Source: https://aiprimetech.io/es/docs/sdks/python/

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# Python 

Ejemplos funcionales con los paquetes anthropic y openai, incluidos streaming y operaciones asíncronas. 

 Copiar página [ Ver como Markdown](/es/docs/sdks/python.md) [ Preguntar a ChatGPT](https://chatgpt.com/?q=Read%20https%3A//aiprimetech.io/docs/sdks/python.md%20and%20answer%20questions%20about%20it.%20Start%20with%20a%20short%20summary.) [ Preguntar en Claude](https://claude.ai/new?q=Read%20https%3A//aiprimetech.io/docs/sdks/python.md%20and%20answer%20questions%20about%20it.%20Start%20with%20a%20short%20summary.)   

## SDK de Anthropic 

```
Copypip install anthropic
```

```
Copyimport os
from anthropic import Anthropic

client = Anthropic(
    api_key=os.environ[class="s">"CLAUDEAPIKEY"],
    base_url=class="s">"https://aiprimetech.io",          class=class="s">"c"># no /v1
)

msg = client.messages.create(
    model=class="s">"claude-sonnet-4-6",
    max_tokens=1024,
    system=class="s">"You are a concise assistant.",
    messages=[{class="s">"role": class="s">"user", class="s">"content": class="s">"Explain HTTP caching in three sentences."}],
)
print(msg.content[0].text)
print(msg.usage.input_tokens, msg.usage.output_tokens)
```

## Transmisión en tiempo real 

```
Copywith client.messages.stream(
    model=class="s">"claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{class="s">"role": class="s">"user", class="s">"content": class="s">"Write a haiku about latency."}],
) as stream:
    for text in stream.text_stream:
        print(text, end=class="s">"", flush=True)
    final = stream.get_final_message()
print(class="s">"\n", final.usage.output_tokens, class="s">"output tokens")
```

## Asíncrono 

```
Copyimport asyncio
from anthropic import AsyncAnthropic

client = AsyncAnthropic(api_key=class="s">"sk-your-key", base_url=class="s">"https://aiprimetech.io")

async def ask(q):
    m = await client.messages.create(
        model=class="s">"claude-haiku-4-5", max_tokens=256,
        messages=[{class="s">"role": class="s">"user", class="s">"content": q}],
    )
    return m.content[0].text

async def main():
    sem = asyncio.Semaphore(8)                 class=class="s">"c"># respect rate limits
    async def guarded(q):
        async with sem:
            return await ask(q)
    print(await asyncio.gather(*(guarded(q) for q in [class="s">"1+1?", class="s">"2+2?", class="s">"3+3?"])))

asyncio.run(main())
```

## SDK de OpenAI 

```
Copyfrom openai import OpenAI

client = OpenAI(api_key=class="s">"sk-your-key", base_url=class="s">"https://aiprimetech.io/v1")   class=class="s">"c"># with /v1

resp = client.chat.completions.create(
    model=class="s">"claude-sonnet-4-6",
    messages=[{class="s">"role": class="s">"user", class="s">"content": class="s">"Hello"}],
)
print(resp.choices[0].message.content)
```

Ambos fragmentos llaman al mismo modelo al mismo precio. Utiliza la biblioteca de la que ya dependa tu proyecto. 

[API de mensajesReferencia de campos](/es/docs/api-reference/messages/)[Transmisión en tiempo realEstructuras de los eventos](/es/docs/api-reference/streaming/)[Buenas prácticasTiempos de espera y reintentos](/es/docs/guides/best-practices/) 

[AnteriorDescripción general de los SDK](/es/docs/sdks/)[SiguienteTypeScript](/es/docs/sdks/typescript/)