LaunchDarkly | Documentation llms.txt 路 AI Docs Browser 路 mdream [llms.txt Tools](https://mdream.dev/llms-txt/launchdarkly/llms-txt) # LaunchDarkly | Documentation llms.txt docs.launchdarkly.com/llms.txt [Raw llms.txt](https://docs.launchdarkly.com/llms.txt) [Raw llms-full.txt](https://docs.launchdarkly.com/llms-full.txt) Copy Markdown [](https://docs.launchdarkly.com/llms.txt) llms.txt Home Account security Account usage metrics Active Directory Federation Services (ADFS) Adding and removing flags from release pipelines Agent graphs AgentControl AgentControl and information privacy AgentControl config autogenerated metrics AgentControl onboarding prompt Agents Alerts Allocating experiment audiences An introduction to contexts Analyzing experiments Anonymous contexts API access tokens Application versions Applications Applications and app versions Applications in Engineering insights Approval request settings Approvals Archiving context kinds Archiving flags Assigning roles to members Assigning roles to teams Autogenerated metrics AWS CloudWatch Metrics AWS FireLens Azure Monitor Bayesian experiment results BigQuery native Experimentation Billing and usage Bitbucket code references Built-in attributes Bulk targeting Calculating billing Change history Change SSO providers Changelog Changelog archive Changing account Owners Changing member roles Choosing a metric type CircleCI code references Clicked or tapped conversion metrics Clone environments Cloning and deleting API access tokens Cloning existing flags Code references Collecting user feedback Compare config variation versions Comparing and copying flag settings Components of a metric Config targeting Configure SAML SSO Configuring approvals for an environment Connect apps and services to LaunchDarkly Connecting Vega to GitHub Context attributes Context instances Context kinds Contexts Converting rules into segments Create and manage AI model configurations Create configs Create environments Create flags Creating a team Creating and editing context kinds Creating and managing progressive rollouts Creating API access tokens Creating custom dashboards Creating experiments Creating experiments using warehouse native metrics Creating flag triggers Creating flag variations Creating guarded rollouts Creating holdouts Creating metric groups Creating metrics Creating metrics from traces Creating migration flags Creating multi-armed bandits Creating new flags Creating private projects with custom roles Creating release pipelines Creating roles and policies Creating scheduled flag changes Creating segments Creating views Creating workflows Custom attributes Custom configuration with ld-find-code-refs Custom conversion binary metrics Custom conversion count metrics Custom flags Custom metrics Custom numeric metrics Custom properties Custom rules Dashboards Databricks native Experimentation Datadog Agent ingestion Datasets Decision making with Bayesian statistics Delete environments Deleting approval requests Deleting flags Deleting release pipelines Deleting roles Deleting teams Deleting workflows Deployments Deprecating flags Disable SSO Domain verification Edit environment settings Edit projects Editing and archiving experiments Editing and archiving multi-armed bandits Editing roles Enable SCIM provisioning Enable SSO Engineering insights Entra ID Environments Error monitoring Events Example roles and policies Experiment configuration Experiment flags Experiment health checks Experiment metric types Experiment results data Experiment sample size and run time Experimentation Experimentation and metric events Experimentation prerequisites Experimentation rate Feature monitoring Feature previews Filtering custom metric events Filtering experiment results Filtering the Contexts list Flag audiences Flag cleanup custom agent for GitHub Copilot Flag evaluations Flag health Flag lifecycle settings Flag links Flag prerequisites Flag settings Flag statuses and lifecycle stages Flag templates Flag triggers Flag types Fluent Bit Frequentist experiment results Get started Get started with engineering insights Getting started in different roles GitHub code references GitHub OAuth GitLab code references Google OAuth Google Workspace Guarded rollout errors Guarded rollouts Health checks for guarded rollouts Holdouts IDE connectors and agents Importing events Importing flags Individual targeting Install the GitHub app IntelliJ IDEA IP allowlist Joining an account JSON targeting Kill switch flags Launch Insights LaunchDarkly agent skills LaunchDarkly architecture