Aiconfig API

AI Config API Access

Access LaunchDarkly AI Configs through the REST API to perform operations not available through SDKs, including creating, updating, and managing AI Configs programmatically.

Prerequisites

• LaunchDarkly account with AI Configs enabled
• Project where you want to manage AI Configs
• Understanding of REST API concepts

API Key Detection

Before prompting the user for an API key, try to detect it automatically:

1. Check Claude MCP config - Read ~/.claude/config.json and look for mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY
2. Check environment variables - Look for LAUNCHDARKLY_API_KEY, LAUNCHDARKLY_API_TOKEN, or LD_API_KEY
3. Prompt user - Only if detection fails, ask the user for their API key

import os
import json
from pathlib import Path

def get_launchdarkly_api_key():
    """Auto-detect LaunchDarkly API key from Claude config or environment."""
    # 1. Check Claude MCP config
    claude_config = Path.home() / ".claude" / "config.json"
    if claude_config.exists():
        try:
            config = json.load(open(claude_config))
            api_key = config.get("mcpServers", {}).get("launchdarkly", {}).get("env", {}).get("LAUNCHDARKLY_API_KEY")
            if api_key:
                return api_key
        except (json.JSONDecodeError, IOError):
            pass

    # 2. Check environment variables
    for var in ["LAUNCHDARKLY_API_KEY", "LAUNCHDARKLY_API_TOKEN", "LD_API_KEY"]:
        if os.environ.get(var):
            return os.environ[var]

    return None

MCP Server vs REST API

The LaunchDarkly MCP server provides basic AI Config operations via natural language in AI clients (Claude, Cursor, etc.).

MCP tools available:

• list-ai-configs, get-ai-config, create-ai-config, update-ai-config, delete-ai-config
• get-ai-config-variation, create-ai-config-variation, update-ai-config-variation, delete-ai-config-variation

Current MCP limitations (see issue #40):

• Cannot configure tools, model.parameters, customParameters, or judgeConfiguration on variations
• No endpoints for managing AI tool definitions (/ai-tools)
• Targeting, segments, and metrics operations not available

Recommendation: Use the REST API (documented below) for full AI Config functionality. MCP is useful for basic listing and skeleton creation only.

Configure MCP Server

If you want to use MCP for basic operations, configure it in your AI client:

Claude Code (~/.claude/mcp.json):

{
  "mcpServers": {
    "launchdarkly": {
      "command": "npx",
      "args": ["-y", "@launchdarkly/mcp-server", "start"],
      "env": {
        "LD_ACCESS_TOKEN": "your-api-token"
      }
    }
  }
}

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "launchdarkly": {
      "command": "npx",
      "args": ["-y", "@launchdarkly/mcp-server", "start"],
      "env": {
        "LD_ACCESS_TOKEN": "your-api-token"
      }
    }
  }
}

Replace your-api-token with your LaunchDarkly API access token. After configuring, restart your AI client.

For more details, see the LaunchDarkly MCP Server Documentation.

Getting an API Access Token

Create a Personal Access Token

1. Navigate to Authorization settings
2. Click Create token
3. Name your token (e.g., "AI Config Management")
4. Set token permissions:
   • Required: ai-configs:write - Full access to AI Configs
   • Optional: projects:read - Read project information
   • Optional: environments:read - Read environment data
5. Set API version (recommended: latest version)
6. Click Save token
7. Copy and save the token immediately - it won't be shown again

Token Security Best Practices

• Never commit tokens to version control
• Store tokens in environment variables or secret managers
• Rotate tokens regularly
• Use service tokens for production applications
• Limit token permissions to minimum required

API Authentication

Include your token in the Authorization header for all requests:

import requests

headers = {
    "Authorization": "YOUR_API_TOKEN",
    "Content-Type": "application/json",
    "LD-API-Version": "beta"  # Required for AI Config endpoints
}

Operations Only Available via API

The following operations can only be done through the API, not through SDKs:

1. Create AI Configs

SDKs can only read AI Configs. Creating new configs requires the API. See: aiconfig-create skill

2. Update AI Configs and Variations

Modifying existing configs, variations, or settings requires the API. See: aiconfig-update and aiconfig-variations skills

3. Delete AI Configs

Removing configs entirely requires the API. See: aiconfig-update skill

4. Manage Tools

Creating, updating, and deleting tools for function calling requires the API. See: aiconfig-tools skill

5. Configure Targeting Rules

Setting up targeting rules for AI Configs requires the API. See: aiconfig-targeting skill

6. List and Search Operations

Listing all AI Configs in a project requires the API:

def list_ai_configs(project_key, api_token):
    """List all AI Configs in a project"""
    url = f"https://app.launchdarkly.com/api/v2/projects/{project_key}/ai-configs"

    headers = {
        "Authorization": api_token,
        "LD-API-Version": "beta"
    }

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        configs = response.json()
        for config in configs.get("items", []):
            print(f"- {config['key']}: {config['name']}")
        return configs
    else:
        print(f"Error: {response.status_code}")
        return None

