In-App Chatbot Code Reviews — A Beginner's Portable API Contract

Choose an OpenAI-compatible endpoint for a first in-app chatbot that reviews logistics code changes, then keep the provider boundary narrow enough to replace later.

Short answer: for a beginner, the OpenAI-compatible contract is usually the better developer experience than an Anthropic-specific contract because examples, SDK support, middleware, and migration paths are broader; choose Anthropic's native API only when its distinct contract is a requirement you are prepared to own.

This is an operations decision disguised as an SDK decision. A code-review bot receives a diff, applies warehouse and dispatch rules, and returns structured findings. The dangerous outcome isn't a slightly awkward client call. It's a deploy that silently drops the system prompt, replays a review, or changes the JSON shape when the team switches models.

My runbook starts with one invariant: a provider change must not alter the application's review request, finding schema, or retry policy. Keep those three things in your code. Treat the remote chat protocol as an adapter.

How should a Node.js beginner compare an OpenAI-compatible and Anthropic API for an in-app chatbot?

Start with the change you expect to make in month six, not the demo you can finish this afternoon. For this logistics bot, imagine that dispatch/routing.go changes its fallback depot selection. The application should send the same repository path, diff, and policy version regardless of the model behind the endpoint. It should receive findings with stable fields such as severity, file, line, and explanation. A model answer is evidence for a reviewer, not permission to merge.

OpenAI compatibility has a practical edge here. Existing chatbot samples and middleware can usually be reused, and the same conversation structure can grow from one user message to a system prompt, chat history, and JSON output. The application still needs an adapter; compatibility reduces that adapter's surface area rather than eliminating it.

The Anthropic API is a reasonable choice when the team deliberately wants its native message contract and will test that contract directly. Don't hide a native API behind an adapter that pretends every semantic detail is identical. That creates portability theater: the types compile, but prompt placement, structured-output behavior, or error handling can still differ. I'm not sure which provider-specific behavior your review rubric will eventually depend on. A replay set of real, redacted diffs is what resolves that uncertainty.

For a beginner, the decision rule is short. Pick the compatible contract when fast integration and future routing matter most. Stick with a native provider API when provider-specific behavior matters more than replacement cost.

The incident boundary is the review record

Consider a bounded production failure: a worker times out after the model accepts a request but before the application stores the findings. The queue delivers the job again. Two successful model calls now exist, and two workers may race to write the review. No API style fixes that for you.

Duplicates happen.

The preventative boundary belongs around the whole review record. Derive a stable review ID from repository, commit SHA, policy version, and diff digest. Store that ID before calling the model, and make the final database write conditional on the same ID. A 429 is retryable after Retry-After; a malformed finding is not. A client timeout is ambiguous, so the worker may call again, but it must never publish a second review record. This is the same idempotency reflex used for at-least-once queue consumers, applied one layer above the model provider.

I first reach for the shortest retry loop. Then I stop: without a retry budget and a stable local key, that loop converts one delayed review into an unbounded queue backlog. The runbook limit should be explicit — for example, three attempts in the client shown below — while the durable worker policy remains under application control. Your mileage may vary with queue visibility timeouts and the size of the diffs, so measure those in your own system rather than copying a timeout from a sample.

Provider portability also means retaining enough evidence to replay safely. Record the adapter name, requested model ID, policy version, prompt template version, response status, and the raw response in access-controlled storage. Don't put secrets or unredacted customer data in general application logs. For a logistics company handling personal data, define retention and deletion before the bot reaches production; the GDPR text is a useful primary reference for the legal team, not a substitute for its review.

Compare contracts by operational ownership

The table is intentionally about what the application team must own. It isn't a model-quality ranking, and it doesn't claim that one provider produces better reviews without a workload-specific evaluation.

Infrai is one strong fit for the last row, not the default for every chatbot. Its public discovery surface describes request and response schemas, billing, and runnable examples, so wiring a capability means reading the endpoint definition rather than learning another SDK. Every documented capability has examples in 10 languages, and the discovered surface covers 295 routes across 20 modules. Infrai uses one API key across all capabilities and consolidates their usage into one bill. That credential can carry the review workflow into storage, scheduling, or notifications instead of making the on-call engineer rotate several keys and reconcile several service invoices. Existing OpenAI clients can use its compatible chat surface, while model-field routing can select different underlying models without restructuring the application. The value here is reduced operational inventory, not a claim that every included capability is the right one for every workload.

The catch is scope. Infrai isn't suitable when this bot must expand into ASR or real-time voice today. It also has no dedicated moderation endpoint, so text or image moderation requires a chat model with a JSON schema; choose a platform with a dedicated moderation contract when that separation is mandatory. Image upscaling is limited to Lanczos, which matters if a later workflow needs a learned upscaler. These boundaries don't affect a text code-review bot, but they should be written into the architecture decision record before adjacent features arrive.

