diff --git a/studio/backend/core/inference/external_provider.py b/studio/backend/core/inference/external_provider.py index 23500b88c8..16caed7858 100644 --- a/studio/backend/core/inference/external_provider.py +++ b/studio/backend/core/inference/external_provider.py @@ -2169,638 +2169,689 @@ class ExternalProviderClient: logger.info("Proxying OpenAI Responses API to %s (model=%s)", url, model) - try: - async with _http_client.stream( - "POST", - url, - json = body, - headers = self._auth_headers(), - timeout = self._stream_timeout, - ) as response: - if response.status_code != 200: - error_body = await response.aread() - error_text = error_body.decode("utf-8", errors = "replace") - logger.error( - "OpenAI Responses returned %d: %s", - response.status_code, - error_text[:500], + def _build_body(container_id_for_this_attempt: Optional[str]) -> dict[str, Any]: + """Snapshot of the request body. Called once for the initial + attempt and again with ``None`` for the post-expiry retry. + Returns a fresh dict so the retry doesn't share state with the + first attempt. + """ + attempt_body = dict(body) + if enabled_tools: + tools_array_attempt: list[dict[str, Any]] = [] + if "web_search" in enabled_tools: + tools_array_attempt.append({"type": "web_search"}) + if code_execution_enabled_openai: + if container_id_for_this_attempt: + env_attempt: dict[str, Any] = { + "type": "container_reference", + "container_id": container_id_for_this_attempt, + } + else: + env_attempt = {"type": "container_auto"} + tools_array_attempt.append( + {"type": "shell", "environment": env_attempt} ) - # Detect stale-container errors so the frontend can - # drop its persisted id. OpenAI doesn't pin an - # error code in the public docs for this case, so - # match a couple of likely substrings. If we sent - # a container_reference and the response is 4xx - # with any hint of "container not found / expired", - # emit container_invalidated; the next turn will - # fall back to container_auto. - if ( - openai_code_exec_container_id - and 400 <= response.status_code < 500 - ): - lowered = error_text.lower() - if "container" in lowered and ( - "expired" in lowered - or "not_found" in lowered - or "not found" in lowered - or "no such container" in lowered - ): + if tools_array_attempt: + attempt_body["tools"] = tools_array_attempt + else: + attempt_body.pop("tools", None) + return attempt_body + + def _is_openai_container_expired_error(error_text: str) -> bool: + """Match the substring patterns OpenAI uses for expired / missing + code-exec containers. There's no official error code in the public + docs, so we substring-match a small set. + """ + lowered = error_text.lower() + if "container" not in lowered: + return False + return ( + "expired" in lowered + or "not_found" in lowered + or "not found" in lowered + or "no such container" in lowered + ) + + try: + retried = False + attempt_container_id = openai_code_exec_container_id + while True: + attempt_body = _build_body(attempt_container_id) + async with _http_client.stream( + "POST", + url, + json = attempt_body, + headers = self._auth_headers(), + timeout = self._stream_timeout, + ) as response: + if response.status_code != 200: + error_body = await response.aread() + error_text = error_body.decode("utf-8", errors = "replace") + logger.error( + "OpenAI Responses returned %d: %s", + response.status_code, + error_text[:500], + ) + expired_container_4xx = ( + attempt_container_id + and 400 <= response.status_code < 500 + and _is_openai_container_expired_error(error_text) + ) + if expired_container_4xx and not retried: yield ( f"data: " f"{_json.dumps({'id': completion_id, 'object': 'chat.completion.chunk', 'choices': [{'index': 0, 'delta': {}, 'finish_reason': None}], '_toolEvent': {'type': 'container_invalidated'}})}" ) - yield _error_sse_line( - response.status_code, error_text, self.provider_type - ) - return - - # NOTE: same manual __anext__ loop as stream_chat_completion — - # see comment there for the GeneratorExit / aclose ordering. - lines_gen = response.aiter_lines().__aiter__() - done_emitted = False - reasoning_open = False - reasoning_emitted = False - # Latched from response.completed / response.incomplete so - # the final log can surface input_tokens_details.cached_tokens — - # the field that proves prompt_cache_retention="24h" is - # actually hitting OpenAI's cache instead of recomputing - # the prefix every turn. - last_usage: Optional[dict[str, Any]] = None - # Per-call state for OpenAI's server-side web_search tool. Mapped - # back into our local _toolEvent shape so the existing chat-UI - # renderer surfaces web_search the same way it does for local - # tool calls: a "Searching…" tool-call card, then a `tool_end` - # carrying citations formatted as - # Title: …\nURL: …\nSnippet: …\n---\n… - # blocks (which the frontend's parseSourcesFromResult lifts - # into source content parts at end of stream). - # web_search_calls preserves insertion order so we can apply - # the aggregated citation list onto the *last* call's - # tool_end — that's the one the frontend's source-pill - # extraction reads (parseSourcesFromResult flatMaps every - # web_search result, so a single non-empty result is enough - # to surface all sources at message tail). - # OpenAI emits url_citation annotations on text deltas, not - # per call — there's no wire field linking a citation back - # to a specific search invocation. Hence the shared list. - # web_search_calls: { item_id -> {query} } - web_search_calls: dict[str, dict[str, Any]] = {} - all_url_citations: list[dict[str, str]] = [] - # Shell-tool (code execution) state. OpenAI emits - # `shell_call` items (model requesting a command list) - # paired with `shell_call_output` items (execution - # results). We mirror the Anthropic code-execution UX - # by emitting one `_toolEvent` tool_start per - # shell_call and one tool_end per shell_call_output; - # they're linked via `shell_call_output.call_id` - # matching `shell_call.id`. Items are independent of - # web_search (different keyed map). - # shell_calls: { call_id -> {commands, output} } - shell_calls: dict[str, dict[str, Any]] = {} - # Container id captured from the response stream. When - # it differs from the inbound id, emit a synthetic - # `container_ready` _toolEvent so the frontend can - # persist it onto the thread record for the next turn. - # Where OpenAI surfaces it is documented loosely; we - # probe two known fields (response.container_id on - # response.completed, item.environment.container_id on - # shell_call output items) and latch the first one we - # see. - latched_container_id: Optional[str] = None - container_id_emitted = False - - def _emit_tool_event(payload: dict[str, Any]) -> str: - chunk = { - "id": completion_id, - "object": "chat.completion.chunk", - "choices": [ - { - "index": 0, - "delta": {}, - "finish_reason": None, - } - ], - "_toolEvent": payload, - } - return f"data: {_json.dumps(chunk)}" - - def _format_shell_output(output: Any) -> str: - """Render an OpenAI `shell_call_output.output` list - as the preformatted text payload the frontend's - CodeExecutionToolUI displays inside a 
. Each
-                    entry has stdout/stderr/outcome — concatenate them
-                    with a separator block per entry and append
-                    `return_code` / `(timeout)` annotations only when
-                    they convey information beyond "succeeded".
