{"version":3,"file":"react_agent_executor.cjs","names":["Runnable","RunnableLambda","SystemMessage","RunnableSequence","RunnableBinding","Annotation","messagesStateReducer","ToolNode","withAgentName","StateGraph","START","isToolMessage","Send","END"],"sources":["../../src/prebuilt/react_agent_executor.ts"],"sourcesContent":["import {\n BaseChatModel,\n BindToolsInput,\n} from \"@langchain/core/language_models/chat_models\";\nimport { LanguageModelLike } from \"@langchain/core/language_models/base\";\nimport {\n BaseMessage,\n BaseMessageLike,\n isAIMessage,\n isBaseMessage,\n isToolMessage,\n SystemMessage,\n type AIMessage,\n} from \"@langchain/core/messages\";\nimport {\n Runnable,\n RunnableLambda,\n RunnableToolLike,\n RunnableSequence,\n RunnableBinding,\n type RunnableLike,\n} from \"@langchain/core/runnables\";\nimport { DynamicTool, StructuredToolInterface } from \"@langchain/core/tools\";\nimport type {\n InteropZodObject,\n InteropZodType,\n} from \"@langchain/core/utils/types\";\nimport {\n All,\n BaseCheckpointSaver,\n BaseStore,\n} from \"@langchain/langgraph-checkpoint\";\n\nimport {\n StateGraph,\n type CompiledStateGraph,\n AnnotationRoot,\n} from \"../graph/index.js\";\nimport { MessagesAnnotation } from \"../graph/messages_annotation.js\";\nimport { ToolNode } from \"./tool_node.js\";\nimport { LangGraphRunnableConfig, Runtime } from \"../pregel/runnable_types.js\";\nimport { Annotation } from \"../graph/annotation.js\";\nimport { Messages, messagesStateReducer } from \"../graph/messages_reducer.js\";\nimport { END, Send, START } from \"../constants.js\";\nimport { withAgentName } from \"./agentName.js\";\nimport type { InteropZodToStateDefinition } from \"../graph/zod/meta.js\";\n\n/**\n * @deprecated `AgentState` has been moved to {@link https://www.npmjs.com/package/langchain langchain} package.\n * Update your import to `import { AgentState } from \"langchain\";`\n */\nexport interface AgentState<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n StructuredResponseType extends Record = Record,\n> {\n messages: BaseMessage[];\n // TODO: This won't be set until we\n // implement managed values in LangGraphJS\n // Will be useful for inserting a message on\n // graph recursion end\n // is_last_step: boolean;\n structuredResponse: StructuredResponseType;\n}\n\nexport type N = typeof START | \"agent\" | \"tools\";\n\ntype StructuredResponseSchemaOptions = {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n schema: InteropZodType | Record;\n prompt?: string;\n\n strict?: boolean;\n [key: string]: unknown;\n};\n\nfunction _convertMessageModifierToPrompt(\n messageModifier: MessageModifier\n): Prompt {\n // Handle string or SystemMessage\n if (\n typeof messageModifier === \"string\" ||\n (isBaseMessage(messageModifier) && messageModifier._getType() === \"system\")\n ) {\n return messageModifier;\n }\n\n // Handle callable function\n if (typeof messageModifier === \"function\") {\n return async (state: typeof MessagesAnnotation.State) =>\n messageModifier(state.messages);\n }\n\n // Handle Runnable\n if (Runnable.isRunnable(messageModifier)) {\n return RunnableLambda.from(\n (state: typeof MessagesAnnotation.State) => state.messages\n ).pipe(messageModifier);\n }\n\n throw new Error(\n `Unexpected type for messageModifier: ${typeof messageModifier}`\n );\n}\n\nconst PROMPT_RUNNABLE_NAME = \"prompt\";\n\nfunction _getPromptRunnable(prompt?: Prompt): Runnable {\n let promptRunnable: Runnable;\n\n if (prompt == null) {\n promptRunnable = RunnableLambda.from(\n (state: typeof MessagesAnnotation.State) => state.messages\n ).withConfig({ runName: PROMPT_RUNNABLE_NAME });\n } else if (typeof prompt === \"string\") {\n const systemMessage = new SystemMessage(prompt);\n promptRunnable = RunnableLambda.from(\n (state: typeof MessagesAnnotation.State) => {\n return [systemMessage, ...