[bot] Instrument ElevenLabs Python SDK (2,446,296 weekly downloads)

[braintrust-bot]

Summary

elevenlabs is the official Python SDK for the ElevenLabs API (https://github.com/elevenlabs/elevenlabs-python), a widely used generative audio provider (text-to-speech, speech-to-text, and conversational voice agents). Its ElevenLabs/AsyncElevenLabs clients expose text_to_speech.convert() and a streaming variant text_to_speech.stream() as the primary generation execution entrypoints, e.g.:

audio = elevenlabs.text_to_speech.convert(
 text="The first move is what sets everything in motion.",
 voice_id="JBFqnCBsd6RMkjVDRZzb",
 model_id="eleven_v3",
 output_format="mp3_44100_128",
)

This is a real generation execution surface (text → generated audio, with a model_id and streaming support) analogous in shape to the completion/generation surfaces this repo already instruments for OpenAI, Anthropic, and Google GenAI — but for a different modality (audio) and a provider not covered at all today.

This repository has zero instrumentation for it — no py/src/braintrust/integrations/elevenlabs* directory, no wrapper, no nox session, no cassette directory, and no entry in py/pyproject.toml's [tool.braintrust.matrix].

Weekly downloads

Weekly downloads: 2,446,296 (as of 2026-07-23; https://pypistats.org/packages/elevenlabs). Cross-checked against https://pepy.tech/projects/elevenlabs, which shows 9.6M downloads over the trailing 30 days (~2.2M/week), consistent with the pypistats figure. Note: this is distinct from the much smaller unofficial elevenlabslib package.

Braintrust docs status

not_found. Checked https://www.braintrust.dev/docs/guides/tracing and https://www.braintrust.dev/docs/guides/tracing/integrations — ElevenLabs is not mentioned in either the provider list or the tracing-integrations directory (OpenTelemetry, Temporal, Vercel AI SDK, OpenRouter SDK, LangChain, LangSmith, LlamaIndex, Agno, Apollo GraphQL, Cloudflare Workers AI, DSPy, Instructor, LiteLLM, Ruby LLM, Traceloop, TrueFoundry).

What needs to be instrumented

• client.text_to_speech.convert() (sync) and the AsyncElevenLabs equivalent — primary TTS generation call, should log input text/voice/model and output audio metadata.
• client.text_to_speech.stream() — streaming generation variant.
• Speech-to-text conversion surface, if present on the current stable client (worth confirming exact method name against the current SDK version during implementation).

Upstream sources

• PyPI: https://pypi.org/project/elevenlabs/
• GitHub (official): https://github.com/elevenlabs/elevenlabs-python
• API docs: https://elevenlabs.io/docs/api-reference

Local repo files inspected

• py/src/braintrust/integrations/ — no elevenlabs directory
• py/src/braintrust/wrappers/ — no ElevenLabs wrapper
• py/pyproject.toml [tool.braintrust.matrix] — no elevenlabs entry
• py/noxfile.py — no test_elevenlabs session
• Repo-wide case-insensitive grep for elevenlabs under py/ — zero matches

[AbhiPrasad]

Spec compliance review

Automated review of the requirements stated in this issue against the Braintrust instrumentation specification (braintrust-spec → skills/instrumentation-spec/references/instrumentation-guide.md and references/features/). This reviews the issue text, not any implementation.

Verdict: UNDERSPECIFIED — the issue is a discovery/justification record, and its single substantive requirement line conflicts with the spec
Category: completion API (multimodal: text-to-speech / speech-to-text), plus a separate agentic/realtime surface (Conversational AI) that is mentioned but not scoped
Already implemented in this repo: no — a repo-wide search for elevenlabs returns zero matches. There is no py/src/braintrust/integrations/elevenlabs* directory, no wrapper under py/src/braintrust/wrappers/, no [tool.braintrust.matrix] entry, no test_elevenlabs nox session, and no cassette directory. The issue's own "Local repo files inspected" section is accurate.

The only instrumentation requirement the issue states is:

client.text_to_speech.convert() (sync) and the AsyncElevenLabs equivalent — primary TTS generation call, should log input text/voice/model and output audio metadata.

Everything below measures that line (and the issue's silences) against the spec. The correct prior art to copy is not pipecat / livekit_agents (those are voice-pipeline integrations that emit task spans), but SpeechWrapper / TranscriptionWrapper / TranslationWrapper in py/src/braintrust/integrations/openai/tracing.py:1643-1874, which already instrument OpenAI's audio.speech, audio.transcriptions, and audio.translations surfaces.

