Vercel AI SDK GenerateObject Fails With Helicone

When you're building applications that leverage the power of AI, integrating different tools and services is key. The Vercel AI SDK is a fantastic choice for simplifying AI interactions, and Helicone offers valuable features like caching and observability for your AI calls. However, you might run into a peculiar issue when trying to use Vercel's `generateObject` function in conjunction with the `@helicone/ai-sdk-provider`. This specific combination can lead to a failure, resulting in an AI_NoObjectGeneratedError, unless you explicitly include the word "json" in your prompt. This might sound a bit strange, but it points to an interesting interaction between how different AI providers are identified and how the AI SDK enforces structured output, particularly for formats like JSON. Let’s dive into what’s happening and how you can navigate this situation to ensure your AI applications run smoothly, especially when working with OpenAI models through Helicone.

Understanding the `generateObject` Function and Provider Identity

The Vercel AI SDK's generateObject function is designed to make it incredibly easy to get structured data back from AI models. Instead of just receiving raw text, you can define a schema (often using libraries like Zod), and the SDK will attempt to parse the AI's response into that defined structure. This is incredibly powerful for building applications that need to process information predictably, like extracting specific details from user input or generating data in a consistent format. To achieve this, the AI SDK needs to communicate with the underlying AI model provider and often relies on specific model features or configurations to ensure the output is structured correctly. For OpenAI models, this often involves enabling a 'JSON mode' or similar functionality. The challenge arises because the AI SDK needs to *know* it's talking to an OpenAI-compatible service in a way that enables these structured output features. When you use a provider like `@helicone/ai-sdk-provider`, it acts as a wrapper or intermediary. While it routes your requests effectively, there's a possibility that it doesn't fully convey the 'identity' of the underlying OpenAI service in a way that the AI SDK expects for its structured output features. This means that even though the model itself might be capable of returning JSON, the AI SDK might not be triggering the necessary parsing or validation steps because it doesn't recognize the provider chain as being fully 'OpenAI' in the context of structured output generation. This is why adding the word "json" to your prompt can sometimes act as a workaround; it's a signal to the AI model itself, and potentially to the intermediary layers, that a structured JSON output is desired, bypassing the need for the SDK's internal provider-specific checks.

The Specific Failure Scenario with Helicone Provider

Let’s get into the specifics of the observed behavior. When you use the `@helicone/ai-sdk-provider` with an OpenAI model, and your prompt *doesn’t* contain the word "json", you’ll encounter the AI_NoObjectGeneratedError. The error message often indicates that the response couldn't be parsed, and looking closer at the raw response often reveals that the AI *did* generate valid JSON, but it was followed by additional explanatory text. For example, you might see something like this: { "name": "Alice", "age": 30, "tags": ["developer", "typescript", "ai"] } followed by a sentence like, "Alice is a 30-year-old developer with expertise in TypeScript and AI." The Vercel AI SDK, when expecting a direct JSON response for generateObject, sees this trailing text as an error, hence the parsing failure. This is different from using the native @ai-sdk/openai provider directly, or even routing through Helicone's OpenAI-compatible gateway using the OpenAI provider. In those cases, even without the word "json" in the prompt, generateObject typically works as expected. This contrast strongly suggests that the issue lies in how the @helicone/ai-sdk-provider modifies or transmits the provider identity information to the AI SDK, preventing it from enabling the precise behavior needed for structured output parsing without explicit prompting. It’s as if the AI SDK sees the Helicone provider and doesn’t enable its most stringent JSON parsing modes, whereas it *does* enable them when it identifies the provider as the direct OpenAI one.

