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langchainjs

Thu, 16 Oct 2025 15:28:47 +0800

langchain-ai/langchainjs

🦜️🔗 LangChain.js

⚡ Building applications with LLMs through composability ⚡

Looking for the Python version? Check out LangChain.

To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.

⚡️ Quick Install

You can use npm, pnpm, or yarn to install LangChain.js

npm install -S langchain or pnpm install langchain or yarn add langchain

🌐 Supported Environments

LangChain is written in TypeScript and can be used in:

Node.js (ESM and CommonJS) - 20.x, 22.x, 24.x
Cloudflare Workers
Vercel / Next.js (Browser, Serverless and Edge functions)
Supabase Edge Functions
Browser
Deno

🤔 What is LangChain?

LangChain is a framework for developing applications powered by language models. It enables applications that:

Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)

This framework consists of several parts.

Open-source libraries: Build your applications using LangChain’s open-source building blocks, components, and third-party integrations. Use LangGraph.js to build stateful agents with first-class streaming and human-in-the-loop support.
Productionization: Use LangSmith to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
Deployment: Turn your LangGraph applications into production-ready APIs and Assistants with LangGraph Cloud.

The LangChain libraries themselves are made up of several different packages.

@langchain/core: Base abstractions.
@langchain/community: Third party integrations.
langchain: Chains, agents, and retrieval strategies that make up an application’s cognitive architecture.
LangGraph.js: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.

Integrations may also be split into their own compatible packages.

This library aims to assist in the development of those types of applications. Common examples of these applications include:

❓Question Answering over specific documents

Documentation
End-to-end Example: Doc-Chatbot

💬 Chatbots

Documentation
End-to-end Example: Chat-LangChain

🚀 How does LangChain help?

The main value props of the LangChain libraries are:

Components: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks

Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.

Components fall into the following modules:

📃 Model I/O:

This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.

📚 Retrieval:

Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.

🤖 Agents:

Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a standard interface for agents, along with LangGraph.js for building custom agents.

📖 Additional Resources

Getting started: installation, setting up the environment, simple examples
Overview of the interfaces, modules and integrations
Full Documentation
Tutorial walkthroughs
Langhain Forum
API Reference

💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see here.

Please report any security issues or concerns following our security guidelines.

🖇️ Relationship with Python LangChain

This is built to integrate as seamlessly as possible with the LangChain Python package. Specifically, this means all objects (prompts, LLMs, chains, etc) are designed in a way where they can be serialized and shared between languages.

Prompt-Engineering-Guide

Tue, 14 Oct 2025 15:30:06 +0800

dair-ai/Prompt-Engineering-Guide

Prompt Engineering Guide

Announcements / Updates

🎓 We now offer self-paced prompt engineering courses under our DAIR.AI Academy. Join Now!
🎓 New course on Prompt Engineering for LLMs announced! Enroll here!
💼 We now offer several services like corporate training, consulting, and talks.
🌐 We now support 13 languages! Welcoming more translations.
👩‍🎓 We crossed 3 million learners in January 2024!
🎉 We have launched a new web version of the guide here
🔥 We reached #1 on Hacker News on 21 Feb 2023
🎉 The First Prompt Engineering Lecture went live here

Join our Discord

Subscribe to our YouTube

Subscribe to our Newsletter

Guides

You can also find the most up-to-date guides on our new website https://www.promptingguide.ai/.

Lecture

We have published a 1 hour lecture that provides a comprehensive overview of prompting techniques, applications, and tools.

Running the guide locally

To run the guide locally, for example to check the correct implementation of a new translation, you will need to:

Install Node >=18.0.0
Install pnpm if not present in your system. Check here for detailed instructions.
Install the dependencies: pnpm i next react react-dom nextra nextra-theme-docs
Boot the guide with pnpm dev
Browse the guide at http://localhost:3000/

Appearances

Some places where we have been featured:

Wall Street Journal - ChatGPT Can Give Great Answers. But Only If You Know How to Ask the Right Question
Forbes - Mom, Dad, I Want To Be A Prompt Engineer
Markettechpost - Best Free Prompt Engineering Resources (2023)

If you are using the guide for your work or research, please cite us as follows:

@article{Saravia_Prompt_Engineering_Guide_2022,
author = {Saravia, Elvis},
journal = {https://github.com/dair-ai/Prompt-Engineering-Guide},
month = {12},
title = {{Prompt Engineering Guide}},
year = {2022}
}

License

MIT License

Feel free to open a PR if you think something is missing here. Always welcome feedback and suggestions. Just open an issue!

genai-toolbox

Thu, 28 Aug 2025 15:29:14 +0800

googleapis/genai-toolbox

MCP Toolbox for Databases

[!NOTE] MCP Toolbox for Databases is currently in beta, and may see breaking changes until the first stable release (v1.0).