-                    """
-                    if not isinstance(output, list):
-                        return ""
-                    parts: list[str] = []
-                    for entry in output:
-                        if not isinstance(entry, dict):
+                            retried = True
+                            attempt_container_id = None
                             continue
-                        stdout = entry.get("stdout") or ""
-                        stderr = entry.get("stderr") or ""
-                        outcome = entry.get("outcome") or {}
-                        chunk_parts: list[str] = []
-                        if stdout:
-                            chunk_parts.append(stdout)
-                        if stderr:
-                            chunk_parts.append(f"--- stderr ---\n{stderr}")
-                        if isinstance(outcome, dict):
-                            outcome_type = outcome.get("type")
-                            if outcome_type == "exit":
-                                exit_code = outcome.get("exit_code")
-                                if isinstance(exit_code, int) and exit_code != 0:
-                                    chunk_parts.append(f"return_code: {exit_code}")
-                            elif outcome_type == "timeout":
-                                chunk_parts.append("(timeout)")
-                        if chunk_parts:
-                            parts.append("\n".join(chunk_parts))
-                    return (
-                        "\n--- next command ---\n".join(parts)
-                        if parts
-                        else "(no output)"
-                    )
+                        yield _error_sse_line(
+                            response.status_code, error_text, self.provider_type
+                        )
+                        return
 
-                def _record_url_citation(payload: dict[str, Any]) -> None:
-                    """Append a url_citation onto the shared all_url_citations
-                    list. Dedup by URL — the same source can be cited multiple
-                    times across deltas. We do NOT try to attribute citations
-                    to individual web_search_call invocations because OpenAI's
-                    annotation events don't carry that linkage."""
-                    if payload.get("type") != "url_citation":
-                        return
-                    url = payload.get("url", "")
-                    if not url:
-                        return
-                    if any(c["url"] == url for c in all_url_citations):
-                        return
-                    title = payload.get("title") or url
-                    snippet = payload.get("snippet") or payload.get("quote") or ""
-                    all_url_citations.append(
-                        {
-                            "url": url,
-                            "title": title,
-                            "snippet": snippet,
+                    # NOTE: same manual __anext__ loop as stream_chat_completion —
+                    # see comment there for the GeneratorExit / aclose ordering.
+                    lines_gen = response.aiter_lines().__aiter__()
+                    done_emitted = False
+                    reasoning_open = False
+                    reasoning_emitted = False
+                    # Latched from response.completed / response.incomplete so
+                    # the final log can surface input_tokens_details.cached_tokens —
+                    # the field that proves prompt_cache_retention="24h" is
+                    # actually hitting OpenAI's cache instead of recomputing
+                    # the prefix every turn.
+                    last_usage: Optional[dict[str, Any]] = None
+                    # Per-call state for OpenAI's server-side web_search tool. Mapped
+                    # back into our local _toolEvent shape so the existing chat-UI
+                    # renderer surfaces web_search the same way it does for local
+                    # tool calls: a "Searching…" tool-call card, then a `tool_end`
+                    # carrying citations formatted as
+                    #   Title: …\nURL: …\nSnippet: …\n---\n…
+                    # blocks (which the frontend's parseSourcesFromResult lifts
+                    # into source content parts at end of stream).
+                    # web_search_calls preserves insertion order so we can apply
+                    # the aggregated citation list onto the *last* call's
+                    # tool_end — that's the one the frontend's source-pill
+                    # extraction reads (parseSourcesFromResult flatMaps every
+                    # web_search result, so a single non-empty result is enough
+                    # to surface all sources at message tail).
+                    # OpenAI emits url_citation annotations on text deltas, not
+                    # per call — there's no wire field linking a citation back
+                    # to a specific search invocation. Hence the shared list.
+                    # web_search_calls: { item_id -> {query} }
+                    web_search_calls: dict[str, dict[str, Any]] = {}
+                    all_url_citations: list[dict[str, str]] = []
+                    # Shell-tool (code execution) state. OpenAI emits
+                    # `shell_call` items (model requesting a command list)
+                    # paired with `shell_call_output` items (execution
+                    # results). We mirror the Anthropic code-execution UX
+                    # by emitting one `_toolEvent` tool_start per
+                    # shell_call and one tool_end per shell_call_output;
+                    # they're linked via `shell_call_output.call_id`
+                    # matching `shell_call.id`. Items are independent of
+                    # web_search (different keyed map).
+                    # shell_calls: { call_id -> {commands, output} }
+                    shell_calls: dict[str, dict[str, Any]] = {}
+                    # Container id captured from the response stream. When
+                    # it differs from the inbound id, emit a synthetic
+                    # `container_ready` _toolEvent so the frontend can
+                    # persist it onto the thread record for the next turn.
+                    # Where OpenAI surfaces it is documented loosely; we
+                    # probe two known fields (response.container_id on
+                    # response.completed, item.environment.container_id on
+                    # shell_call output items) and latch the first one we
+                    # see.
