Connecting to a LangGraph Platform Deployment Guide
Connecting to a LangGraph Platform Deployment Deployment Creation We just created a <!--~deployment~ --> deployment for the task_maistro app from module 5. * We used the the LangGraph CLI to build a Docker image for the LangGraph Server with our task_maistro graph. * We used the provided docker-compose.yml file to create three separate containers based on the services defined: * langgraph-redis: Creates a new container using the official Redis image. * langgraph-postgres: Creates a new container using the official Postgres image. * langgraph-api: Creates a new container using our pre-built task_maistro Docker image. `` $ cd module-6/deployment $ docker compose up `` Once running, we can access the deployment through: * API: http://localhost:8123 * Docs: http://localhost:8123/docs * LangGraph Studio: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:8123 Using the API LangGraph Server exposes many API endpoints for interacting with the deployed agent. We can group these endpoints into a few common agent needs: * **Runs**: Atomic agent executions * **Threads**: Multi-turn interactions or human in the loop * **Store**: Long-term memory We can test requests directly in the API docs. SDK The LangGraph SDKs (Python and JS) provide a developer-friendly interface to interact with the LangGraph Server API presented above.
When to use Connecting to a LangGraph Platform Deployment
Connecting to a LangGraph Platform Deployment Deployment Creation We just created a <!--~deployment~ --> deployment for the task_maistro app from module 5. * We used the the LangGraph CLI to build a Docker image for the LangGraph Server with our task_maistro graph. * We used the provided docker-compose.yml file to create three separate containers based on the services defined: * langgraph-redis: Creates a new container using the official Redis image. * langgraph-postgres: Creates a new container using the official Postgres image. * langgraph-api: Creates a new container using our pre-built task_maistro Docker image. `` $ cd module-6/deployment $ docker compose up `` Once running, we can access the deployment through: * API: http://localhost:8123 * Docs: http://localhost:8123/docs * LangGraph Studio: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:8123 Using the API LangGraph Server exposes many API endpoints for interacting with the deployed agent. We can group these endpoints into a few common agent needs: * **Runs**: Atomic agent executions * **Threads**: Multi-turn interactions or human in the loop * **Store**: Long-term memory We can test requests directly in the API docs. SDK The LangGraph SDKs (Python and JS) provide a developer-friendly interface to interact with the LangGraph Server API presented above.
How to use Connecting to a LangGraph Platform Deployment
Connecting to a LangGraph Platform Deployment is a autonomous loop agent built on the LangGraph framework. Set it up from the source repository, configure your model credentials, and invoke it for tasks that match its description. Review the safety profile below before running it against production data or systems.
Safety profile
Autonomy
Semi-autonomous
Sandbox-aware
No declared sandbox guidance
Network access
Unspecified
Filesystem access
Unspecified