MCP Toolbox for Databases is an open source MCP server for databases. It enables you to develop tools easier, faster, and more securely by handling the complexities such as connection pooling, authentication, and more.

This README provides a brief overview. For comprehensive details, see the full documentation.

[!NOTE] This solution was originally named “Gen AI Toolbox for Databases” as its initial development predated MCP, but was renamed to align with recently added MCP compatibility.

Why Toolbox?

Toolbox helps you build Gen AI tools that let your agents access data in your database. Toolbox provides:

Simplified development: Integrate tools to your agent in less than 10 lines of code, reuse tools between multiple agents or frameworks, and deploy new versions of tools more easily.
Better performance: Best practices such as connection pooling, authentication, and more.
Enhanced security: Integrated auth for more secure access to your data
End-to-end observability: Out of the box metrics and tracing with built-in support for OpenTelemetry.

⚡ Supercharge Your Workflow with an AI Database Assistant ⚡

Stop context-switching and let your AI assistant become a true co-developer. By connecting your IDE to your databases with MCP Toolbox, you can delegate complex and time-consuming database tasks, allowing you to build faster and focus on what matters. This isn’t just about code completion; it’s about giving your AI the context it needs to handle the entire development lifecycle.

Here’s how it will save you time:

Query in Plain English: Interact with your data using natural language right from your IDE. Ask complex questions like, “How many orders were delivered in 2024, and what items were in them?” without writing any SQL.
Automate Database Management: Simply describe your data needs, and let the AI assistant manage your database for you. It can handle generating queries, creating tables, adding indexes, and more.
Generate Context-Aware Code: Empower your AI assistant to generate application code and tests with a deep understanding of your real-time database schema. This accelerates the development cycle by ensuring the generated code is directly usable.
Slash Development Overhead: Radically reduce the time spent on manual setup and boilerplate. MCP Toolbox helps streamline lengthy database configurations, repetitive code, and error-prone schema migrations.

Learn how to connect your AI tools (IDEs) to Toolbox using MCP.

General Architecture

Toolbox sits between your application’s orchestration framework and your database, providing a control plane that is used to modify, distribute, or invoke tools. It simplifies the management of your tools by providing you with a centralized location to store and update tools, allowing you to share tools between agents and applications and update those tools without necessarily redeploying your application.

Getting Started

Installing the server

For the latest version, check the releases page and use the following instructions for your OS and CPU architecture.

Binary

To install Toolbox as a binary:

# see releases page for other versions
export VERSION=0.13.0
curl -O https://storage.googleapis.com/genai-toolbox/v$VERSION/linux/amd64/toolbox
chmod +x toolbox

Container image

You can also install Toolbox as a container:

1
2
3

# see releases page for other versions
export VERSION=0.13.0
docker pull us-central1-docker.pkg.dev/database-toolbox/toolbox/toolbox:$VERSION

Homebrew

To install Toolbox using Homebrew on macOS or Linux:

`1`	`brew install mcp-toolbox`

Compile from source

To install from source, ensure you have the latest version of Go installed, and then run the following command:

`1`	`go install github.com/googleapis/genai-toolbox@v0.13.0`

Running the server

Configure a tools.yaml to define your tools, and then execute toolbox to start the server:

`1`	`./toolbox --tools-file "tools.yaml"`

[!NOTE] Toolbox enables dynamic reloading by default. To disable, use the --disable-reload flag.

Homebrew Users

If you installed Toolbox using Homebrew, the toolbox binary is available in your system path. You can start the server with the same command:

`1`	`toolbox --tools-file "tools.yaml"`

You can use toolbox help for a full list of flags! To stop the server, send a terminate signal (ctrl+c on most platforms).