+                    latched_container_id: Optional[str] = None
+                    container_id_emitted = False
+
+                    def _emit_tool_event(payload: dict[str, Any]) -> str:
+                        chunk = {
+                            "id": completion_id,
+                            "object": "chat.completion.chunk",
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {},
+                                    "finish_reason": None,
+                                }
+                            ],
+                            "_toolEvent": payload,
                         }
-                    )
+                        return f"data: {_json.dumps(chunk)}"
 
-                def _extract_reasoning_text(payload: Any) -> str:
-                    if payload is None:
-                        return ""
-                    if isinstance(payload, str):
-                        return payload
-                    if isinstance(payload, list):
-                        out: list[str] = []
-                        for item in payload:
-                            text = _extract_reasoning_text(item)
-                            if text:
-                                out.append(text)
-                        return "".join(out)
-                    if isinstance(payload, dict):
-                        # OpenAI responses may carry reasoning summaries in
-                        # different envelope fields across event variants.
-                        for key in ("text", "delta", "content", "summary"):
-                            if key in payload:
-                                text = _extract_reasoning_text(payload.get(key))
-                                if text:
-                                    return text
-                        if payload.get("type") == "summary_text":
-                            return _extract_reasoning_text(payload.get("text"))
-                    return ""
+                    def _format_shell_output(output: Any) -> str:
+                        """Render an OpenAI `shell_call_output.output` list
+                        as the preformatted text payload the frontend's
+                        CodeExecutionToolUI displays inside a 
. Each
+                        entry has stdout/stderr/outcome — concatenate them
+                        with a separator block per entry and append
+                        `return_code` / `(timeout)` annotations only when
+                        they convey information beyond "succeeded".
+                        """
+                        if not isinstance(output, list):
+                            return ""
+                        parts: list[str] = []
+                        for entry in output:
+                            if not isinstance(entry, dict):
+                                continue
+                            stdout = entry.get("stdout") or ""
+                            stderr = entry.get("stderr") or ""
+                            outcome = entry.get("outcome") or {}
+                            chunk_parts: list[str] = []
+                            if stdout:
+                                chunk_parts.append(stdout)
+                            if stderr:
+                                chunk_parts.append(f"--- stderr ---\n{stderr}")
+                            if isinstance(outcome, dict):
+                                outcome_type = outcome.get("type")
+                                if outcome_type == "exit":
+                                    exit_code = outcome.get("exit_code")
+                                    if isinstance(exit_code, int) and exit_code != 0:
+                                        chunk_parts.append(f"return_code: {exit_code}")
+                                elif outcome_type == "timeout":
+                                    chunk_parts.append("(timeout)")
+                            if chunk_parts:
+                                parts.append("\n".join(chunk_parts))
+                        return (
+                            "\n--- next command ---\n".join(parts)
+                            if parts
+                            else "(no output)"
+                        )
 
-                def _chunk_with_text(text: str) -> str:
-                    chunk = {
-                        "id": completion_id,
-                        "object": "chat.completion.chunk",
-                        "choices": [
+                    def _record_url_citation(payload: dict[str, Any]) -> None:
+                        """Append a url_citation onto the shared all_url_citations
+                        list. Dedup by URL — the same source can be cited multiple
+                        times across deltas. We do NOT try to attribute citations
+                        to individual web_search_call invocations because OpenAI's
+                        annotation events don't carry that linkage."""
+                        if payload.get("type") != "url_citation":
+                            return
+                        url = payload.get("url", "")
+                        if not url:
+                            return
+                        if any(c["url"] == url for c in all_url_citations):
+                            return
+                        title = payload.get("title") or url
+                        snippet = payload.get("snippet") or payload.get("quote") or ""
+                        all_url_citations.append(
                             {
-                                "index": 0,
-                                "delta": {"content": text},
-                                "finish_reason": None,
+                                "url": url,
+                                "title": title,
+                                "snippet": snippet,
                             }
-                        ],
-                    }
-                    return f"data: {_json.dumps(chunk)}"
+                        )
 
-                try:
-                    while True:
-                        try:
-                            line = await lines_gen.__anext__()
-                        except StopAsyncIteration:
-                            break
-                        if not line or line.startswith("event:"):
-                            continue
-                        if not line.startswith("data:"):
-                            continue
+                    def _extract_reasoning_text(payload: Any) -> str:
+                        if payload is None:
+                            return ""
+                        if isinstance(payload, str):
+                            return payload
+                        if isinstance(payload, list):
+                            out: list[str] = []
+                            for item in payload:
+                                text = _extract_reasoning_text(item)
+                                if text:
+                                    out.append(text)
+                            return "".join(out)
+                        if isinstance(payload, dict):
+                            # OpenAI responses may carry reasoning summaries in
+                            # different envelope fields across event variants.
+                            for key in ("text", "delta", "content", "summary"):
+                                if key in payload:
+                                    text = _extract_reasoning_text(payload.get(key))
+                                    if text:
+                                        return text
+                            if payload.get("type") == "summary_text":
+                                return _extract_reasoning_text(payload.get("text"))
+                        return ""
 
-                        data_str = line[len("data:") :].strip()
-                        if not data_str:
-                            continue
-                        if data_str == "[DONE]":
-                            if not done_emitted:
-                                yield "data: [DONE]"
-                                done_emitted = True
-                            break
+                    def _chunk_with_text(text: str) -> str:
+                        chunk = {
+                            "id": completion_id,
+                            "object": "chat.completion.chunk",
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {"content": text},
+                                    "finish_reason": None,
+                                }
+                            ],
+                        }
+                        return f"data: {_json.dumps(chunk)}"
 
-                        try:
-                            event = _json.loads(data_str)
-                        except _json.JSONDecodeError:
-                            continue
+                    try:
+                        while True:
+                            try:
+                                line = await lines_gen.__anext__()
+                            except StopAsyncIteration:
+                                break
+                            if not line or line.startswith("event:"):
+                                continue
+                            if not line.startswith("data:"):
+                                continue
 
-                        event_type = event.get("type")
+                            data_str = line[len("data:") :].strip()
+                            if not data_str:
+                                continue
+                            if data_str == "[DONE]":
+                                if not done_emitted:
+                                    yield "data: [DONE]"
+                                    done_emitted = True
+                                break
 
-                        if event_type == "response.output_text.delta":
-                            delta_text = event.get("delta", "")
-                            if delta_text:
-                                if reasoning_open:
-                                    yield _chunk_with_text("")
-                                    reasoning_open = False
-                                yield _chunk_with_text(delta_text)
-                            # Some API versions inline url citations on the
-                            # delta event itself rather than as a separate
-                            # response.output_text.annotation.added event.
