> ## Documentation Index
> Fetch the complete documentation index at: https://student-213fb9fc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Python

> Use AJ STUDIOZ Cloud Infra with Python via the Ollama SDK or OpenAI SDK

## Installation

Install the SDK you prefer — both work with AJ STUDIOZ Cloud Infra:

<CodeGroup>
  ```bash Ollama SDK theme={null}
  pip install ollama
  ```

  ```bash OpenAI SDK theme={null}
  pip install openai
  ```
</CodeGroup>

***

## Ollama SDK

Point the Ollama client to `https://api.ajstudioz.co.in` and pass your API key as a header:

```python Setup theme={null}
from ollama import Client

client = Client(
    host="https://api.ajstudioz.co.in",
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)
```

### Chat

```python Chat theme={null}
response = client.chat(
    model="gemma3:27b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is quantum entanglement?"}
    ]
)
print(response.message.content)
```

### Streaming

```python Streaming theme={null}
stream = client.chat(
    model="deepseek-v3.2",
    messages=[{"role": "user", "content": "Write a short poem about the ocean."}],
    stream=True
)

for chunk in stream:
    print(chunk["message"]["content"], end="", flush=True)
```

### Text Generation

```python Generate theme={null}
response = client.generate(
    model="gemma3:27b",
    prompt="Explain the difference between supervised and unsupervised learning."
)
print(response["response"])
```

### List Models

```python List Models theme={null}
models = client.list()
for model in models.models:
    size_gb = model.size / 1e9
    print(f"{model.model:<40} {size_gb:>8.1f} GB")
```

### Embeddings

```python Embeddings theme={null}
result = client.embeddings(
    model="gemma3:4b",
    prompt="AJ STUDIOZ Cloud Infra provides frontier AI inference"
)
embedding = result.embedding
print(f"Dimensions: {len(embedding)}, first 5: {embedding[:5]}")
```

***

## OpenAI SDK

Point the OpenAI client to `https://api.ajstudioz.co.in/v1`:

```python Setup theme={null}
from openai import OpenAI

client = OpenAI(
    base_url="https://api.ajstudioz.co.in/v1",
    api_key="YOUR_API_KEY"
)
```

### Chat Completions

```python Chat Completions theme={null}
response = client.chat.completions.create(
    model="gemma3:27b",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "How do I read a file line by line in Python?"}
    ],
    temperature=0.7,
    max_tokens=512
)
print(response.choices[0].message.content)
```

### Streaming

```python Streaming theme={null}
stream = client.chat.completions.create(
    model="deepseek-v3.2",
    messages=[{"role": "user", "content": "Describe the web3 ecosystem in 3 paragraphs."}],
    stream=True
)

for chunk in stream:
    delta = chunk.choices[0].delta
    if delta.content:
        print(delta.content, end="", flush=True)
```

### Function Calling

```python Function Calling theme={null}
import json

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name, e.g. 'London'"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gemma3:27b",
    messages=[{"role": "user", "content": "What's the weather like in Tokyo?"}],
    tools=tools,
    tool_choice="auto"
)

if response.choices[0].finish_reason == "tool_calls":
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")
```

### Embeddings

```python Embeddings theme={null}
response = client.embeddings.create(
    model="gemma3:4b",
    input="The future of AI is bright and transformative"
)
embedding = response.data[0].embedding
print(f"Embedding dim: {len(embedding)}")
```

***

## Environment Variables

Store your API key in a `.env` file:

```bash .env theme={null}
AJSTUDIOZ_API_KEY=your_api_key_here
```

Load it using `python-dotenv`:

```python Load from .env theme={null}
from dotenv import load_dotenv
import os
from openai import OpenAI

load_dotenv()

client = OpenAI(
    base_url="https://api.ajstudioz.co.in/v1",
    api_key=os.environ["AJSTUDIOZ_API_KEY"]
)
```

***

## Next Steps

<CardGroup cols={2}>
  <Card title="Available Models" icon="cpu" href="/models">
    Browse all 32 models with sizes and capabilities
  </Card>

  <Card title="Streaming Guide" icon="wave-sine" href="/guides/streaming">
    In-depth streaming patterns for Python
  </Card>

  <Card title="Function Calling" icon="function" href="/guides/function-calling">
    Build tool-using agents with Python
  </Card>

  <Card title="Embeddings Guide" icon="vector-square" href="/guides/embeddings">
    RAG pipelines and semantic search
  </Card>
</CardGroup>

## Usage

1. Get an API key from [firecrawl.dev](https://firecrawl.dev)
2. Set the API key as an environment variable named `FIRECRAWL_API_KEY` or pass it as a parameter to the `Firecrawl` class.