For more detailed documentation on deploying to different environments, check out the resources in the How-to section

Integrating your application

Once your server is up and running, you can load the tools into your application. See below the list of Client SDKs for using various frameworks:

Python (Github)

Core

Install Toolbox Core SDK:
1

pip install toolbox-core

Load tools:

from toolbox_core import ToolboxClient

# update the url to point to your server
async with ToolboxClient("http://127.0.0.1:5000") as client:

    # these tools can be passed to your application!
    tools = await client.load_toolset("toolset_name")

For more detailed instructions on using the Toolbox Core SDK, see the project’s README.

LangChain / LangGraph

Install Toolbox LangChain SDK:
1

pip install toolbox-langchain

Load tools:

from toolbox_langchain import ToolboxClient

# update the url to point to your server
async with ToolboxClient("http://127.0.0.1:5000") as client:

    # these tools can be passed to your application!
    tools = client.load_toolset()

For more detailed instructions on using the Toolbox LangChain SDK, see the project’s README.

LlamaIndex

Install Toolbox Llamaindex SDK:
1

pip install toolbox-llamaindex

Load tools:

from toolbox_llamaindex import ToolboxClient

# update the url to point to your server
async with ToolboxClient("http://127.0.0.1:5000") as client:

    # these tools can be passed to your application!
    tools = client.load_toolset()

For more detailed instructions on using the Toolbox Llamaindex SDK, see the project’s README.

Javascript/Typescript (Github)

Core

Install Toolbox Core SDK:
1

npm install @toolbox-sdk/core

Load tools:

import { ToolboxClient } from '@toolbox-sdk/core';

// update the url to point to your server
const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);

// these tools can be passed to your application!
const tools = await client.loadToolset('toolsetName');

For more detailed instructions on using the Toolbox Core SDK, see the project’s README.

LangChain / LangGraph

Install Toolbox Core SDK:
1

npm install @toolbox-sdk/core

Load tools:

import { ToolboxClient } from '@toolbox-sdk/core';

// update the url to point to your server
const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);

// these tools can be passed to your application!
const toolboxTools = await client.loadToolset('toolsetName');

// Define the basics of the tool: name, description, schema and core logic
const getTool = (toolboxTool) => tool(currTool, {
    name: toolboxTool.getName(),
    description: toolboxTool.getDescription(),
    schema: toolboxTool.getParamSchema()
});

// Use these tools in your Langchain/Langraph applications
const tools = toolboxTools.map(getTool);

Genkit

Install Toolbox Core SDK:
1

npm install @toolbox-sdk/core

Load tools:

import { ToolboxClient } from '@toolbox-sdk/core';
import { genkit } from 'genkit';

// Initialise genkit
const ai = genkit({
    plugins: [
        googleAI({
            apiKey: process.env.GEMINI_API_KEY || process.env.GOOGLE_API_KEY
        })
    ],
    model: googleAI.model('gemini-2.0-flash'),
});

// update the url to point to your server
const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);

// these tools can be passed to your application!
const toolboxTools = await client.loadToolset('toolsetName');

// Define the basics of the tool: name, description, schema and core logic
const getTool = (toolboxTool) => ai.defineTool({
    name: toolboxTool.getName(),
    description: toolboxTool.getDescription(),
    schema: toolboxTool.getParamSchema()
}, toolboxTool)

// Use these tools in your Genkit applications
const tools = toolboxTools.map(getTool);

Go (Github)

Core

Install Toolbox Go SDK:

`1`	`go get github.com/googleapis/mcp-toolbox-sdk-go`

Load tools:

package main

import (
  "github.com/googleapis/mcp-toolbox-sdk-go/core"
  "context"
)

func main() {
  // Make sure to add the error checks
  // update the url to point to your server
  URL := "http://127.0.0.1:5000";
  ctx := context.Background()

  client, err := core.NewToolboxClient(URL)

  // Framework agnostic tools
  tools, err := client.LoadToolset("toolsetName", ctx)
}

For more detailed instructions on using the Toolbox Go SDK, see the project’s README.

