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docs: update why langgraph and other typos (#2799)

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Eugene Yurtsev
2024-12-17 15:36:41 -05:00
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docs/docs/concepts/double_texting.md
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@@ -16,7 +16,7 @@ If you do not want to use LangGraph Platform, we describe the options we have im
## Reject
This is the simplest option, this just rejects any follow up runs and does not allow double texting.
This is the simplest option, this just rejects any follow-up runs and does not allow double texting.
See the [how-to guide](../cloud/how-tos/reject_concurrent.md) for configuring the reject double text option.
## Enqueue
docs/docs/concepts/faq.md
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@@ -22,21 +22,21 @@ Yes. LangGraph is an MIT-licensed open-source library and is free to use.
LangGraph is a stateful, orchestration framework that brings added control to agent workflows. LangGraph Platform is a service for deploying and scaling LangGraph applications, with an opinionated API for building agent UXs, plus an integrated developer studio.
| Features | LangGraph (open source) | LangGraph Platform |
|----------|------------------------|-------------------|
| Description | Stateful orchestration framework for agentic applications | Scalable infrastructure for deploying LangGraph applications |
| SDKs | Python and JavaScript | Python and JavaScript |
| HTTP APIs | None | Yes - useful for retrieving & updating state or long-term memory, or creating a configurable assistant |
| Streaming | Basic | Dedicated mode for token-by-token messages |
| Checkpointer | Community contributed | Supported out-of-the-box |
| Persistence Layer | Self-managed | Managed Postgres with efficient storage |
| Deployment | Self-managed | • Cloud SaaS <br> • Free self-hosted <br> • Enterprise (BYOC or paid self-hosted) |
| Scalability | Self-managed | Auto-scaling of task queues and servers |
| Fault-tolerance | Self-managed | Automated retries |
| Concurrency Control | Simple threading | Supports double-texting |
| Scheduling | None | Cron scheduling |
| Monitoring | None | Integrated with LangSmith for observability |
| IDE integration | LangGraph Studio for Desktop | LangGraph Studio for Desktop & Cloud |
| Features | LangGraph (open source) | LangGraph Platform |
|---------------------|-----------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| Description | Stateful orchestration framework for agentic applications | Scalable infrastructure for deploying LangGraph applications |
| SDKs | Python and JavaScript | Python and JavaScript |
| HTTP APIs | None | Yes - useful for retrieving & updating state or long-term memory, or creating a configurable assistant |
| Streaming | Basic | Dedicated mode for token-by-token messages |
| Checkpointer | Community contributed | Supported out-of-the-box |
| Persistence Layer | Self-managed | Managed Postgres with efficient storage |
| Deployment | Self-managed | • Cloud SaaS <br> • Free self-hosted <br> • Enterprise (BYOC or paid self-hosted) |
| Scalability | Self-managed | Auto-scaling of task queues and servers |
| Fault-tolerance | Self-managed | Automated retries |
| Concurrency Control | Simple threading | Supports double-texting |
| Scheduling | None | Cron scheduling |
| Monitoring | None | Integrated with LangSmith for observability |
| IDE integration | LangGraph Studio for Desktop | LangGraph Studio for Desktop & Cloud |
## What are my deployment options for LangGraph Platform?
docs/docs/concepts/langgraph_platform.md
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@@ -18,32 +18,7 @@ The LangGraph Platform offers a few different deployment options described in th
## Why Use LangGraph Platform?
LangGraph Platform is designed to make deploying agentic applications seamless and production-ready.
For simpler applications, deploying a LangGraph agent can be as straightforward as using your own server logic—for example, setting up a FastAPI endpoint and invoking LangGraph directly.
### Option 1: Deploying with Custom Server Logic
For basic LangGraph applications, you may choose to handle deployment using your custom server infrastructure. Setting up endpoints with frameworks like [FastAPI](https://fastapi.tiangolo.com/) allows you to quickly deploy and run LangGraph as you would any other Python application:
```python
from fastapi import FastAPI
from your_agent_package import graph
app = FastAPI()
@app.get("/foo")
async def foo(...):
return await graph.ainvoke({...})
```
This approach works well for simple applications with straightforward needs and provides you with full control over the deployment setup. For example, you might use this for a single-assistant application that doesn’t require long-running sessions or persistent memory.
### Option 2: Leveraging LangGraph Platform for Complex Deployments
As your applications scale or add complex features, the deployment requirements often evolve. Running an application with more nodes, longer processing times, or a need for persistent memory can introduce challenges that quickly become time-consuming and difficult to manage manually. [LangGraph Platform](./langgraph_platform.md) is built to handle these challenges seamlessly, allowing you to focus on agent logic rather than server infrastructure.
Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
**LangGraph Platform** handles common issues that arise when deploying LLM applications to production, allowing you to focus on agent logic instead of managing server infrastructure.
- **[Streaming Support](streaming.md)**: As agents grow more sophisticated, they often benefit from streaming both token outputs and intermediate states back to the user. Without this, users are left waiting for potentially long operations with no feedback. LangGraph Server provides [multiple streaming modes](streaming.md) optimized for various application needs.