# Arize AX

```json
{
  "name": "Arize AX",
  "slug": "arize_ax",
  "url": "https://composio.dev/toolkits/arize_ax",
  "markdown_url": "https://composio.dev/toolkits/arize_ax.md",
  "logo_url": "https://logos.composio.dev/api/arize_ax",
  "categories": [
    "ai & machine learning"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-08-13T07:35:52.211Z"
}
```

![Arize AX logo](https://logos.composio.dev/api/arize_ax)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Arize AX MCP or direct API to inspect traces, analyze spans, manage datasets, run evaluations, and review experiments through natural language.

## Summary

Arize AX is an AI engineering platform for tracing, evaluating, and improving AI applications.
Use it to debug LLM behavior, compare experiments, and improve production AI quality.

## Categories

- ai & machine learning

## Toolkit Details

- Tools: 24

## Images

- Logo: https://logos.composio.dev/api/arize_ax

## Authentication

- **Api Key**
  - Type: `api_key`
  - Description: Api Key authentication for Arize AX.
  - Setup:
    - Configure Api Key credentials for Arize AX.
    - Use the credentials when creating an auth config in Composio.

## Suggested Prompts

- Summarize failed spans from latest traces
- Compare experiment results across prompt versions
- Create dataset from production traces

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ARIZE_AX_ADD_DATASET_EXAMPLES` | Add Dataset Examples | Append arbitrary-field examples to an Arize dataset or a specified dataset version. |
| `ARIZE_AX_ADD_EXPERIMENT_RUNS` | Add Experiment Runs | Append one or more runs to an existing Arize experiment. |
| `ARIZE_AX_CREATE_DATASET` | Create Dataset | Create an Arize dataset in a space with an explicit initial array of examples. |
| `ARIZE_AX_CREATE_EXPERIMENT` | Create Experiment | Create an Arize experiment with initial runs, associated with exactly one dataset or space. |
| `ARIZE_AX_CREATE_PROMPT` | Create Prompt | Create an Arize prompt and its initial version in a space. |
| `ARIZE_AX_CREATE_PROMPT_VERSION` | Create Prompt Version | Create a new immutable version of an existing Arize prompt. |
| `ARIZE_AX_GET_DATASET` | Get Dataset | Get one Arize dataset and its versions by dataset ID. |
| `ARIZE_AX_GET_EXPERIMENT` | Get Experiment | Get one Arize experiment by its ID, without its experiment runs. |
| `ARIZE_AX_GET_PROJECT` | Get Project | Get one accessible Arize project by its ID. |
| `ARIZE_AX_GET_PROMPT` | Get Prompt | Get an Arize prompt with its current, explicitly selected, or labeled version. |
| `ARIZE_AX_GET_SPACE` | Get Space | Get one accessible Arize space by its ID. |
| `ARIZE_AX_LIST_DATASET_EXAMPLES` | List Dataset Examples | List one page of examples from an Arize dataset or a specific dataset version, preserving user-defined example fields. |
| `ARIZE_AX_LIST_DATASETS` | List Datasets | List one page of Arize datasets, optionally filtered by space or dataset name. |
| `ARIZE_AX_LIST_EXPERIMENT_RUNS` | List Experiment Runs | List one page of runs and outputs for an Arize experiment, preserving additional run fields. |
| `ARIZE_AX_LIST_EXPERIMENTS` | List Experiments | List one page of Arize experiments, optionally filtered by dataset, space, or name. |
| `ARIZE_AX_LIST_PROJECTS` | List Projects | List one page of accessible Arize projects, optionally filtered by space or project name. |
| `ARIZE_AX_LIST_PROMPTS` | List Prompts | List one page of Arize prompts, optionally filtered by space or prompt name. |
| `ARIZE_AX_LIST_PROMPT_VERSIONS` | List Prompt Versions | List one page of versions for an Arize prompt, newest first. |
| `ARIZE_AX_LIST_SPACES` | List Spaces | List one page of Arize spaces accessible to the connected API key, optionally filtered by organization or name. |
| `ARIZE_AX_LIST_SPANS` | List Spans | Read one bounded page of spans for an Arize project using optional time and filter constraints. |
| `ARIZE_AX_LIST_TRACES` | List Traces | Read one bounded page of traces and nested spans for an Arize project using optional time and filter constraints; nested spans may be truncated per trace. |
| `ARIZE_AX_UPDATE_DATASET` | Update Dataset | Rename an existing Arize dataset. |
| `ARIZE_AX_UPDATE_DATASET_EXAMPLES` | Update Dataset Examples | Update existing Arize dataset examples by ID, optionally creating a named dataset version. IDs that do not match existing examples are ignored. |
| `ARIZE_AX_UPDATE_PROMPT` | Update Prompt | Update or clear an Arize prompt's description. |

## Supported Triggers

None listed.