LangChain Go

Install Toolbox Go SDK:

`1`	`go get github.com/googleapis/mcp-toolbox-sdk-go`

Load tools:

package main

import (
  "context"
  "encoding/json"

  "github.com/googleapis/mcp-toolbox-sdk-go/core"
  "github.com/tmc/langchaingo/llms"
)

func main() {
  // Make sure to add the error checks
  // update the url to point to your server
  URL := "http://127.0.0.1:5000"
  ctx := context.Background()

  client, err := core.NewToolboxClient(URL)

  // Framework agnostic tool
  tool, err := client.LoadTool("toolName", ctx)

  // Fetch the tool's input schema
  inputschema, err := tool.InputSchema()

  var paramsSchema map[string]any
  _ = json.Unmarshal(inputschema, &paramsSchema)

  // Use this tool with LangChainGo
  langChainTool := llms.Tool{
    Type: "function",
    Function: &llms.FunctionDefinition{
      Name:        tool.Name(),
      Description: tool.Description(),
      Parameters:  paramsSchema,
    },
  }
}

Genkit

Install Toolbox Go SDK:

`1`	`go get github.com/googleapis/mcp-toolbox-sdk-go`

Load tools:

package main
import (
  "context"
  "encoding/json"

  "github.com/firebase/genkit/go/ai"
  "github.com/firebase/genkit/go/genkit"
  "github.com/googleapis/mcp-toolbox-sdk-go/core"
  "github.com/googleapis/mcp-toolbox-sdk-go/tbgenkit"
  "github.com/invopop/jsonschema"
)

func main() {
  // Make sure to add the error checks
  // Update the url to point to your server
  URL := "http://127.0.0.1:5000"
  ctx := context.Background()
  g, err := genkit.Init(ctx)

  client, err := core.NewToolboxClient(URL)

  // Framework agnostic tool
  tool, err := client.LoadTool("toolName", ctx)

  // Convert the tool using the tbgenkit package
  // Use this tool with Genkit Go
  genkitTool, err := tbgenkit.ToGenkitTool(tool, g)
  if err != nil {
    log.Fatalf("Failed to convert tool: %v\n", err)
  }
}

Go GenAI

Install Toolbox Go SDK:

`1`	`go get github.com/googleapis/mcp-toolbox-sdk-go`

Load tools:

package main

import (
  "context"
  "encoding/json"

  "github.com/googleapis/mcp-toolbox-sdk-go/core"
  "google.golang.org/genai"
)

func main() {
  // Make sure to add the error checks
  // Update the url to point to your server
  URL := "http://127.0.0.1:5000"
  ctx := context.Background()

  client, err := core.NewToolboxClient(URL)

  // Framework agnostic tool
  tool, err := client.LoadTool("toolName", ctx)

  // Fetch the tool's input schema
  inputschema, err := tool.InputSchema()

  var schema *genai.Schema
  _ = json.Unmarshal(inputschema, &schema)

  funcDeclaration := &genai.FunctionDeclaration{
    Name:        tool.Name(),
    Description: tool.Description(),
    Parameters:  schema,
  }

  // Use this tool with Go GenAI
  genAITool := &genai.Tool{
    FunctionDeclarations: []*genai.FunctionDeclaration{funcDeclaration},
  }
}

OpenAI Go

Install Toolbox Go SDK:

`1`	`go get github.com/googleapis/mcp-toolbox-sdk-go`

Load tools:

package main

import (
  "context"
  "encoding/json"

  "github.com/googleapis/mcp-toolbox-sdk-go/core"
  openai "github.com/openai/openai-go"
)

func main() {
  // Make sure to add the error checks
  // Update the url to point to your server
  URL := "http://127.0.0.1:5000"
  ctx := context.Background()

  client, err := core.NewToolboxClient(URL)

  // Framework agnostic tool
  tool, err := client.LoadTool("toolName", ctx)

  // Fetch the tool's input schema
  inputschema, err := tool.InputSchema()

  var paramsSchema openai.FunctionParameters
  _ = json.Unmarshal(inputschema, &paramsSchema)

  // Use this tool with OpenAI Go
  openAITool := openai.ChatCompletionToolParam{
    Function: openai.FunctionDefinitionParam{
      Name:        tool.Name(),
      Description: openai.String(tool.Description()),
      Parameters:  paramsSchema,
    },
  }

}

Configuration

The primary way to configure Toolbox is through the tools.yaml file. If you have multiple files, you can tell toolbox which to load with the --tools-file tools.yaml flag.

