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54 lines
3.3 KiB
Markdown
54 lines
3.3 KiB
Markdown
---
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boost: 2
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---
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# LangGraph Server
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**LangGraph Server** offers an API for creating and managing agent-based applications. It is built on the concept of [assistants](assistants.md), which are agents configured for specific tasks, and includes built-in [persistence](persistence.md#memory-store) and a **task queue**. This versatile API supports a wide range of agentic application use cases, from background processing to real-time interactions.
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Use LangGraph Server to create and manage [assistants](assistants.md), [threads](./persistence.md#threads), [runs](./assistants.md#execution), [cron jobs](../cloud/concepts/cron_jobs.md), [webhooks](../cloud/concepts/webhooks.md), and more.
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!!! tip "API reference"
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For detailed information on the API endpoints and data models, see [LangGraph Platform API reference docs](../cloud/reference/api/api_ref.html).
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## Application structure
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To deploy a LangGraph Server application, you need to specify the graph(s) you want to deploy, as well as any relevant configuration settings, such as dependencies and environment variables.
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Read the [application structure](./application_structure.md) guide to learn how to structure your LangGraph application for deployment.
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## Parts of a deployment
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When you deploy LangGraph Server, you are deploying one or more [graphs](#graphs), a database for [persistence](persistence.md), and a task queue.
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### Graphs
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When you deploy a graph with LangGraph Server, you are deploying a "blueprint" for an [Assistant](assistants.md).
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An [Assistant](assistants.md) is a graph paired with specific configuration settings. You can create multiple assistants per graph, each with unique settings to accommodate different use cases
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that can be served by the same graph.
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Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
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!!! note
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We often think of a graph as implementing an [agent](agentic_concepts.md), but a graph does not necessarily need to implement an agent. For example, a graph could implement a simple
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chatbot that only supports back-and-forth conversation, without the ability to influence any application control flow. In reality, as applications get more complex, a graph will often implement a more complex flow that may use [multiple agents](./multi_agent.md) working in tandem.
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### Persistence and task queue
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LangGraph Server leverages a database for [persistence](persistence.md) and a task queue.
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Currently, only [Postgres](https://www.postgresql.org/) is supported as a database for LangGraph Server and [Redis](https://redis.io/) as the task queue.
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If you're deploying using [LangGraph Platform](./langgraph_cloud.md), these components are managed for you. If you're deploying LangGraph Server on your own infrastructure, you'll need to set up and manage these components yourself.
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Please review the [deployment options](./deployment_options.md) guide for more information on how these components are set up and managed.
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## Learn more
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* LangGraph [Application Structure](./application_structure.md) guide explains how to structure your LangGraph application for deployment.
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* The [LangGraph Platform API Reference](../cloud/reference/api/api_ref.html) provides detailed information on the API endpoints and data models.
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