---

Conflicts with the spec

1. voice must not be part of the span input.

   should log input text/voice/model

   voice_id is request configuration, not conversation content. This is the same rule the guide applies to tool definitions in §"Available tool definitions": "They are request configuration, not conversation content — so they MUST NOT appear in the input messages array."
   Correction: span input carries the synthesis text only. Voice selection belongs in metadata — subject to item 2, which is the harder constraint.

2. voice_id is not on the metadata allowlist, so it cannot be emitted at all today.
   instrumentation-guide.md §"Metadata" permits exactly model and provider (both MUST), plus this closed list of request-configuration fields: temperature, top_p, max_tokens, frequency_penalty, presence_penalty, stop, response_format. It closes with "Any additional metadata field requires a specification update before SDKs emit it." §"Data capturing policy" states this as an absolute: "Instrumentation MUST NOT capture any data or fields unless this guide explicitly requires or allows them… New captured fields MUST be added to this specification before SDKs emit them."

   That makes all of the following unsanctioned for an ElevenLabs integration: voice_id, voice_settings, output_format, language_code, previous_text / next_text, seed, apply_text_normalization.

   Note for reviewers: the existing OpenAI integration does emit voice, speed, instructions, and stream_format — see _SPEECH_METADATA_PARAMS at py/src/braintrust/integrations/openai/tracing.py:130-137. That is pre-existing behavior that exceeds the written allowlist; it is not license for a new integration to do the same. §"Data capturing policy" explicitly anticipates this case: "It is encouraged to expand this specification for any data that may already be captured (preceding this policy), however extensions of this specification should go through critical review."

   Correction: restrict metadata to model + provider + response_format (map ElevenLabs output_format onto the sanctioned response_format key rather than inventing a new one), or land a braintrust-spec amendment adding a TTS request-parameter set before implementation.

3. A character_count / characters metric would be a hard violation.
   §"Metrics Reference" is unambiguous: "SDKs MUST NOT add metrics beyond the keys listed in this guide. Add new metric keys to this specification before emitting them." Neither the guide nor features/token-and-cost-metrics.md defines characters, character_count, billable_characters, audio_duration, latency, request_latency, or cost_credits.

   This matters specifically for ElevenLabs because it bills per character and returns character usage on the response headers, so the temptation to capture it is real.

   Correction: emit only start, end, and — for streaming calls — time_to_first_token. Do not emit token metrics at all: features/token-and-cost-metrics.md §"Canonical metrics" requires "Omit a metric when the provider did not report it and the SDK cannot compute it accurately. Do not fabricate zero values to hide missing provider data."

4. "output audio metadata" is the wrong disposition for the generated audio bytes.

   and output audio metadata

   §"Multimodal / Attachments" requires: "When conversion succeeds, raw media bytes MUST NOT remain inline in input or output." §"Output payloads" adds: "Generated media MUST be logged in the output payload, not in metadata." And §"Multimodal API surfaces" names this exact case: "Text-to-speech: log input text as normal request input and log generated audio as an attachment."

   So the MP3/PCM bytes must be replaced by a braintrust_attachment reference at the exact field that held them — summarizing them as metadata is non-compliant in two directions at once (media in metadata, and unsanctioned metadata keys).

   Correction: reuse the existing helper _extract_audio_output() at py/src/braintrust/integrations/utils.py:498-530, which already produces the sanctioned shape:

   {"type": "audio", "mime_type": ..., "audio_size_bytes": ..., **resolved_attachment.multimodal_part_payload}

   MIME type and byte size are the two audio fields §"Output payloads" explicitly sanctions: "Chat audio outputs: attach binary audio data, preserve compact transcript/text fields, and record these audio metadata fields when the provider reports them: MIME type, byte size, and audio token metrics." The reference object shape is fixed by §"Attachment reference" (type, content_type, filename, key as a UUID v4).

5. Speech-to-text is listed as an open question, but the spec already dictates its shape.

   Speech-to-text conversion surface, if present on the current stable client (worth confirming exact method name against the current SDK version during implementation).

   The method-name confirmation is fine as an implementation detail (client.speech_to_text.convert(file=...) — verify against the pinned version). The shape is not open: §"Output payloads" states "Speech-to-text and OCR: log input media or documents as attachments. Log transcripts, pages, detected text, and structured extraction results as text/JSON.", and §"Multimodal API surfaces" repeats "Speech-to-text attaches input audio and logs transcript output."

   Correction: state the requirement in the issue — input audio converted to a braintrust_attachment, transcript logged as output. Prior art: _AudioFileWrapper._parse_params / TranscriptionWrapper.process_output at py/src/braintrust/integrations/openai/tracing.py:1653-1861.

