> ## Documentation Index
> Fetch the complete documentation index at: https://docs.phala.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Migration and Integrations

> Connect existing OpenAI-compatible clients, OpenRouter-style apps, LangChain, Vercel AI SDK, observability tools, and desktop clients to Confidential AI.

Phala Confidential AI is OpenAI-compatible. Most integrations require changing only the base URL and API key:

```text theme={"system"}
https://inference.phala.com/v1
```

After migration, responses can be verified with `x-receipt-id`, [Attestation Report](/phala-cloud/confidential-ai/confidential-model/api-reference/attestation), and [Get Receipt](/phala-cloud/confidential-ai/confidential-model/api-reference/receipts).

## Migrate from OpenAI

```python theme={"system"}
from openai import OpenAI

# Before
openai_client = OpenAI(api_key="sk-...")

# After
phala_client = OpenAI(
    api_key="YOUR_PHALA_API_KEY",
    base_url="https://inference.phala.com/v1",
)
```

Everything else stays familiar: chat completions, streaming, tool calling, vision, structured output, and embeddings use OpenAI-compatible request shapes.

## Migrate from OpenRouter

```python theme={"system"}
from openai import OpenAI

client = OpenAI(
    base_url="https://inference.phala.com/v1",
    api_key="YOUR_PHALA_API_KEY",
)
```

Model ids use the provider/model format returned by `/v1/models`. Use `is_tee: true` to find models that can be served confidentially, then verify the actual response receipt.

## OpenAI SDK

<CodeGroup>
  ```python Python theme={"system"}
  from openai import OpenAI

  client = OpenAI(
      api_key="YOUR_PHALA_API_KEY",
      base_url="https://inference.phala.com/v1",
  )

  response = client.chat.completions.create(
      model="phala/qwen3.5-27b",
      messages=[{"role": "user", "content": "Explain TEE privacy."}],
  )

  print(response.choices[0].message.content)
  ```

  ```typescript TypeScript theme={"system"}
  import OpenAI from "openai";

  const client = new OpenAI({
    apiKey: process.env.API_KEY,
    baseURL: "https://inference.phala.com/v1",
  });

  const response = await client.chat.completions.create({
    model: "phala/qwen3.5-27b",
    messages: [{ role: "user", content: "Explain TEE privacy." }],
  });
  ```
</CodeGroup>

## LangChain

```python theme={"system"}
from langchain_openai import ChatOpenAI

chat = ChatOpenAI(
    model="phala/qwen3.5-27b",
    api_key="YOUR_PHALA_API_KEY",
    base_url="https://inference.phala.com/v1",
)

response = chat.invoke("Explain confidential inference in one paragraph.")
print(response.content)
```

## Vercel AI SDK

```typescript theme={"system"}
import { generateText } from "ai";
import { createOpenAI } from "@ai-sdk/openai";

const phala = createOpenAI({
  apiKey: process.env.API_KEY,
  baseURL: "https://inference.phala.com/v1",
});

const { text } = await generateText({
  model: phala("phala/qwen3.5-27b"),
  prompt: "Explain attestation.",
});
```

## Langfuse

```python theme={"system"}
import os
from langfuse.openai import OpenAI

os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com"

client = OpenAI(
    api_key="YOUR_PHALA_API_KEY",
    base_url="https://inference.phala.com/v1",
)
```

<Warning>
  Observability tools may store prompts, responses, metadata, or traces. For sensitive data, configure redaction or self-hosting before sending production traffic.
</Warning>

## Coding Assistants and Desktop Clients

For tools that support an OpenAI-compatible provider, use:

| Setting       | Value                            |
| ------------- | -------------------------------- |
| Provider type | OpenAI compatible                |
| Base URL      | `https://inference.phala.com/v1` |
| API key       | Your Phala API key               |
| Model         | Any id from `/v1/models`         |

This works for tools such as Continue, Cline-compatible assistants, Cherry Studio, and other clients that let you set an OpenAI-compatible base URL.

## Migration Checklist

* [ ] Create a Phala API key.
* [ ] Change the base URL to `https://inference.phala.com/v1`.
* [ ] List models with `GET /v1/models`.
* [ ] Use `is_tee: true` for confidential-capable models.
* [ ] Capture `x-receipt-id` in raw HTTP responses when you need verification.
* [ ] Verify one representative response before production rollout.

## Related

<CardGroup cols={2}>
  <Card title="On-demand API" icon="bolt" href="/phala-cloud/confidential-ai/confidential-model/confidential-ai-api" />

  <Card title="Error Handling" icon="triangle-alert" href="/phala-cloud/confidential-ai/confidential-model/error-handling" />
</CardGroup>