LaunchDarkly CLI LaunchDarkly CLI commands LaunchDarkly developer toolbar LaunchDarkly developer tools LaunchDarkly extension for GitHub Copilot LaunchDarkly hosted MCP server LaunchDarkly in China and Pakistan LaunchDarkly in environments requiring FIPS 140-3 validated encryption modules LaunchDarkly in federal environments LaunchDarkly in the European Union (EU) LaunchDarkly local MCP server LaunchDarkly MCP server LaunchDarkly subscriptions and plans LaunchDarkly vocabulary Lead time Live events LLM observability Logging in with SSO Logs Manage AgentControl Manage flags Manage targeting rules Manage views Managing experiments Managing flag triggers Managing guarded rollouts Managing holdouts Managing metrics Managing release pipelines Managing roles Managing roles assignments Managing scheduled flag changes Managing segments Managing sessions Managing team maintainers Managing teams Manually instrument LLM spans Member role concepts Members Metric data sources Metric groups Metric health checks Metric impact Metric measurement window Metric winsorization Migration flag metrics Migration flags Mobile targeting Monitor config performance Multi-armed bandit results Multi-armed bandits Multi-contexts Multi-factor authentication Mutually exclusive experiments OAuth applications Observability Observability autogenerated metrics Observability integrations Observability settings Offline evaluations Okta Onboarding OneLogin Online evaluations OpenTelemetry autogenerated metrics Organization access settings Organization announcements Organization roles Outliers in Bayesian experiments Page viewed conversion metrics Percentage rollouts Percentage rollouts by context attribute PingIdentity Playgrounds Previewing event data Product analytics events Progressive rollout health checks Progressive rollouts Project metrics Project overview Project roles Project-level flag settings Projects Prompt snippets Public IP list Quickstart for AgentControl Quickstart for Experimentation Randomization units Ratio metrics Redshift native Experimentation Regression detection Release flags Release management tools Release pipelines Release policies Releases Releasing features with LaunchDarkly Removing member roles Requesting approvals Required comments Required confirmation Resetting access tokens Resetting your password Restoring previous flag versions Reviewing approval requests Roles Run experiments with AgentControl Scheduled flag changes SDK and integration configuration for segments SDK credentials Search specification Segment types Segments Segments synced from external tools Send deployment information Service connections Service map Session replay Set up .NET (server-side) SDK Set up Android SDK Set up code references Set up Go SDK Set up iOS SDK Set up Java SDK Set up JavaScript SDK Set up Node.js (client-side) SDK Set up Node.js (server-side) SDK Set up Python SDK Set up React Native SDK Set up React Web SDK Set up Vue SDK Setting up an SDK Setting up contexts for guarded rollouts Setting up external warehouses Setting up the LaunchDarkly developer toolbar Setting up the Snowflake integration Shipping the winning variation Single sign-on Snowflake common questions Snowflake data requirements Snowflake native Experimentation Starting and stopping experiment iterations Support options Syncing segments with Amplitude cohorts Syncing segments with Fivetran (formerly Census) Syncing segments with Heap Syncing segments with Hightouch Syncing segments with RudderStack Audiences Syncing segments with Tealium Audiences Syncing segments with Twilio Segment Audiences Syncing segments with Zeotap Syslog log drain Tags Target with flags Targeting contexts in larger list-based segments Targeting contexts in rule-based and smaller list-based segments Targeting rules Targeting segments Targeting with migration flags Teams Testing changes to flag targeting The Billing page The context details page The Contexts list The default rule The Flags list The LaunchDarkly API The off variation The trends explorer Traces Turning flags on and off Understand AI impact with AI Insights Using actions Using feature management Using one email for multiple accounts Using policies Using private context attributes Using resources Using role scope Using the advanced editor Using the LaunchDarkly developer toolbar Using the Metrics list Using the SCIM API Using Vega in Slack Variation reassignment Vega Vega for auto-remediation Vega for flag cleanup Vercel Viewing context kinds Viewing flags across environments Viewing incoming events Viewing member roles