(state.messages ?? [])];\n }\n ).withConfig({ runName: PROMPT_RUNNABLE_NAME });\n } else if (isBaseMessage(prompt) && prompt._getType() === \"system\") {\n promptRunnable = RunnableLambda.from(\n (state: typeof MessagesAnnotation.State) => [prompt, ...state.messages]\n ).withConfig({ runName: PROMPT_RUNNABLE_NAME });\n } else if (typeof prompt === \"function\") {\n promptRunnable = RunnableLambda.from(prompt).withConfig({\n runName: PROMPT_RUNNABLE_NAME,\n });\n } else if (Runnable.isRunnable(prompt)) {\n promptRunnable = prompt;\n } else {\n throw new Error(`Got unexpected type for 'prompt': ${typeof prompt}`);\n }\n\n return promptRunnable;\n}\n\ntype ServerTool = Record;\ntype ClientTool = StructuredToolInterface | DynamicTool | RunnableToolLike;\n\nfunction isClientTool(tool: ClientTool | ServerTool): tool is ClientTool {\n return Runnable.isRunnable(tool);\n}\n\nfunction _getPrompt(\n prompt?: Prompt,\n stateModifier?: CreateReactAgentParams[\"stateModifier\"],\n messageModifier?: CreateReactAgentParams[\"messageModifier\"]\n): Runnable {\n // Check if multiple modifiers exist\n const definedCount = [prompt, stateModifier, messageModifier].filter(\n (x) => x != null\n ).length;\n if (definedCount > 1) {\n throw new Error(\n \"Expected only one of prompt, stateModifier, or messageModifier, got multiple values\"\n );\n }\n\n let finalPrompt = prompt;\n if (stateModifier != null) {\n finalPrompt = stateModifier;\n } else if (messageModifier != null) {\n finalPrompt = _convertMessageModifierToPrompt(messageModifier);\n }\n\n return _getPromptRunnable(finalPrompt);\n}\n\nfunction _isBaseChatModel(model: LanguageModelLike): model is BaseChatModel {\n return (\n \"invoke\" in model &&\n typeof model.invoke === \"function\" &&\n \"_modelType\" in model\n );\n}\n\ninterface ConfigurableModelInterface {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n _queuedMethodOperations: Record;\n _model: () => Promise;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nfunction _isConfigurableModel(model: any): model is ConfigurableModelInterface {\n return (\n \"_queuedMethodOperations\" in model &&\n \"_model\" in model &&\n typeof model._model === \"function\"\n );\n}\n\nfunction _isChatModelWithBindTools(\n llm: LanguageModelLike\n): llm is BaseChatModel & Required> {\n if (!_isBaseChatModel(llm)) return false;\n return \"bindTools\" in llm && typeof llm.bindTools === \"function\";\n}\n\nexport async function _shouldBindTools(\n llm: LanguageModelLike,\n tools: (ClientTool | ServerTool)[]\n): Promise {\n // If model is a RunnableSequence, find a RunnableBinding or BaseChatModel in its steps\n let model = llm;\n if (RunnableSequence.isRunnableSequence(model)) {\n model =\n model.steps.find(\n (step) =>\n RunnableBinding.isRunnableBinding(step) ||\n _isBaseChatModel(step) ||\n _isConfigurableModel(step)\n ) || model;\n }\n\n if (_isConfigurableModel(model)) {\n model = await model._model();\n }\n\n // If not a RunnableBinding, we should bind tools\n if (!RunnableBinding.isRunnableBinding(model)) {\n return true;\n }\n\n let boundTools = (() => {\n // check if model.kwargs contain the tools key\n if (\n model.kwargs != null &&\n typeof model.kwargs === \"object\" &&\n \"tools\" in model.kwargs &&\n Array.isArray(model.kwargs.tools)\n ) {\n return (model.kwargs.tools ?? null) as BindToolsInput[] | null;\n }\n\n // Some models can bind the tools via `withConfig()` instead of `bind()`\n if (\n model.config != null &&\n typeof model.config === \"object\" &&\n \"tools\" in model.config &&\n Array.isArray(model.config.tools)\n ) {\n return (model.config.tools ?? null) as BindToolsInput[] | null;\n }\n\n return null;\n })();\n\n // google-style\n if (\n boundTools != null &&\n boundTools.length === 1 &&\n \"functionDeclarations\" in boundTools[0]\n ) {\n boundTools = boundTools[0].functionDeclarations;\n }\n\n // If no tools in kwargs, we should bind tools\n if (boundTools == null) return true;\n\n // Check if tools count matches\n if (tools.length !== boundTools.length) {\n throw new Error(\n \"Number of tools in the model.bindTools() and tools passed to createReactAgent must match\"\n );\n }\n\n const toolNames = new Set(\n tools.flatMap((tool) => (isClientTool(tool) ? tool.name : []))\n );\n\n const boundToolNames = new Set();\n\n for (const boundTool of boundTools) {\n let boundToolName: string | undefined;\n\n // OpenAI-style tool\n if (\"type\" in boundTool && boundTool.type === \"function\") {\n boundToolName = boundTool.function.name;\n }\n // Anthropic or Google-style tool\n else if (\"name\" in boundTool) {\n boundToolName = boundTool.name;\n }\n // Bedrock-style tool\n else if (\"toolSpec\" in boundTool && \"name\" in boundTool.toolSpec) {\n boundToolName = boundTool.toolSpec.name;\n }\n // unknown tool type so we'll ignore it\n else {\n continue;\n }\n\n if (boundToolName) {\n boundToolNames.add(boundToolName);\n }\n }\n\n const missingTools = [...toolNames].filter((x) => !boundToolNames.has(x));\n if (missingTools.length > 0) {\n throw new Error(\n `Missing tools '${missingTools}' in the model.bindTools().` +\n `Tools in the model.bindTools() must match the tools passed to createReactAgent.`\n );\n }\n\n return false;\n}\n\nconst _simpleBindTools = (\n llm: LanguageModelLike,\n toolClasses: (ClientTool | ServerTool)[]\n) => {\n if (_isChatModelWithBindTools(llm)) {\n return llm.bindTools(toolClasses);\n }\n\n if (\n RunnableBinding.isRunnableBinding(llm) &&\n _isChatModelWithBindTools(llm.bound)\n ) {\n const newBound = llm.bound.bindTools(toolClasses);\n\n if (RunnableBinding.isRunnableBinding(newBound)) {\n return new RunnableBinding({\n bound: newBound.bound,\n config: { ...llm.config, ...newBound.config },\n kwargs: { ...llm.kwargs, ...newBound.kwargs },\n configFactories: newBound.configFactories ?? llm.configFactories,\n });\n }\n\n return new RunnableBinding({\n bound: newBound,\n config: llm.config,\n kwargs: llm.kwargs,\n configFactories: llm.configFactories,\n });\n }\n\n return null;\n};\n\nexport async function _bindTools(\n llm: LanguageModelLike,\n toolClasses: (ClientTool | ServerTool)[]\n) {\n const model = _simpleBindTools(llm, toolClasses);\n if (model) return model;\n\n if (_isConfigurableModel(llm)) {\n const model = _simpleBindTools(await llm._model(), toolClasses);\n if (model) return model;\n }\n\n if (RunnableSequence.isRunnableSequence(llm)) {\n const modelStep = llm.steps.findIndex(\n (step) =>\n RunnableBinding.isRunnableBinding(step) ||\n _isBaseChatModel(step) ||\n _isConfigurableModel(step)\n );\n\n if (modelStep >= 0) {\n const model = _simpleBindTools(llm.steps[modelStep], toolClasses);\n if (model) {\n const nextSteps: unknown[] = llm.steps.slice();\n nextSteps.splice(modelStep, 1, model);\n\n return RunnableSequence.from(\n nextSteps as [RunnableLike, ...RunnableLike[], RunnableLike]\n );\n }\n }\n }\n\n throw new Error(`llm ${llm} must define bindTools method.