---

Missing requirements

1. Span type, span count, and span name. Every instrumented call MUST produce exactly one span (§"One span per API call": "Each discrete API call MUST produce exactly one llm span. A streaming call produces a single span that closes when the stream finishes, not one span per chunk.") with span_attributes.type = "llm". §"Multimodal API surfaces" gives "One llm span per model execution" for the analogous image-generation row, and features/token-and-cost-metrics.md §"Span identity" makes span_attributes.type = "llm" a MUST. Both SpeechWrapper and TranscriptionWrapper use SpanTypeAttribute.LLM (openai/tracing.py:1749, :1805). The issue names no span type. It also names no span-name convention — §"Span naming": "Span names SHOULD reflect the provider and operation."

   Caution: do not copy livekit_agents / pipecat here. livekit_agents/tracing.py:466 emits tts_request as SpanTypeAttribute.TASK, and pipecat/tracing.py:323 emits tts_response as SpanTypeAttribute.TASK. Those are session-pipeline spans, not direct provider calls. See the spec-gap section below.

2. metadata.provider and metadata.model are MUST. §"Metadata" and features/token-and-cost-metrics.md §"Span identity" both require them on every LLM span; the latter notes provider is required "even when a model name appears globally unique. Resellers and gateways can sell the same model at different prices." provider must be "elevenlabs". model comes from the request model_id (e.g. eleven_v3) because convert() returns raw audio, not a JSON envelope — acceptable, since resolving model from the response is only a SHOULD.

3. Streaming semantics are entirely absent. The issue lists text_to_speech.stream() as a target but states nothing about it. Required:

   • One span that closes when the stream finishes (§"One span per API call").
   • metrics.time_to_first_token — §"Streaming" → "This metric MUST be captured for all streaming calls. It is measured by the SDK, not reported by the provider." Measure to the first audio chunk.
   • Chunk aggregation — §"Streaming multimodal outputs": "Streaming instrumentation MUST aggregate media chunks into the final output rather than dropping them or logging raw chunks as separate opaque blobs… Streaming audio chunks SHOULD be aggregated into transcript/audio output. If binary audio data is retained, it MUST be converted to an attachment."

   Implementation note: in current elevenlabs versions text_to_speech.convert() itself returns a byte iterator, not a bytes object, so even the "non-streaming" path needs tee-ing that does not consume the caller's iterator. Confirm this against the pinned version — it changes the design materially. Prior art for the tee pattern: _TracedStream / _AsyncTracedStream as used at py/src/braintrust/integrations/openai/tracing.py:1794 and :1850.

4. Async coverage is stated for TTS only. The issue mentions AsyncElevenLabs once, for text_to_speech.convert(). The same requirements apply to every async entrypoint, including stream() and speech_to_text.convert().

5. Error handling. Failures MUST be recorded in the top-level error field (§"Span Data Model"). Separately, attachment conversion is best-effort: §"Multimodal / Attachments" → "If conversion or upload fails, instrumentation MUST preserve the original payload and MUST NOT throw an exception that prevents span export." features/attachments.md §"error handling" adds the per-span vs. upload-pipeline distinction and "in either case, do not throw exceptions that would prevent the span from being exported."

6. context.span_origin provenance. §"Context": "Braintrust-created spans MUST also include span-origin provenance." Repo convention is internal={"instrumentation": "elevenlabs-auto"} — see the SpanInternalOptions.instrumentation docstring at py/src/braintrust/logger.py:150-162 ("Stamped into context.span_origin.instrumentation.name. Set by SDK integrations (openai-auto, anthropic-auto, etc.)"), with live examples at py/src/braintrust/integrations/dspy/tracing.py:17, py/src/braintrust/integrations/cohere/tracing.py:32, and py/src/braintrust/integrations/llamaindex/tracing.py:21. Integration tests assert it directly, e.g. py/src/braintrust/integrations/dspy/test_dspy.py:50.

7. Repo plumbing the issue does not list (repo convention per CLAUDE.md, not a spec rule): a [tool.braintrust.matrix] version pin in py/pyproject.toml, a test_elevenlabs session in py/noxfile.py, a [tool.braintrust.cassette-dirs] entry, per-version VCR cassettes under py/src/braintrust/integrations/elevenlabs/cassettes/<version>/, and auto_instrument() wiring. Cassette-backed tests are mandatory for provider work; since ElevenLabs responses are binary audio, verify early that VCR records the audio body faithfully.