API vs SDK Comparison

Common API Patterns

Error Handling

def api_request(method, url, headers, json_data=None):
    """Make API request with error handling"""
    try:
        if method == "GET":
            response = requests.get(url, headers=headers)
        elif method == "POST":
            response = requests.post(url, headers=headers, json=json_data)
        elif method == "PATCH":
            response = requests.patch(url, headers=headers, json=json_data)
        elif method == "DELETE":
            response = requests.delete(url, headers=headers)

        if response.status_code in [200, 201, 204]:
            return {"success": True, "data": response.json() if response.text else None}
        elif response.status_code == 400:
            return {"success": False, "error": "Bad request - check your data"}
        elif response.status_code == 401:
            return {"success": False, "error": "Invalid or missing API token"}
        elif response.status_code == 403:
            return {"success": False, "error": "Insufficient permissions"}
        elif response.status_code == 404:
            return {"success": False, "error": "Resource not found"}
        elif response.status_code == 429:
            return {"success": False, "error": "Rate limited - wait and retry"}
        else:
            return {"success": False, "error": f"HTTP {response.status_code}"}

    except requests.exceptions.RequestException as e:
        return {"success": False, "error": str(e)}

Pagination

Many API endpoints return paginated results:

def get_all_configs(project_key, api_token):
    """Get all AI Configs with pagination"""
    all_configs = []
    limit = 20
    offset = 0

    while True:
        url = f"https://app.launchdarkly.com/api/v2/projects/{project_key}/ai-configs"
        url += f"?limit={limit}&offset={offset}"

        result = api_request("GET", url, {"Authorization": api_token, "LD-API-Version": "beta"})

        if not result["success"]:
            break

        items = result["data"].get("items", [])
        all_configs.extend(items)

        # Check if more pages exist
        if len(items) < limit:
            break

        offset += limit

    return all_configs

Environment-Specific URLs

Production (Default)

https://app.launchdarkly.com/api/v2

Federal

https://app.launchdarkly.us/api/v2

European Union

https://app.eu.launchdarkly.com/api/v2

Rate Limiting

LaunchDarkly API has rate limits to ensure availability:

• Global limit: Account-wide limit per 10 seconds
• Route limit: Per-endpoint limit per 10 seconds
• 429 responses: Indicate rate limiting
• Retry-After header: Specifies wait time

Handle rate limits gracefully:

import time

def retry_with_backoff(func, max_retries=3):
    """Retry function with exponential backoff"""
    for attempt in range(max_retries):
        result = func()

        if result.get("success"):
            return result

        if "rate limited" in result.get("error", "").lower():
            wait_time = 2 ** attempt  # Exponential backoff
            print(f"Rate limited, waiting {wait_time} seconds...")
            time.sleep(wait_time)
        else:
            return result

    return {"success": False, "error": "Max retries exceeded"}

Quick Reference

Essential Headers

headers = {
    "Authorization": "YOUR_API_TOKEN",     # Required
    "Content-Type": "application/json",    # For POST/PATCH
    "LD-API-Version": "beta"               # For AI Config endpoints
}

Base URLs

# Choose based on your environment
BASE_URL = "https://app.launchdarkly.com/api/v2"  # Production
# BASE_URL = "https://app.launchdarkly.us/api/v2"  # Federal
# BASE_URL = "https://app.eu.launchdarkly.com/api/v2"  # EU

Common Endpoints

# AI Configs
f"{BASE_URL}/projects/{project}/ai-configs"  # List/Create
f"{BASE_URL}/projects/{project}/ai-configs/{key}"  # Get/Update/Delete

# Variations
f"{BASE_URL}/projects/{project}/ai-configs/{key}/variations"  # List/Create
f"{BASE_URL}/projects/{project}/ai-configs/{key}/variations/{var_key}"  # Get/Update/Delete

# Tools
f"{BASE_URL}/projects/{project}/ai-tools"  # List/Create
f"{BASE_URL}/projects/{project}/ai-tools/{key}"  # Get/Update/Delete

# AI Config Metrics (read AI usage metrics)
f"{BASE_URL}/projects/{project}/ai-configs/{key}/metrics?from={start}&to={end}&env={env}"  # Get

# Custom Metrics (manage metric definitions)
f"{BASE_URL}/metrics/{project}"  # List/Create
f"{BASE_URL}/metrics/{project}/{metric_key}"  # Get/Update/Delete

# Segments (for targeting)
f"{BASE_URL}/segments/{project}/{env}"  # List/Create
f"{BASE_URL}/segments/{project}/{env}/{segment_key}"  # Get/Update/Delete

# Projects
f"{BASE_URL}/projects"  # List/Create
f"{BASE_URL}/projects/{project}"  # Get/Update/Delete
f"{BASE_URL}/projects/{project}/environments"  # List environments

Next Steps

After setting up API access:

1. Create AI Configs - See aiconfig-create
2. Manage variations - See aiconfig-variations
3. Configure tools - See aiconfig-tools
4. Set up targeting - See aiconfig-targeting
5. Integrate with SDKs - See aiconfig-sdk

Related Skills

Core Operations

• aiconfig-create - Create configs via API
• aiconfig-update - Update configs via API
• aiconfig-projects - Manage projects via API

SDK Integration

• aiconfig-sdk - SDK vs API usage
• aiconfig-variations - Manage variations via API
• aiconfig-tools - Manage tools via API

References

• LaunchDarkly API Documentation
• AI Configs API Reference
• Authentication Guide
• API Best Practices
• Rate Limiting