Customizing AgentControl configs

Use the Config function to customize an AgentControl config. Customization substitutes context attributes and variables into the prompt messages defined in the config variation. You must call Config each time you generate content from your AI model.

The Config function takes the config key, a Context, a fallback value, and optional additional variables to substitute into your prompt. It performs the evaluation, then returns the customized prompt and model along with a tracker instance for recording prompt metrics. You can pass the customized prompt directly to your AI.

The fallback value is the value of the config variation that your application should use in the event of an error, for example, if the config key is not valid, or if there is a problem connecting to LaunchDarkly. You can use LdAiConfig.Disabled as a fallback value and handle the disabled case in your application.

Alternatively, you can create a custom LdAiConfig object using LdAiConfig.New().

Here is an example of calling the Config method:

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var fallbackConfig = LdAiConfig.New()
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  .SetEnabled(false)
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  .Build()
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var tracker = aiClient.Config(
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  "example-config-key",
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  context,
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  fallbackConfig,
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  new Dictionary<string, object> {
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    { "exampleCustomVariable", "exampleCustomValue" }
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  }
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);
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if (tracker.Config.Enabled == true) {
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  // Send a request to your AI provider, using the details from the customized Config
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} else {
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  // Application path to take when the tracker.Config is disabled
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}

After the function call, you can view the context that you provided to it on the Contexts list.

To learn more, read Config.

Use the Config() function to customize an AgentControl config. Customization substitutes context attributes and variables into the prompt messages defined in the config variation.

The Config() function takes a config key, a context, and a fallback value. It performs the evaluation and returns a Config object with the customized messages and model, along with a Tracker object to capture performance metrics. You can pass the customized messages directly to your AI.

The fallback value is used when an error occurs, such as when the config key is invalid or LaunchDarkly is unreachable. You can use an empty, disabled Config as a fallback value by calling ldai.Disabled() and handle the disabled case in your application. Alternatively, you can create a custom Config using NewConfig.

Here is an example of calling the Config() method:

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fallbackValue := NewConfig().Build() // by default, the Config is disabled
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cfg, tracker := aiClient.Config("example-config-key", context, fallbackValue, map[string]interface{}{"exampleCustomVariable": "exampleCustomValue"})
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if cfg.Enabled() {
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  // Send a request to your AI provider, using details from the customized cfg
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} else {
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  // Application path to take when the cfg is disabled
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}

After the function call, you can view the context that you provided to it on the Contexts list.

To learn more, read Config.

The completionConfig(), agentConfig(), and agentConfigs() functions customize the AgentControl config at runtime. Customization substitutes context attributes and optional variables into the messages or instructions defined in the config variation.

The customization behavior depends on whether the config uses completion mode or agent mode. You select the mode when you create the config in the LaunchDarkly UI.

Customize configs in completion mode

In completion mode, each config variation includes a single set of roles and messages used to prompt your generative AI model.

In completion mode, use completionConfig() to customize the config. The completionConfig function takes the config key, a Context, a fallback value, and optional additional variables to substitute into your prompt. It performs the evaluation, then returns the customized prompt and model along with a tracker instance for recording prompt metrics. You can pass the customized prompt directly to your AI.

The fallback value is used when an error occurs, such as when the config key is invalid or LaunchDarkly is unreachable. You can use an empty, disabled configuration as a fallback value and handle the disabled case in your application. Alternatively, you can construct a fallback configuration using values from a config variation defined in the LaunchDarkly UI.

Here is an example of calling the completionConfig() method:

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const fallbackConfig = { enabled: false };
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const aiConfig: LDAIConfig = await aiClient.completionConfig(
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  'example-config-key',
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  context,
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  fallbackConfig,
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  { exampleCustomVariable: 'exampleCustomValue' },
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);
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if (aiConfig.enabled) {
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  // Send a request to your AI provider using the customized config
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} else {
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  // Application path to take when the config is disabled
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}

Customize configs in agent mode

In agent mode, each config variation includes a set of instructions that enable multi-step workflows.

In agent mode, use agentConfig() or agentConfigs() to customize the config. The agentConfig() function customizes a single agent config, while the agentConfigs() function customizes an array of agent configurations.

The instructions returned by the SDK come directly from the instructions you define for the config variation in the LaunchDarkly UI. The goal or task shown in the UI is delivered unchanged as the instructions field in the SDK.

Customization requires a config key, a fallback value, optional variables, and a context. Both functions perform the evaluation and return the customized instructions along with a tracker instance for recording metrics. If the SDK cannot perform the evaluation or LaunchDarkly is unreachable, it returns the fallback value.

