aiconfig-update

name	aiconfig-update
description	Update and delete LaunchDarkly AI Configs. Learn how to modify existing AI Configs and manage their lifecycle.
compatibility	Requires LaunchDarkly project with AI Configs enabled and API access token.
metadata	{"author":"launchdarkly","version":"0.1.0"}

AI Config Update and Delete Operations

Update existing LaunchDarkly AI Configs and manage their lifecycle. This skill covers updating config properties, archiving configs, and deleting them when no longer needed.

Prerequisites

• LaunchDarkly project with AI Configs enabled
• API access token with write permissions
• Existing AI Config to update or delete

Note: The LaunchDarkly MCP server has update-ai-config and delete-ai-config tools for basic operations. Use the REST API below for full functionality. See aiconfig-api for details.

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

Core Concepts

AI Config lifecycle management includes:

• Updating: Modify name, description, and other properties
• Archiving: Temporarily disable configs while preserving data
• Deleting: Permanently remove configs (cannot be undone)

Update AI Config

Update an existing AI Config's properties using a PATCH request:

import requests
import json

# Configuration
API_TOKEN = "your-api-token"
PROJECT_KEY = "your-project"
CONFIG_KEY = "your-config-key"

# Update config properties
url = f"https://app.launchdarkly.com/api/v2/projects/{PROJECT_KEY}/ai-configs/{CONFIG_KEY}"

headers = {
    "Authorization": f"{API_TOKEN}",
    "Content-Type": "application/json"
}

# Update data - simple object with fields to update
patch_data = {
    "name": "Updated AI Config Name",
    "description": "Updated description for the AI Config"
}

response = requests.patch(url, headers=headers, json=patch_data)

if response.status_code == 200:
    print("[OK] AI Config updated successfully")
    data = response.json()
    print(f"  Name: {data.get('name')}")
    print(f"  Description: {data.get('description')}")
else:
    print(f"[ERROR] {response.status_code}: {response.text}")

Archive AI Config

Archiving temporarily disables an AI Config while preserving all its data:

def archive_config(project_key, config_key, api_token, archive=True):
    """Archive or unarchive an AI Config"""
    url = f"https://app.launchdarkly.com/api/v2/projects/{project_key}/ai-configs/{config_key}"

    headers = {
        "Authorization": f"{api_token}",
        "Content-Type": "application/json"
    }

    patch_data = {"archived": archive}

    response = requests.patch(url, headers=headers, json=patch_data)

    if response.status_code == 200:
        status = "archived" if archive else "unarchived"
        print(f"[OK] AI Config {status} successfully")
        return response.json()
    else:
        print(f"[ERROR] {response.status_code}: {response.text}")
        return None

# Archive a config
archive_config(PROJECT_KEY, CONFIG_KEY, API_TOKEN, archive=True)

# Unarchive a config
archive_config(PROJECT_KEY, CONFIG_KEY, API_TOKEN, archive=False)

Delete AI Config

Permanently delete an AI Config. This operation cannot be undone:

def delete_config(project_key, config_key, api_token, confirm=False):
    """Permanently delete an AI Config

    Args:
        confirm: Must be True to actually delete (safety check)
    """
    url = f"https://app.launchdarkly.com/api/v2/projects/{project_key}/ai-configs/{config_key}"

    headers = {
        "Authorization": f"{api_token}"
    }

    if not confirm:
        print(f"[WARNING] Delete skipped - set confirm=True to delete '{config_key}'")
        return False

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

    if response.status_code == 204:
        print(f"[OK] AI Config '{config_key}' deleted successfully")
        return True
    else:
        print(f"[ERROR] deleting config: {response.status_code}")
        print(response.text)
        return False

