@langchain/langgraph-sdk
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Client library for interacting with the LangGraph API
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text/typescript
//#region src/types.messages.d.ts
type ImageDetail = "auto" | "low" | "high";
type MessageContentImageUrl = {
type: "image_url";
image_url: string | {
url: string;
detail?: ImageDetail | undefined;
};
};
type MessageContentText = {
type: "text";
text: string;
};
type MessageContentComplex = MessageContentText | MessageContentImageUrl;
type MessageContent = string | MessageContentComplex[];
/**
* Model-specific additional kwargs, which is passed back to the underlying LLM.
*/
type MessageAdditionalKwargs = Record<string, unknown>;
type BaseMessage = {
additional_kwargs?: MessageAdditionalKwargs | undefined;
content: MessageContent;
id?: string | undefined;
name?: string | undefined;
response_metadata?: Record<string, unknown> | undefined;
};
type HumanMessage = BaseMessage & {
type: "human";
example?: boolean | undefined;
};
/**
* Default tool call type when no specific tool definitions are provided.
*/
type DefaultToolCall = {
name: string;
args: {
[x: string]: any;
};
id?: string | undefined;
type?: "tool_call" | undefined;
};
/**
* Invalid tool call type.
*/
type InvalidToolCall = {
name?: string | undefined;
args?: string | undefined;
id?: string | undefined;
error?: string | undefined;
type?: "invalid_tool_call" | undefined;
};
/**
* Usage metadata for AI messages.
*/
type UsageMetadata = {
input_tokens: number;
output_tokens: number;
total_tokens: number;
input_token_details?: {
audio?: number | undefined;
cache_read?: number | undefined;
cache_creation?: number | undefined;
} | undefined;
output_token_details?: {
audio?: number | undefined;
reasoning?: number | undefined;
} | undefined;
};
/**
* AI message type that can be parameterized with custom tool call types.
*
* @template ToolCall The type of tool calls, defaults to DefaultToolCall.
* Provide a discriminated union for type-safe tool call handling.
*
* @example
* ```ts
* // Define typed tool calls as a discriminated union
* type MyToolCalls =
* | { name: "get_weather"; args: { location: string }; id?: string }
* | { name: "search"; args: { query: string; limit?: number }; id?: string };
*
* // Use with AIMessage
* const message: AIMessage<MyToolCalls> = ...;
*
* // Now tool.name === "get_weather" narrows tool.args type
* if (message.tool_calls) {
* for (const tool of message.tool_calls) {
* if (tool.name === "get_weather") {
* // tool.args is now { location: string }
* console.log(tool.args.location);
* }
* }
* }
* ```
*/
type AIMessage<ToolCall = DefaultToolCall> = BaseMessage & {
type: "ai";
example?: boolean | undefined;
tool_calls?: ToolCall[] | undefined;
invalid_tool_calls?: InvalidToolCall[] | undefined;
usage_metadata?: UsageMetadata | undefined;
};
type ToolMessage = BaseMessage & {
type: "tool";
status?: "error" | "success" | undefined;
tool_call_id: string;
/**
* Artifact of the Tool execution which is not meant to be sent to the model.
*
* Should only be specified if it is different from the message content, e.g. if only
* a subset of the full tool output is being passed as message content but the full
* output is needed in other parts of the code.
*/
artifact?: any;
};
type SystemMessage = BaseMessage & {
type: "system";
};
type FunctionMessage = BaseMessage & {
type: "function";
};
type RemoveMessage = BaseMessage & {
type: "remove";
};
/**
* Union of all message types.
*
* @template ToolCall The type of tool calls for AIMessage, defaults to DefaultToolCall.
*/
type Message<ToolCall = DefaultToolCall> = HumanMessage | AIMessage<ToolCall> | ToolMessage | SystemMessage | FunctionMessage | RemoveMessage;
/**
* Helper type to infer schema input type, supporting both Zod v3 and v4.
* - Zod v4 uses `_zod.input` property
* - Zod v3 uses `_input` property
*
* Note: Zod v4 is checked first as it's the more specific structure.