-                            for ann in event.get("annotations") or []:
+                            try:
+                                event = _json.loads(data_str)
+                            except _json.JSONDecodeError:
+                                continue
+
+                            event_type = event.get("type")
+
+                            if event_type == "response.output_text.delta":
+                                delta_text = event.get("delta", "")
+                                if delta_text:
+                                    if reasoning_open:
+                                        yield _chunk_with_text("")
+                                        reasoning_open = False
+                                    yield _chunk_with_text(delta_text)
+                                # Some API versions inline url citations on the
+                                # delta event itself rather than as a separate
+                                # response.output_text.annotation.added event.
+                                for ann in event.get("annotations") or []:
+                                    if isinstance(ann, dict):
+                                        _record_url_citation(ann)
+
+                            elif event_type == "response.output_text.annotation.added":
+                                ann = event.get("annotation")
                                 if isinstance(ann, dict):
                                     _record_url_citation(ann)
 
-                        elif event_type == "response.output_text.annotation.added":
-                            ann = event.get("annotation")
-                            if isinstance(ann, dict):
-                                _record_url_citation(ann)
+                            elif event_type == "response.output_item.added":
+                                # Track the call early but do NOT emit tool_start
+                                # yet — action.query is not reliably populated on
+                                # added across OpenAI API versions, and the
+                                # frontend's tool_start is a one-shot push (no
+                                # update mechanism). Wait for output_item.done.
+                                item = event.get("item", {})
+                                if (
+                                    isinstance(item, dict)
+                                    and item.get("type") == "web_search_call"
+                                ):
+                                    item_id = item.get("id", "") or (
+                                        f"ws_{len(web_search_calls)}"
+                                    )
+                                    web_search_calls.setdefault(item_id, {"query": ""})
+                                # Shell-tool: register the call eagerly so
+                                # the matching shell_call_output can link
+                                # back even if `done` arrives out of order.
+                                # Also probe for container_id on the
+                                # environment field — when container_auto
+                                # auto-creates one, this is the first place
+                                # the new id might surface (OpenAI doesn't
+                                # promise this in docs, but the field is
+                                # cheap to scan and lets us emit
+                                # container_ready earlier than
+                                # response.completed).
+                                if (
+                                    isinstance(item, dict)
+                                    and item.get("type") == "shell_call"
+                                ):
+                                    item_id = item.get("id", "") or (
+                                        f"sc_{len(shell_calls)}"
+                                    )
+                                    shell_calls.setdefault(
+                                        item_id,
+                                        {"commands": [], "output": None},
+                                    )
+                                    env = item.get("environment")
+                                    if isinstance(env, dict):
+                                        probe = env.get("container_id") or env.get("id")
+                                        if (
+                                            isinstance(probe, str)
+                                            and probe.startswith("cntr_")
+                                            and latched_container_id is None
+                                        ):
+                                            latched_container_id = probe
 
-                        elif event_type == "response.output_item.added":
-                            # Track the call early but do NOT emit tool_start
-                            # yet — action.query is not reliably populated on
-                            # added across OpenAI API versions, and the
-                            # frontend's tool_start is a one-shot push (no
-                            # update mechanism). Wait for output_item.done.
-                            item = event.get("item", {})
-                            if (
-                                isinstance(item, dict)
-                                and item.get("type") == "web_search_call"
+                            elif event_type == "response.output_item.done":
+                                item = event.get("item", {})
+                                if not isinstance(item, dict):
+                                    continue
+                                if item.get("type") == "reasoning":
+                                    summary_text = _extract_reasoning_text(
+                                        item.get("summary")
+                                    )
+                                    if summary_text and not reasoning_emitted:
+                                        if not reasoning_open:
+                                            summary_text = f"{summary_text}"
+                                            reasoning_open = True
+                                        yield _chunk_with_text(summary_text)
+                                        reasoning_emitted = True
+                                elif item.get("type") == "web_search_call":
+                                    # done is the canonical place to read the
+                                    # query, so emit both tool_start and tool_end
+                                    # here. Frontend then renders a card per call
+                                    # with the proper "Searching: " label.
+                                    # Citations are aggregated separately and the
+                                    # *last* call's result is overwritten at
+                                    # response.completed with the citation list
+                                    # (so the source-pill extraction at message
+                                    # tail surfaces them once).
+                                    item_id = item.get("id", "") or (
+                                        f"ws_{len(web_search_calls)}"
+                                    )
+                                    action = item.get("action")
+                                    query = (
+                                        action.get("query", "")
+                                        if isinstance(action, dict)
+                                        else ""
+                                    )
+                                    web_search_calls[item_id] = {"query": query}
+                                    yield _emit_tool_event(
+                                        {
+                                            "type": "tool_start",
+                                            "tool_name": "web_search",
+                                            "tool_call_id": item_id,
+                                            "arguments": (
+                                                {"query": query} if query else {}
+                                            ),
+                                        }
+                                    )
+                                    yield _emit_tool_event(
+                                        {
+                                            "type": "tool_end",
+                                            "tool_call_id": item_id,
+                                            # Empty result — the last call gets
+                                            # overwritten with citations at
+                                            # response.completed.
+                                            "result": "",
+                                        }
+                                    )
+                                elif item.get("type") == "shell_call":
+                                    # OpenAI ships the commands array on the
+                                    # action field. Join them onto one
+                                    # command string for the tool card —
+                                    # the renderer is shared with Anthropic
+                                    # bash, which only carries a single
+                                    # `command`. Multiple commands in one
+                                    # shell_call get joined with newlines so
+                                    # they still render as one card.
