[bot] Instrument Writer AI Python SDK (197,670 weekly downloads)

[braintrust-bot]

Summary

The Writer AI Python SDK (writer-sdk, importable as writerai) is the official Stainless-generated Python client for Writer's Palmyra model family (e.g. palmyra-x5). Its Writer/AsyncWriter clients expose a chat completions execution surface with streaming support. This repository has zero instrumentation for any Writer SDK surface — no integration directory, no wrapper, no patcher, no auto_instrument() support.

Weekly downloads: 197,670 (as of 2026-08-03; https://pypistats.org/packages/writer-sdk)

What's missing

• client.chat.chat(messages=[...], model="palmyra-x5", ...) — the primary chat completions execution surface. Response exposes choices[0].message.content.
• Streaming — stream=True returns chunks where content arrives via choices[0].delta.content instead of choices[0].message.content; the streaming span lifecycle (start on call, accumulate deltas, finalize on exhaustion) is unhandled.
• Async client — AsyncWriter mirrors the sync client's chat.chat() surface and is equally uninstrumented.
• Optional aiohttp backend (pip install writer-sdk[aiohttp]) — an alternate transport path for the async client, also uncovered.

None of these surfaces appear in py/src/braintrust/integrations/, py/src/braintrust/wrappers/, py/noxfile.py nox sessions, py/src/braintrust/integrations/versioning.py, or py/pyproject.toml's [tool.braintrust.matrix]. A case-insensitive grep for writer across py/src/braintrust/ returns only incidental matches (stream_writer, WAV file writer objects in the LiveKit integration) — no actual Writer AI SDK references.

Braintrust docs status

not_found — Writer / Palmyra is not mentioned on https://www.braintrust.dev/docs/guides/tracing/integrations (checked 2026-08-03), which lists model-provider and agent-framework integrations including OpenAI, Anthropic (Claude Agent SDK), LangChain, CrewAI, AutoGen, AgentScope, Google ADK, LiveKit Agents, Pydantic AI, Strands, and others, but no Writer/Palmyra entry.

Upstream sources

• PyPI: https://pypi.org/project/writer-sdk/
• GitHub: https://github.com/writer/writer-python
• SDKs overview: https://dev.writer.com/home/sdks
• API quickstart (chat completions, streaming behavior): https://dev.writer.com/api-guides/quickstart

Braintrust docs sources checked

• https://www.braintrust.dev/docs/guides/tracing/integrations (checked 2026-08-03)

Local repo files inspected

• py/src/braintrust/integrations/__init__.py — Writer not listed in the integration registry
• py/src/braintrust/integrations/ — no writer/ directory
• py/src/braintrust/wrappers/ — no Writer wrapper
• py/src/braintrust/integrations/versioning.py — no Writer version entry
• py/pyproject.toml — no writer/writer-sdk/writerai entry in [tool.braintrust.matrix]
• py/noxfile.py — no test_writer session
• Case-insensitive grep for writer across py/src/braintrust/ — only incidental matches (stream_writer, WAV writer objects), no real SDK coverage

[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
Category: completion API (client.chat.chat is a single request-response chat completions call), plus several non-chat model surfaces the issue does not mention
Already implemented in this repo: no — there is no py/src/braintrust/integrations/writer/, no py/src/braintrust/wrappers/ Writer wrapper, no entry in py/src/braintrust/integrations/__init__.py, no writer key in [tool.braintrust.matrix] in py/pyproject.toml, no test_writer session in py/noxfile.py, and no entry in py/src/braintrust/integrations/versioning.py. The issue's own "Local repo files inspected" list is accurate on all six points.

This issue is a gap audit — it correctly identifies which surfaces are uninstrumented, but it states almost nothing normative about the resulting span shape. Span type, span name, input/output payload format, metadata allowlist, metrics, tool-call shape, error handling, and span-origin provenance are all absent. As written it cannot be implemented against without an implementer inventing the parts the spec actually mandates, and the two closest existing integrations in this repo (Mistral, Cohere) contain patterns that would produce a non-conformant Writer integration if copied. Details below.

Conflicts with the spec

1. Output described as the extracted content string, not the choices array.

Response exposes choices[0].message.content.