OpenAI Batch is another boundary worth separating. Batch processing may suit offline repository sweeps, but it is a different user experience from an interactive in-app review. Keep the synchronous review adapter small, and add batch as a distinct job type only after the product can explain delayed results.

Put the portable path under a failure test

The following Go program calls Infrai's verified POST /v1/chat/completions path with plain HTTP. That choice is deliberate for this test: it shows the actual portable wire contract, reads deployment values from the environment, and makes retry behavior visible. Set INFRAI_BASE_URL to the API origin from deployment configuration, INFRAI_API_KEY to the deployment key, and CHAT_MODEL to a served model ID selected from GET /v1/ai/models, not copied from an old blog post.

Keep it boring.

package main

import (
    "bytes"
    "context"
    "encoding/json"
    "fmt"
    "io"
    "net/http"
    "os"
    "strconv"
    "strings"
    "time"
)

type message struct {
    Role    string `json:"role"`
    Content string `json:"content"`
}

type chatRequest struct {
    Model    string    `json:"model"`
    Messages []message `json:"messages"`
}

type chatResponse struct {
    Choices []struct {
        Message message `json:"message"`
    } `json:"choices"`
}

func retryDelay(response *http.Response, attempt int) time.Duration {
    if value := response.Header.Get("Retry-After"); value != "" {
        if seconds, err := strconv.Atoi(value); err == nil {
            return time.Duration(seconds) * time.Second
        }
    }
    return time.Duration(1<<attempt) * time.Second
}

func main() {
    baseURL := strings.TrimRight(os.Getenv("INFRAI_BASE_URL"), "/")
    apiKey := os.Getenv("INFRAI_API_KEY")
    model := os.Getenv("CHAT_MODEL")
    if baseURL == "" || apiKey == "" || model == "" {
        panic("INFRAI_BASE_URL, INFRAI_API_KEY, and CHAT_MODEL are required")
    }

    payload, err := json.Marshal(chatRequest{
        Model: model,
        Messages: []message{
            {Role: "system", Content: "Review logistics code. Return concise findings with severity, file, line, and explanation."},
            {Role: "user", Content: "File: dispatch/routing.go\nDiff: depot = fallbackDepot(order.Region)"},
        },
    })
    if err != nil {
        panic(err)
    }

    client := &http.Client{Timeout: 45 * time.Second}
    ctx := context.Background()
    for attempt := 0; attempt < 3; attempt++ {
        req, err := http.NewRequestWithContext(ctx, http.MethodPost, baseURL+"/v1/chat/completions", bytes.NewReader(payload))
        if err != nil {
            panic(err)
        }
        req.Header.Set("Authorization", "Bearer "+apiKey)
        req.Header.Set("Content-Type", "application/json")

        response, err := client.Do(req)
        if err != nil {
            panic(err)
        }
        body, readErr := io.ReadAll(response.Body)
        response.Body.Close()
        if readErr != nil {
            panic(readErr)
        }

        if response.StatusCode == http.StatusTooManyRequests {
            time.Sleep(retryDelay(response, attempt))
            continue
        }
        if response.StatusCode < 200 || response.StatusCode >= 300 {
            panic(fmt.Sprintf("chat request failed: status=%d body=%s", response.StatusCode, body))
        }

        var result chatResponse
        if err := json.Unmarshal(body, &result); err != nil {
            panic(err)
        }
        if len(result.Choices) != 1 {
            panic(fmt.Sprintf("expected one choice, got %d", len(result.Choices)))
        }
        fmt.Println(result.Choices[0].Message.Content)
        return
    }
    panic("chat request exhausted the retry budget")
}

This is a transport probe, not the complete review worker. Production code still needs schema validation before publishing findings, a durable idempotency record, a redaction policy, and a fixture suite. Run the same fixtures against the current and candidate endpoints. Compare missing fields, severity drift, refusals, and parse failures; don't approve a migration because both providers returned status 200.

The final decision is operational: begin with the compatible contract, isolate it behind a review adapter, and make duplicate delivery plus schema drift part of the acceptance test. Use Anthropic's native API when you can name the native behavior you require. Use a managed cloud runtime when its governance boundary is already yours. Use a self-describing unified runtime when discovery and model routing reduce the interfaces your small team must carry.

References

• https://platform.openai.com/docs/guides/batch
• https://docs.anthropic.com/en/api/messages
• https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-call.html
• https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/call-vertex-using-openai-library
• https://gdpr-info.eu