`);\n}\n\nexport async function _getModel(\n llm: LanguageModelLike | ConfigurableModelInterface\n): Promise {\n // If model is a RunnableSequence, find a RunnableBinding or BaseChatModel in its steps\n let model = llm;\n if (RunnableSequence.isRunnableSequence(model)) {\n model =\n model.steps.find(\n (step) =>\n RunnableBinding.isRunnableBinding(step) ||\n _isBaseChatModel(step) ||\n _isConfigurableModel(step)\n ) || model;\n }\n\n if (_isConfigurableModel(model)) {\n model = await model._model();\n }\n\n // Get the underlying model from a RunnableBinding\n if (RunnableBinding.isRunnableBinding(model)) {\n model = model.bound;\n }\n\n if (!_isBaseChatModel(model)) {\n throw new Error(\n `Expected \\`llm\\` to be a ChatModel or RunnableBinding (e.g. llm.bind_tools(...)) with invoke() and generate() methods, got ${model.constructor.name}`\n );\n }\n\n return model;\n}\n\nexport type Prompt =\n | SystemMessage\n | string\n | ((\n state: typeof MessagesAnnotation.State,\n config: LangGraphRunnableConfig\n ) => BaseMessageLike[])\n | ((\n state: typeof MessagesAnnotation.State,\n config: LangGraphRunnableConfig\n ) => Promise)\n | Runnable;\n\n/** @deprecated Use Prompt instead. */\nexport type StateModifier = Prompt;\n\n/** @deprecated Use Prompt instead. */\nexport type MessageModifier =\n | SystemMessage\n | string\n | ((messages: BaseMessage[]) => BaseMessage[])\n | ((messages: BaseMessage[]) => Promise)\n | Runnable;\n\nexport const createReactAgentAnnotation = <\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n T extends Record = Record,\n>() =>\n Annotation.Root({\n messages: Annotation({\n reducer: messagesStateReducer,\n default: () => [],\n }),\n structuredResponse: Annotation,\n });\n\ntype WithStateGraphNodes =\n Graph extends StateGraph<\n infer SD,\n infer S,\n infer U,\n infer N,\n infer I,\n infer O,\n infer C\n >\n ? StateGraph\n : never;\n\nconst PreHookAnnotation = Annotation.Root({\n llmInputMessages: Annotation({\n reducer: (_, update) => messagesStateReducer([], update),\n default: () => [],\n }),\n});\n\ntype PreHookAnnotation = typeof PreHookAnnotation;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\ntype AnyAnnotationRoot = AnnotationRoot;\n\ntype ToAnnotationRoot =\n A extends AnyAnnotationRoot\n ? A\n : A extends InteropZodObject\n ? AnnotationRoot>\n : never;\n\n/**\n * @deprecated `CreateReactAgentParams` has been moved to {@link https://www.npmjs.com/package/langchain langchain} package.\n * Update your import to `import { CreateAgentParams } from \"langchain\";`\n */\nexport type CreateReactAgentParams<\n A extends AnyAnnotationRoot | InteropZodObject = AnyAnnotationRoot,\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n StructuredResponseType extends Record = Record,\n C extends AnyAnnotationRoot | InteropZodObject = AnyAnnotationRoot,\n> = {\n /** The chat model that can utilize OpenAI-style tool calling. */\n llm:\n | LanguageModelLike\n | ((\n state: ToAnnotationRoot[\"State\"] & PreHookAnnotation[\"State\"],\n runtime: Runtime[\"State\"]>\n ) => Promise | LanguageModelLike);\n\n /** A list of tools or a ToolNode. */\n tools: ToolNode | (ServerTool | ClientTool)[];\n\n /**\n * @deprecated Use prompt instead.\n */\n messageModifier?: MessageModifier;\n\n /**\n * @deprecated Use prompt instead.\n */\n stateModifier?: StateModifier;\n\n /**\n * An optional prompt for the LLM. This takes full graph state BEFORE the LLM is called and prepares the input to LLM.\n *\n * Can take a few different forms:\n *\n * - str: This is converted to a SystemMessage and added to the beginning of the list of messages in state[\"messages\"].