Views Visual Studio Code (VSCode) Warehouse health checks Warehouse native metrics Webhooks Workflow templates Workflows Your member profile SDKs .NET (client-side) SDK 1.x to 2.0 migration guide .NET (client-side) SDK 2.x to 3.0 migration guide .NET (client-side) SDK 3.x to 4.0 migration guide .NET (client-side) SDK 4.x to 5.0 migration guide .NET (server-side) SDK 5.x to 6.0 migration guide .NET (server-side) SDK 6.x to 7.0 migration guide .NET (server-side) SDK 7.x to 8.0 migration guide .NET (server-side) SDK observability reference .NET AI SDK reference .NET MAUI SDK observability reference .NET SDK reference (client-side) .NET SDK reference (server-side) AI SDKs Akamai SDK reference Aliasing users Analytics events Android SDK 2.x to 3.0 migration guide Android SDK 3.x to 4.0 migration guide Android SDK 4.x to 5.0 migration guide Android SDK observability reference Android SDK reference Anonymous contexts Apex SDK reference Apple App Store data collection policy Application metadata configuration Automatic configuration Automatic environment attributes Big segments Bootstrapping C/C++ (client-side) SDK 1.x to 2.0 migration guide C++ (client-side) SDK 2.x to 3.0 migration guide C++ (server-side) SDK 2.x to 3.0 migration guide C++ SDK reference (client-side) C++ SDK reference (server-side) Caching contexts to local storage Choosing an SDK type Client-side SDKs Cloudflare SDK 1.x to 2.0 migration guide Cloudflare SDK reference Configuration for client-side observability Configuration for server-side observability Configuration for session replay Configuring and deploying the Relay Proxy Configuring SDKs for the Relay Proxy Consul Context configuration Contributor's guide Customizing AgentControl configs Data saving mode Determining whether to use the Relay Proxy Domain list DynamoDB Edge SDKs Electron SDK reference Eliminating flicker when using default flag values Erlang (server-side) SDK 1.x to 2.0 migration guide Erlang (server-side) SDK 2.x to 3.0 migration guide Erlang SDK reference Evaluation reasons Experimentation Fastly SDK reference Flag evaluation rules in server-side SDKs Flag variation evaluation Flag variation evaluation details Flushing events Flutter SDK 1.x to 2.0 migration guide Flutter SDK 2.x to 3.0 migration guide Flutter SDK 3.x to 4.0 migration guide Flutter SDK observability reference Flutter SDK reference Gatsby plugin Getting all flags Getting started with SDKs Go AI SDK reference Go SDK 4.x to 5.0 migration guide Go SDK 5.x to 6.0 migration guide Go SDK 6.x to 7.0 migration guide Go SDK observability reference Go SDK reference Haskell SDK 3.x to 4.0 migration guide Haskell SDK reference Hooks HTMX observability reference Identifying and changing contexts Implementing the Relay Proxy Inspectors iOS SDK 5.x to 6.0 migration guide for Objective-C iOS SDK 5.x to 6.0 migration guide for Swift iOS SDK 7.x to 8.0 migration guide for Objective-C iOS SDK 7.x to 8.0 migration guide for Swift iOS SDK 8.x to 9.0 migration guide iOS SDK observability reference iOS SDK reference Java SDK 4.x to 5.0 migration guide Java SDK 5.x to 6.0 migration guide Java SDK 6.x to 7.0 migration guide Java SDK reference JavaScript SDK 2.x to 3.0 migration guide JavaScript SDK 3.x to 4.0 migration guide JavaScript SDK observability reference JavaScript SDK reference Logging configuration Lua (server-side) SDK 1.x to 2.0 migration guide Lua SDK reference Migration configuration Migrations Monitoring SDK status Monitoring the Relay Proxy Multiple environments Node.js (client-side) SDK 1.x to 2.0 migration guide Node.js (client-side) SDK 2.x to 3.0 migration guide Node.js (server-side) AI SDK reference Node.js (server-side) SDK 5.x to 6.0 migration guide Node.js (server-side) SDK 6.x to 7.0 migration guide Node.js (server-side) SDK 7.x to 8.0 migration guide Node.js (server-side) SDK 8.x to 9.0 migration guide Node.js (server-side) SDK observability reference Node.js SDK reference (client-side) Node.js SDK reference (server-side) Observability SDKs Offline mode Offline mode OpenFeature provider for .NET (server-side) SDK OpenFeature provider for Java SDK OpenFeature provider for Node.js (server-side) SDK OpenFeature provider for PHP SDK OpenFeature providers OpenTelemetry in client-side SDKs OpenTelemetry in server-side SDKs OpenTelemetry versions and instrumentation support Persistent data stores PHP SDK 3.x to 4.0 migration guide PHP SDK 4.x to 5.0 migration guide PHP SDK 5.x to 6.0 migration guide PHP SDK reference Private attributes Python AI SDK reference Python SDK 7.x to 8.0 migration guide