Here's an example of how to use the SDK:

<ScrapeAndCrawlExamplePython />

### Scraping a URL

To scrape a single URL, use the `scrape` method. It takes the URL as a parameter and returns the scraped document.

<ScrapePythonShort />

### Crawl a Website

To crawl a website, use the `crawl` method. It takes the starting URL and optional options as arguments. The options allow you to specify additional settings for the crawl job, such as the maximum number of pages to crawl, allowed domains, and the output format. See [Pagination](#pagination) for auto/manual pagination and limiting.

<CrawlPythonShort />

### Sitemap-Only Crawl

Use `sitemap="only"` to crawl sitemap URLs only (the start URL is always included, and HTML link discovery is skipped).

<CrawlSitemapOnlyPython />

### Start a Crawl

<Tip>Prefer non-blocking? Check out the [Async Class](#async-class) section below.</Tip>

Start a job without waiting using `start_crawl`. It returns a job `ID` you can use to check status. Use `crawl` when you want a waiter that blocks until completion. See [Pagination](#pagination) for paging behavior and limits.

<StartCrawlPythonShort />

### Checking Crawl Status

To check the status of a crawl job, use the `get_crawl_status` method. It takes the job ID as a parameter and returns the current status of the crawl job.

<CheckCrawlStatusPythonShort />

### Cancelling a Crawl

To cancel an crawl job, use the `cancel_crawl` method. It takes the job ID of the `start_crawl` as a parameter and returns the cancellation status.

<CancelCrawlPythonShort />

### Map a Website

Use `map` to generate a list of URLs from a website. The options let you customize the mapping process, including excluding subdomains or utilizing the sitemap.

<MapPythonShort />

### Crawling a Website with WebSockets

To crawl a website with WebSockets, start the job with `start_crawl` and subscribe using the `watcher` helper. Create a watcher with the job ID and attach handlers (e.g., for page, completed, failed) before calling `start()`.

<CrawlWebSocketPythonBase />

### Pagination

Firecrawl endpoints for crawl and batch scrape return a `next` URL when more data is available. The Python SDK auto-paginates by default and aggregates all documents; in that case `next` will be `None`. You can disable auto-pagination or set limits to control pagination behavior.

#### PaginationConfig

Use `PaginationConfig` to control pagination behavior when calling `get_crawl_status` or `get_batch_scrape_status`:

```python Python theme={null}
from firecrawl.v2.types import PaginationConfig
```

| Option          | Type   | Default | Description                                                                                                      |
| --------------- | ------ | ------- | ---------------------------------------------------------------------------------------------------------------- |
| `auto_paginate` | `bool` | `True`  | When `True`, automatically fetches all pages and aggregates results. Set to `False` to fetch one page at a time. |
| `max_pages`     | `int`  | `None`  | Stop after fetching this many pages (only applies when `auto_paginate=True`).                                    |
| `max_results`   | `int`  | `None`  | Stop after collecting this many documents (only applies when `auto_paginate=True`).                              |
| `max_wait_time` | `int`  | `None`  | Stop after this many seconds (only applies when `auto_paginate=True`).                                           |

#### Manual Pagination Helpers

When `auto_paginate=False`, the response includes a `next` URL if more data is available. Use these helper methods to fetch subsequent pages:

* **`get_crawl_status_page(next_url)`** - Fetch the next page of crawl results using the opaque `next` URL from a previous response.
* **`get_batch_scrape_status_page(next_url)`** - Fetch the next page of batch scrape results using the opaque `next` URL from a previous response.

These methods return the same response type as the original status call, including a new `next` URL if more pages remain.

#### Crawl

Use the waiter method `crawl` for the simplest experience, or start a job and page manually.

##### Simple crawl (auto-pagination, default)

* See the default flow in [Crawl a Website](#crawl-a-website).

