chore: Next AI SDK Release

package.json

@@ -48,6 +48,7 @@
     "packages/sdk/server-ai/examples/chat-judge",
     "packages/sdk/server-ai/examples/direct-judge",
     "packages/sdk/server-ai/examples/openai",
+    "packages/sdk/server-ai/examples/managed-agent",
     "packages/sdk/server-ai/examples/tracked-chat",
     "packages/sdk/server-ai/examples/chat-observability",
     "packages/sdk/server-ai/examples/openai-observability",

packages/ai-providers/server-ai-langchain/README.md

@@ -20,7 +20,7 @@
 
 ## Quick Setup
 
-This package provides LangChain integration for the LaunchDarkly AI SDK. The simplest way to use it is with the LaunchDarkly AI SDK's `initChat` method:
+This package provides LangChain integration for the LaunchDarkly AI SDK. The simplest way to use it is with the LaunchDarkly AI SDK's `createModel` method:
 
 1. Install the required packages:
 
@@ -30,7 +30,7 @@ npm install @launchdarkly/server-sdk-ai @launchdarkly/server-sdk-ai-langchain --
 yarn add @launchdarkly/server-sdk-ai @launchdarkly/server-sdk-ai-langchain
 ```
 
-2. Create a chat session and use it:
+2. Create a managed model and run it:
 
 ```typescript
 import { init } from '@launchdarkly/node-server-sdk';
@@ -40,17 +40,17 @@ import { initAi } from '@launchdarkly/server-sdk-ai';
 const ldClient = init(sdkKey);
 const aiClient = initAi(ldClient);
 
-// Create a chat session
-const defaultConfig = { 
-  enabled: true, 
+// Create a managed model
+const defaultConfig = {
+  enabled: true,
   model: { name: 'gpt-4' },
   provider: { name: 'openai' }
 };
-const chat = await aiClient.initChat('my-chat-config', context, defaultConfig);
+const model = await aiClient.createModel('my-chat-config', context, defaultConfig);
 
