LangSmith Deployment Guide

LangSmith Deployment Guide

Current deployment guidance for LangChain/LangGraph apps on LangSmith.

Deployment options

LangSmith supports three deployment patterns:

1. Cloud: Managed hosting; deploy from GitHub.
2. Hybrid / Self-hosted with control plane: Control plane + data plane in your environment.
3. Standalone server: Run Agent Server directly without the control plane UI.

Official references:

• https://docs.langchain.com/langsmith/deployments
• https://docs.langchain.com/langsmith/deploy-hybrid
• https://docs.langchain.com/langsmith/self-hosted
• https://docs.langchain.com/langsmith/deploy-with-control-plane
• https://docs.langchain.com/langsmith/deploy-standalone-server

Prerequisites (all deployment modes)

A deployable app should include:

• langgraph.json
• Graph implementation(s)
• Dependency definition (pyproject.toml, requirements.txt, or package.json)
• Optional .env file for local development

Minimal langgraph.json (Python)

{
  "graphs": {
    "agent": "./src/agent.py:graph"
  },
  "dependencies": ["."],
  "env": ".env",
  "python_version": "3.12",
  "image_distro": "wolfi",
  "pip_installer": "uv"
}

Notes based on current config reference:

• graphs and dependencies are required.
• python_version supports 3.11, 3.12, 3.13.
• pip_installer supports auto, pip, uv.

Reference: https://docs.langchain.com/langsmith/cli

Cloud deployment

Cloud is the simplest path: connect repo in LangSmith and deploy from branch/commit.

Typical flow:

1. Connect GitHub integration in LangSmith.
2. Create deployment from repository.
3. Set environment variables in deployment config.
4. Create revisions from new commits.
5. Observe traces, dashboards, and alerts.

Reference:

• https://docs.langchain.com/oss/python/langgraph/deploy
• https://docs.langchain.com/langsmith/deployment-quickstart

Hybrid / Self-hosted with control plane

Use when you need a managed deployment UX with infrastructure in your own cloud.

High-level flow:

1. Build image with LangGraph CLI.
2. Push image to registry accessible by your cluster.
3. Create/update deployment in LangSmith control plane.

# Build image
langgraph build -t my-registry/my-agent:v1.0.0

# Push image
docker push my-registry/my-agent:v1.0.0

References:

• https://docs.langchain.com/langsmith/deploy-with-control-plane
• https://docs.langchain.com/langsmith/api-ref-control-plane

Standalone server deployment

Standalone is the lightest self-hosted option (no control plane UI).

Required environment variables (documented):

• REDIS_URI
• DATABASE_URI
• LANGSMITH_API_KEY
• LANGGRAPH_CLOUD_LICENSE_KEY

Optional:

• LANGSMITH_ENDPOINT (for self-hosted LangSmith tracing endpoint)

Reference: https://docs.langchain.com/langsmith/deploy-standalone-server

Important standalone notes

• Do not run standalone servers in serverless/scale-to-zero environments.
• Allow egress to https://beacon.langchain.com for license verification/usage reporting unless air-gapped mode is configured.

References:

• https://docs.langchain.com/langsmith/self-hosted
• https://docs.langchain.com/langsmith/deploy-standalone-server

Verify a deployment with SDK

Use LangGraph SDK for smoke tests.

import asyncio
from langgraph_sdk import get_client


async def smoke_test() -> None:
    client = get_client(url="<deployment-url>", api_key="<langsmith-api-key>")

    async for chunk in client.runs.stream(
        None,
        "agent",
        input={
            "messages": [{"role": "human", "content": "What is LangGraph?"}]
        },
        stream_mode="updates",
    ):
        print(chunk.event)


if __name__ == "__main__":
    asyncio.run(smoke_test())

Reference: https://docs.langchain.com/langsmith/deployment-quickstart

Operational best practices

• Keep langgraph.json minimal and explicit.
• Keep secrets out of source control.
• Use immutable image tags for deployments.
• Validate with CI tests + evaluation before production.
• Use project dashboards and project-scoped alerts after deployment.