# Delete a config (requires confirm=True)
delete_config(PROJECT_KEY, CONFIG_KEY, API_TOKEN, confirm=True)

Batch Operations

Update multiple configs efficiently:

def batch_update_configs(project_key, updates, api_token):
    """Update multiple AI Configs"""
    results = []

    for config_key, patch_data in updates.items():
        url = f"https://app.launchdarkly.com/api/v2/projects/{project_key}/ai-configs/{config_key}"

        headers = {
            "Authorization": f"{api_token}",
            "Content-Type": "application/json"
        }

        response = requests.patch(url, headers=headers, json=patch_data)

        results.append({
            "config_key": config_key,
            "success": response.status_code == 200,
            "status_code": response.status_code
        })

    return results

# Example batch update
updates = {
    "config-1": {"name": "Updated Config 1"},
    "config-2": {"archived": True},
    "config-3": {"description": "New description"}
}

results = batch_update_configs(PROJECT_KEY, updates, API_TOKEN)
for result in results:
    print(f"{result['config_key']}: {'Success' if result['success'] else 'Failed'}")

Updatable Fields

The AI Config update endpoint accepts a simple object with fields to update:

Field	Description	Value Type
name	Config display name	string
description	Config description	string
archived	Archive status	boolean
tags	Config tags	array of strings

Error Handling

Handle common update and delete errors:

def safe_update_config(project_key, config_key, patch_data, api_token):
    """Update config with comprehensive error handling"""
    url = f"https://app.launchdarkly.com/api/v2/projects/{project_key}/ai-configs/{config_key}"

    headers = {
        "Authorization": f"{api_token}",
        "Content-Type": "application/json"
    }

    try:
        response = requests.patch(url, headers=headers, json=patch_data)

        if response.status_code == 200:
            return {"success": True, "data": response.json()}
        elif response.status_code == 400:
            return {"success": False, "error": "Invalid request format"}
        elif response.status_code == 404:
            return {"success": False, "error": "AI Config not found"}
        elif response.status_code == 409:
            return {"success": False, "error": "Conflict - config may have been modified"}
        else:
            return {"success": False, "error": f"Unexpected error: {response.status_code}"}

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

# Use with error handling
result = safe_update_config(
    PROJECT_KEY,
    CONFIG_KEY,
    {"name": "New Name"},
    API_TOKEN
)

if result["success"]:
    print("[OK] Update successful")
else:
    print(f"[ERROR] Update failed: {result['error']}")

Best Practices

1. Backup Before Delete

Always export config data before deletion using the API to GET the config and save to a file.

2. Use Archiving First

Archive configs before deletion to ensure they're no longer needed:

1. Archive the config
2. Monitor for any issues
3. Delete after confirmation period

3. Validate Updates

Check the update object before applying - ensure it only contains valid fields (name, description, archived, tags).

Complete Example

Update, archive, and manage an AI Config lifecycle:

import requests
import json

class AIConfigManager:
    def __init__(self, project_key, api_token):
        self.project_key = project_key
        self.api_token = api_token
        self.base_url = f"https://app.launchdarkly.com/api/v2/projects/{project_key}"
        self.headers = {
            "Authorization": api_token,
            "Content-Type": "application/json"
        }

    def update(self, config_key, patch_data):
        """Update an AI Config"""
        url = f"{self.base_url}/ai-configs/{config_key}"
        response = requests.patch(url, headers=self.headers, json=patch_data)
        return response.status_code == 200, response

    def archive(self, config_key):
        """Archive an AI Config"""
        return self.update(config_key, {"archived": True})

    def unarchive(self, config_key):
        """Unarchive an AI Config"""
        return self.update(config_key, {"archived": False})

    def delete(self, config_key):
        """Delete an AI Config"""
        url = f"{self.base_url}/ai-configs/{config_key}"
        response = requests.delete(url, headers={"Authorization": self.api_token})
        return response.status_code == 204

# Usage
manager = AIConfigManager(PROJECT_KEY, API_TOKEN)
success, response = manager.update("test-config", {"description": "Updated via AIConfigManager"})

Related Skills

Management Workflow

• aiconfig-create - Create configs first
• aiconfig-api - API reference for updates
• aiconfig-variations - Update variations

Related Operations

• aiconfig-tools - Update tool attachments
• aiconfig-targeting - Update targeting rules
• aiconfig-experiments - Update experiment settings

References

• LaunchDarkly API Documentation
• AI Configs Documentation