Python SDK 8.x to 9.0 migration guide Python SDK observability reference Python SDK reference React Native SDK 6.x to 7.0 migration guide React Native SDK 7.x to 8.0 migration guide React Native SDK 8.x to 9.0 migration guide React Native SDK 9.x to 10.0 migration guide React Native SDK observability reference React Native SDK reference React SDK reference React Web SDK 2.x to 3.0 migration guide React Web SDK 3.x to 4.0 migration guide React Web SDK observability reference React Web SDK reference Reading flags from a file Recording metrics Recording traces Redis Relay Proxy configuration Relay Proxy deployment strategies Relay Proxy Enterprise Relay Proxy guidelines Relay Proxy use cases Requirements and polyfills Roku SDK 1.x to 2.0 migration guide Roku SDK reference Ruby AI SDK reference Ruby SDK 6.x to 7.0 migration guide Ruby SDK 7.x to 8.0 migration guide Ruby SDK observability reference Ruby SDK reference Rust SDK 2.x to 3.0 migration guide Rust SDK reference Rust SDK v1 implementation guide Sample applications SDK concepts SDK configuration SDK data handling behavior SDK features SDK features for observability SDKs Secure mode Self-hosting an OpenTelemetry Collector Sending errors Sending logs Server-side SDK bandwidth usage Server-side SDKs Service endpoint configuration Shutting down Storing data Subscribing to flag changes Supported versions Test data sources The Relay Proxy Tracking AI metrics Tracking custom events Upgrading the Relay Proxy User feedback Using daemon mode Using flag types Using proxy mode Vercel SDK reference Version notifications Versioning policy Versioning policy Vue SDK 1.x to 2.0 migration guide Vue SDK observability reference Vue SDK reference Web proxy configuration Integrations Adding a custom approvals integration configuration Akamai Amazon Kinesis Ansible Collection AppDynamics Approvals capability AWS CloudTrail Lake AWS CloudWatch RUM AWS PrivateLink Azure DevOps Azure Event Hubs Backstage, by Roadie BigQuery Data Export Bitbucket Pipelines Bitrise Release Management Building partner integrations Change history events hook capability Chronosphere ClickHouse Data Export Cloudflare CloudQuery Collaboration tools Compass Configuring environments to use a custom approvals integration Confluence Contentful integration Convex Cortex Creating your own approval application Crossplane Custom approvals Data Export Databricks Data Export Datadog Datadog RUM Defining an integration manifest Ditto DynamoDB Dynatrace Edge tools Elastic (ELK) Stack Endpoints capability Environments as a Service integrations Experimentation and metric integrations Fastly Feature stores capability Find Code References in Pull Request Fivetran (formerly Census) Flag Evaluations Flag links capability FullStory Getting started GitHub Actions Google Cloud Pub/Sub Grafana Honeycomb Integrations Internal developer platforms Jira Cloud Kosli Last9 Managing approvals in Slack Managing integrations Mattermost incoming webhooks Mezmo Microsoft Teams Microsoft Teams incoming webhooks Migrating to Jira Forge Monitoring tools More integrations mParticle mParticle schema reference Netlify New Relic One ngrok Okteto Osano PagerDuty for guarded rollouts Port Receiving approval notifications in Microsoft Teams Receiving flag change notifications in Microsoft Teams Redis Redshift Data Export Registering a LaunchDarkly OAuth client Release Reserved custom properties Roost.ai Salesforce Sanity CMS integration Segments integrations Sentry ServiceNow approvals Setting up Slack accounts and permissions Setting up the Microsoft Teams integration Setting up the ServiceNow approvals integration Slack app Slack incoming webhooks Sleuth Snowflake Data Export Snowplow Splunk Splunk Observability Cloud (formerly SignalFx) Sprig Streaming Data Export Streaming Data Export schema reference Subscribing to notifications Synced segments capability Tealium Terraform Terraform provider v1.x to v2 migration guide Toggling flag targeting on and off Tray Trello Triggers capability Twilio Segment for Data Export Twilio Segment for metrics Twilio Segment schema reference Unfurling and linking flags in Microsoft Teams Using flag triggers with Datadog Using flag triggers with Dynatrace Using flag triggers with Honeycomb Using flag triggers with New Relic One Using flag triggers with Splunk Observability Cloud Using form variables Using integration framework capabilities Using the Datadog dashboard widget Using the Datadog events integration Using the Dynatrace events integration Using the Honeycomb events integration Using the