You can find more detailed reference documentation to all resource types in the Resources.

Sources

The sources section of your tools.yaml defines what data sources your Toolbox should have access to. Most tools will have at least one source to execute against.

sources:
  my-pg-source:
    kind: postgres
    host: 127.0.0.1
    port: 5432
    database: toolbox_db
    user: toolbox_user
    password: my-password

For more details on configuring different types of sources, see the Sources.

Tools

The tools section of a tools.yaml define the actions an agent can take: what kind of tool it is, which source(s) it affects, what parameters it uses, etc.

tools:
  search-hotels-by-name:
    kind: postgres-sql
    source: my-pg-source
    description: Search for hotels based on name.
    parameters:
      - name: name
        type: string
        description: The name of the hotel.
    statement: SELECT * FROM hotels WHERE name ILIKE '%' || $1 || '%';

For more details on configuring different types of tools, see the Tools.

Toolsets

The toolsets section of your tools.yaml allows you to define groups of tools that you want to be able to load together. This can be useful for defining different groups based on agent or application.

toolsets:
    my_first_toolset:
        - my_first_tool
        - my_second_tool
    my_second_toolset:
        - my_second_tool
        - my_third_tool

You can load toolsets by name:

# This will load all tools
all_tools = client.load_toolset()

# This will only load the tools listed in 'my_second_toolset'
my_second_toolset = client.load_toolset("my_second_toolset")

Versioning

This project uses semantic versioning (MAJOR.MINOR.PATCH). Since the project is in a pre-release stage (version 0.x.y), we follow the standard conventions for initial development:

Pre-1.0.0 Versioning

While the major version is 0, the public API should be considered unstable. The version will be incremented as follows:

0.MINOR.PATCH: The MINOR version is incremented when we add new functionality or make breaking, incompatible API changes.
0.MINOR.PATCH: The PATCH version is incremented for backward-compatible bug fixes.

Post-1.0.0 Versioning

Once the project reaches a stable 1.0.0 release, the versioning will follow the more common convention:

MAJOR.MINOR.PATCH: Incremented for incompatible API changes.
MAJOR.MINOR.PATCH: Incremented for new, backward-compatible functionality.
MAJOR.MINOR.PATCH: Incremented for backward-compatible bug fixes.

The public API that this applies to is the CLI associated with Toolbox, the interactions with official SDKs, and the definitions in the tools.yaml file.

Contributing

Contributions are welcome. Please, see the CONTRIBUTING to get started.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See Contributor Code of Conduct for more information.

Community

Join our discord community to connect with our developers!

reactos

Mon, 26 May 2025 15:39:52 +0800

reactos/reactos

Quick Links

Website • Official chat • Wiki • Forum • Community Discord • JIRA Bug Tracker • ReactOS Git mirror • Testman

What is ReactOS?

ReactOS™ is an Open Source effort to develop a quality operating system that is compatible with applications and drivers written for the Microsoft® Windows™ NT family of operating systems (NT4, 2000, XP, 2003, Vista, 7).

The ReactOS project, although currently focused on Windows Server 2003 compatibility, is always keeping an eye toward compatibility with Windows Vista and future Windows NT releases.

The code of ReactOS is licensed under GNU GPL 2.0.

Product quality warning

ReactOS is currently an Alpha quality operating system. This means that ReactOS is under heavy development and you have to be ready to encounter some problems. Different things may not work well and it can corrupt the data present on your hard disk. It is HIGHLY recommended to test ReactOS on a virtual machine or on a computer with no sensitive or critical data!

Building

To build the system it is strongly advised to use the ReactOS Build Environment (RosBE). Up-to-date versions for Windows and for Unix/GNU-Linux are available from our download page at: “Build Environment”.

Alternatively one can use Microsoft Visual C++ (MSVC) version 2019+. Building with MSVC is covered here: “Visual Studio or Microsoft Visual C++”.

See “Building ReactOS” article for more details.

Binaries

To build ReactOS you must run the configure script in the directory you want to have your build files. Choose configure.cmd or configure.sh depending on your system. Then run ninja <modulename> to build a module you want or just ninja to build all modules.

Bootable images

To build a bootable CD image run ninja bootcd from the build directory. This will create a CD image with a filename bootcd.iso.