-if (chat) {
-  const response = await chat.invoke('What is the capital of France?');
-  console.log(response.message.content);
+if (model) {
+  const result = await model.run('What is the capital of France?');
+  console.log(result.content);
 }
 ```
 

packages/ai-providers/server-ai-langchain/__tests__/LangChainAgentRunner.test.ts

@@ -0,0 +1,87 @@
+import { AIMessage, HumanMessage, ToolMessage } from '@langchain/core/messages';
+
+import { CompiledAgent, LangChainAgentRunner } from '../src/LangChainAgentRunner';
+
+const mockLogger = {
+  warn: jest.fn(),
+  info: jest.fn(),
+  error: jest.fn(),
+  debug: jest.fn(),
+};
+
+function makeAgent(invoke: jest.Mock): CompiledAgent {
+  return { invoke };
+}
+
+it('returns content with no toolCalls when the agent returns a simple response', async () => {
+  const finalMsg = new AIMessage('done');
+  finalMsg.usage_metadata = { total_tokens: 6, input_tokens: 4, output_tokens: 2 };
+
+  const agent = makeAgent(jest.fn().mockResolvedValue({ messages: [finalMsg] }));
+  const runner = new LangChainAgentRunner(agent, mockLogger);
+  const result = await runner.run('hi');
+
+  expect(agent.invoke).toHaveBeenCalledWith({
+    messages: [{ role: 'user', content: 'hi' }],
+  });
+  expect(result.content).toBe('done');
+  expect(result.metrics.success).toBe(true);
+  expect(result.metrics.toolCalls).toBeUndefined();
+  expect(result.metrics.tokens).toEqual({ total: 6, input: 4, output: 2 });
+});
+
+it('extracts tool calls and aggregates usage from multi-step agent messages', async () => {
+  const toolCallMsg = new AIMessage('');
+  toolCallMsg.tool_calls = [{ id: 'call_1', name: 'lookup', args: { id: 42 } }];
+  toolCallMsg.usage_metadata = { total_tokens: 14, input_tokens: 10, output_tokens: 4 };
+
+  const toolResultMsg = new ToolMessage({ tool_call_id: 'call_1', content: '{"value":42}' });
+
+  const finalMsg = new AIMessage('Answer is 42.');
+  finalMsg.usage_metadata = { total_tokens: 14, input_tokens: 6, output_tokens: 8 };
+
+  const agent = makeAgent(
+    jest.fn().mockResolvedValue({
+      messages: [
+        new HumanMessage('Look up 42'),
+        toolCallMsg,
+        toolResultMsg,
+        finalMsg,
+      ],
+    }),
+  );
+
+  const runner = new LangChainAgentRunner(agent, mockLogger);
+  const result = await runner.run('Look up 42');
+
+  expect(result.content).toBe('Answer is 42.');
+  expect(result.metrics.toolCalls).toEqual(['lookup']);
+  expect(result.metrics.tokens).toEqual({ total: 28, input: 16, output: 12 });
+});
+
+it('returns success=false when the agent throws', async () => {
+  const agent = makeAgent(jest.fn().mockRejectedValue(new Error('boom')));
+  const runner = new LangChainAgentRunner(agent, mockLogger);
+  const result = await runner.run('hi');
+
+  expect(result.content).toBe('');
+  expect(result.metrics.success).toBe(false);
+  expect(mockLogger.warn).toHaveBeenCalled();
+});
+
+it('returns the underlying agent via getAgent()', () => {
+  const agent = makeAgent(jest.fn());
+  const runner = new LangChainAgentRunner(agent, mockLogger);
+  expect(runner.getAgent()).toBe(agent);
+});
+
+it('handles empty messages array gracefully', async () => {
+  const agent = makeAgent(jest.fn().mockResolvedValue({ messages: [] }));
+  const runner = new LangChainAgentRunner(agent, mockLogger);
+  const result = await runner.run('hi');
+
+  expect(result.content).toBe('');
+  expect(result.metrics.success).toBe(true);
+  expect(result.metrics.toolCalls).toBeUndefined();
+  expect(result.metrics.tokens).toBeUndefined();
+});

packages/ai-providers/server-ai-langchain/__tests__/LangChainHelper.test.ts

@@ -0,0 +1,219 @@
+import { AIMessage, HumanMessage, SystemMessage, ToolMessage } from '@langchain/core/messages';
+import { initChatModel } from 'langchain/chat_models/universal';
+
+import {
+  buildStructuredTools,
+  convertMessagesToLangChain,
+  createLangChainModel,
+  extractLastMessageContent,
+  extractToolCalls,
+  getAIMetricsFromResponse,
+  getAIUsageFromResponse,
+  mapProviderName,
+  sumTokenUsageFromMessages,
+} from '../src/LangChainHelper';
+
+jest.mock('langchain/chat_models/universal', () => ({
+  initChatModel: jest.fn(),
+}));
+
+const mockInitChatModel = initChatModel as jest.MockedFunction<typeof initChatModel>;
+
+describe('createLangChainModel', () => {
+  const fakeLLM = { invoke: jest.fn() };
+
+  beforeEach(() => {
+    mockInitChatModel.mockReset();
+    mockInitChatModel.mockResolvedValue(fakeLLM as any);
+  });
+
+  it('calls initChatModel with model name and mapped provider', async () => {
+    await createLangChainModel({
+      key: 'cfg',
+      enabled: true,
+      provider: { name: 'openai' },
+      model: { name: 'gpt-4o', parameters: { temperature: 0.5 } },
+      createTracker: jest.fn(),
+    });
+
+    expect(mockInitChatModel).toHaveBeenCalledWith('gpt-4o', {
+      temperature: 0.5,
+      modelProvider: 'openai',
+    });
+  });
+
+  it('maps gemini to google-genai', async () => {
+    await createLangChainModel({
+      key: 'cfg',
+      enabled: true,
+      provider: { name: 'gemini' },
+      model: { name: 'gemini-2.0' },
+      createTracker: jest.fn(),
+    });
+
+    expect(mockInitChatModel).toHaveBeenCalledWith('gemini-2.0', {
+      modelProvider: 'google-genai',
+    });
+  });
+});
+
+it('converts system, user, and assistant messages to LangChain instances', () => {
+  const result = convertMessagesToLangChain([
+    { role: 'system', content: 'sys' },
