From 318de5bb81dcf7945fa4f863d229ec8756370861 Mon Sep 17 00:00:00 2001 From: Chester Curme Date: Fri, 17 Jan 2025 13:31:41 -0500 Subject: [PATCH] update readme --- README.md | 35 +++++++++++++++++++++++++---------- libs/langgraph/README.md | 35 +++++++++++++++++++++++++---------- 2 files changed, 50 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 648f45f4b..9dbafabc1 100644 --- a/README.md +++ b/README.md @@ -12,25 +12,40 @@ ## Overview -[LangGraph](https://langchain-ai.github.io/langgraph/) 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](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. + 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, but can be used without LangChain. -[LangGraph Platform](https://langchain-ai.github.io/langgraph/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/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger), +### Why use LangGraph? -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 provides fine-grained control over both the flow and state of your +agent applications. It implements a central persistence layer, enabling features that +are common to most agent architectures: -### Key Features +- **Memory**: LangGraph supports conversational memory within and across user +interactions; +- **Human-in-the-loop**: Execution can be interrupted and resumed, allowing for +decisions, validation, and corrections at key stages via human input. -- **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](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them). +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 +debugging, tracing and observability, evaluation, and deployment. + +LangGraph integrates seamlessly with +[LangChain](https://python.langchain.com/docs/introduction/) 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 is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. +[LangGraph Platform](https://langchain-ai.github.io/langgraph/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/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger). + 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/langgraph/concepts/streaming) optimized for various application needs diff --git a/libs/langgraph/README.md b/libs/langgraph/README.md index 648f45f4b..9397bd668 100644 --- a/libs/langgraph/README.md +++ b/libs/langgraph/README.md @@ -12,25 +12,40 @@ ## Overview -[LangGraph](https://langchain-ai.github.io/langgraph/) 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](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. + 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, but can be used without LangChain. -[LangGraph Platform](https://langchain-ai.github.io/langgraph/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/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger), +### Why use LangGraph? -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 provides fine-grained control over both the flow and state of your +agent applications. It implements a central persistence layer, enabling features that +are common to most agent architectures: -### Key Features +- **Memory**: LangGraph supports conversational memory within and across user +interactions; +- **Human-in-the-loop**: Execution can be interrupted and resumed, allowing for +decisions, validation, and corrections at key stages via human input. -- **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](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them). +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 +debugging, tracing and observability, evaluation, and deployment. + +LangGraph integrates seamlessly with +[LangChain](https://github.com/langchain-ai/langchain/) 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 is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. +[LangGraph Platform](https://langchain-ai.github.io/langgraph/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/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger). + 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/langgraph/concepts/streaming) optimized for various application needs