Read as a requirement, this points implementers at capturing a string. The guide's Output capture → Default: OpenAI Chat Completions format requires output to be an array of OpenAI-style choice objects:

[
 {
 "index": 0,
 "finish_reason": "stop",
 "message": { "role": "assistant", "content": "..." }
 }
]

with finish_reason marked MUST and message marked MUST in the Choice fields table. Writer is not one of the three providers with a built-in UI normalizer — Canonical payload format states: "For the three providers that Braintrust ships built-in UI normalizers for (OpenAI, Anthropic, Google), instrumentation MAY preserve the provider-native payload as-is… For all other providers (Mistral, Cohere, Groq, Fireworks, Together, xAI, Perplexity, custom providers, etc.), instrumentation MUST convert payloads into the OpenAI Chat Completions format so that the UI can render them."

Concrete correction: require output = response.choices and input = messages (an ordered array of OpenAI-style message objects). Writer's ChatCompletion / ChatCompletionChoice / ChatCompletionMessage types are already OpenAI Chat Completions-shaped, so this is a pass-through, not a rewrite — but the issue must say so, because "capture the native response" is only legal for OpenAI/Anthropic/Google.

2. Streaming reduced to content deltas.

stream=True returns chunks where content arrives via choices[0].delta.content instead of choices[0].message.content; the streaming span lifecycle (start on call, accumulate deltas, finalize on exhaustion) is unhandled.

The lifecycle sentence is right. The delta sentence is incomplete in a way that produces a non-conformant span. Streaming → Accumulation requires: "The SDK MUST accumulate streamed chunks and produce a single complete input and output on the span, identical in structure to a non-streaming call." Content is not the only thing that streams — Writer ships a ToolCallStreaming shared type, so tool_calls deltas (function.name and function.arguments fragments, indexed by tool_call.index) and per-choice finish_reason must also be accumulated into the final choices array.

Concrete correction: require full choice reconstruction (role, content, tool_calls with concatenated function.arguments string, finish_reason), plus metrics.time_to_first_token (MUST for streaming spans per Streaming → Additional metrics), plus usage capture from the terminal chunk (Token counts from stream metadata: "If the provider does not include usage in the stream, the SDK SHOULD still attempt to capture token counts if they become available (e.g. from a final stream event)").

3. The aiohttp backend listed as a separate uninstrumented surface.

Optional aiohttp backend (pip install writer-sdk[aiohttp]) — an alternate transport path for the async client, also uncovered.

This frames instrumentation as transport-level coverage. Completion API Instrumentation → One span per API call requires exactly one llm span per discrete API call. aiohttp vs the default httpx is a transport swap underneath the same AsyncWriter.chat.chat() method; patching at the client-method level — which is what every existing integration in this repo does (py/src/braintrust/integrations/mistral/patchers.py, py/src/braintrust/integrations/openrouter/patchers.py) — covers it automatically. Treating it as a distinct surface invites a second instrumentation point and duplicate spans.

Concrete correction: demote this from a coverage requirement to a test-matrix assertion ("verify exactly one span per call and no span loss when the async client is constructed with the aiohttp transport").

4. Scope is silently narrowed to client.chat.chat.

The issue's "What's missing" enumerates only chat.chat, streaming, async, and the aiohttp transport. Per the upstream api.md for writer/writer-python, the client also exposes model-backed surfaces the spec has opinions about:

• client.completions.create (+ streaming; Completion / CompletionChunk / CompletionParams)
• client.graphs.question (+ streaming; Question / QuestionResponseChunk)
• client.applications.generate_content (+ ApplicationGenerateContentChunk streaming and async client.applications.jobs.create / retrieve / retry)
• client.tools.parse_pdf — OCR / document understanding, explicitly covered by the guide's Multimodal API surfaces table
• client.tools.web_search
• client.vision.analyze (VisionRequest / VisionResponse) — image input, covered by Multimodal / Attachments

Concrete correction: either enumerate these with their required span shapes, or explicitly declare them out of scope for a first PR. Silence reads as "not applicable", which is wrong for vision.analyze and tools.parse_pdf in particular.