---

Blocked on spec changes (braintrustdata/braintrust-spec)

1. The canonical input shape for non-chat TTS spans is undefined. §"Canonical payload format" requires that providers without a dedicated UI normalizer (ElevenLabs is one) "MUST convert payloads into the OpenAI Chat Completions format so that the UI can render them", and §"Input capture" says the input "MUST be an ordered array of OpenAI-style message objects." But §"Multimodal API surfaces" says TTS should "log text input as normal request input" without saying whether that means a bare string, {"text": ...}, or [{"role": "user", "content": "..."}]. The existing SpeechWrapper logs a bare value. The spec needs to state which shape wins for non-chat generation surfaces.

2. There is no sanctioned way to record character-based TTS cost. ElevenLabs bills per character and reports character usage, but no characters-style metric key exists, and metrics.estimated_cost (MAY, features/token-and-cost-metrics.md) is the only escape hatch. If character-driven cost attribution is wanted for this integration, a new metric key must be added to the spec first.

3. Conversational AI / voice agents are an unspecified surface. §"Multimodal API surfaces" routes realtime/live APIs to "a parent task span for the session with child spans for model turns, media exchanges, and tool calls" but immediately caveats "Detailed event lifecycle conventions are still a separate TODO", and the guide's closing §TODO lists Realtime APIs as unspecified (event lifecycle, session finalization, interruption/cancellation). The existing repo prior art is already divergent on exactly this point — livekit_agents and pipecat emit TTS/STT as task spans while openai emits them as llm spans. ElevenLabs Conversational AI should be explicitly deferred out of this issue until the realtime spec lands.

4. Audio-capture opt-in gating is undefined for direct providers. livekit_agents and pipecat gate audio attachments behind BRAINTRUST_CAPTURE_AGENT_AUDIO_ATTACHMENTS, default off (py/src/braintrust/env.py:213, resolver at py/src/braintrust/integrations/utils.py:452-484, usage at py/src/braintrust/integrations/livekit_agents/tracing.py:443 and :683), whereas the OpenAI Speech path attaches unconditionally. features/attachments.md §"Configuration" only defines BRAINTRUST_AUTO_CONVERT_AI_ATTACHMENTS (default true). The spec should say which regime applies to a direct TTS/STT provider. Absent that ruling, follow the OpenAI Speech path (unconditional attachment) rather than the voice-pipeline gate.

5. Not a gap, just a scoping note: features/filter-ai-spans.md does not apply if this is built on the native integrations API (start_span) rather than OTel — its prefix allowlist (gen_ai., braintrust., llm., ai., traceloop.) only inspects OTel span attributes.

---

Suggested edits to this issue

• Rewrite the scope line under "What needs to be instrumented": instrument text_to_speech.convert(), text_to_speech.stream(), and speech_to_text.convert() on both ElevenLabs and AsyncElevenLabs. Explicitly out of scope: Conversational AI / realtime agents (blocked on the realtime spec TODO), dubbing, sound-effects, and music endpoints.
• Replace should log input text/voice/model and output audio metadata with: one llm span per call; input = the synthesis text only; output = generated audio as a braintrust_attachment produced via _extract_audio_output() (py/src/braintrust/integrations/utils.py:498). For STT: input = source audio as a braintrust_attachment, output = transcript text.
• Add an explicit metadata line: provider: "elevenlabs" (MUST), model from model_id (MUST), response_format derived from output_format. Then state plainly: no other metadata keys — voice_id / voice_settings / language_code require a braintrust-spec amendment first.
• Add an explicit metrics line: start, end, and time_to_first_token (streaming only). No token metrics — ElevenLabs reports none, so omit rather than zero-fill. Explicitly forbid characters / character_count / audio_duration / latency.
• Add a streaming requirement: single span closing at stream end; chunks tee'd and aggregated into one attachment; time_to_first_token measured to the first audio chunk; the caller's iterator must not be consumed by instrumentation. Flag that convert() returns a byte iterator in current versions, so this applies to it too.
• Add error handling: exceptions recorded in the top-level error field; attachment conversion failure falls back to the original payload and never raises.
• Add provenance: context.span_origin.instrumentation.name = "elevenlabs-auto" via internal={"instrumentation": ...}, asserted in tests.
• Add the repo checklist: [tool.braintrust.matrix] pin, test_elevenlabs nox session, [tool.braintrust.cassette-dirs] entry, per-version VCR cassettes (verify binary audio bodies record correctly), and auto_instrument() registration.
• Add two open questions for spec owners: (a) the canonical input shape for non-chat TTS spans; (b) whether a character-count metric or metrics.estimated_cost is the sanctioned path for ElevenLabs cost attribution.