openai.yamlconfig/samples/gateway_vertex_ai.yamldocs/getting-started.md
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Checked the samples against api/v1alpha1 and the repo's own scripts — field shapes all match, the Agent Sandbox recipe matches hack/start-kind.sh exactly, and the Makefile targets and kubectl logs $SANDBOX step all check out. Three fixes below, plus CodeRabbit's Anthropic model-ID catch.
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Actionable comments posted: 5
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Inline comments:
In `@docs/getting-started.md`:
- Around line 136-142: The AWS_REGION value in the getting-started credential
setup must match the endpoint configured by gateway_aws_bedrock.yaml. Replace
the unrestricted region placeholder with us-east-1, or explicitly instruct users
to update the Gateway endpoint and model when choosing another region before
running kubectl apply.
- Around line 166-170: Update the getting-started instructions around applying
the example Agent and AgentRun manifests to state that Option A is required
because both samples reference the Vertex AI Gateway, or instruct users
following Options B–D to change both manifests to reference the Gateway they
created.
- Around line 118-140: Update the credential setup examples for OpenAI,
Anthropic, and AWS Bedrock to avoid placing secret values in kubectl command
arguments; use a protected-file, stdin-generated Secret, or external
secret-manager pattern while preserving the existing secret names and provider
configuration references.
- Around line 257-267: Update the cleanup instructions to use a dedicated
tutorial namespace and explicitly target that namespace for each kubectl
deletion command instead of deleting resources across the current namespace. Add
a clear warning before the make undeploy and make uninstall commands that they
remove CRDs and matching custom resources across namespaces.
- Around line 9-11: Update the Kubernetes prerequisite in the getting-started
documentation to Kubernetes v1.36+ so it matches the stable ImageVolume support
required by corev1.ImageVolumeSource; retain the existing OpenShift and Agent
Sandbox prerequisites.
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📒 Files selected for processing (8)
README.mdconfig/samples/agentrun_example.yamlconfig/samples/gateway_anthropic.yamlconfig/samples/gateway_aws_bedrock.yamlconfig/samples/gateway_openai.yamlconfig/samples/gateway_vertex_ai.yamldocs/getting-started.mdharness/internal/goose/lifecycle_test.go
🚧 Files skipped from review as they are similar to previous changes (6)
- config/samples/gateway_openai.yaml
- config/samples/gateway_anthropic.yaml
- config/samples/gateway_aws_bedrock.yaml
- config/samples/agentrun_example.yaml
- README.md
- config/samples/gateway_vertex_ai.yaml
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Re-checked the new head — my three earlier items all landed (Bedrock ID + test, Vertex @ snapshot + scheme, ADR link), thanks.
This pass I walked steps 3–5 against the harness/controller source and a scratch apiserver, not just the CRD shapes, and the run step can't succeed as written today. Three things block §5, one bites on OpenShift specifically, plus a couple of nits — details inline:
agent-java's entrypoint requires a Konveyor Hub (HUB_BASE_URL/APP_ID/TARGET_BRANCH) and resolves the repo from Hub; the sample AgentRun sets none of these, and itssource_url/target_branchparams aren't read by anything.quay.io/konveyor/skills:javaee-to-quarkusisn't published — CI only pushesskills/examples/*.- Option A: goose needs
GCP_PROJECT_ID; nothing sets it. kubectl get gateways/delete gateways --allresolve to Gateway API'sgatewayson any cluster with those CRDs (OpenShift 4.19+ installs them by default) — reproduced on envtest, the konveyor Gateway is invisible/undeleted.gateways.konveyor.iofixes it.
(1) is really a product-shape question — Hub-backed vs. standalone runs — more than a doc fix; happy to pair on it if useful.
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| # Supply values for the Agent's declared parameters. | ||
| params: | ||
| - name: source_url |
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This run can't start with quay.io/konveyor/agent-java:latest as written. The image's entrypoint is migration-harness run; its first step, config.LoadFromEnv(), hard-requires HUB_BASE_URL, APP_ID and TARGET_BRANCH (harness/internal/config/config.go:71-81), and its second step resolves the repository URL + git credentials from Hub by APP_ID (harness/cmd/migration-harness/main.go:59-103). So the sandbox exits at startup with config: required env var … is not set (visible in kubectl logs $SANDBOX), and source_url / target_branch land as KONVEYOR_PARAM_SOURCE_URL / KONVEYOR_PARAM_TARGET_BRANCH, which nothing reads — the harness reads TARGET_BRANCH, not the param, and the repo comes from Hub, not source_url.
The repo's own working shape is hack/harness-test/workflow-resources.yaml:157-171 — a spec.env block with HUB_BASE_URL, HUB_TOKEN, HUB_TOKEN_ID, APP_ID, TARGET_BRANCH. The controller passes spec.env straight through (agentrun_controller.go:530), so mirroring that here + dropping the two dead params + adding "a Konveyor Hub with the application registered" to Prerequisites would make §5 honest. If we'd rather not put Hub in the getting-started path, the alternative is to say plainly that standalone runs aren't supported yet and link the entry-point work (#122). Either way, the current text promises something the image can't do.