You can always download fresh binary builds of bootable images from the “Daily builds” page.

Installing

By default, ReactOS currently can only be installed on a machine that has a FAT16 or FAT32 partition as the active (bootable) partition. The partition on which ReactOS is to be installed (which may or may not be the bootable partition) must also be formatted as FAT16 or FAT32. ReactOS Setup can format the partitions if needed.

Starting with 0.4.10, ReactOS can be installed using the BtrFS file system. But consider this as an experimental feature and thus regressions not triggered on FAT setup may be observed.

To install ReactOS from the bootable CD distribution, extract the archive contents. Then burn the CD image, boot from it, and follow the instructions.

See “Installing ReactOS” Wiki page or INSTALL for more details.

Testing

If you discover a bug in ReactOS search on JIRA first - it might be reported already. If not report the bug providing logs and as much information as possible.

See “File Bugs” for a guide.

NOTE: The bug tracker is not for discussions. Please use our official chat or our forum.

Contributing

We are always looking for developers! Check how to contribute if you are willing to participate.

Legal notice: If you have seen proprietary Microsoft Windows source code (including but not limited to the leaked Windows NT 3.5, NT 4, 2000 source code and the Windows Research Kernel), your contribution won’t be accepted because of potential copyright violation.

Try out cloud-based ReactOS development using Gitpod and Docker:

You can also support ReactOS by donating! We rely on our backers to maintain our servers and accelerate development by hiring full-time devs.

More information

ReactOS is a Free and Open Source operating system based on the Windows architecture, providing support for existing applications and drivers, and an alternative to the current dominant consumer operating system.

It is not another wrapper built on Linux, like WINE. It does not attempt or plan to compete with WINE; in fact, the user-mode part of ReactOS is almost entirely WINE-based and our two teams have cooperated closely in the past.

ReactOS is also not “yet another OS”. It does not attempt to be a third player like any other alternative OS out there. People are not meant to uninstall Linux and use ReactOS instead; ReactOS is a replacement for Windows users who want a Windows replacement that behaves just like Windows.

More information is available at: reactos.org.

Also see the media/doc subdirectory for some sparse notes.

Who is responsible

Active devs are listed as members of GitHub organization. See also the CREDITS file for others.

Code mirrors

The main development is done on GitHub. We have an alternative mirror in case GitHub is down.

There is also an obsolete SVN archive repository that is kept for historical purposes.

WSL

Fri, 23 May 2025 15:28:31 +0800

microsoft/WSL

Welcome to the Windows Subsystem for Linux (WSL) repository

Learn more about WSL | Downloads & Release notes | Contributing to WSL

About

Windows Subsystem for Linux (WSL) is a powerful way for you to run your Linux command-line tools, utilities and applications, all unmodified and directly on Windows without the overhead of a traditional virtual machine or dual boot setup.

You can install WSL right away by running this command inside of your Windows command line:

`1`	`wsl --install`

You can learn more about best practices for setup, overviews of WSL and more at our WSL documentation page.

WSL also has related open source repositories:

microsoft/WSL2-Linux-Kernel - The Linux kernel shipped with WSL
microsoft/WSLg - Support for Linux GUI apps in WSL
microsoftdocs/wsl - WSL documentation at aka.ms/wsldocs

Contributing

This project welcomes contributions of all types, including coding features / bug fixes, documentation fixes, design proposals and more.

We ask that before you start working on a contribution, please read our Contributor’s Guide.

For guidance on developing for WSL, please read the developer docs for instructions on how to build WSL from source and details on its architecture.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

Privacy and telemetry

The application logs basic diagnostic data (telemetry). For more information on privacy and what we collect, see our data and privacy documentation.

The software may collect information about you and your use of the software and send it to Microsoft. Microsoft may use this information to provide services and improve our products and services. You may turn off the telemetry as described in the repository. There are also some features in the software that may enable you and Microsoft to collect data from users of your applications. If you use these features, you must comply with applicable law, including providing appropriate notices to users of your applications together with a copy of Microsoft’s privacy statement. Our privacy statement is located at https://go.microsoft.com/fwlink/?LinkID=824704. You can learn more about data collection and use in the help documentation and our privacy statement. Your use of the software operates as your consent to these practices.

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Privacy and telemetry