\n * - SystemMessage: this is added to the beginning of the list of messages in state[\"messages\"].\n * - Function: This function should take in full graph state and the output is then passed to the language model.\n * - Runnable: This runnable should take in full graph state and the output is then passed to the language model.\n *\n * Note:\n * Prior to `v0.2.46`, the prompt was set using `stateModifier` / `messagesModifier` parameters.\n * This is now deprecated and will be removed in a future release.\n */\n prompt?: Prompt;\n\n /**\n * Additional state schema for the agent.\n */\n stateSchema?: A;\n\n /**\n * An optional schema for the context.\n */\n contextSchema?: C;\n /** An optional checkpoint saver to persist the agent's state. */\n checkpointSaver?: BaseCheckpointSaver | boolean;\n /** An optional checkpoint saver to persist the agent's state. Alias of \"checkpointSaver\". */\n checkpointer?: BaseCheckpointSaver | boolean;\n /** An optional list of node names to interrupt before running. */\n interruptBefore?: N[] | All;\n /** An optional list of node names to interrupt after running. */\n interruptAfter?: N[] | All;\n store?: BaseStore;\n /**\n * An optional schema for the final agent output.\n *\n * If provided, output will be formatted to match the given schema and returned in the 'structuredResponse' state key.\n * If not provided, `structuredResponse` will not be present in the output state.\n *\n * Can be passed in as:\n * - Zod schema\n * - JSON schema\n * - { prompt, schema }, where schema is one of the above.\n * The prompt will be used together with the model that is being used to generate the structured response.\n *\n * @remarks\n * **Important**: `responseFormat` requires the model to support `.withStructuredOutput()`.\n *\n * **Note**: The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished.\n * This is not the only strategy to get structured responses, see more options in [this guide](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/).\n */\n responseFormat?:\n | InteropZodType\n | StructuredResponseSchemaOptions\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record;\n\n /**\n * An optional name for the agent.\n */\n name?: string;\n\n /**\n * An optional description for the agent.\n * This can be used to describe the agent to the underlying supervisor LLM.\n */\n description?: string | undefined;\n\n /**\n * Use to specify how to expose the agent name to the underlying supervisor LLM.\n\n - undefined: Relies on the LLM provider {@link AIMessage#name}. Currently, only OpenAI supports this.\n - `\"inline\"`: Add the agent name directly into the content field of the {@link AIMessage} using XML-style tags.\n Example: `\"How can I help you\"` -> `\"agent_nameHow can I help you?\"`\n */\n includeAgentName?: \"inline\" | undefined;\n\n /**\n * An optional node to add before the `agent` node (i.e., the node that calls the LLM).\n * Useful for managing long message histories (e.g., message trimming, summarization, etc.).\n */\n preModelHook?: RunnableLike<\n ToAnnotationRoot[\"State\"] & PreHookAnnotation[\"State\"],\n ToAnnotationRoot[\"Update\"] & PreHookAnnotation[\"Update\"],\n LangGraphRunnableConfig\n >;\n\n /**\n * An optional node to add after the `agent` node (i.e., the node that calls the LLM).\n * Useful for implementing human-in-the-loop, guardrails, validation, or other post-processing.\n */\n postModelHook?: RunnableLike<\n ToAnnotationRoot[\"State\"],\n ToAnnotationRoot[\"Update\"],\n LangGraphRunnableConfig\n >;\n\n /**\n * Determines the version of the graph to create.