LaunchDarkly integration framework Using the New Relic One events integration Using the ServiceNow approvals integration Using the Splunk Observability Cloud events integration Validating an integration Vercel Warehouse Data Export Warehouse Data Export schema reference Workflow management tools Zapier Zendesk Guides About LaunchDarkly integrations Account management Additional resources AgentControl Bayesian versus frequentist statistics Best practices for upgrading users to contexts Building a change history event hook integration Building a chatbot with multiple AI providers using AgentControl configs Building a metric import and sync integration Building a synced segments integration Building an ephemeral Environments as a Service integration Building teams in LaunchDarkly Building your first experiment Cheatsheets Choosing a statistical methodology Comparing LaunchDarkly's SDKs and REST API Control and govern AI agents in production Covariate adjustment and CUPED methodology Creating an A/B experiment using a funnel metric group Creating flags Creating mutually exclusive experiments Creating roles Deciding between flag targeting strategies Defining and targeting mobile applications Deployment and release strategies Designing experiments Enable self-healing systems with runtime controls Example experiments Example metrics Experimentation Experimentation best practices Feature flag hierarchy Feature flags Flag conventions Getting started with Amazon Bedrock and AgentControl configs Getting started with Anthropic Claude and AgentControl configs Getting started with Google Gemini Getting started with LangChain and AgentControl configs Getting started with LangGraph and AgentControl configs Getting started with observability for Android Getting started with OpenAI and AgentControl configs Getting started with Strands and AgentControl configs Guides Improving flag usage in code Infrastructure Integrations Integrations use cases LaunchDarkly CLI dev-server reference guide LaunchDarkly for large enterprise teams Maintaining consistency across user sessions when running experiments Managing AI model configuration outside of code with the Node.js AI SDK Managing flags with Terraform Measuring Experimentation impact with holdouts Merging LaunchDarkly accounts Metrics Migrating from Statsig to LaunchDarkly Experimentation Migrating your existing feature flag solution to LaunchDarkly Minimizing LaunchDarkly's access to end user data Multiple comparisons correction Observability Onboarding to preset roles Onboarding to views Optimize AI performance and cost with AgentControl configs Performing multi-stage migrations with migration flags Proving ROI with data-driven AI agent experiments Reducing technical debt from feature flags Resilient architecture patterns for LaunchDarkly SDKs REST API REST API migration guide: 20220603 to 20240415 Run continuous experiments in production Sample ratio mismatch Sample size calculations for frequentist experiments SDKs Ship AI-built code with AgentControl or CodeControl Statistical methodology Statistical methodology for Bayesian experiments Statistical methodology for frequentist experiments Stratified sampling Teams and custom roles Testing code that uses feature flags Understanding LaunchDarkly metrics Use cases for SDK wrappers Using entitlements to manage customer experience Using flag triggers with Dynatrace Using flags on static sites with JavaScript Using LaunchDarkly in serverless environments Using LaunchDarkly with Cloudflare Workers Using LaunchDarkly with Protected Health Information (PHI) Using LaunchDarkly without a supported SDK Using metrics from engineering insights Using mobile SDKs Using segments and targeting to manage early access programs Using SSO with LaunchDarkly Using targeting to manage AI model usage by tier with the Python AI SDK Using the JavaScript SDK in Salesforce Lightning Web Components Using the LaunchDarkly CLI for local testing Using the LaunchDarkly REST API Using the Lua SDK with HAProxy Using the Lua SDK with NGINX When to use completion mode vs agent mode in LaunchDarkly for AI applications: a guide for LangGraph, OpenAI, and Multi-Agent Systems Tutorials 3 Levels of Data Validation in a Full Stack Application With React 4 hacks to turbocharge your Cursor productivity A Deeper Look at LaunchDarkly Architecture: More than Feature Flags Add observability to your React Native application in 5 minutes Agent Graphs: Taming Multi-Agent Architecture with LaunchDarkly AgentControl CI/CD Pipeline: Automated Quality Gates and Safe Deployment