ChatAnthropicMessages

Anthropic API key

Anthropic API key

Optional array of beta features to enable for the Anthropic API. Beta features are experimental capabilities that may change or be removed. See https://docs.claude.com/en/api/beta-headers for available beta features.

The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.

Overridable Anthropic ClientOptions

Configuration for context management. See https://docs.claude.com/en/docs/build-with-claude/context-editing

Optional method that returns an initialized underlying Anthropic client. Useful for accessing Anthropic models hosted on other cloud services such as Google Vertex.

Specifies the geographic region for inference processing. US-only inference is available at 1.1x pricing for models released after February 1, 2026.

Holds any additional parameters that are valid to pass to anthropic.messages that are not explicitly specified on this class.

A path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.

A maximum number of tokens to generate before stopping.

Model name to use

Configuration options for the model's output, such as effort level and output format. The effort parameter controls how many tokens Claude uses when responding, trading off between response thoroughness and token efficiency.

Effort levels: "low", "medium", "high" (default), "max" (Opus 4.6 only).

A list of strings upon which to stop generating. You probably want ["\n\nHuman:"], as that's the cue for the next turn in the dialog agent.

Whether to stream the results or not

Whether or not to include token usage data in streamed chunks.

Amount of randomness injected into the response. Ranges from 0 to 1. Use temperature closer to 0 for analytical / multiple choice, and temperature closer to 1 for creative and generative tasks.

Options for extended thinking.

Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses.

Does nucleus sampling, in which we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by top_p. Note that you should either alter temperature or top_p, but not both.

Whether to print out response text.

Internal method that handles batching and configuration for a runnable It takes a function, input values, and optional configuration, and returns a promise that resolves to the output values.

Create a unique cache key for a specific call to a specific language model.

Default streaming implementation. Subclasses should override this method if they support streaming output.

Helper method to transform an Iterator of Input values into an Iterator of Output values, with callbacks. Use this to implement stream() or transform() in Runnable subclasses.

Assigns new fields to the dict output of this runnable. Returns a new runnable.

Convert a runnable to a tool. Return a new instance of RunnableToolLike which contains the runnable, name, description and schema.

Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.

Bind tool-like objects to this chat model.

Creates a streaming request with retry.

Formats LangChain StructuredTools to AnthropicTools.

Generates chat based on the input messages.

Generates a prompt based on the input prompt values.

Get the number of tokens in the content.

Get the identifying parameters for the model

Get the parameters used to invoke the model

Invokes the chat model with a single input.

Pick keys from the dict output of this runnable. Returns a new runnable.

Create a new runnable sequence that runs each individual runnable in series, piping the output of one runnable into another runnable or runnable-like.

Stream output in chunks.

Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.

Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.

Bind config to a Runnable, returning a new Runnable.

Create a new runnable from the current one that will try invoking other passed fallback runnables if the initial invocation fails.

Bind lifecycle listeners to a Runnable, returning a new Runnable. The Run object contains information about the run, including its id, type, input, output, error, startTime, endTime, and any tags or metadata added to the run.

Add retry logic to an existing runnable.

The name of the serializable. Override to provide an alias or to preserve the serialized module name in minified environments.

Implemented as a static method to support loading logic.

Generate a stream of events emitted by the internal steps of the runnable.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

• event: string - Event names are of the format: on_[runnable_type]_(start|stream|end).
• name: string - The name of the runnable that generated the event.
• run_id: string - Randomly generated ID associated with the given execution of the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a parent runnable is assigned its own unique ID.
• tags: string[] - The tags of the runnable that generated the event.
• metadata: Record<string, any> - The metadata of the runnable that generated the event.
• data: Record<string, any>

Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

ATTENTION This reference table is for the V2 version of the schema.

+----------------------+-----------------------------+------------------------------------------+
| event                | input                       | output/chunk                             |
+======================+=============================+==========================================+
| on_chat_model_start  | {"messages": BaseMessage[]} |                                          |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_stream |                             | AIMessageChunk("hello")                  |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_end    | {"messages": BaseMessage[]} | AIMessageChunk("hello world")            |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_start         | {'input': 'hello'}          |                                          |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_stream        |                             | 'Hello'                                  |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_end           | 'Hello human!'              |                                          |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_start       |                             |                                          |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_stream      |                             | "hello world!"                           |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_end         | [Document(...)]             | "hello world!, goodbye world!"           |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_start        | {"x": 1, "y": "2"}          |                                          |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_end          |                             | {"x": 1, "y": "2"}                       |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_start   | {"query": "hello"}          |                                          |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_end     | {"query": "hello"}          | [Document(...), ..]                      |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_start      | {"question": "hello"}       |                                          |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_end        | {"question": "hello"}       | ChatPromptValue(messages: BaseMessage[]) |
+----------------------+-----------------------------+------------------------------------------+

The "on_chain_*" events are the default for Runnables that don't fit one of the above categories.

In addition to the standard events above, users can also dispatch custom events.

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

+-----------+------+------------------------------------------------------------+
| Attribute | Type | Description                                                |
+===========+======+============================================================+
| name      | str  | A user defined name for the event.                         |
+-----------+------+------------------------------------------------------------+
| data      | Any  | The data associated with the event. This can be anything.  |
+-----------+------+------------------------------------------------------------+

Here's an example:

import { RunnableLambda } from "@langchain/core/runnables";
import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch";
// Use this import for web environments that don't support "async_hooks"
// and manually pass config to child runs.
// import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch/web";

const slowThing = RunnableLambda.from(async (someInput: string) => {
  // Placeholder for some slow operation
  await new Promise((resolve) => setTimeout(resolve, 100));
  await dispatchCustomEvent("progress_event", {
   message: "Finished step 1 of 2",
 });
 await new Promise((resolve) => setTimeout(resolve, 100));
 return "Done";
});

const eventStream = await slowThing.streamEvents("hello world", {
  version: "v2",
});

for await (const event of eventStream) {
 if (event.event === "on_custom_event") {
   console.log(event);
 }
}