[bot] Google GenAI Batches API is not instrumented (mis-tagged as a generic LLM span)

Comienza con braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java, especialmente con tagSpan(), y después inspecciona BraintrustInstrumentation.java para confirmar el cliente y los puntos de entrada de los lotes asíncronos. Compara las estructuras de la google-genai Batches API con la instrumentación existente e identifica las pruebas que se deben añadir para create, get, list, cancel y los resultados completados. Se considera terminado cuando las operaciones de lotes tienen datos de span adecuados y cobertura de regresión, sin tratar los metadatos de BatchJob como salida de generación.

Summary

The genai_1_18_0 module instruments Google's google-genai Java SDK by subclassing its package-private ApiClient (com.google.genai.BraintrustApiClient) and swapping it into every service object on the Client, including client.batches and client.async.batches (BraintrustInstrumentation.wrapClient, lines 36–52). This means a call to client.batches.create(...)/.get(...)/.list(...)/.cancel(...)/.createEmbeddings(...) does produce a span — but BraintrustApiClient.tagSpan() has no awareness of the Batches API's request/response shape, so the span is mis-tagged: it's marked span_attributes.type = "llm" as if it were a real generation call, but the request/response field extraction (built for generateContent-shaped bodies) finds essentially nothing meaningful to populate.

What is missing

In braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java, tagSpan() (lines 50–161):

• Request metadata extraction (lines 65–76) looks for top-level model, systemInstruction, tools, toolConfig, safetySettings, cachedContent. A batchGenerateContent request body wraps everything under a batch object ({"batch": {"displayName": ..., "inputConfig": {...}}}), and a createEmbeddings batch job body is shaped around EmbeddingsBatchJobSource/CreateEmbeddingsBatchJobConfig — none of the expected top-level fields exist, so metadata stays essentially empty.
• input_json construction (lines 97–113) only populates model/contents/config (from generationConfig) — a batch-create body has none of these at top level (the model is only present in the URL path, e.g. {model}:batchGenerateContent, and getModel(genAIEndpoint) (used as a fallback at line 102) is the only path by which model would end up in input_json at all); the batch's actual per-item contents/generation requests (whether inline or file/GCS-referenced) are never captured.
• Response handling (lines 116–150) dumps the whole response body as output_json (line 125) — for a batch create/get call, that whole body is just BatchJob resource metadata (name, state, createTime, etc.), not generative output — and reads usageMetadata for metrics (line 128), which a BatchJob response never has. There is no instrumentation at all of retrieving a completed batch's actual per-request results (where real generation outputs and usageMetadata become available), so even a fully successful batch job produces a span with type: "llm" but no meaningful input, output, or token metrics.
• No test or example anywhere in the repo exercises client.batches in any form (confirmed via grep for batch under braintrust-sdk/instrumentation/genai_1_18_0/ — the only match is BraintrustInstrumentation.java's own field-swap wiring, not a test).

Braintrust docs status: not_found

Checked https://www.braintrust.dev/docs/integrations/ai-providers/gemini in full: no mention of batchGenerateContent, Batches, or batch/async generation jobs anywhere, in any language. The only "batch"-adjacent mention is batchEmbedContents, and only in the unrelated context of AI proxy/gateway passthrough support for embeddings ("The gateway also supports Gemini's native embedContent and batchEmbedContents endpoints") — not span/tracing coverage, and not the Batches job API this issue is about.

Note: this repo's own gap-audit history already treats "Batch API not instrumented, mis-tagged as a generic LLM span" as a valid, in-scope finding — see the already-filed and still-open #155 for Anthropic's Message Batches API, which this issue mirrors for the Google GenAI provider (a distinct upstream API/SDK/module, not a duplicate).

Upstream sources

• Gemini API "Batch Mode" (batchGenerateContent) — official docs: https://ai.google.dev/gemini-api/docs/batch-api and https://ai.google.dev/gemini-api/docs/batch-mode. Async, 50%-discounted, ~24h turnaround; supports inline requests or file/JSONL upload.
• Vertex AI batch predictions for Gemini (and partner models) via GCS/BigQuery: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/maas/capabilities/batch-prediction, with a Java-specific sample using the same com.google.genai.Batches class: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/samples/googlegenaisdk-batchpredict-with-gcs.
• Official google-genai Java SDK exposes this directly: com.google.genai.Batches (googleapis/java-genai, src/main/java/com/google/genai/Batches.java), javadoc at https://googleapis.github.io/java-genai/javadoc/com/google/genai/Batches.html — create(String model, BatchJobSource, CreateBatchJobConfig), createEmbeddings(...) (experimental), get(...), cancel(...), delete(...), list(...), all returning/operating on BatchJob.

Local repo files inspected

• braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan)
• braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — lines 22–57 (wrapClient), confirming client.batches/client.async.batches are explicitly wired to the same instrumented ApiClient (lines 37, 47) and therefore do produce (mis-tagged) spans rather than bypassing instrumentation entirely
• Repo-wide grep for batch/Batch/batchGenerateContent/BatchJob under braintrust-sdk/instrumentation/genai_1_18_0/ — only match is the field-swap wiring above; no test or example exercises the Batches API