Deploy and operate production agent servers with LangSmith Deployment. Use when work involves choosing Cloud vs Hybrid/Self-hosted-with-control-plane vs Standalone, preparing/validating langgraph.json, creating deployments or revisions, rolling back revisions, wiring CI/CD to control-plane APIs, configuring environment variables and secrets, setting monitoring/alerts/webhooks, or troubleshooting deployment/runtime/scaling issues for LangChain/LangGraph applications.
npx skills add https://github.com/Lubu-Labs/langchain-agent-skills --skill langsmith-deployment
Use this skill to deploy, revise, monitor, and troubleshoot LangGraph-based agents in LangSmith Deployment.
langgraph.json validation and deployment compatibility checks.| Model | Use when | Build/Source | Operates infra |
|---|---|---|---|
| Cloud | Fastest managed production path | GitHub repo via control plane | LangSmith |
| Hybrid/Self-hosted with control plane | You need private data plane + centralized deployment UI/API | Container image + control plane | You |
| Standalone server | You want direct Agent Server hosting without control plane | Containerized server | You |
langgraph.jsonuv run python skills/langsmith-deployment/scripts/validate_deployment.py --config langgraph.json --target cloud
uv run python skills/langsmith-deployment/scripts/deploy_to_langsmith.py \
--name "my-agent-prod" \
--owner my-org \
--repo my-agent-repo \
--branch main \
--config langgraph.json
uv run python skills/langsmith-deployment/scripts/deploy_to_langsmith.py \
--name "my-agent-prod" \
--owner my-org \
--repo my-agent-repo \
--region eu
uv run python skills/langsmith-deployment/scripts/deploy_to_langsmith.py \
--name "my-agent-prod" \
--owner my-org \
--repo my-agent-repo \
--control-plane-url https://<your-langsmith-host>/api-host
uv run python skills/langsmith-deployment/scripts/deploy_to_langsmith.py \
--name "my-agent-prod" \
--owner my-org \
--repo my-agent-repo \
--deployment-id <deployment-id> \
--branch main
uv run python skills/langsmith-deployment/scripts/rollback_deployment.py \
--deployment-id <deployment-id> \
--list-revisions
uv run python skills/langsmith-deployment/scripts/rollback_deployment.py \
--deployment-id <deployment-id>
uv run python skills/langsmith-deployment/scripts/setup_monitoring.py \
--project my-agent-prod \
--output-json /tmp/monitoring-plan.json
Note: setup_monitoring.py generates a docs-aligned setup plan/templates. Alerts are configured in LangSmith UI per project.
graphs is required in langgraph.json.dependencies is required.node_version present), dependencies may be handled via package.json.env may be either a string path to an env file or an inline object map.python_version should be one of 3.11, 3.12, 3.13 when set.pip_installer should be one of auto, pip, uv when set.node_version currently documented for LangGraph.js as 20.https://api.host.langchain.com.https://eu.api.host.langchain.com.https://<host>/api-host.X-Tenant-Id), exposed by scripts as --tenant-id.langgraph.json or source code.LANGSMITH_API_KEY env var.DATABASE_URI, REDIS_URI, license key, and any app provider keys)./docs etc.).references/deployment-guide.md: Deployment model choice and end-to-end execution.references/cicd-integration.md: CI/CD stages, control-plane automation patterns, preview/prod strategy.references/environment-management.md: Env var sources, secrets patterns, standalone required vars.references/monitoring-alerts.md: Dashboards, alert model, webhook payload guidance.references/scaling-configuration.md: Scaling responsibilities by model and tuning knobs.references/troubleshooting-deployment.md: Failure triage and rollback strategy.scripts/validate_deployment.py: Validate langgraph.json and deployment readiness.scripts/deploy_to_langsmith.py: Create deployment or revision via control-plane API.scripts/deploy_to_langsmith.ts: TypeScript equivalent deploy/revision script.scripts/rollback_deployment.py: List and rollback revisions.scripts/setup_monitoring.py: Generate alert/dashboard/webhook setup plan.assets/templates/langgraph-cloud.json: Cloud-oriented starter config.assets/templates/github-actions-deploy.yml: CI/CD template for deployment automation.assets/templates/kubernetes-deployment.yaml: Kubernetes template for self-managed environments.assets/templates/.env.example: Env var template for safe sharing.Automate creating a GitHub repository and deploying a web project to Vercel. Use when the user asks to deploy a website/app to production, publish a project, or set up GitHub + Vercel deployment.
Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Take lubu-labs/langsmith-deployment from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.