Published February 13, 2026
This tutorial was published in February 2026, before LaunchDarkly shipped agent graphs. The walkthrough still works, but the Agent Skills can now also generate newer constructs:
Tell your AI assistant to scaffold these constructs after the configs.
To learn more, read AgentControl.
LaunchDarkly Agent Skills let you build AgentControl configs by describing what you want. Tell your coding assistant to create an agent, and it handles the API calls, targeting rules, and tool definitions for you.
In this quickstart, you’ll create configs using natural language, then run a sample LangGraph app that consumes them. You’ll build a “Side Project Launcher”—a three-agent pipeline that validates ideas, writes landing pages, and recommends tech stacks.
Prefer video? Watch Build a multi-agent system with LaunchDarkly Agent Skills for a walkthrough of this tutorial.
A three-agent pipeline called “Side Project Launcher”:
By the end, you’ll have working configs in LaunchDarkly and a sample app that fetches them at runtime.
LD_API_KEY): Used by Agent Skills to create projects and configs. Get it from Authorization settings. Requires writer role or custom role with createProject and createAIConfig permissions.LAUNCHDARKLY_SDK_KEY): Used by your app at runtime to fetch configs. Found in your project’s SDK settings after creation.ANTHROPIC_API_KEY): Used to call the model. Get it from your provider (Anthropic, OpenAI, etc.).Store all keys in .env and never commit them to version control.
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If you just want to get started, here’s the fastest path:
1. Install skills:
Or ask your editor: “Download and install skills from https://github.com/launchdarkly/agent-skills”
Restart your editor after installing.
2. Set your token:
3. Build something:
Use the prompt in Build a multi-agent project below, or describe your own agents. The assistant creates everything and gives you links to view them in LaunchDarkly.
Agent Skills work with any editor that supports the Agent Skills specification.
You have two options:
Option A: Use skills.sh (recommended)
skills.sh is an open directory for agent skills. Install LaunchDarkly skills with one command:
Option B: Ask your AI assistant
Open your editor and ask:
Both methods install the same skills.
Close and reopen your editor. The skills load on startup.
How to verify: Type /configs in Claude Code. You should see autocomplete suggestions. In Cursor, ask “what LaunchDarkly skills do you have?” and the assistant should list them.
Get your token from LaunchDarkly Authorization settings. The writer role works, or use a custom role with createProject and createAIConfig permissions.
Now let’s build something real: a Side Project Launcher that helps you validate ideas, write landing pages, and pick the right tech stack. Tell the assistant:
The assistant uses several skills automatically:
Expected output:
The variables ({{idea}}, {{target_audience}}, etc.) get filled in at runtime when you call the SDK. That’s how each user gets personalized output.
After creation, your LaunchDarkly project contains:
Each agent has its own configuration with instructions, variables, and tools. Here’s the idea-validator:
The landing-page-writer and tech-stack-advisor follow the same pattern with their own instructions and tools.
The full working code is available on GitHub: launchdarkly-labs/side-project-researcher
Clone it and run:
You’ll need both the LaunchDarkly SDK key (from your project’s SDK settings) and your Anthropic API key in the .env file. The assistant can surface the SDK key from your project details, but store it in .env rather than hardcoding it.
The app prompts you for your idea details:
Then each agent runs in sequence, fetching its config from LaunchDarkly and generating output:
The config stores your model, instructions, and tools. The SDK fetches the config and handles variable substitution automatically.
The snippets below show the integration pattern. They omit imports, error handling, and tool wiring for brevity. For complete, runnable code, use the sample repo.
LangGraph orchestrates multi-agent workflows as a graph of nodes, but you can use any orchestrator—CrewAI, LlamaIndex, Bedrock AgentCore, or custom code. To compare options, read Compare AI orchestrators.
By wiring configs to each node, your agents fetch their model, instructions, and tools dynamically from LaunchDarkly. This lets you swap models within a provider (e.g., Sonnet to Haiku), update prompts, or disable agents without redeploying.
The config defines tool schemas, but your code must implement the actual tool handlers. The sample repo shows how to bind config.tools to LangChain tool functions. For this tutorial, the tools are defined but not wired—the agents respond based on their instructions alone.
Each agent becomes a node in your graph:
To see a full example running across LangGraph, Strands, and OpenAI Swarm, read Compare AI orchestrators.
Once your agents are in LaunchDarkly:
To learn more about targeting and experimentation, read AgentControl best practices.
Skills installed but not working: Restart your editor after installing skills. They load on startup.
“Permission denied” errors: Check that your API token has createProject and createAIConfig permissions. The writer role includes both.
Config comes back disabled: Your targeting rules may not match the context you’re passing. Check that default targeting is enabled, or that your context attributes match your rules.
Tools defined but not executing: The config defines tool schemas, but your code must implement handlers. See the sample repo for tool binding examples.
Can’t find SDK key: After Agent Skills creates your project, find the SDK key in your project’s Settings > Environments > SDK key. Copy it to your .env file.
Agent Skills work in any editor that supports the Agent Skills specification. This includes Claude Code, Cursor, and Windsurf. The installation process is the same.
Both give your AI assistant access to LaunchDarkly. Agent Skills are text-based playbooks that teach the assistant workflows. The MCP server exposes LaunchDarkly’s API as tools. You can use either or both.
The writer role works, or use a custom role with createProject and createAIConfig permissions.
In the LaunchDarkly UI: go to your project, then AgentControl in the left sidebar. Each config shows its instructions, model, tools, and targeting rules.
In the LaunchDarkly UI, open the config and click Archive (or Delete if available). Or ask the assistant: “Delete the config called researcher-agent in project valentines-day.”
Yes. The SDK returns model name, instructions, and tools as data. You wire that into whatever framework you use: CrewAI, LlamaIndex, Bedrock AgentCore, or custom code.
Both. Use ai_client.completion_config() for completion mode (chat with message arrays) or ai_client.agent_config() for agent mode (instructions for multi-step workflows). To learn more, read Agent mode vs completion mode.