All I Want for Christmas is Observable Multi-Modal Agentic Systems: How Session Replay + Online Evals Revealed How My Holiday Pet App Actually Works Application Tracing in .NET for Performance Monitoring Benchmark LangGraph, Strands, OpenAI Agents, and Google ADK on the same agent graph Beyond n8n for workflow automation: agent graphs as your universal agent harness Build a LangGraph Multi-Agent system in 20 Minutes with AgentControl Build a multi-agent system with LaunchDarkly Agent Skills Build a production LLM data extraction pipeline with AgentControl and Vercel AI Gateway Build a User Frustration Detection & Response System by Connecting Rage Click Detection to Guarded Releases Build AgentControl configs with Agent Skills in Claude Code, Cursor, or Windsurf Building Framework-Agnostic AI Swarms: Compare LangGraph, Strands, and OpenAI Swarm Catch your first silent AI failure with Vega AI in under 10 minutes Collecting user feedback in your app with feature flags Compression: The simple, powerful upgrade for your web stack Create a feature flag in your IDE in 5 minutes with LaunchDarkly's MCP server Day 1 | 馃巹 Observability under the Tree: What Changed in 2025 Day 10 | Why observability and feature flags go together like milk and cookies Day 11 | 鉁夛笍 Letters to Santa: What engineering teams really want from Observability in 2026 Day 12 | 馃帄 New Year, New Observability Day 12 | 馃帄 New Year, New Observability Day 2 | 馃巺 He knows if you have been bad or good... But what if he gets it wrong? Day 3 | 馃敂 Jingle All the Way to Zero-Config Observability Day 4 | 鉂勶笍 Tracing the impact of holiday styling in your Node.js app Day 5 | 馃Ч Using a Popular Tidying Method to Consolidate Your Observability Stack Day 5 | 馃Ч Using a Popular Tidying Method to Consolidate Your Observability Stack Day 6 | 馃捀 The famous green character that stole your cloud budget: the cardinality problem Day 7 | 馃巹鉁═he Rockefeller tree in NYC: SLOs that actually drive decisions Day 9 | 馃懟 The Three Ghosts Haunting Your AI This Holiday Season DeepSeek vs Qwen: local model showdown featuring AgentControl Detecting User Frustration: Understanding rage clicks and session replay Detection to Resolution: Real world debugging with rage clicks and session replay Distributed Tracing in Next.js Apps Evaluate LLM code generation with LLM-as-judge evaluators Filtering and Sampling LaunchDarkly Ingest How to implement WebSockets and kill switches in a Python application How to instrument your React Native app with OpenTelemetry How to roll out a feature with LaunchDarkly How to run safe migrations using feature flags How to set up a kill switch for any feature in production How to Set Up Your Production AWS MSK Kafka Cluster How to target users in production without redeploying code How to Use OpenTelemetry to Monitor Next.js Applications How To Use The Chrome Inspector & Debugger How to vibe code safely using LaunchDarkly feature flags Introducing Judges: Enhancing AI Response Quality Monitoring LLM evaluation guide: When to add online evals to your AI application Migrate a Hardcoded LangGraph Agent to AgentControl in 20 Minutes Monitoring Browser Applications with OpenTelemetry Observability for Your Go ORM: OpenTelemetry Integration with GORM Offline Evaluation of RAG-Grounded Answers in AgentControl OpenTelemetry for LLM Applications: A Practical Guide with LaunchDarkly and Langfuse Publishing an NPM Package with Private pnpm Monorepo Dependencies Real-time Monitoring in Django: Essential Tools and Techniques Smart AI Agent Targeting with MCP Tools Snowflake Cortex Completion API + LaunchDarkly SDK Integration The AI Iteration Loop for Deploying Reliable Agents with LangGraph The complete guide to OpenTelemetry in Next.js The complete guide to OpenTelemetry in Python The human-in-the-loop alternative to autonomous remediation The power of the monorepo: Keep your fullstack app in sync! The Ultimate Guide to Ruby Logging: Best Libraries and Practices Tracing Distributed Systems in Next.js Tutorials Using AgentControl to review database changes Using LaunchDarkly feature flags and Experimentation with WordPress Using Materialized Views in ClickHouse (vs. Postgres) Video tutorials What is OpenTelemetry and Why Should I Care? Blog 5 Best Node.js Logging Libraries 5 Best Practices for Maintaining a Clean ReactJS App 5 strategies to monitor the health of your web application 5 takeaways from my first PyCon JP conference 52 Blog Posts, Claude, 3 Prompts, Under an Hour 8 Tips to Help You Maximize Chrome DevTools A quick tool for npm package scanning