+                                    item_id = item.get("id", "") or (
+                                        f"sc_{len(shell_calls)}"
+                                    )
+                                    action = item.get("action") or {}
+                                    commands = (
+                                        action.get("commands")
+                                        if isinstance(action, dict)
+                                        else None
+                                    ) or []
+                                    joined_command = (
+                                        "\n".join(str(c) for c in commands)
+                                        if isinstance(commands, list)
+                                        else ""
+                                    )
+                                    shell_calls.setdefault(
+                                        item_id,
+                                        {"commands": [], "output": None},
+                                    )
+                                    shell_calls[item_id]["commands"] = (
+                                        list(commands)
+                                        if isinstance(commands, list)
+                                        else []
+                                    )
+                                    yield _emit_tool_event(
+                                        {
+                                            "type": "tool_start",
+                                            "tool_name": "code_execution",
+                                            "tool_call_id": item_id,
+                                            "arguments": {
+                                                "kind": "bash",
+                                                "command": joined_command,
+                                            },
+                                        }
+                                    )
+                                elif item.get("type") == "shell_call_output":
+                                    # `call_id` links back to the shell_call's
+                                    # `id`, which is what we used as the
+                                    # tool_call_id on tool_start. Match on
+                                    # call_id when present so the matching
+                                    # card transitions to complete.
+                                    call_id = (
+                                        item.get("call_id") or item.get("id") or ""
+                                    )
+                                    output = item.get("output") or []
+                                    if call_id in shell_calls:
+                                        shell_calls[call_id]["output"] = output
+                                    result_text = _format_shell_output(output)
+                                    yield _emit_tool_event(
+                                        {
+                                            "type": "tool_end",
+                                            "tool_call_id": call_id,
+                                            "result": result_text,
+                                        }
+                                    )
+
+                            elif (
+                                isinstance(event_type, str)
+                                and "reasoning" in event_type
                             ):
-                                item_id = item.get("id", "") or (
-                                    f"ws_{len(web_search_calls)}"
+                                reasoning_delta = _extract_reasoning_text(event)
+                                if reasoning_delta:
+                                    if not reasoning_open:
+                                        reasoning_delta = f"{reasoning_delta}"
+                                        reasoning_open = True
+                                    yield _chunk_with_text(reasoning_delta)
+                                    reasoning_emitted = True
+
+                            elif event_type == "response.completed":
+                                completed_usage = (event.get("response") or {}).get(
+                                    "usage"
                                 )
-                                web_search_calls.setdefault(item_id, {"query": ""})
-                            # Shell-tool: register the call eagerly so
-                            # the matching shell_call_output can link
-                            # back even if `done` arrives out of order.
-                            # Also probe for container_id on the
-                            # environment field — when container_auto
-                            # auto-creates one, this is the first place
-                            # the new id might surface (OpenAI doesn't
-                            # promise this in docs, but the field is
-                            # cheap to scan and lets us emit
-                            # container_ready earlier than
-                            # response.completed).
-                            if (
-                                isinstance(item, dict)
-                                and item.get("type") == "shell_call"
-                            ):
-                                item_id = item.get("id", "") or (
-                                    f"sc_{len(shell_calls)}"
-                                )
-                                shell_calls.setdefault(
-                                    item_id,
-                                    {"commands": [], "output": None},
-                                )
-                                env = item.get("environment")
-                                if isinstance(env, dict):
-                                    probe = env.get("container_id") or env.get("id")
+                                if isinstance(completed_usage, dict):
+                                    last_usage = completed_usage
+                                if reasoning_open:
+                                    yield _chunk_with_text("")
+                                    reasoning_open = False
+                                # Probe response.container_id (top-level) and
+                                # response.container.id for the shell-tool
+                                # container id. OpenAI's docs don't pin the
+                                # exact field, so we scan both. Emit
+                                # `container_ready` only when the value
+                                # differs from the inbound one — no churn on
+                                # reuse.
+                                response_obj = event.get("response") or {}
+                                if isinstance(response_obj, dict):
+                                    probe_id = response_obj.get("container_id")
+                                    if not probe_id:
+                                        container_field = response_obj.get("container")
+                                        if isinstance(container_field, dict):
+                                            probe_id = container_field.get("id")
                                     if (
-                                        isinstance(probe, str)
-                                        and probe.startswith("cntr_")
+                                        isinstance(probe_id, str)
+                                        and probe_id.startswith("cntr_")
                                         and latched_container_id is None
                                     ):
-                                        latched_container_id = probe
-
-                        elif event_type == "response.output_item.done":
-                            item = event.get("item", {})
-                            if not isinstance(item, dict):
-                                continue
-                            if item.get("type") == "reasoning":
-                                summary_text = _extract_reasoning_text(
-                                    item.get("summary")
-                                )
-                                if summary_text and not reasoning_emitted:
-                                    if not reasoning_open:
-                                        summary_text = f"{summary_text}"
-                                        reasoning_open = True
-                                    yield _chunk_with_text(summary_text)
-                                    reasoning_emitted = True
-                            elif item.get("type") == "web_search_call":
-                                # done is the canonical place to read the
-                                # query, so emit both tool_start and tool_end
-                                # here. Frontend then renders a card per call
-                                # with the proper "Searching: " label.
-                                # Citations are aggregated separately and the
-                                # *last* call's result is overwritten at
-                                # response.completed with the citation list
-                                # (so the source-pill extraction at message
-                                # tail surfaces them once).
-                                item_id = item.get("id", "") or (
-                                    f"ws_{len(web_search_calls)}"
-                                )
-                                action = item.get("action")
-                                query = (
-                                    action.get("query", "")
-                                    if isinstance(action, dict)
-                                    else ""
-                                )
-                                web_search_calls[item_id] = {"query": query}
-                                yield _emit_tool_event(
-                                    {
-                                        "type": "tool_start",
-                                        "tool_name": "web_search",
-                                        "tool_call_id": item_id,
-                                        "arguments": (
-                                            {"query": query} if query else {}
-                                        ),
-                                    }
-                                )
-                                yield _emit_tool_event(
-                                    {
-                                        "type": "tool_end",
-                                        "tool_call_id": item_id,
-                                        # Empty result — the last call gets
-                                        # overwritten with citations at
-                                        # response.completed.