Workarounds and Potential Fixes

Given this behavior, you might be wondering how to proceed. Fortunately, there are effective workarounds, and understanding the root cause can guide potential fixes. The most common and robust workaround, as highlighted in the discussion, is to route your OpenAI calls through Helicone’s OpenAI-compatible gateway. Instead of using createHelicone() directly as the model provider, you would configure the native @ai-sdk/openai provider with Helicone's `baseURL` and necessary authentication headers. Specifically, you’d set `baseURL: "https://oai.helicone.ai/v1"` and include the `Helicone-Auth` header. This approach leverages the official OpenAI provider while still benefiting from Helicone's features like caching and observability, as these can often be controlled via headers like `Helicone-Cache-Enabled`. From a troubleshooting perspective, the hypothesis is that the Vercel AI SDK's structured output enforcement is tightly coupled to the detected provider type. When it sees the native OpenAI provider, it enables the necessary JSON mode and strict parsing. When it sees the `@helicone/ai-sdk-provider`, it might not be correctly signaling this, or the provider itself isn't passing along the right signals. A potential fix from the Helicone side could involve ensuring that when OpenAI models are used through their provider, the SDK correctly mimics the behavior of the native OpenAI provider concerning structured outputs. This might mean internally delegating to the OpenAI provider for specific model calls or ensuring that the provider metadata is accurately passed to satisfy the AI SDK's expectations for structured data generation. Until such a fix is implemented, using the OpenAI provider with Helicone's gateway remains the most reliable method for ensuring generateObject works seamlessly.

Testing the Scenario: A Minimal Reproduction

To truly understand and verify the issue, a minimal reproduction script is invaluable. The provided script demonstrates the exact problem scenario clearly. It sets up different provider configurations: the direct OpenAI provider, the Helicone provider, and the OpenAI provider routed via Helicone's gateway. It then uses a Zod schema to define the expected output structure and tests two prompts: one without the word "json" and one with it. When you run this script, you’ll observe the distinct behaviors: the direct OpenAI provider and the OpenAI provider via Helicone gateway successfully handle the prompt without "json", while the Helicone provider fails unless "json" is explicitly mentioned. The script is crucial because it isolates the variables, confirming that the issue isn't with your schema, your OpenAI API key, or the model itself, but specifically with the interaction between the `@helicone/ai-sdk-provider` and the Vercel AI SDK's structured output generation logic. Seeing the detailed failure output, including the `cause` and `text` properties, provides concrete evidence of the JSON parsing error due to trailing text. This kind of reproducible test case is fundamental for reporting bugs and collaborating on solutions with the respective SDK maintainers, ensuring that the AI ecosystem becomes more robust and predictable for all developers.

Conclusion: Ensuring Seamless Structured Data with AI SDKs

Navigating the complexities of AI SDKs and provider integrations can sometimes lead to unexpected challenges, as seen with the Vercel AI SDK's generateObject function and the `@helicone/ai-sdk-provider`. The core of the issue appears to stem from how provider identity is communicated and interpreted by the AI SDK, particularly concerning the nuanced requirements for structured data generation like JSON output. While the direct OpenAI provider and routing through Helicone's gateway using the OpenAI provider work seamlessly, the dedicated Helicone provider requires an explicit "json" in the prompt to avoid parsing errors. This is likely because the AI SDK performs specific checks tied to provider types to enable structured output features, and the Helicone provider, in this configuration, may not be signaling these correctly. The good news is that the workaround of using the OpenAI provider configured with Helicone's `baseURL` and headers is effective, allowing you to benefit from both Helicone's features and the AI SDK's structured output capabilities. For developers encountering this, understanding this workaround is key to maintaining production stability. For the future, potential fixes lie in enhancing the @helicone/ai-sdk-provider to better emulate the necessary provider signals for the AI SDK, ensuring a more transparent and consistent experience across different integration paths. Continuous collaboration and detailed bug reporting, like the minimal reproduction example provided, are vital for improving the robustness of these powerful AI development tools.

For more information on Vercel AI SDK best practices, you can refer to the official **Vercel AI SDK Documentation**. For details on optimizing AI calls with Helicone, check out their **Helicone Documentation**.