Anthropic TypeScript API Library
This library provides convenient access to the Anthropic REST API from server-side TypeScript or JavaScript.
The REST API documentation can be found on docs.anthropic.com. The full API of this library can be found in api.md.
Installation
npm install @anthropic-ai/sdk
Usage
The full API of this library can be found in api.md.
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
apiKey: process.env['ANTHROPIC_API_KEY'],
});
async function main() {
const message = await client.messages.create({
max_tokens: 1024,
messages: [{ role: 'user', content: 'Hello, Claude' }],
model: 'claude-3-opus-20240229',
});
console.log(message.content);
}
main();
Streaming responses
We provide support for streaming responses using Server Sent Events (SSE).
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
const stream = await client.messages.create({
max_tokens: 1024,
messages: [{ role: 'user', content: 'Hello, Claude' }],
model: 'claude-3-opus-20240229',
stream: true,
});
for await (const messageStreamEvent of stream) {
console.log(messageStreamEvent.type);
}
If you need to cancel a stream, you can break
from the loop
or call stream.controller.abort()
.
Request & Response types
This library includes TypeScript definitions for all request params and response fields. You may import and use them like so:
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
apiKey: process.env['ANTHROPIC_API_KEY'],
});
async function main() {
const params: Anthropic.MessageCreateParams = {
max_tokens: 1024,
messages: [{ role: 'user', content: 'Hello, Claude' }],
model: 'claude-3-opus-20240229',
};
const message: Anthropic.Message = await client.messages.create(params);
}
main();
Documentation for each method, request param, and response field are available in docstrings and will appear on hover in most modern editors.
Counting Tokens
You can see the exact usage for a given request through the usage
response property, e.g.
const message = await client.messages.create(...)
console.log(message.usage)
Streaming Helpers
This library provides several conveniences for streaming messages, for example:
import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic();
async function main() {
const stream = anthropic.messages
.stream({
model: 'claude-3-opus-20240229',
max_tokens: 1024,
messages: [
{
role: 'user',
content: 'Say hello there!',
},
],
})
.on('text', (text) => {
console.log(text);
});
const message = await stream.finalMessage();
console.log(message);
}
main();
Streaming with client.messages.stream(...)
exposes various helpers for your convenience including event handlers and accumulation.
Alternatively, you can use client.messages.create({ ..., stream: true })
which only returns an async iterable of the events in the stream and thus uses less memory (it does not build up a final message object for you).
Tool use beta
This SDK provides beta support for tool use, aka function calling. More details can be found in the documentation.
AWS Bedrock
We provide support for the Anthropic Bedrock API through a separate package.
Handling errors
When the library is unable to connect to the API,
or if the API returns a non-success status code (i.e., 4xx or 5xx response),
a subclass of APIError
will be thrown:
async function main() {
const message = await client.messages
.create({
max_tokens: 1024,
messages: [{ role: 'user', content: 'Hello, Claude' }],
model: 'claude-3-opus-20240229',
})
.catch(async (err) => {
if (err instanceof Anthropic.APIError) {
console.log(err.status);
console.log(err.name);
console.log(err.headers);
} else {
throw err;
}
});
}
main();
Error codes are as followed:
Status Code | Error Type |
---|
400 | BadRequestError |
401 | AuthenticationError |
403 | PermissionDeniedError |
404 | NotFoundError |
422 | UnprocessableEntityError |
429 | RateLimitError |
>=500 | InternalServerError |
N/A | APIConnectionError |
Retries
Certain errors will be automatically retried 2 times by default, with a short exponential backoff.
Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,
429 Rate Limit, and >=500 Internal errors will all be retried by default.
You can use the maxRetries
option to configure or disable this:
const client = new Anthropic({
maxRetries: 0,
});
await client.messages.create({ max_tokens: 1024, messages: [{ role: 'user', content: 'Hello, Claude' }], model: 'claude-3-opus-20240229' }, {
maxRetries: 5,
});
Timeouts
Requests time out after 10 minutes by default. You can configure this with a timeout
option:
const client = new Anthropic({
timeout: 20 * 1000,
});
await client.messages.create({ max_tokens: 1024, messages: [{ role: 'user', content: 'Hello, Claude' }], model: 'claude-3-opus-20240229' }, {
timeout: 5 * 1000,
});
On timeout, an APIConnectionTimeoutError
is thrown.
Note that requests which time out will be retried twice by default.
We automatically send the anthropic-version
header set to 2023-06-01
.
If you need to, you can override it by setting default headers on a per-request basis.
Be aware that doing so may result in incorrect types and other unexpected or undefined behavior in the SDK.
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
const message = await client.messages.create(
{
max_tokens: 1024,
messages: [{ role: 'user', content: 'Hello, Claude' }],
model: 'claude-3-opus-20240229',
},
{ headers: { 'anthropic-version': 'My-Custom-Value' } },
);
Advanced Usage
The "raw" Response
returned by fetch()
can be accessed through the .asResponse()
method on the APIPromise
type that all methods return.