+    { role: 'user', content: 'u' },
+    { role: 'assistant', content: 'a' },
+  ]);
+
+  expect(result).toHaveLength(3);
+  expect(result[0]).toBeInstanceOf(SystemMessage);
+  expect(result[1]).toBeInstanceOf(HumanMessage);
+  expect(result[2]).toBeInstanceOf(AIMessage);
+});
+
+it('throws on an unsupported role', () => {
+  expect(() => convertMessagesToLangChain([{ role: 'tool' as any, content: 'x' }])).toThrow(
+    'Unsupported message role: tool',
+  );
+});
+
+it('maps gemini to google-genai (case-insensitive)', () => {
+  expect(mapProviderName('gemini')).toBe('google-genai');
+  expect(mapProviderName('Gemini')).toBe('google-genai');
+  expect(mapProviderName('GEMINI')).toBe('google-genai');
+});
+
+it('returns the provider unchanged when no mapping exists', () => {
+  expect(mapProviderName('openai')).toBe('openai');
+  expect(mapProviderName('anthropic')).toBe('anthropic');
+});
+
+it('returns undefined when usage_metadata is absent', () => {
+  expect(getAIUsageFromResponse(new AIMessage('x'))).toBeUndefined();
+});
+
+it('maps usage_metadata to LDTokenUsage', () => {
+  const message = new AIMessage('x');
+  message.usage_metadata = { total_tokens: 30, input_tokens: 10, output_tokens: 20 };
+  expect(getAIUsageFromResponse(message)).toEqual({ total: 30, input: 10, output: 20 });
+});
+
+it('returns success=true with usage from the response', () => {
+  const message = new AIMessage('x');
+  message.usage_metadata = { total_tokens: 3, input_tokens: 1, output_tokens: 2 };
+  expect(getAIMetricsFromResponse(message)).toEqual({
+    success: true,
+    tokens: { total: 3, input: 1, output: 2 },
+  });
+});
+
+describe('buildStructuredTools', () => {
+  const mockLogger = { warn: jest.fn(), info: jest.fn(), error: jest.fn(), debug: jest.fn() };
+
+  beforeEach(() => jest.clearAllMocks());
+
+  it('builds a StructuredTool from a valid tool definition', () => {
+    const toolDefs = [{ name: 'lookup', description: 'looks up a value' }];
+    const registry = { lookup: jest.fn().mockReturnValue('result') };
+
+    const result = buildStructuredTools(toolDefs, registry, mockLogger);
+
+    expect(result).toHaveLength(1);
+    expect(result[0].name).toBe('lookup');
+    expect(result[0].description).toBe('looks up a value');
+  });
+
+  it('skips tools missing from the registry and logs a warning', () => {
+    const toolDefs = [{ name: 'missing', description: 'not in registry' }];
+
+    const result = buildStructuredTools(toolDefs, {}, mockLogger);
+
+    expect(result).toHaveLength(0);
+    expect(mockLogger.warn).toHaveBeenCalledWith(
+      expect.stringContaining("Tool 'missing'"),
+    );
+  });
+
+  it('skips non-function built-in tools and logs a warning', () => {
+    const toolDefs = [{ type: 'code_interpreter', name: 'ci' }];
+
+    const result = buildStructuredTools(toolDefs, { ci: jest.fn() }, mockLogger);
+
+    expect(result).toHaveLength(0);
+    expect(mockLogger.warn).toHaveBeenCalledWith(
+      expect.stringContaining("Built-in tool 'code_interpreter'"),
+    );
+  });
+
+  it('handles function-style tool definitions with nested function.name', () => {
+    const toolDefs = [
+      { type: 'function', function: { name: 'search', description: 'searches' } },
+    ];
+    const registry = { search: jest.fn() };
+
+    const result = buildStructuredTools(toolDefs, registry, mockLogger);
+
+    expect(result).toHaveLength(1);
+    expect(result[0].name).toBe('search');
+  });
+
+  it('uses a default description when none is provided', () => {
+    const toolDefs = [{ name: 'mytool' }];
+    const registry = { mytool: jest.fn() };
+
+    const result = buildStructuredTools(toolDefs, registry);
+
+    expect(result[0].description).toBe('Tool mytool');
+  });
+});
+
+it('extracts tool call names from AIMessages with tool_calls', () => {
+  const msg1 = new AIMessage('');
+  msg1.tool_calls = [
+    { id: 'c1', name: 'lookup', args: {} },
+    { id: 'c2', name: 'search', args: {} },
+  ];
+  const msg2 = new AIMessage('done');
+
+  expect(extractToolCalls([msg1, msg2])).toEqual(['lookup', 'search']);
+});
+
+it('returns an empty array when no tool calls are present', () => {
+  expect(extractToolCalls([new AIMessage('done')])).toEqual([]);
+});
+
+it('handles empty messages for extractToolCalls', () => {
+  expect(extractToolCalls([])).toEqual([]);
+});
+
+it('extracts string content from the last message', () => {
+  expect(
+    extractLastMessageContent([new HumanMessage('hi'), new AIMessage('hello')]),
+  ).toBe('hello');
+});
+
+it('returns empty string for empty array', () => {
+  expect(extractLastMessageContent([])).toBe('');
+});
+
+it('returns empty string when last message content is not a string', () => {
+  const msg = new AIMessage({ content: [{ type: 'text', text: 'hi' }] });
+  expect(extractLastMessageContent([msg])).toBe('');
+});
+
+it('sums usage across multiple messages', () => {
+  const m1 = new AIMessage('');
+  m1.usage_metadata = { total_tokens: 10, input_tokens: 6, output_tokens: 4 };
+  const m2 = new AIMessage('done');
+  m2.usage_metadata = { total_tokens: 8, input_tokens: 3, output_tokens: 5 };
+  const toolMsg = new ToolMessage({ tool_call_id: 'x', content: 'res' });
+
+  expect(sumTokenUsageFromMessages([m1, toolMsg, m2])).toEqual({
+    total: 18,
+    input: 9,
+    output: 9,
+  });
+});
+
+it('returns undefined when no messages have usage', () => {
+  expect(sumTokenUsageFromMessages([new AIMessage('hi')])).toBeUndefined();
+});