Control Plane UI
From the control plane UI, you can:- View a list of outstanding deployments.
- View details of an individual deployment.
- Create a new deployment.
- Update a deployment.
- Update environment variables for a deployment.
- View build and server logs of a deployment.
- View deployment metrics such as CPU and memory usage.
- Delete a deployment.
Control Plane API
This section describes the data model of the control plane API. The API is used to create, update, and delete deployments. See the control plane API reference for more details.Integrations
An integration is an abstraction for agit repository provider (e.g. GitHub). It contains all of the required metadata needed to connect with and deploy from a git repository.
Deployments
A deployment is an instance of a LangGraph Server. A single deployment can have many revisions.Revisions
A revision is an iteration of a deployment. When a new deployment is created, an initial revision is automatically created. To deploy code changes or update secrets for a deployment, a new revision must be created.Listeners
A listener is an instance of a “listener” application. A listener contains metadata about the application (e.g. version) and metadata about the compute infrastructure where it can deploy to (e.g. Kubernetes namespaces). The listener data model only applies for Hybrid and Self-Hosted deployments.Control Plane Features
This section describes various features of the control plane.Deployment Types
For simplicity, the control plane offers two deployment types with different resource allocations:Development and Production.
| Deployment Type | CPU/Memory | Scaling | Database |
|---|---|---|---|
| Development | 1 CPU, 1 GB RAM | Up to 1 replica | 10 GB disk, no backups |
| Production | 2 CPU, 2 GB RAM | Up to 10 replicas | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
Immutable Deployment Type
Once a deployment is created, the deployment type cannot be changed.
Self-Hosted Deployment
Resources for Hybrid and Self-Hosted deployments can be fully customized. Deployment types are only applicable for Cloud deployments.
Production
Production type deployments are suitable for “production” workloads. For example, select Production for customer-facing applications in the critical path.
Resources for Production type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
Development
Development type deployments are suitable development and testing. For example, select Development for internal testing environments. Development type deployments are not suitable for “production” workloads.
Preemptible Compute Infrastructure
Development type deployments (API server, queue server, and database) are provisioned on preemptible compute infrastructure. This means the compute infrastructure may be terminated at any time without notice. This may result in intermittent…- Redis connection timeouts/errors
- Postgres connection timeouts/errors
- Failed or retrying background runs
Development type deployment. By design, LangGraph Server is fault-tolerant. The implementation will automatically attempt to recover from Redis/Postgres connection errors and retry failed background runs.Production type deployments are provisioned on durable compute infrastructure, not preemptible compute infrastructure.Development type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, TTLs should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.
Database Provisioning
The control plane and LangGraph Data Plane “listener” application coordinate to automatically create a Postgres database for each deployment. The database serves as the persistence layer for the deployment. When implementing a LangGraph application, a checkpointer does not need to be configured by the developer. Instead, a checkpointer is automatically configured for the graph. Any checkpointer configured for a graph will be replaced by the one that is automatically configured. There is no direct access to the database. All access to the database occurs through the LangGraph Server. The database is never deleted until the deployment itself is deleted.A custom Postgres instance can be configured for Hybrid and Self-Hosted deployments.
Asynchronous Deployment
Infrastructure for deployments and revisions are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.- When a new deployment is created, a new database is created for the deployment. Database creation is a one-time step. This step contributes to a longer deployment time for the initial revision of the deployment.
- When a subsequent revision is created for a deployment, there is no database creation step. The deployment time for a subsequent revision is significantly faster compared to the deployment time of the initial revision.
- The deployment process for each revision contains a build step, which can take up to a few minutes.
Monitoring
After a deployment is ready, the control plane monitors the deployment and records various metrics, such as:- CPU and memory usage of the deployment.
- Number of container restarts.
- Number of replicas (this will increase with autoscaling).
- Postgres CPU, memory usage, and disk usage.
- LangGraph Server queue pending/active run count.
- LangGraph Server API success response count, error response count, and latency.
LangSmith Integration
A LangSmith tracing project is automatically created for each deployment. The tracing project has the same name as the deployment. When creating a deployment, theLANGCHAIN_TRACING and LANGSMITH_API_KEY/LANGCHAIN_API_KEY environment variables do not need to be specified; they are set automatically by the control plane.
When a deployment is deleted, the traces and the tracing project are not deleted.