-                                        "result": "",
-                                    }
-                                )
-                            elif item.get("type") == "shell_call":
-                                # OpenAI ships the commands array on the
-                                # action field. Join them onto one
-                                # command string for the tool card —
-                                # the renderer is shared with Anthropic
-                                # bash, which only carries a single
-                                # `command`. Multiple commands in one
-                                # shell_call get joined with newlines so
-                                # they still render as one card.
-                                item_id = item.get("id", "") or (
-                                    f"sc_{len(shell_calls)}"
-                                )
-                                action = item.get("action") or {}
-                                commands = (
-                                    action.get("commands")
-                                    if isinstance(action, dict)
-                                    else None
-                                ) or []
-                                joined_command = (
-                                    "\n".join(str(c) for c in commands)
-                                    if isinstance(commands, list)
-                                    else ""
-                                )
-                                shell_calls.setdefault(
-                                    item_id,
-                                    {"commands": [], "output": None},
-                                )
-                                shell_calls[item_id]["commands"] = (
-                                    list(commands) if isinstance(commands, list) else []
-                                )
-                                yield _emit_tool_event(
-                                    {
-                                        "type": "tool_start",
-                                        "tool_name": "code_execution",
-                                        "tool_call_id": item_id,
-                                        "arguments": {
-                                            "kind": "bash",
-                                            "command": joined_command,
-                                        },
-                                    }
-                                )
-                            elif item.get("type") == "shell_call_output":
-                                # `call_id` links back to the shell_call's
-                                # `id`, which is what we used as the
-                                # tool_call_id on tool_start. Match on
-                                # call_id when present so the matching
-                                # card transitions to complete.
-                                call_id = item.get("call_id") or item.get("id") or ""
-                                output = item.get("output") or []
-                                if call_id in shell_calls:
-                                    shell_calls[call_id]["output"] = output
-                                result_text = _format_shell_output(output)
-                                yield _emit_tool_event(
-                                    {
-                                        "type": "tool_end",
-                                        "tool_call_id": call_id,
-                                        "result": result_text,
-                                    }
-                                )
-
-                        elif isinstance(event_type, str) and "reasoning" in event_type:
-                            reasoning_delta = _extract_reasoning_text(event)
-                            if reasoning_delta:
-                                if not reasoning_open:
-                                    reasoning_delta = f"{reasoning_delta}"
-                                    reasoning_open = True
-                                yield _chunk_with_text(reasoning_delta)
-                                reasoning_emitted = True
-
-                        elif event_type == "response.completed":
-                            completed_usage = (event.get("response") or {}).get("usage")
-                            if isinstance(completed_usage, dict):
-                                last_usage = completed_usage
-                            if reasoning_open:
-                                yield _chunk_with_text("")
-                                reasoning_open = False
-                            # Probe response.container_id (top-level) and
-                            # response.container.id for the shell-tool
-                            # container id. OpenAI's docs don't pin the
-                            # exact field, so we scan both. Emit
-                            # `container_ready` only when the value
-                            # differs from the inbound one — no churn on
-                            # reuse.
-                            response_obj = event.get("response") or {}
-                            if isinstance(response_obj, dict):
-                                probe_id = response_obj.get("container_id")
-                                if not probe_id:
-                                    container_field = response_obj.get("container")
-                                    if isinstance(container_field, dict):
-                                        probe_id = container_field.get("id")
+                                        latched_container_id = probe_id
                                 if (
-                                    isinstance(probe_id, str)
-                                    and probe_id.startswith("cntr_")
-                                    and latched_container_id is None
+                                    latched_container_id
+                                    and not container_id_emitted
+                                    and latched_container_id
+                                    != openai_code_exec_container_id
                                 ):
-                                    latched_container_id = probe_id
-                            if (
-                                latched_container_id
-                                and not container_id_emitted
-                                and latched_container_id
-                                != openai_code_exec_container_id
-                            ):
-                                yield _emit_tool_event(
-                                    {
-                                        "type": "container_ready",
-                                        "container_id": latched_container_id,
-                                    }
-                                )
-                                container_id_emitted = True
-                            # Apply the aggregated citation list onto the
-                            # *last* web_search call by overwriting its
-                            # tool_end result. The frontend's
-                            # parseSourcesFromResult flatMaps every
-                            # web_search tool-call result, so a single
-                            # non-empty result is enough to surface the
-                            # whole source-pill set at the message tail —
-                            # no need to fan out across every card (which
-                            # would just duplicate the same pills).
-                            if web_search_calls and all_url_citations:
-                                last_id = list(web_search_calls.keys())[-1]
-                                blocks: list[str] = []
-                                for cit in all_url_citations:
-                                    line = (
-                                        f"Title: {cit['title']}\n" f"URL: {cit['url']}"
+                                    yield _emit_tool_event(
+                                        {
+                                            "type": "container_ready",
+                                            "container_id": latched_container_id,
+                                        }
                                     )
-                                    if cit.get("snippet"):
-                                        line += f"\nSnippet: {cit['snippet']}"
-                                    blocks.append(line)
-                                yield _emit_tool_event(
-                                    {
-                                        "type": "tool_end",
-                                        "tool_call_id": last_id,
-                                        "result": "\n---\n".join(blocks),
-                                    }
-                                )
-                            chunk = {
-                                "id": completion_id,
-                                "object": "chat.completion.chunk",
-                                "choices": [
-                                    {
-                                        "index": 0,
-                                        "delta": {},
-                                        "finish_reason": "stop",
-                                    }
-                                ],
-                            }
-                            yield f"data: {_json.dumps(chunk)}"
-
-                        elif event_type == "response.incomplete":
-                            incomplete_usage = (event.get("response") or {}).get(
-                                "usage"
-                            )
-                            if isinstance(incomplete_usage, dict):
-                                last_usage = incomplete_usage
-                            if reasoning_open:
-                                yield _chunk_with_text("")
-                                reasoning_open = False
-                            # Same backfill as response.completed — apply
-                            # whatever citations we managed to gather
-                            # before truncation onto the last call. All
-                            # earlier tool cards already have their proper
-                            # query + empty placeholder result from the
-                            # output_item.done emissions above.