\n *\n * Can be one of\n * - `\"v1\"`: The tool node processes a single message. All tool calls in the message are\n * executed in parallel within the tool node.\n * - `\"v2\"`: The tool node processes a single tool call. Tool calls are distributed across\n * multiple instances of the tool node using the Send API.\n *\n * @default `\"v1\"`\n */\n version?: \"v1\" | \"v2\";\n};\n\n/**\n * @deprecated `createReactAgent` has been moved to {@link https://www.npmjs.com/package/langchain langchain} package.\n * Update your import to `import { createAgent } from \"langchain\";`\n *\n * Creates a StateGraph agent that relies on a chat model utilizing tool calling.\n *\n * @example\n * ```ts\n * import { ChatOpenAI } from \"@langchain/openai\";\n * import { tool } from \"@langchain/core/tools\";\n * import { z } from \"zod\";\n * import { createReactAgent } from \"@langchain/langgraph/prebuilt\";\n *\n * const model = new ChatOpenAI({\n * model: \"gpt-4o\",\n * });\n *\n * const getWeather = tool((input) => {\n * if ([\"sf\", \"san francisco\"].includes(input.location.toLowerCase())) {\n * return \"It's 60 degrees and foggy.\";\n * } else {\n * return \"It's 90 degrees and sunny.\";\n * }\n * }, {\n * name: \"get_weather\",\n * description: \"Call to get the current weather.\",\n * schema: z.object({\n * location: z.string().describe(\"Location to get the weather for.\"),\n * })\n * })\n *\n * const agent = createReactAgent({ llm: model, tools: [getWeather] });\n *\n * const inputs = {\n * messages: [{ role: \"user\", content: \"what is the weather in SF?\" }],\n * };\n *\n * const stream = await agent.stream(inputs, { streamMode: \"values\" });\n *\n * for await (const { messages } of stream) {\n * console.log(messages);\n * }\n * // Returns the messages in the state at each step of execution\n * ```\n */\nexport function createReactAgent<\n A extends AnyAnnotationRoot | InteropZodObject = typeof MessagesAnnotation,\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n StructuredResponseFormat extends Record = Record,\n C extends AnyAnnotationRoot | InteropZodObject = AnyAnnotationRoot,\n>(\n params: CreateReactAgentParams\n): CompiledStateGraph<\n ToAnnotationRoot[\"State\"],\n ToAnnotationRoot[\"Update\"],\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n any,\n typeof MessagesAnnotation.spec & ToAnnotationRoot[\"spec\"],\n ReturnType<\n typeof createReactAgentAnnotation\n >[\"spec\"] &\n ToAnnotationRoot[\"spec\"]\n> {\n const {\n llm,\n tools,\n messageModifier,\n stateModifier,\n prompt,\n stateSchema,\n contextSchema,\n checkpointSaver,\n checkpointer,\n interruptBefore,\n interruptAfter,\n store,\n responseFormat,\n preModelHook,\n postModelHook,\n name,\n description,\n version = \"v1\",\n includeAgentName,\n } = params;\n\n let toolClasses: (ClientTool | ServerTool)[];\n\n let toolNode: ToolNode;\n if (!Array.isArray(tools)) {\n toolClasses = tools.tools;\n toolNode = tools;\n } else {\n toolClasses = tools;\n toolNode = new ToolNode(toolClasses.filter(isClientTool));\n }\n\n let cachedStaticModel: Runnable | null = null;\n\n const _getStaticModel = async (llm: LanguageModelLike): Promise => {\n if (cachedStaticModel) return cachedStaticModel;\n\n let modelWithTools: LanguageModelLike;\n if (await _shouldBindTools(llm, toolClasses)) {\n modelWithTools = await _bindTools(llm, toolClasses);\n } else {\n modelWithTools = llm;\n }\n\n const promptRunnable = _getPrompt(prompt, stateModifier, messageModifier);\n const modelRunnable =\n includeAgentName === \"inline\"\n ? withAgentName(modelWithTools, includeAgentName)\n : modelWithTools;\n\n cachedStaticModel = promptRunnable.pipe(modelRunnable);\n return cachedStaticModel;\n };\n\n const _getDynamicModel = async (\n llm: (\n state: AgentState & PreHookAnnotation[\"State\"],\n runtime: Runtime[\"State\"]>\n ) => Promise | LanguageModelLike,\n state: AgentState & PreHookAnnotation[\"State\"],\n config: Runtime[\"State\"]>\n ) => {\n const model = await llm(state, config);\n\n return _getPrompt(prompt, stateModifier, messageModifier).pipe(\n includeAgentName === \"inline\"\n ? withAgentName(model, includeAgentName)\n : model\n );\n };\n\n // If any of the tools are configured to return_directly after running,\n // our graph needs to check if these were called\n const shouldReturnDirect = new Set(\n toolClasses\n .filter(isClientTool)\n .filter((tool) => \"returnDirect\" in tool && tool.returnDirect)\n .map((tool) => tool.name)\n );\n\n function getModelInputState(\n state: AgentState & PreHookAnnotation[\"State\"]\n ): Omit, \"llmInputMessages\"> {\n const { messages, llmInputMessages, ...rest } = state;\n if (llmInputMessages != null && llmInputMessages.length > 0) {\n return { messages: llmInputMessages, ...rest };\n }\n return { messages, ...rest };\n }\n\n const generateStructuredResponse = async (\n state: AgentState & PreHookAnnotation[\"State\"],\n config: Runtime[\"State\"]>\n ) => {\n if (responseFormat == null) {\n throw new Error(\n \"Attempted to generate structured output with no passed response schema. Please contact us for help.\"\n );\n }\n const messages = [...state.messages];\n let modelWithStructuredOutput;\n\n const model: LanguageModelLike =\n typeof llm === \"function\"\n ? await llm(state, config)\n : await _getModel(llm);\n\n if (!_isBaseChatModel(model)) {\n throw new Error(\n `Expected \\`llm\\` to be a ChatModel with .withStructuredOutput() method, got ${model.constructor.name}`\n );\n }\n\n if (typeof responseFormat === \"object\" && \"schema\" in responseFormat) {\n const { prompt, schema, ...options } =\n responseFormat as StructuredResponseSchemaOptions;\n\n modelWithStructuredOutput = model.withStructuredOutput(schema, options);\n if (prompt != null) {\n messages.unshift(new SystemMessage({ content: prompt }));\n }\n } else {\n modelWithStructuredOutput = model.withStructuredOutput(responseFormat);\n }\n\n const response = await modelWithStructuredOutput.invoke(messages, config);\n return { structuredResponse: response };\n };\n\n const callModel = async (\n state: AgentState & PreHookAnnotation[\"State\"],\n config: Runtime[\"State\"]>\n ) => {\n // NOTE: we're dynamically creating the model runnable here\n // to ensure that we can validate ConfigurableModel properly\n const modelRunnable: Runnable =\n typeof llm === \"function\"\n ? await _getDynamicModel(llm, state, config)\n : await _getStaticModel(llm);\n\n // TODO: Auto-promote streaming.\n const response = (await modelRunnable.invoke(\n getModelInputState(state),\n config\n )) as BaseMessage;\n // add agent name to the AIMessage\n // TODO: figure out if we can avoid mutating the message directly\n response.name = name;\n response.lc_kwargs.name = name;\n return { messages: [response] };\n };\n\n const schema =\n stateSchema ?? createReactAgentAnnotation();\n\n const workflow = new StateGraph(\n schema as AnyAnnotationRoot,\n contextSchema\n ).addNode(\"tools\", toolNode);\n\n if (!(\"messages\" in workflow._schemaDefinition)) {\n throw new Error(\"Missing required `messages` key in state schema.