You can also use the .withResponse()
method to get the raw Response
along with the parsed data.
const client = new Anthropic();
const response = await client.messages
.create({
max_tokens: 1024,
messages: [{ role: 'user', content: 'Hello, Claude' }],
model: 'claude-3-opus-20240229',
})
.asResponse();
console.log(response.headers.get('X-My-Header'));
console.log(response.statusText);
const { data: message, response: raw } = await client.messages
.create({
max_tokens: 1024,
messages: [{ role: 'user', content: 'Hello, Claude' }],
model: 'claude-3-opus-20240229',
})
.withResponse();
console.log(raw.headers.get('X-My-Header'));
console.log(message.content);
Making custom/undocumented requests
This library is typed for convenient access to the documented API. If you need to access undocumented
endpoints, params, or response properties, the library can still be used.
Undocumented endpoints
To make requests to undocumented endpoints, you can use client.get
, client.post
, and other HTTP verbs.
Options on the client, such as retries, will be respected when making these requests.
await client.post('/some/path', {
body: { some_prop: 'foo' },
query: { some_query_arg: 'bar' },
});
Undocumented request params
To make requests using undocumented parameters, you may use // @ts-expect-error
on the undocumented
parameter. This library doesn't validate at runtime that the request matches the type, so any extra values you
send will be sent as-is.
client.foo.create({
foo: 'my_param',
bar: 12,
baz: 'undocumented option',
});
For requests with the GET
verb, any extra params will be in the query, all other requests will send the
extra param in the body.
If you want to explicitly send an extra argument, you can do so with the query
, body
, and headers
request
options.
Undocumented response properties
To access undocumented response properties, you may access the response object with // @ts-expect-error
on
the response object, or cast the response object to the requisite type. Like the request params, we do not
validate or strip extra properties from the response from the API.
Customizing the fetch client
By default, this library uses node-fetch
in Node, and expects a global fetch
function in other environments.
If you would prefer to use a global, web-standards-compliant fetch
function even in a Node environment,
(for example, if you are running Node with --experimental-fetch
or using NextJS which polyfills with undici
),
add the following import before your first import from "Anthropic"
:
import '@anthropic-ai/sdk/shims/web';
import Anthropic from '@anthropic-ai/sdk';
To do the inverse, add import "@anthropic-ai/sdk/shims/node"
(which does import polyfills).
This can also be useful if you are getting the wrong TypeScript types for Response
(more details).
Logging and middleware
You may also provide a custom fetch
function when instantiating the client,
which can be used to inspect or alter the Request
or Response
before/after each request:
import { fetch } from 'undici';
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
fetch: async (url: RequestInfo, init?: RequestInit): Promise<Response> => {
console.log('About to make a request', url, init);
const response = await fetch(url, init);
console.log('Got response', response);
return response;
},
});
Note that if given a DEBUG=true
environment variable, this library will log all requests and responses automatically.
This is intended for debugging purposes only and may change in the future without notice.
Configuring an HTTP(S) Agent (e.g., for proxies)
By default, this library uses a stable agent for all http/https requests to reuse TCP connections, eliminating many TCP & TLS handshakes and shaving around 100ms off most requests.
If you would like to disable or customize this behavior, for example to use the API behind a proxy, you can pass an httpAgent
which is used for all requests (be they http or https), for example:
import http from 'http';
import { HttpsProxyAgent } from 'https-proxy-agent';
const client = new Anthropic({
httpAgent: new HttpsProxyAgent(process.env.PROXY_URL),
});
await client.messages.create(
{
max_tokens: 1024,
messages: [{ role: 'user', content: 'Hello, Claude' }],
model: 'claude-3-opus-20240229',
},
{
httpAgent: new http.Agent({ keepAlive: false }),
},
);
Semantic versioning
This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:
- Changes that only affect static types, without breaking runtime behavior.
- Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals).
- Changes that we do not expect to impact the vast majority of users in practice.
We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.
We are keen for your feedback; please open an issue with questions, bugs, or suggestions.
Requirements
TypeScript >= 4.5 is supported.
The following runtimes are supported:
- Node.js 18 LTS or later (non-EOL) versions.
- Deno v1.28.0 or higher, using
import Anthropic from "npm:@anthropic-ai/sdk"
. - Bun 1.0 or later.
- Cloudflare Workers.
- Vercel Edge Runtime.
- Jest 28 or greater with the
"node"
environment ("jsdom"
is not supported at this time). - Nitro v2.6 or greater.
- Web browsers: disabled by default to avoid exposing your secret API credentials (see our help center for best practices). Enable browser support by explicitly setting
dangerouslyAllowBrowser
to true
.
More explanation
Why is this dangerous?
Enabling the dangerouslyAllowBrowser
option can be dangerous because it exposes your secret API credentials in the client-side code. Web browsers are inherently less secure than server environments,
any user with access to the browser can potentially inspect, extract, and misuse these credentials. This could lead to unauthorized access using your credentials and potentially compromise sensitive data or functionality.
When might this not be dangerous?
In certain scenarios where enabling browser support might not pose significant risks:
- Internal Tools: If the application is used solely within a controlled internal environment where the users are trusted, the risk of credential exposure can be mitigated.
- Development or debugging purpose: Enabling this feature temporarily might be acceptable, provided the credentials are short-lived, aren't also used in production environments, or are frequently rotated.
Note that React Native is not supported at this time.
If you are interested in other runtime environments, please open or upvote an issue on GitHub.