-                            if web_search_calls and all_url_citations:
-                                last_id = list(web_search_calls.keys())[-1]
-                                blocks = []
-                                for cit in all_url_citations:
-                                    line = (
-                                        f"Title: {cit['title']}\n" f"URL: {cit['url']}"
+                                    container_id_emitted = True
+                                # Apply the aggregated citation list onto the
+                                # *last* web_search call by overwriting its
+                                # tool_end result. The frontend's
+                                # parseSourcesFromResult flatMaps every
+                                # web_search tool-call result, so a single
+                                # non-empty result is enough to surface the
+                                # whole source-pill set at the message tail —
+                                # no need to fan out across every card (which
+                                # would just duplicate the same pills).
+                                if web_search_calls and all_url_citations:
+                                    last_id = list(web_search_calls.keys())[-1]
+                                    blocks: list[str] = []
+                                    for cit in all_url_citations:
+                                        line = (
+                                            f"Title: {cit['title']}\n"
+                                            f"URL: {cit['url']}"
+                                        )
+                                        if cit.get("snippet"):
+                                            line += f"\nSnippet: {cit['snippet']}"
+                                        blocks.append(line)
+                                    yield _emit_tool_event(
+                                        {
+                                            "type": "tool_end",
+                                            "tool_call_id": last_id,
+                                            "result": "\n---\n".join(blocks),
+                                        }
                                     )
-                                    if cit.get("snippet"):
-                                        line += f"\nSnippet: {cit['snippet']}"
-                                    blocks.append(line)
-                                yield _emit_tool_event(
-                                    {
-                                        "type": "tool_end",
-                                        "tool_call_id": last_id,
-                                        "result": "\n---\n".join(blocks),
-                                    }
-                                )
-                            chunk = {
-                                "id": completion_id,
-                                "object": "chat.completion.chunk",
-                                "choices": [
-                                    {
-                                        "index": 0,
-                                        "delta": {},
-                                        "finish_reason": "length",
-                                    }
-                                ],
-                            }
-                            yield f"data: {_json.dumps(chunk)}"
+                                chunk = {
+                                    "id": completion_id,
+                                    "object": "chat.completion.chunk",
+                                    "choices": [
+                                        {
+                                            "index": 0,
+                                            "delta": {},
+                                            "finish_reason": "stop",
+                                        }
+                                    ],
+                                }
+                                yield f"data: {_json.dumps(chunk)}"
 
-                        elif event_type in ("response.failed", "error"):
-                            # Surface the failure to the client; let the
-                            # outer route emit [DONE] as part of its cleanup.
-                            error_payload = event.get("response", {}).get(
-                                "error", {}
-                            ) or {
-                                "message": event.get("message", "Unknown error"),
-                                "code": event.get("code"),
-                            }
-                            yield _error_sse_line(
-                                502,
-                                _json.dumps(error_payload),
-                                self.provider_type,
-                            )
-                            break
-                except GeneratorExit:
-                    await response.aclose()
-                    await lines_gen.aclose()
-                    raise
-                finally:
-                    # Summarise what the model actually did this turn so
-                    # support reports of "I clicked Search and got nothing"
-                    # can be triaged at a glance: was the tool requested,
-                    # did OpenAI invoke it, and how many sources came back?
-                    web_search_requested = bool(
-                        enabled_tools and "web_search" in enabled_tools
-                    )
-                    web_search_invocations = len(web_search_calls)
-                    total_citations = len(all_url_citations)
-                    queries = [
-                        sc["query"]
-                        for sc in web_search_calls.values()
-                        if sc.get("query")
-                    ]
-                    # cached_input_tokens > 0 on turn N proves
-                    # prompt_cache_retention="24h" is letting the previous
-                    # turn's prefix hit the cache instead of being
-                    # recomputed. On /v1/responses the field is nested as
-                    # usage.input_tokens_details.cached_tokens (not
-                    # prompt_tokens_details, which is the /v1/chat/completions
-                    # shape).
-                    cached_input_tokens = None
-                    if isinstance(last_usage, dict):
-                        details = last_usage.get("input_tokens_details")
-                        if isinstance(details, dict):
-                            cached_input_tokens = details.get("cached_tokens")
-                    code_execution_requested = code_execution_enabled_openai
-                    code_execution_invocations = len(shell_calls)
-                    code_execution_results = sum(
-                        1 for sc in shell_calls.values() if sc.get("output") is not None
-                    )
-                    logger.info(
-                        "OpenAI Responses stream complete (model=%s, "
-                        "web_search_requested=%s, web_search_invocations=%s, "
-                        "citations=%s, queries=%s, reasoning_emitted=%s, "
-                        "code_execution_requested=%s, "
-                        "code_execution_invocations=%s, "
-                        "code_execution_results=%s, "
-                        "container_id_in=%s, container_id_out=%s, "
-                        "input_tokens=%s, output_tokens=%s, "
-                        "cached_input_tokens=%s)",
-                        model,
-                        web_search_requested,
-                        web_search_invocations,
-                        total_citations,
-                        queries,
-                        reasoning_emitted,
-                        code_execution_requested,
-                        code_execution_invocations,
-                        code_execution_results,
-                        openai_code_exec_container_id,
-                        latched_container_id,
-                        (last_usage or {}).get("input_tokens"),
-                        (last_usage or {}).get("output_tokens"),
-                        cached_input_tokens,
-                    )
-                    await response.aclose()
-                    await lines_gen.aclose()
+                            elif event_type == "response.incomplete":
+                                incomplete_usage = (event.get("response") or {}).get(
+                                    "usage"
+                                )
+                                if isinstance(incomplete_usage, dict):
+                                    last_usage = incomplete_usage
+                                if reasoning_open:
+                                    yield _chunk_with_text("")
+                                    reasoning_open = False
+                                # Same backfill as response.completed — apply
+                                # whatever citations we managed to gather
+                                # before truncation onto the last call. All
+                                # earlier tool cards already have their proper
+                                # query + empty placeholder result from the
+                                # output_item.done emissions above.
