From 91afd1f9677e32afed1cb973da294b3974a3c971 Mon Sep 17 00:00:00 2001 From: Chester Curme Date: Fri, 24 Jan 2025 11:18:48 -0500 Subject: [PATCH] update readme --- README.md | 54 ++++++++++++++++++++++++++++++++++------ libs/langgraph/README.md | 54 ++++++++++++++++++++++++++++++++++------ 2 files changed, 94 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index d582fb2c..4ba7b575 100644 --- a/README.md +++ b/README.md @@ -9,17 +9,57 @@ ## Overview -[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features. +[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a library for building +stateful, multi-actor applications with LLMs, used to create agent and multi-agent +workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraphjs/tutorials/quickstart/). + LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of [LangChain](https://github.com/langchain-ai/langchainjs), but can be used without LangChain. -### Key Features +### Why use LangGraph? + +LangGraph provides fine-grained control over both the flow and state of your +agent applications. It implements a central +[persistence layer](https://langchain-ai.github.io/langgraphjs/concepts/persistence/), +enabling features that are common to most agent architectures: + +- **Memory**: LangGraph persists arbitrary aspects of your application's state, +supporting memory of conversations and other updates within and across user +interactions; +- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted +and resumed, allowing for decisions, validation, and corrections at key stages via +human input. + +Standardizing these components allows individuals and teams to focus on the behavior +of their agent, instead of its supporting infrastructure. + +Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for +the development, deployment, debugging, and monitoring of your applications. + +LangGraph integrates seamlessly with +[LangChain](https://github.com/langchain-ai/langchainjs) and +[LangSmith](https://docs.smith.langchain.com/) (but does not require them). + +To learn more about LangGraph, check out our first LangChain Academy +course, *Introduction to LangGraph*, available for free +[here](https://academy.langchain.com/courses/intro-to-langgraph). + + +### LangGraph Platform + +[LangGraph Platform](https://langchain-ai.github.io/langgraphjs/concepts/langgraph_platform/) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraphjs/concepts/langgraph_server/) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraphjs/concepts/sdk/) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraphjs/concepts/langgraph_cli/) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraphjs/concepts/langgraph_studio/) (UI/debugger). + +See deployment options [here](https://langchain-ai.github.io/langgraphjs/concepts/deployment_options/) +(includes a free tier). + +Here are some common issues that arise in complex deployments, which LangGraph Platform addresses: + +- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraphjs/concepts/streaming/) optimized for various application needs +- **Background runs**: Runs agents asynchronously in the background +- **Support for long running agents**: Infrastructure that can handle long running processes +- **[Double texting](https://langchain-ai.github.io/langgraphjs/concepts/double_texting/)**: Handle the case where you get two messages from the user before the agent can respond +- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads -- **Cycles and Branching**: Implement loops and conditionals in your apps. -- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more. -- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent. -- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming). -- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain.js](https://github.com/langchain-ai/langchainjs/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them). ## Installation diff --git a/libs/langgraph/README.md b/libs/langgraph/README.md index d582fb2c..4ba7b575 100644 --- a/libs/langgraph/README.md +++ b/libs/langgraph/README.md @@ -9,17 +9,57 @@ ## Overview -[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features. +[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a library for building +stateful, multi-actor applications with LLMs, used to create agent and multi-agent +workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraphjs/tutorials/quickstart/). + LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of [LangChain](https://github.com/langchain-ai/langchainjs), but can be used without LangChain. -### Key Features +### Why use LangGraph? + +LangGraph provides fine-grained control over both the flow and state of your +agent applications. It implements a central +[persistence layer](https://langchain-ai.github.io/langgraphjs/concepts/persistence/), +enabling features that are common to most agent architectures: + +- **Memory**: LangGraph persists arbitrary aspects of your application's state, +supporting memory of conversations and other updates within and across user +interactions; +- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted +and resumed, allowing for decisions, validation, and corrections at key stages via +human input. + +Standardizing these components allows individuals and teams to focus on the behavior +of their agent, instead of its supporting infrastructure. + +Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for +the development, deployment, debugging, and monitoring of your applications. + +LangGraph integrates seamlessly with +[LangChain](https://github.com/langchain-ai/langchainjs) and +[LangSmith](https://docs.smith.langchain.com/) (but does not require them). + +To learn more about LangGraph, check out our first LangChain Academy +course, *Introduction to LangGraph*, available for free +[here](https://academy.langchain.com/courses/intro-to-langgraph). + + +### LangGraph Platform + +[LangGraph Platform](https://langchain-ai.github.io/langgraphjs/concepts/langgraph_platform/) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraphjs/concepts/langgraph_server/) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraphjs/concepts/sdk/) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraphjs/concepts/langgraph_cli/) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraphjs/concepts/langgraph_studio/) (UI/debugger). + +See deployment options [here](https://langchain-ai.github.io/langgraphjs/concepts/deployment_options/) +(includes a free tier). + +Here are some common issues that arise in complex deployments, which LangGraph Platform addresses: + +- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraphjs/concepts/streaming/) optimized for various application needs +- **Background runs**: Runs agents asynchronously in the background +- **Support for long running agents**: Infrastructure that can handle long running processes +- **[Double texting](https://langchain-ai.github.io/langgraphjs/concepts/double_texting/)**: Handle the case where you get two messages from the user before the agent can respond +- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads -- **Cycles and Branching**: Implement loops and conditionals in your apps. -- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more. -- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent. -- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming). -- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain.js](https://github.com/langchain-ai/langchainjs/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them). ## Installation