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| # Skills available to the agent. | ||
| skillCards: | ||
| - ref: javaee-to-quarkus |
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quay.io/konveyor/skills:javaee-to-quarkus doesn't exist on quay. make skill-push (what skills.yml runs) only publishes skills/examples/* — the live tag list is ejb-to-cdi, maven-migration, no-javax-imports. skills/javaee-to-quarkus/ is only built locally by hack/harness-test/setup.sh and kind loaded. Because the SkillCard controller marks spec.image cards Ready without pulling (skillcard_controller.go:96), kubectl get skillcards looks fine and this surfaces late, as an image-volume pull failure on the sandbox pod. Same for javax-to-jakarta-ee via the SkillCollection.
Either point the sample at a published skill (ejb-to-cdi matches the AgentRun's "EJB-to-CDI first" instructions nicely) or move skills/javaee-to-quarkus under skills/examples/ so CI ships it.
| gcloud auth application-default login | ||
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| kubectl create secret generic vertex-credentials \ | ||
| --from-file=GOOGLE_APPLICATION_CREDENTIALS_JSON="$HOME/.config/gcloud/application_default_credentials.json" |
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Option A also needs the GCP project. goose's Vertex provider requires GCP_PROJECT_ID — no default, from_env errors without it (goose v1.45.0 as pinned in agent-base, gcpvertexai.rs) — and the harness sets nothing for it: providerEnv only writes the ADC file and ignores endpoint for Vertex (lifecycle.go:224-235, 244-245).
Cheapest fix that keeps the sample CRs placeholder-free: put it in the same Secret. A keyless credentialRef is exposed whole via envFrom (agentrun_controller.go:501-519), which is already how AWS_REGION rides along in Option D:
kubectl create secret generic vertex-credentials \
--from-file=GOOGLE_APPLICATION_CREDENTIALS_JSON="$HOME/.config/gcloud/application_default_credentials.json" \
--from-literal=GCP_PROJECT_ID="$(gcloud config get-value project)" \
--from-literal=GCP_LOCATION=globalGCP_LOCATION is optional (goose defaults to us-central1 and falls back to us-east5 for Claude) but global matches the sample endpoint and what hack/harness-test uses. The header comment in gateway_vertex_ai.yaml wants the same two lines.
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Thank you; this was very helpful. Agree with Ian's comments but big improvement
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Follow-up to my thread on agentrun_example.yaml, with a data point from today: a teammate applied these samples on a Hub-integrated cluster and launched from the console. The console renders every declared param it can't resolve as a free-text box, so they got a required source_url field (which the harness ignores — it clones the application's repo from Hub by APP_ID) plus a second branch box next to the console's own Target branch (the one that becomes TARGET_BRANCH).
Two small edits fix it, suggestions inline:
- if
source_urlstays, annotate it withkonveyor.io/param-sources(ADR 0013) so the console fills it from the selected application instead of asking the user to retype it (resolver); - drop
target_brancheither way — it collides withTARGET_BRANCH, and the agent ends up told one branch while the harness pushes to another.
Not blocking; happy to fold these into whichever direction the env/Hub-prereq thread lands.
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| name: migration-agent |
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| name: migration-agent | |
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| name: migration-agent | |
| annotations: | |
| # Konveyor console: fill source_url from the selected application's | |
| # repository instead of asking the user to retype it (ADR 0013). | |
| konveyor.io/param-sources: | | |
| {"source_url": "konveyor.io/application-repository-url"} |
Add docs/getting-started.md with step-by-step instructions for deploying the controller, configuring LLM credentials, creating Gateways, Agents, and AgentRuns. Add sample CRs to config/samples/ for all four supported providers (Vertex AI, OpenAI, Anthropic, AWS Bedrock), plus an example Agent and AgentRun that reference the default migration skills. These are reference examples — not added to the samples kustomization since they require user-specific credentials. Update README.md to link to the new getting-started guide. Signed-off-by: David Zager <david.j.zager@gmail.com>
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Signed-off-by: Dylan Murray <dymurray@redhat.com>
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Signed-off-by: David Zager <david.j.zager@gmail.com>
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Summary
Adds user-facing documentation and sample manifests so new contributors
and testers can deploy the controller and configure agent workloads
without reverse-engineering the codebase.
What's included
Getting-started guide (
docs/getting-started.md):make deploy)Sample CRs (
config/samples/):gateway_vertex_ai.yaml— GCP Vertex AI with Claudegateway_openai.yaml— OpenAI GPT-4ogateway_anthropic.yaml— Anthropic direct APIgateway_aws_bedrock.yaml— AWS Bedrockagent_example.yaml— Java migration agent referencing a gateway and skillagentrun_example.yaml— Triggers the migration agentSample CRs are self-contained reference examples with inline
comments explaining prerequisites (e.g. secret creation commands).
They are not added to the samples kustomization since they require
user-specific credentials.
README update: Added a "Getting started" section linking to the
new guide.
Context
From team discussion: multiple people were blocked on testing because
there was no documentation on how to configure things after deploying
the controller. The
LLMProviderCRD was renamed toGatewayandsome team members were still on the old version without realizing.
ADR compatibility
Reviewed all open ADR PRs (#108, #106, #138) before writing. The
guide stays at the CRD-level user interface and does not describe
internal delivery mechanisms (params.json, skill loading, ACP
transport) that are in flux. Nothing here contradicts pending
decisions.
Summary by CodeRabbit