## Installation and MCP Setup

### Path 1: SDK Installation

#### Path 1, Step 1: Install Composio

Install the Composio SDK
```python
pip install composio_openai
```

```typescript
npm install @composio/openai
```

#### Path 1, Step 2: Initialize Composio and Create Tool Router Session

Import and initialize Composio client, then create a Tool Router session
```python
from openai import OpenAI
from composio import Composio
from composio_openai import OpenAIResponsesProvider

composio = Composio(provider=OpenAIResponsesProvider())
openai = OpenAI()
session = composio.create(user_id='your-user-id')
```

```typescript
import OpenAI from 'openai';
import { Composio } from '@composio/core';
import { OpenAIResponsesProvider } from '@composio/openai';

const composio = new Composio({
  provider: new OpenAIResponsesProvider(),
});
const openai = new OpenAI({});
const session = await composio.create('your-user-id');
```

#### Path 1, Step 3: Execute Arize AX Tools via Tool Router with Your Agent

Get tools from Tool Router session and execute Arize AX actions with your Agent
```python
tools = session.tools
response = openai.responses.create(
  model='gpt-4.1',
  tools=tools,
  input=[{
    'role': 'user',
    'content': 'List recent traces in my Arize AX project and summarize failed spans'
  }]
)
result = composio.provider.handle_tool_calls(
  response=response,
  user_id='your-user-id'
)
print(result)
```

```typescript
const tools = session.tools;
const response = await openai.responses.create({
  model: 'gpt-4.1',
  tools: tools,
  input: [{
    role: 'user',
    content: 'List recent traces in my Arize AX project and summarize failed spans'
  }],
});
const result = await composio.provider.handleToolCalls(
  'your-user-id',
  response.output
);
console.log(result);
```

### Path 2: MCP Server Setup

#### Path 2, Step 1: Install Composio

Install the Composio SDK for Python or TypeScript
```python
pip install composio claude-agent-sdk
```

```typescript
npm install @composio/core ai @ai-sdk/openai @ai-sdk/mcp
```

#### Path 2, Step 2: Initialize Client and Create Tool Router Session

Import and initialize the Composio client, then create a Tool Router session for Arize AX
```python
from composio import Composio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions

composio = Composio(api_key='your-composio-api-key')
session = composio.create(user_id='your-user-id')
url = session.mcp.url
```

```typescript
import { Composio } from '@composio/core';

const composio = new Composio({ apiKey: 'your-api-key' });
const session = await composio.create('your-user-id');
console.log(`Tool Router session created: ${session.mcp.url}`);
```

#### Path 2, Step 3: Connect to AI Agent

Use the MCP server with your AI agent (Anthropic Claude or Mastra)
```python
import asyncio

options = ClaudeAgentOptions(
    permission_mode='bypassPermissions',
    mcp_servers={
        'tool_router': {
            'type': 'http',
            'url': url,
            'headers': {
                'x-api-key': 'your-composio-api-key'
            }
        }
    },
    system_prompt='You are a helpful assistant with access to Arize AX tools.',
    max_turns=10
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query('List recent traces in my Arize AX project and summarize failed spans')
        async for message in client.receive_response():
            if hasattr(message, 'content'):
                for block in message.content:
                    if hasattr(block, 'text'):
                        print(block.text)

asyncio.run(main())
```

```typescript
import { openai } from '@ai-sdk/openai';
import { experimental_createMCPClient as createMCPClient } from '@ai-sdk/mcp';
import { generateText } from 'ai';

const client = await createMCPClient({
  transport: {
    type: 'http',
    url: session.mcp.url,
    headers: {
      'x-api-key': 'your-composio-api-key',
    },
  },
});

const tools = await client.tools();
const { text } = await generateText({
  model: openai('gpt-4o'),
  tools,
  messages: [{
    role: 'user',
    content: 'List recent traces in my Arize AX project and summarize failed spans'
  }],
  maxSteps: 5,
});

console.log(`Agent: ${text}`);
```

## Why Use Composio?

### 1. AI Native Arize AX Integration

- Supports both Arize AX MCP and direct API based integrations
- Structured, LLM-friendly schemas for reliable trace, span, dataset, prompt, and experiment workflows
- Rich coverage for reading, writing, and querying your Arize AX observability and evaluation data

### 2. Managed Auth

- Secure API key handling without hard-coding Arize AX credentials in your agent code
- Central place to manage, scope, and revoke Arize AX access across users and environments
- Per user and per environment credentials for safer AI engineering workflows

### 3. Agent Optimized Design

- Tools are shaped for language models, so agents can find projects, inspect traces, and reason over evaluation results more reliably
- Comprehensive execution logs so you always know what ran, when, and on whose behalf
- Built to help agents move from vague debugging questions to concrete Arize AX actions

### 4. Enterprise Grade Security

- Fine-grained RBAC so you control which agents and users can access Arize AX
- Scoped, least privilege access to Arize AX resources like projects, datasets, traces, spans, prompts, and experiments
- Full audit trail of agent actions to support review, debugging, and compliance

## Use Arize AX with any AI Agent Framework

Choose a framework you want to connect Arize AX with:

- [ChatGPT Work](https://composio.dev/toolkits/arize_ax/framework/chatgpt)
- [Claude Cowork](https://composio.dev/toolkits/arize_ax/framework/claude-cowork)
- [Hermes](https://composio.dev/toolkits/arize_ax/framework/hermes-agent)

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- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
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- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.

## Frequently Asked Questions

### Do I need my own developer credentials to use Arize AX with Composio?

Yes, Arize AX requires you to configure your own API key credentials. Once set up, Composio handles secure credential storage and API request handling for you.

### Can I use multiple toolkits together?

Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview).

### Is Composio secure?

Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev).

### What if the API changes?

Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)