open-index/open-npm huggingface dataset:
npm package: @rollyjoely/mlflow-anthropic
Description: Anthropic integration package for MLflow Tracing
Author: MLflow
Latest version: 0.1.3

# MLflow Typescript SDK - Anthropic

Seamlessly integrate [MLflow Tracing](https://github.com/mlflow/mlflow/tree/main/libs/typescript) with Anthropic to automatically trace your Claude API calls.

| Package                | NPM                                                                                                                                               | Description                                     |
| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- |
| [mlflow-anthropic](./) | [![npm package](https://img.shields.io/npm/v/mlflow-tracing-anthropic?style=flat-square)](https://www.npmjs.com/package/mlflow-tracing-anthropic) | Auto-instrumentation integration for Anthropic. |

## Installation

```bash
npm install mlflow-anthropic
```

The package includes the [`mlflow-tracing`](https://github.com/mlflow/mlflow/tree/main/libs/typescript) package and `@anthropic-ai/sdk` package as peer dependencies. Depending on your package manager, you may need to install these two packages separately.

## Quickstart

Start MLflow Tracking Server if you don't have one already:

```bash
pip install mlflow
mlflow server --backend-store-uri sqlite:///mlruns.db --port 5000
```

Self-hosting MLflow server requires Python 3.10 or higher. If you don't have one, you can also use [managed MLflow service](https://mlflow.org/#get-started) for free to get started quickly.

Instantiate MLflow SDK in your application:

```typescript
import * as mlflow from 'mlflow-tracing';

mlflow.init({
  trackingUri: 'http://localhost:5000',
  experimentId: '<experiment-id>',
});
```

Create a trace for Anthropic Claude:

```typescript
import Anthropic from '@anthropic-ai/sdk';
import { tracedAnthropic } from 'mlflow-anthropic';

const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
const client = tracedAnthropic(anthropic);

const response = await client.messages.create({
  model: 'claude-3-7-sonnet-20250219',
  max_tokens: 256,
  messages: [{ role: 'user', content: 'Hello Claude' }],
});
```

View traces in MLflow UI:

![MLflow Tracing UI](https://github.com/mlflow/mlflow/blob/master/docs/static/images/llms/anthropic/anthropic-tracing.png?raw=True)

## Documentation 📘

Official documentation for MLflow Typescript SDK can be found [here](https://mlflow.org/docs/latest/genai/tracing/quickstart).

## License

This project is licensed under the [Apache License 2.0](https://github.com/mlflow/mlflow/blob/master/LICENSE.txt).
npm package: @rollyjoely/mlflow-langchain
Description: LangChain integration package for MLflow Tracing
Author: MLflow
Latest version: 0.1.3

# MLflow LangChain Integration

Auto-instrumentation for [LangChain](https://js.langchain.com/) chat models with MLflow Tracing.

## Installation

```bash
npm install mlflow-langchain mlflow-tracing @langchain/core
```

## Usage

```typescript
import * as mlflow from 'mlflow-tracing';
import { tracedModel } from 'mlflow-langchain';
import { ChatAnthropic } from '@langchain/anthropic';

mlflow.init({
  trackingUri: 'http://localhost:5000',
  experimentId: '<experiment-id>',
});

const model = tracedModel(new ChatAnthropic({ model: 'claude-sonnet-4-5-20250514' }));

// Both invoke() and stream() are automatically traced
const result = await model.invoke([{ role: 'user', content: 'Hello!' }]);
```

## Supported Models

- `ChatAnthropic` (`@langchain/anthropic`)
- `ChatOpenAI` (`@langchain/openai`)
- `ChatXAI` (`@langchain/xai`)
- Any `BaseChatModel` subclass with `invoke()` / `stream()` methods

## Features

- Traces `invoke()` and `stream()` calls as LLM spans
- Captures input messages and output content
- Extracts token usage from `usage_metadata`
- Auto-detects message format from model class name
- Preserves tracing through `bindTools()` calls
npm package: @rollyjoely/mlflow-tracing
Description: TypeScript implementation of MLflow Tracing SDK for LLM observability
Author: MLflow
Latest version: 0.1.3

# MLflow Typescript SDK - Core

This is the core package of the [MLflow Typescript SDK](https://github.com/mlflow/mlflow/tree/main/libs/typescript). It is a skinny package that includes the core tracing functionality and manual instrumentation.

| Package              | NPM                                                                                                                           | Description                                                |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------- |
| [mlflow-tracing](./) | [![npm package](https://img.shields.io/npm/v/mlflow-tracing?style=flat-square)](https://www.npmjs.com/package/mlflow-tracing) | The core tracing functionality and manual instrumentation. |

## Installation

```bash
npm install mlflow-tracing
```

## Quickstart

Start MLflow Tracking Server. If you have a local Python environment, you can run the following command:

```bash
pip install mlflow
mlflow server --backend-store-uri sqlite:///mlruns.db --port 5000
```

If you don't have Python environment locally, MLflow also supports Docker deployment or managed services. See [Self-Hosting Guide](https://mlflow.org/docs/latest/self-hosting/index.html) for getting started.

Instantiate MLflow SDK in your application:

```typescript
import * as mlflow from 'mlflow-tracing';

mlflow.init({
  trackingUri: 'http://localhost:5000',
  experimentId: '<experiment-id>',
});
```

Create a trace:

```typescript
// Wrap a function with mlflow.trace to generate a span when the function is called.
// MLflow will automatically record the function name, arguments, return value,
// latency, and exception information to the span.
const getWeather = mlflow.trace(
  (city: string) => {
    return `The weather in ${city} is sunny`;
  },
  // Pass options to set span name. See https://mlflow.org/docs/latest/genai/tracing/quickstart
  // for the full list of options.
  { name: 'get-weather' },
);
getWeather('San Francisco');

// Alternatively, start and end span manually
const span = mlflow.startSpan({ name: 'my-span' });
span.end();
```

## Documentation 📘

Official documentation for MLflow Typescript SDK can be found [here](https://mlflow.org/docs/latest/genai/tracing/quickstart).

## License

This project is licensed under the [Apache License 2.0](https://github.com/mlflow/mlflow/blob/master/LICENSE.txt).