##### Manual crawl with pagination control

Start a job, then fetch one page at a time with `auto_paginate=False`. Use `get_crawl_status_page` to fetch subsequent pages:

```python Python theme={null}
crawl_job = client.start_crawl("https://example.com", limit=100)

# Fetch first page
status = client.get_crawl_status(
    crawl_job.id,
    pagination_config=PaginationConfig(auto_paginate=False)
)
print("First page:", len(status.data), "docs")

# Fetch subsequent pages using get_crawl_status_page
while status.next:
    status = client.get_crawl_status_page(status.next)
    print("Next page:", len(status.data), "docs")
```

##### Manual crawl with limits (auto-pagination + early stop)

Keep auto-pagination on but stop early with `max_pages`, `max_results`, or `max_wait_time`:

```python Python theme={null}
status = client.get_crawl_status(
    crawl_job.id,
    pagination_config=PaginationConfig(max_pages=2, max_results=50, max_wait_time=15),
)
print("crawl limited:", status.status, "docs:", len(status.data), "next:", status.next)
```

#### Batch Scrape

Use the waiter method `batch_scrape`, or start a job and page manually.

##### Simple batch scrape (auto-pagination, default)

* See the default flow in [Batch Scrape](/features/batch-scrape).

##### Manual batch scrape with pagination control

Start a job, then fetch one page at a time with `auto_paginate=False`. Use `get_batch_scrape_status_page` to fetch subsequent pages:

```python Python theme={null}
batch_job = client.start_batch_scrape(urls)

# Fetch first page
status = client.get_batch_scrape_status(
    batch_job.id,
    pagination_config=PaginationConfig(auto_paginate=False)
)
print("First page:", len(status.data), "docs")

# Fetch subsequent pages using get_batch_scrape_status_page
while status.next:
    status = client.get_batch_scrape_status_page(status.next)
    print("Next page:", len(status.data), "docs")
```

##### Manual batch scrape with limits (auto-pagination + early stop)

Keep auto-pagination on but stop early with `max_pages`, `max_results`, or `max_wait_time`:

```python Python theme={null}
status = client.get_batch_scrape_status(
    batch_job.id,
    pagination_config=PaginationConfig(max_pages=2, max_results=100, max_wait_time=20),
)
print("batch limited:", status.status, "docs:", len(status.data), "next:", status.next)
```

## Error Handling

The SDK handles errors returned by the Firecrawl API and raises appropriate exceptions. If an error occurs during a request, an exception will be raised with a descriptive error message.

## Async Class

For async operations, use the `AsyncFirecrawl` class. Its methods mirror `Firecrawl`, but they don't block the main thread.

<AIOPython />

## Browser

Launch cloud browser sessions and execute code remotely.

### Create a Session

```python Python theme={null}
from firecrawl import Firecrawl

app = Firecrawl(api_key="fc-YOUR-API-KEY")

session = app.browser()
print(session.id)             # session ID
print(session.cdp_url)        # wss://cdp-proxy.firecrawl.dev/cdp/...
print(session.live_view_url)  # https://liveview.firecrawl.dev/...
```

### Execute Code

```python Python theme={null}
result = app.browser_execute(
    session.id,
    code='await page.goto("https://news.ycombinator.com")\ntitle = await page.title()\nprint(title)',
    language="python",
)
print(result.result)  # "Hacker News"
```

Execute JavaScript instead of Python:

```python Python theme={null}
result = app.browser_execute(
    session.id,
    code='await page.goto("https://example.com"); const t = await page.title(); console.log(t);',
    language="node",
)
```

### Profiles

Save and reuse browser state (cookies, localStorage, etc.) across sessions:

<PersistentPython />

### Connect via CDP

For full Playwright control, connect directly using the CDP URL:

```python Python theme={null}
from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    browser = p.chromium.connect_over_cdp(session.cdp_url)
    context = browser.contexts[0]
    page = context.pages[0] if context.pages else context.new_page()

    page.goto("https://example.com")
    print(page.title())

    browser.close()
```

### List & Close Sessions

```python Python theme={null}
# List active sessions
sessions = app.list_browsers(status="active")
for s in sessions.sessions:
    print(s.id, s.status, s.created_at)

# Close a session
app.delete_browser(session.id)
```