For example, you might use an empty, disabled agent-mode configuration as a fallback value and handle the disabled case in your application.

Here is an example of customizing a config in agent mode:

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agent_config = LDAIAgentConfig(
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  key='example-config-key',
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  default_value=LDAIAgentDefaults(
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    enabled=False
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  ),
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  variables={ 'example_custom_variable': 'example_custom_value' }
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)
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const agent: LDAIAgentConfig = await aiClient.agentConfig(
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  'example-config-key',
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  context,
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  fallbackConfig,
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  { 'exampleCustomVariable': 'exampleCustomValue' },
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);

To learn more, read agent and agents.

The completion_config(), agent_config(), and agent_configs() functions customize the AgentControl config at runtime. Customization substitutes context attributes and optional variables into the messages or instructions defined in the config variation.

The customization behavior depends on whether the config uses completion mode or agent mode. You select the mode when you create the config in the LaunchDarkly UI.

Customize configs in completion mode

In completion mode, each config variation includes a single set of roles and messages used to prompt the generative AI model.

In completion mode, use completion_config() to customize the config. The completion_config() function takes a config key, a context, and a fallback value. It performs the evaluation, then returns the customized messages and model along with a tracker instance for recording metrics.

The fallback value is used when an error occurs, such as when the config key is invalid or LaunchDarkly is unreachable. You can use an empty, disabled configuration as a fallback value and handle the disabled case in your application. Alternatively, you can construct a fallback configuration using values from a config variation defined in the LaunchDarkly UI.

Here is an example of calling the completion_config() method:

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key = 'example-config-key'
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context = Context.builder('example-context-key') \
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  .kind('user') \
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  .set('name', 'Sandy') \
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  .build()
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fallback_value = AICompletionConfigDefault(enabled=False)
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variables = { 'example_custom_variable': 'example_custom_value' }
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config, tracker = aiclient.completion_config(key, context, fallback_value, variables)
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if config.enabled:
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  # Send a request to your AI provider using the customized config
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else:
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  # Application path to take when the config is disabled

After the function call, you can view the context that you provided to it on the Contexts list.

To learn more, read config.

Customize configs in agent mode

In agent mode, each config variation includes a set of instructions that enable multi-step workflows.

In agent mode, use agent_config() or agent_configs() to customize the config. The agent_config() function customizes a single agent config, while the agent_configs() function customizes a list of them.

The instructions returned by the SDK come directly from the instructions you define for the config variation in the LaunchDarkly UI. The goal or task shown in the UI is delivered unchanged as the instructions field in the SDK.

Customization requires a config key, a fallback value, optional variables, and a context. Both functions perform the evaluation and return the customized instructions along with a tracker instance for recording metrics. If the SDK cannot perform the evaluation or LaunchDarkly is unreachable, it returns the fallback value.

For example, you might use an empty, disabled agent-mode configuration as a fallback value and handle the disabled case in your application.

Here is an example of customizing a config in agent mode:

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from ldai import AIAgentConfigDefault
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agent = aiclient.agent_config(
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  'example-config-key',
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  context,
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  AIAgentConfigDefault(
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    enabled=False
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  ),
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  { 'example_custom_variable': 'example_custom_value'}
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)

Use the config function to customize an AgentControl config. Customization substitutes context attributes and optional variables into the prompt messages defined in the config variation. You must call config each time you generate content from your AI model.

The config function takes a config key, a Context, a fallback value, and optional variables for substitution. It performs the evaluation and returns the customized prompt and model along with a tracker instance for recording metrics. You can pass the customized prompt directly to your AI provider.

The fallback value is used when an error occurs, such as when the config key is invalid or LaunchDarkly is unreachable. You can use an empty, disabled configuration as a fallback value and handle the disabled case in your application. Alternatively, you can construct a fallback configuration using values from a config variation defined in the LaunchDarkly UI.

Here is an example of calling the config method:

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key = 'example-config-key'
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context = LaunchDarkly::LDContext.create({ key: 'example-user-key', kind: 'user', name: 'Sandy' })
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fallback_value = LaunchDarkly::AI::AIConfig.new(enabled: false)
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variables = { 'example_custom_variable' => 'example_custom_value' }
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ai_config = ai_client.config(key, context, fallback_value, variables)
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if ai_config.enabled
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  # Send a request to your AI provider using the customized config
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else
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  # Application path to take when the config is disabled
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end