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[bot] Google GenAI: Chat session API (`chats.create`, `send_message`) not instrumented in Python (documented as traced in JS)

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Bewertung

Schwierigkeit
3/5
Geschätzter Aufwand
1-2 Tage
Anfängerfreundlichkeit
65/100
Issue-Typ
Feature
Klarheit
Klar beschrieben
Aktivitätsstatus
Aktiv
Tech-Stack
python
Bereich
devtools, observability

Rechercherichtung

The issue is to add instrumentation for the Chat session API in the Google GenAI Python SDK. Start by examining the existing patchers in py/src/braintrust/integrations/google_genai/patchers.py and integration.py. Look at the upstream google/genai/chats.py to understand the Chat/AsyncChat classes and their send_message methods. The goal is to create a new patcher that captures session-level spans for chat lifecycle and history, similar to the JS/TS implementation. Test by running the existing integration tests and verifying that chat spans appear in traces.

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Beschreibung

Summary

The google-genai Python SDK's Chat session API (client.chats.create(model=...) returning a Chat/AsyncChat with send_message()/send_message_stream()) has no dedicated instrumentation in this repo's Python integration. Braintrust's own docs confirm this capability is already designed and shipped for the legacy JS/TS SDK ("Chat spans (send_message, send_message_stream): message input and model as metadata") — it is simply missing from the Python integration.

What is missing

py/src/braintrust/integrations/google_genai/patchers.py and integration.py register patchers only for Models/AsyncModels (generate_content, generate_content_stream, embed_content, generate_images) and Interactions/GAOSInteractions (create/get/cancel/delete, sync and async). There is no patcher targeting google.genai.chats.Chats/AsyncChats/Chat/AsyncChat anywhere in the file, and no reference to chat, Chat, or send_message in tracing.py or patchers.py.

Verified against the pinned upstream version (google-genai==2.24.0, per py/uv.lock): Chat.send_message() does internally delegate to self._modules.generate_content(...) — i.e. it reuses the already-patched Models.generate_content path, so a generate_content span is still produced per turn. However, Chat/AsyncChat (subclass _BaseChat) separately maintains cross-turn conversation state — self._curated_history and self._comprehensive_history, updated via record_history() after every turn — that is invisible to any single generate_content span. There is no session-level span capturing the chat object's lifecycle, turn count, or curated-vs-comprehensive history divergence (e.g. when a turn is marked invalid and excluded from curated history sent to the model on subsequent turns).

Braintrust docs status: supported (JS/TS) / not_found (Python)

The Gemini integration page, Python section, documents only: content generation spans (generate_content/generate_content_stream), embedding spans (embed_content), image generation spans (generate_images), and interaction spans (interactions.create/get/cancel/delete). It contains zero mentions of chat, chats, send_message, or ChatSession in the Python section.

The same page's legacy-SDK section (for @google/generative-ai, JS/TS) explicitly documents: "Chat spans (send_message, send_message_stream): message input and model as metadata." This confirms Braintrust considers chat-session tracing a real, intended capability — it has just not been ported to the Python google-genai integration.

Upstream sources

Local repo files inspected

  • py/src/braintrust/integrations/google_genai/integration.py (full file — patcher list)
  • py/src/braintrust/integrations/google_genai/patchers.py (full file — no Chat/chats patcher)
  • py/src/braintrust/integrations/google_genai/tracing.py (grepped for all top-level class/def names and chat — no matches)
  • py/uv.lock (confirms pinned google-genai==2.24.0)
Vorherrschende Sprache
Python
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19
Forks
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1 T. 5 Std.
Gemergte PRs (30 T.)
61

Entwicklungsumgebung

Erste Schritte

  1. Lesen Sie das ganze Issue und danach den Beitragsleitfaden des Projekts.
  2. Schreiben Sie ins Issue, dass Sie es übernehmen — das erspart doppelte Arbeit.
  3. Forken Sie das Repository und arbeiten Sie in einem Branch.
  4. Öffnen Sie einen Pull Request, der die Issue-Nummer nennt.

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