Missing requirements

1. Span type and name. Nothing states span_attributes.type = "llm", which is a MUST in both Span types and features/token-and-cost-metrics.md §Span identity ("span_attributes.type … MUST … Set to "llm" for model-call spans"). Without it the span is excluded from LLM views and cost aggregation. Repo convention for span names is provider-dotted (mistral.chat.complete at py/src/braintrust/integrations/mistral/tracing.py:1331, cohere.chat_stream at py/src/braintrust/integrations/cohere/tracing.py:740), so writer.chat.chat and writer.completions.create.

2. metadata.model and metadata.provider. Both are MUST per Metadata and features/token-and-cost-metrics.md §Span identity. model SHOULD come from the API response (ChatCompletion.model) rather than the requested string. provider must be "writer" and is required even though Palmyra models are unique to Writer — the spec is explicit: "metadata.provider is required even when a model name appears globally unique. Resellers and gateways can sell the same model at different prices."

3. A metadata allowlist. The issue proposes no metadata policy at all, and the guide's Data capturing policy is a strict allowlist: "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." Permitted request-config fields are exactly temperature, top_p, max_tokens, frequency_penalty, presence_penalty, stop, response_format (Metadata), plus tools / tool_choice / parallel_tool_calls / max_tool_calls (Available tool definitions) and prompt (Prompt metadata). Writer request params such as logprobs, top_logprobs, n, seed, stream_options, graph_data, application_id, and subqueries MUST NOT be captured.

   ⚠️ Do not use the nearest prior art as a template here. py/src/braintrust/integrations/mistral/tracing.py:42-61 (_CHAT_METADATA_KEYS) captures random_seed, n, prediction, reasoning_effort, prompt_mode, guardrails, safe_prompt, and py/src/braintrust/integrations/mistral/tracing.py:455-464 adds id, object, created — none of which are sanctioned by the current spec. py/src/braintrust/integrations/cohere/tracing.py does the same ("a small allowlist of request params plus provider/response ids"). Those predate the data-capturing policy; a new integration should not inherit them.

4. Tool-call requirements. Writer's shared types include FunctionDefinition, FunctionParams, ToolCall, ToolCallStreaming, ToolChoiceString, ToolChoiceJsonObject, ToolParam — so tools are fully in play, and the issue says nothing. Required:

   • metadata.tools on every llm span whose request makes tools available, in OpenAI Chat Completions tool shape (Available tool definitions: "MUST be placed in metadata.tools… regardless of the underlying provider"). "If the user passed no tools in the request, metadata.tools MUST be omitted (do NOT emit an empty array)."
   • metadata.tool_choice, normalized to one of "auto" / "none" / "required" / {"type":"function","function":{"name":"..."}}. Writer's ToolChoiceString and ToolChoiceJsonObject must be mapped onto those values.
   • function.arguments in output tool calls MUST be a JSON-encoded string, not an object (Tool call object fields), and "MAY be malformed JSON if the model produced invalid output — in which case it MUST be passed through as-is rather than dropped".
   • Tool definitions MUST NOT appear in the input messages array, and executable handlers/closures MUST NOT be logged.

5. An explicit "no child tool spans" statement. This needs to be written down precisely because the closest prior art violates it. Completion APIs states: "If the model returns tool calls, the completion API span captures them in its output, but does NOT execute them or create tool spans," and the Spans per user call table gives "Completion API → 1" with "Tool execution spans → None (not SDK's job)". client.chat.chat is a completion API — the user executes tools and makes the follow-up call. But py/src/braintrust/integrations/mistral/tracing.py:995-1017 (_log_completion_tool_spans, called from _finalize_completion_response at line 1055 and from _finalize_completion_stream at line 1125) synthesizes a tool span for every tool call in a completion response, and py/src/braintrust/integrations/cohere/tracing.py:643 (_log_tool_call_spans) does the same. A Writer integration copying that pattern would emit fabricated tool spans for tool calls that Writer never executed. Requirement: one llm span, no children, no task parent.