A tale of three rate limiters Adventures in dogfooding: Guarded Releases Alert Evaluations: Incremental Merges in ClickHouse An open-source session replay benchmark Building GitHub Enhanced Stacktraces Building Logging Integrations at LaunchDarkly Configuring OpenSearch for a Write-Heavy Workload Data isolation with ClickHouse row policies Dungeons & Downtimes: XP gained from our adventure Finding Interesting Sessions with Markov Chains Flagship, an engineering blog Fun with JS streams If you can survive a toddler, you can ship LLMs in production InfluxDB: Visualizing Millions of Customers' Metrics using a Time Series Database Ingest and Visualization for OpenTelemetry Metrics Is Kafka the Key? The Evolution of LaunchDarkly's Ingest LLM-based Grouping of Errors Make every launch a big deal Managing our design tokens at LaunchDarkly Maximizing Our Machines: Worker Pools At LaunchDarkly Migrating from OpenSearch to ClickHouse Moonshots XXII: Hack to the Future recap My approach to React app architecture in 2025 My DEF CON 33 experience My good friend Claude Optimizing ClickHouse: The Tactics That Worked for Us Our Commitment to OpenTelemetry Revamping Privacy Mode: A Better Way to Obfuscate Sensitive Data Shipping from Oakland: An Observability Hackathon Recap Supporting Outside Contributions at LaunchDarkly The 5 Best Logging Libraries for Ruby The beauty of contact-first API design The Debugging Process and Techniques for Web Applications (Part 2/2) The Network Request Details Panel Use Reverse Proxy for custom domain requests Using Github as a Headless CMS Vercel Edge Runtime Support What are rage clicks and how to detect them What is Frontend Monitoring and What Tools Help You Do It? What Is Full Stack Monitoring and How Does It Work? Your Source Maps Should Be Public Flags-in-docs.mdx Flags in docs Internal-only.mdx Internal only ## What is LaunchDarkly | Documentation's llms.txt? LaunchDarkly | Documentation publishes an `/llms.txt` file that provides AI systems with a structured index of its documentation. It follows the [llms.txt specification](https://llmstxt.org), organizing links to guides, API references, and tutorials under section headings so that LLMs like ChatGPT, Claude, and Gemini can quickly understand what LaunchDarkly | Documentation offers and where to find details. Pass Spec 8 Sections 955 Links 196.2 KB Size ~50.2k Tokens Sections: Home 路 SDKs 路 Integrations 路 Guides 路 Tutorials 路 Blog 路 and 2 more ## Add LaunchDarkly | Documentation Docs to Your AI Assistant Select your tool to see how to add LaunchDarkly | Documentation's llms.txt as a documentation source. Cursor Windsurf Claude Code ChatGPT Zed Copilot 1. 1 Open any chat or composer panel 2. 2 Type @Docs and select "Add new doc" 3. 3 Paste the URL: https://docs.launchdarkly.com/llms.txt 4. 4 Wait for the green dot (indexing complete) 5. 5 Reference @Docs in chat when asking about LaunchDarkly | Documentation Cursor crawls the URL and indexes all subpages. Add a trailing slash to index child pages too. ## Spec Compliance 4 notes ## Frequently Asked Questions Where is LaunchDarkly | Documentation's llms.txt file? LaunchDarkly | Documentation publishes its llms.txt at https://docs.launchdarkly.com/llms.txt. This file provides a structured, markdown-formatted index of LaunchDarkly | Documentation's documentation that AI systems can consume to understand the project's APIs, guides, and references. How do I use LaunchDarkly | Documentation's llms.txt with AI coding assistants? Copy the llms.txt content and paste it into your AI assistant (Cursor, Windsurf, Claude, ChatGPT) as context. This gives the AI an accurate map of LaunchDarkly | Documentation's documentation so it can provide better code suggestions and answers about LaunchDarkly | Documentation. What does LaunchDarkly | Documentation's llms.txt contain? LaunchDarkly | Documentation's llms.txt contains 8 sections and 955 documentation links in 196.2 KB (~50.2k tokens). Key sections include Home, SDKs, Integrations, Guides, Tutorials, Blog. How many tokens does LaunchDarkly | Documentation's llms.txt use? The concise llms.txt index is approximately 50.2k tokens (196.2 KB). Most AI assistants can fit this within their context window. For the full expanded documentation, look for an llms-full.txt variant which embeds all content inline. Can I generate an llms.txt for my own project? Yes. Use the free llms.txt generator at mdream.dev/tools/llms-txt/generator to crawl your site and produce a spec-compliant llms.txt file. 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