Interface LDAIClient

Deprecated

Use agentConfig instead. This method will be removed in a future version.

Retrieves and processes a single AI Config agent based on the provided key, LaunchDarkly context, and variables. This includes the model configuration and the customized instructions.

Example

const key = "research_agent";
const context = {...};
const variables = { topic: 'climate change' };
const agentConfig = await client.agentConfig(key, context, {
  enabled: true,
  model: { name: 'gpt-4' },
  provider: { name: 'openai' },
  instructions: 'You are a research assistant.',
}, variables);

const researchResult = agentConfig.instructions; // Interpolated instructions
agentConfig.tracker.trackSuccess();
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Retrieves and processes multiple AI Config agents based on the provided agent configurations and LaunchDarkly context. This includes the model configuration and the customized instructions.

Example

const agentConfigsList = [
  {
    key: 'research_agent',
    defaultValue: {
      enabled: true,
      model: { name: 'gpt-4' },
      provider: { name: 'openai' },
      instructions: 'You are a research assistant.'
    },
    variables: { topic: 'climate change' }
  },
  {
    key: 'writing_agent',
    defaultValue: {
      enabled: true,
      model: { name: 'gpt-4' },
      provider: { name: 'openai' },
      instructions: 'You are a writing assistant.'
    },
    variables: { style: 'academic' }
  }
] as const;
const context = {...};

const configs = await client.agentConfigs(agentConfigsList, context);
const researchResult = configs["research_agent"].instructions; // Interpolated instructions
configs["research_agent"].tracker.trackSuccess();
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Fetches an agent graph configuration from LaunchDarkly and returns an AgentGraphDefinition.

When the graph is enabled the method validates that:

• The graph flag can be evaluated.
• A single root node is present.
• All nodes in the graph are reachable from the root (no disconnected nodes).
• Every referenced agent config can be fetched and is enabled.

If any validation check fails, the returned definition has enabled set to false with an empty node collection. When the logger level is DEBUG, a message describing the failure is emitted.

Example

const graph = await aiClient.agentGraph('my-agent-graph', context, { userName: 'Sandy' });
if (graph.enabled) {
  graph.traverse((node, ctx) => {
    // build your provider-specific node here
  });
}
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Deprecated

Use agentConfigs instead. This method will be removed in a future version.

Retrieves and processes a completion AI Config based on the provided key, LaunchDarkly context, and variables. This includes the model configuration and the customized messages.

Example

const key = "welcome_prompt";
const context = {...};
const variables = {username: 'john'};
const defaultValue = {
 enabled: true,
 model: { name: 'gpt-4' },
 provider: { name: 'openai' },
};

const result = completionConfig(key, context, defaultValue, variables);
// Output:
{
  enabled: true,
  config: {
    modelId: "gpt-4o",
    temperature: 0.2,
    maxTokens: 4096,
    userDefinedKey: "myValue",
  },
  messages: [
    {
      role: "system",
      content: "You are an amazing GPT."
    },
    {
      role: "user",
      content: "Explain how you're an amazing GPT."
    }
  ],
  tracker: ...
}
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Deprecated

Use completionConfig instead. This method will be removed in a future version.

Returns a TrackedChat instance for chat interactions. This method serves as the primary entry point for creating TrackedChat instances from configuration.

Example

const key = "customer_support_chat";
const context = {...};
const defaultValue = {
  enabled: true,
  model: { name: "gpt-4" },
  provider: { name: "openai" },
  messages: [
    { role: "system", content: "You are a helpful customer support agent." }
  ]
};
const variables = { customerName: 'John' };

const chat = await client.createChat(key, context, defaultValue, variables);
if (chat) {
  const response = await chat.invoke("I need help with my order");
  console.log(response.message.content);
}
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Reconstructs an LDGraphTracker from a resumption token, preserving the original runId so events from a resumed session are correlated correctly.

Security note: The token encodes the flag variation key and version. Keep it server-side; do not expose it to untrusted clients.

Creates and returns a new Judge instance for AI evaluation.

Example

const judge = await client.createJudge(
  "relevance-judge",
  context,
  {
    enabled: true,
    model: { name: "gpt-4" },
    provider: { name: "openai" },
    evaluationMetricKey: '$ld:ai:judge:relevance',
    messages: [{ role: 'system', content: 'You are a relevance judge.' }]
  },
  { metric: "relevance" }
);

if (judge) {
  const result = await judge.evaluate("User question", "AI response");
  console.log('Relevance score:', result.evals.relevance?.score);
}
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Reconstructs an AIConfigTracker from a resumption token string previously obtained from a tracker's resumptionToken property. Use this to associate deferred events (such as user feedback) with the original invocation's runId.

Deprecated

Use createChat instead. This method will be removed in a future version.

Retrieves and processes a Judge AI Config based on the provided key, LaunchDarkly context, and variables. This includes the model configuration and the customized messages for evaluation.

Example

const judgeConf = await client.judgeConfig(key, context, {
  enabled: true,
  model: { name: 'gpt-4' },
  provider: { name: 'openai' },
  evaluationMetricKey: '$ld:ai:judge:relevance',
  messages: [{ role: 'system', content: 'You are a relevance judge.' }]
}, variables);

const config = judgeConf.config; // Interpolated configuration
judgeConf.tracker.trackSuccess();
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