6. Metrics. No metrics are specified. Required per Metrics Reference and features/token-and-cost-metrics.md:

   • start, end (MUST, all spans)
   • prompt_tokens, completion_tokens, tokens (MUST when reported). Writer's ChatCompletionUsage reports prompt_tokens / completion_tokens / total_tokens, so total_tokens maps to tokens.
   • time_to_first_token (MUST, streaming spans) — "measured by the SDK, not reported by the provider".
   • Missing values MUST be omitted, never fabricated: "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."
   • "SDKs MUST NOT add metrics beyond the keys listed in this guide." The generic camel/snake usage-dict passthrough at py/src/braintrust/integrations/mistral/tracing.py:415-429 (_parse_usage_metrics iterates every key in usage and emits it as a metric) would silently leak any new field Writer adds to usage as an unsanctioned metric key — the Writer integration should map a fixed set of usage fields explicitly.

7. Error handling. Nothing requires that a provider or transport failure populates the span's top-level error field and still ends/exports the span. This matters for the streaming path in particular: the span must be finalized on exception, on StopIteration/StopAsyncIteration, and on context-manager exit, without double-closing.

8. context.span_origin provenance. The Context section: "Braintrust-created spans MUST also include span-origin provenance." Required for a Python SDK integration: context.span_origin.name = "braintrust.sdk.python" (the braintrust. prefix is reserved for Braintrust-maintained SDKs), a version, and context.span_origin.instrumentation.name identifying the integration module. Note that the provider SDK itself is not the span origin: "Provider/client SDKs such as openai or anthropic are not the span origin and SHOULD continue to appear in metadata.provider." Repo pattern is a module-local start_span wrapper that injects internal["instrumentation"] — see py/src/braintrust/integrations/mistral/tracing.py:22-29 and py/src/braintrust/integrations/openrouter/tracing.py:21-28; Writer should use _INSTRUMENTATION = "writer-auto".

9. Attachments / multimodal handling. Writer chat accepts image content parts, client.vision.analyze takes images, and client.tools.parse_pdf takes documents. Multimodal / Attachments requires: "SDKs MUST replace the raw media with Braintrust attachment references inside the log row's input or output payload. Do not add a separate attachment list," using the {"type":"braintrust_attachment","content_type":…,"filename":…,"key":…} shape. Since Writer has no UI normalizer, the normalized {"type":"image_url","image_url":{"url": <ref>}} / {"type":"file","file":{"filename":…,"file_data": <ref>}} content parts apply. features/attachments.md §partial-replacement-safety additionally requires all-or-nothing rewrite, and the guide requires that conversion failure "MUST NOT throw an exception that prevents span export".

10. Integration wiring and test plan. None of the repo-mandated scaffolding is named:

    • a WriterIntegration(BaseIntegration) in py/src/braintrust/integrations/writer/integration.py with name = "writer", import_names = ("writerai",), min_version, and a patchers tuple (cf. py/src/braintrust/integrations/openrouter/integration.py)
    • registration in py/src/braintrust/integrations/__init__.py so auto_instrument() picks it up, plus a manual wrap_writer(client) helper
    • a writer entry in [tool.braintrust.matrix] in py/pyproject.toml with an explicit latest pin plus at least one older version
    • a parametrized test_writer session in py/noxfile.py, and inclusion in .github/workflows/checks.yaml
    • VCR cassettes under py/src/braintrust/integrations/writer/cassettes/<version>/, plus a [tool.braintrust.cassette-dirs] entry so py/scripts/check-stale-cassettes.py can detect orphans
    • a versioning.py entry if any behavior is version-gated

    Note the import-name/distribution-name mismatch is a real trap: the distribution is writer-sdk but the import is writerai. import_names must be ("writerai",), while the matrix/nox pin installs writer-sdk.

Blocked on spec changes (braintrustdata/braintrust-spec)

1. Legacy text completion (client.completions.create). The guide's canonical formats are message-in / choice-out. There is no defined mapping for a prompt-in / text-out completion endpoint — whether to synthesize [{"role":"user","content":prompt}] as input and a choices array as output, or to log the raw prompt string. Completion / CompletionChunk cannot be instrumented consistently until the spec says which.