*/
type InferSchemaInput<S> = S extends {
_zod: {
input: infer Args;
};
} ? Args : S extends {
_input: infer Args;
} ? Args : never;
/**
* Helper type to extract the input type from a DynamicStructuredTool's _call method.
* This is more reliable than trying to infer from the schema directly because
* DynamicStructuredTool has the input type baked into its _call signature.
*/
type InferToolInput<T> = T extends {
_call: (arg: infer Args, ...rest: any[]) => any;
} ? Args : T extends {
schema: infer S;
} ? InferSchemaInput<S> : never;
/**
* Infer a tool call type from a single tool.
* Works with tools created via `tool()` from `@langchain/core/tools`.
*
* For DynamicStructuredTool, this extracts the input type from the _call method,
* which is the most reliable source as it's the pre-computed input type.
*
* @template T The tool type (e.g., DynamicStructuredTool)
*
* @example
* ```ts
* import { tool } from "@langchain/core/tools";
* import { z } from "zod";
*
* const getWeather = tool(
* async ({ location }) => `Weather in ${location}`,
* { name: "get_weather", schema: z.object({ location: z.string() }) }
* );
*
* // Infer: { name: "get_weather"; args: { location: string }; id?: string; type?: "tool_call" }
* type WeatherToolCall = ToolCallFromTool<typeof getWeather>;
* ```
*/
type ToolCallFromTool<T> = T extends {
name: infer N;
} ? InferToolInput<T> extends infer Args ? Args extends never ? never : Args extends Record<string, any> ? {
name: N;
args: Args;
id?: string;
type?: "tool_call";
} : never : never : never;
/**
* Infer a union of tool call types from an array of tools.
* Works with tools created via `tool()` from `@langchain/core/tools`.
*
* @template T A tuple/array of tools
*
* @example
* ```ts
* import { tool } from "@langchain/core/tools";
* import { z } from "zod";
*
* const getWeather = tool(
* async ({ location }) => `Weather in ${location}`,
* { name: "get_weather", schema: z.object({ location: z.string() }) }
* );
*
* const search = tool(
* async ({ query }) => `Results for ${query}`,
* { name: "search", schema: z.object({ query: z.string() }) }
* );
*
* const tools = [getWeather, search] as const;
*
* // Infer union:
* // | { name: "get_weather"; args: { location: string }; id?: string; type?: "tool_call" }
* // | { name: "search"; args: { query: string }; id?: string; type?: "tool_call" }
* type MyToolCalls = ToolCallsFromTools<typeof tools>;
* ```
*/
type ToolCallsFromTools<T extends readonly unknown[]> = T extends readonly [infer First, ...infer Rest] ? ToolCallFromTool<First> | ToolCallsFromTools<Rest> : never;
/**
* The lifecycle state of a tool call.
*
* - `pending`: Tool call received, awaiting result
* - `completed`: Tool execution finished successfully
* - `error`: Tool execution failed (result.status === "error")
*/
type ToolCallState = "pending" | "completed" | "error";
/**
* Represents a tool call paired with its result.
* Useful for rendering tool invocations and their outputs together.
*
* @template ToolCall The type of the tool call.
*/
type ToolCallWithResult<ToolCall = DefaultToolCall> = {
/**
* Unique identifier for this tool call.
* Uses the tool call's id if available, otherwise generates one from aiMessage.id and index.
*/
id: string;
/**
* The tool call from the AI message.
*/
call: ToolCall;
/**
* The result message from tool execution.
* `undefined` if the tool is still being executed or no result was received.
*/
result: ToolMessage | undefined;
/**
* The AI message that initiated this tool call.
*/
aiMessage: AIMessage<ToolCall>;
/**
* Index of this tool call within the AI message's tool_calls array.
*/
index: number;
/**
* The current lifecycle state of the tool call.
*
* - `pending`: No result yet
* - `completed`: Has result with success status
* - `error`: Has result with error status
*/
state: ToolCallState;
};
//#endregion
export { AIMessage, DefaultToolCall, FunctionMessage, HumanMessage, Message, RemoveMessage, SystemMessage, ToolCallFromTool, ToolCallState, ToolCallWithResult, ToolCallsFromTools, ToolMessage };
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