\");\n }\n\n const allNodeWorkflows = workflow as WithStateGraphNodes<\n | \"pre_model_hook\"\n | \"post_model_hook\"\n | \"generate_structured_response\"\n | \"agent\",\n typeof workflow\n >;\n\n const conditionalMap = (map: Record) => {\n return Object.fromEntries(\n Object.entries(map).filter(([_, v]) => v != null) as [string, T][]\n );\n };\n\n let entrypoint: \"agent\" | \"pre_model_hook\" = \"agent\";\n let inputSchema: AnnotationRoot[\"spec\"]> | undefined;\n if (preModelHook != null) {\n allNodeWorkflows\n .addNode(\"pre_model_hook\", preModelHook)\n .addEdge(\"pre_model_hook\", \"agent\");\n entrypoint = \"pre_model_hook\";\n\n inputSchema = Annotation.Root({\n ...workflow._schemaDefinition,\n ...PreHookAnnotation.spec,\n });\n } else {\n entrypoint = \"agent\";\n }\n\n allNodeWorkflows\n .addNode(\"agent\", callModel, { input: inputSchema })\n .addEdge(START, entrypoint);\n\n if (postModelHook != null) {\n allNodeWorkflows\n .addNode(\"post_model_hook\", postModelHook)\n .addEdge(\"agent\", \"post_model_hook\")\n .addConditionalEdges(\n \"post_model_hook\",\n (state) => {\n const { messages } = state as AgentState;\n\n const toolMessageIds: Set = new Set(\n messages.filter(isToolMessage).map((msg) => msg.tool_call_id)\n );\n\n let lastAiMessage: AIMessage | undefined;\n for (let i = messages.length - 1; i >= 0; i -= 1) {\n const message = messages[i];\n if (isAIMessage(message)) {\n lastAiMessage = message;\n break;\n }\n }\n\n const pendingToolCalls =\n lastAiMessage?.tool_calls?.filter(\n (i) => i.id == null || !toolMessageIds.has(i.id)\n ) ?? [];\n\n const lastMessage = messages[messages.length - 1];\n if (pendingToolCalls.length > 0) {\n if (version === \"v2\") {\n return pendingToolCalls.map(\n (toolCall) =>\n new Send(\"tools\", { ...state, lg_tool_call: toolCall })\n );\n }\n return \"tools\";\n }\n\n if (lastMessage && isToolMessage(lastMessage)) return entrypoint;\n if (responseFormat != null) return \"generate_structured_response\";\n return END;\n },\n conditionalMap({\n tools: \"tools\",\n [entrypoint]: entrypoint,\n generate_structured_response:\n responseFormat != null ? \"generate_structured_response\" : null,\n [END]: responseFormat != null ? null : END,\n })\n );\n }\n\n if (responseFormat !== undefined) {\n workflow\n .addNode(\"generate_structured_response\", generateStructuredResponse)\n .addEdge(\"generate_structured_response\", END);\n }\n\n if (postModelHook == null) {\n allNodeWorkflows.addConditionalEdges(\n \"agent\",\n (state) => {\n const { messages } = state;\n const lastMessage = messages[messages.length - 1];\n\n // if there's no function call, we finish\n if (!isAIMessage(lastMessage) || !lastMessage.tool_calls?.length) {\n if (responseFormat != null) return \"generate_structured_response\";\n return END;\n }\n\n // there are function calls, we continue\n if (version === \"v2\") {\n return lastMessage.tool_calls.map(\n (toolCall) =>\n new Send(\"tools\", { ...state, lg_tool_call: toolCall })\n );\n }\n\n return \"tools\";\n },\n conditionalMap({\n tools: \"tools\",\n generate_structured_response:\n responseFormat != null ? \"generate_structured_response\" : null,\n [END]: responseFormat != null ? null : END,\n })\n );\n }\n\n if (shouldReturnDirect.size > 0) {\n allNodeWorkflows.addConditionalEdges(\n \"tools\",\n (state) => {\n const agentState = state;\n // Check the last consecutive tool calls\n for (let i = agentState.messages.length - 1; i >= 0; i -= 1) {\n const message = agentState.messages[i];\n if (!isToolMessage(message)) break;\n\n // Check if this 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