2. RAG / knowledge-graph question answering. client.graphs.question returns a Question carrying retrieved sources, and chat requests accept graph_data with sources echoed back on the response message. The guide defines no retrieval/RAG span shape and no sanctioned location for citations or retrieved sources. Under the Data capturing policy, passing Writer's native graph_data.sources through inside the choices array is not sanctioned, but dropping it destroys the most useful part of the trace. The TODO section covers Reranking APIs, not retrieval. Needs a spec addition.

3. Provider-hosted search as a standalone endpoint. client.tools.web_search is a direct API call by user code, not a model-initiated call inside an agent loop. The tool span type is defined as "A model-initiated external call (API, database query, retrieval)", which does not describe this. No defined span shape.

4. Long-running text generation jobs. client.applications.jobs.create / retrieve / retry is an async submit-then-poll flow. The guide addresses long-running media operations only ("Video generation and other long-running media operations: when the wrapper waits or polls for completion, log the initial request and final media result on the llm span"). There is nothing on long-running text generation, span-per-poll semantics, or correlating a job-result span back to the submit span.

5. Provider-string registry / pricing. The spec mandates metadata.provider but does not define how an SDK picks a value the backend pricing registry will match. Unless writer plus palmyra-* pricing exists in the model registry, token-derived estimated_cost silently yields nothing for every Writer span (features/token-and-cost-metrics.md §Cost computation semantics). Worth confirming registry coverage before build, not after.

Suggested edits to this issue

• Add an explicit Required span shape section rather than leaving it implicit: one llm span per chat.chat() call, no child spans, no task parent; span name writer.chat.chat; input = the messages array; output = response.choices.
• Replace "> Response exposes choices[0].message.content" with the full choice-object requirement (index, finish_reason MUST, message MUST), and add the note that Writer's response is already OpenAI Chat Completions-shaped so conversion is a pass-through — while stating plainly that Writer has no UI normalizer, so native-shape passthrough is only acceptable because the native shape already is the canonical shape.
• Rewrite the streaming bullet: accumulate content and tool_calls deltas (ToolCallStreaming) and finish_reason into one final choices array; close the span exactly once on exhaustion, exception, or context-manager exit; emit time_to_first_token; pull usage from the terminal chunk.
• Add a metadata section: metadata.model from the response, metadata.provider = "writer", and the explicit allowlist (temperature, top_p, max_tokens, frequency_penalty, presence_penalty, stop, response_format, tools, tool_choice, parallel_tool_calls, max_tool_calls). Add an explicit MUST NOT list: logprobs, top_logprobs, n, seed, stream_options, graph_data, application_id, subqueries, response id/object/created.
• Add a tools section: metadata.tools in OpenAI shape (omitted entirely when no tools were passed, never []), normalized metadata.tool_choice, function.arguments preserved as a JSON-encoded string including malformed output.
• Add an explicit non-goal: no synthesized tool spans, with a pointer to py/src/braintrust/integrations/mistral/tracing.py:995 and py/src/braintrust/integrations/cohere/tracing.py:643 as non-conformant precedent that must not be copied.
• Add a metrics section: start, end, prompt_tokens, completion_tokens, tokens (from total_tokens), time_to_first_token for streams; omit unreported values; no other metric keys; map usage fields explicitly rather than iterating the usage dict.
• Add error handling (top-level error populated, span still ended/exported on both sync and streaming paths) and context.span_origin provenance with instrumentation = "writer-auto".
• Demote the aiohttp backend bullet from a coverage gap to a test-matrix assertion.
• Either enumerate completions.create, graphs.question, applications.generate_content / jobs, tools.parse_pdf, tools.web_search, and vision.analyze with their intended span shapes — flagging items 1–4 under "Blocked on spec changes" as prerequisites — or state that v1 covers chat.chat only.
• Add the repo checklist: WriterIntegration(BaseIntegration) with import_names = ("writerai",) (distribution is writer-sdk, import is writerai), auto_instrument() registration, min_version, [tool.braintrust.matrix] pin with latest + one older version, test_writer nox session, .github/workflows/checks.yaml inclusion, VCR cassettes under py/src/braintrust/integrations/writer/cassettes/<version>/, and a [tool.braintrust.cassette-dirs] entry.