+                                if web_search_calls and all_url_citations:
+                                    last_id = list(web_search_calls.keys())[-1]
+                                    blocks = []
+                                    for cit in all_url_citations:
+                                        line = (
+                                            f"Title: {cit['title']}\n"
+                                            f"URL: {cit['url']}"
+                                        )
+                                        if cit.get("snippet"):
+                                            line += f"\nSnippet: {cit['snippet']}"
+                                        blocks.append(line)
+                                    yield _emit_tool_event(
+                                        {
+                                            "type": "tool_end",
+                                            "tool_call_id": last_id,
+                                            "result": "\n---\n".join(blocks),
+                                        }
+                                    )
+                                chunk = {
+                                    "id": completion_id,
+                                    "object": "chat.completion.chunk",
+                                    "choices": [
+                                        {
+                                            "index": 0,
+                                            "delta": {},
+                                            "finish_reason": "length",
+                                        }
+                                    ],
+                                }
+                                yield f"data: {_json.dumps(chunk)}"
+
+                            elif event_type in ("response.failed", "error"):
+                                # Surface the failure to the client; let the
+                                # outer route emit [DONE] as part of its cleanup.
+                                error_payload = event.get("response", {}).get(
+                                    "error", {}
+                                ) or {
+                                    "message": event.get("message", "Unknown error"),
+                                    "code": event.get("code"),
+                                }
+                                yield _error_sse_line(
+                                    502,
+                                    _json.dumps(error_payload),
+                                    self.provider_type,
+                                )
+                                break
+                    except GeneratorExit:
+                        await response.aclose()
+                        await lines_gen.aclose()
+                        raise
+                    finally:
+                        # Summarise what the model actually did this turn so
+                        # support reports of "I clicked Search and got nothing"
+                        # can be triaged at a glance: was the tool requested,
+                        # did OpenAI invoke it, and how many sources came back?
+                        web_search_requested = bool(
+                            enabled_tools and "web_search" in enabled_tools
+                        )
+                        web_search_invocations = len(web_search_calls)
+                        total_citations = len(all_url_citations)
+                        queries = [
+                            sc["query"]
+                            for sc in web_search_calls.values()
+                            if sc.get("query")
+                        ]
+                        # cached_input_tokens > 0 on turn N proves
+                        # prompt_cache_retention="24h" is letting the previous
+                        # turn's prefix hit the cache instead of being
+                        # recomputed. On /v1/responses the field is nested as
+                        # usage.input_tokens_details.cached_tokens (not
+                        # prompt_tokens_details, which is the /v1/chat/completions
+                        # shape).
+                        cached_input_tokens = None
+                        if isinstance(last_usage, dict):
+                            details = last_usage.get("input_tokens_details")
+                            if isinstance(details, dict):
+                                cached_input_tokens = details.get("cached_tokens")
+                        code_execution_requested = code_execution_enabled_openai
+                        code_execution_invocations = len(shell_calls)
+                        code_execution_results = sum(
+                            1
+                            for sc in shell_calls.values()
+                            if sc.get("output") is not None
+                        )
+                        logger.info(
+                            "OpenAI Responses stream complete (model=%s, "
+                            "web_search_requested=%s, web_search_invocations=%s, "
+                            "citations=%s, queries=%s, reasoning_emitted=%s, "
+                            "code_execution_requested=%s, "
+                            "code_execution_invocations=%s, "
+                            "code_execution_results=%s, "
+                            "container_id_in=%s, container_id_out=%s, "
+                            "input_tokens=%s, output_tokens=%s, "
+                            "cached_input_tokens=%s)",
+                            model,
+                            web_search_requested,
+                            web_search_invocations,
+                            total_citations,
+                            queries,
+                            reasoning_emitted,
+                            code_execution_requested,
+                            code_execution_invocations,
+                            code_execution_results,
+                            openai_code_exec_container_id,
+                            latched_container_id,
+                            (last_usage or {}).get("input_tokens"),
+                            (last_usage or {}).get("output_tokens"),
+                            cached_input_tokens,
+                        )
+                        await response.aclose()
+                        await lines_gen.aclose()
+                    return
 
         except httpx.ConnectError as exc:
             logger.error("Connection error to %s: %s", self.provider_type, exc)
diff --git a/studio/backend/tests/test_openai_code_execution.py b/studio/backend/tests/test_openai_code_execution.py
index 88ff1171ef..3d179371e3 100644
--- a/studio/backend/tests/test_openai_code_execution.py
+++ b/studio/backend/tests/test_openai_code_execution.py
@@ -389,3 +389,149 @@ def test_stale_container_emits_invalidated(monkeypatch):
     events = _tool_events(lines)
     invalidated = [e for e in events if e["type"] == "container_invalidated"]
     assert len(invalidated) == 1
+
+
+def test_expired_container_triggers_transparent_retry(monkeypatch):
+    """When OpenAI 400s with 'Container is expired' on a request that
+    carried container_reference, the streamer retries once with the
+    container field stripped. The user never sees an error line — only
+    container_invalidated, then the normal stream from the retry.
+    """
+    calls: list[dict] = []
+
+    def handler(request: httpx.Request) -> httpx.Response:
+        body = json.loads(request.content.decode("utf-8"))
+        calls.append(body)
+        # Find the shell tool entry to inspect environment.type.
+        shell_env_type = None
+        for tool in body.get("tools", []) or []:
+            if tool.get("type") == "shell":
+                shell_env_type = tool.get("environment", {}).get("type")
+                break
+        # First call carries container_reference -> 400 expired.
+        # Retry omits container -> normal SSE stream.
+        if shell_env_type == "container_reference":
+            return httpx.Response(
+                400,
+                content = json.dumps(
+                    {
+                        "error": {
+                            "message": "Container is expired.",
+                            "type": "invalid_request_error",
+                        }
+                    }
+                ).encode("utf-8"),
+                headers = {"content-type": "application/json"},
+            )
+        # Successful retry: minimal SSE — a completed response with a
+        # fresh container_id so container_ready latches.
+        sse = _openai_sse(
+            [
+                {
+                    "type": "response.completed",
+                    "response": {"container_id": "cntr_fresh_111"},
+                },
+            ]
+        )
+        return httpx.Response(
+            200,
+            content = sse,
+            headers = {"content-type": "text/event-stream"},
+        )
+
+    _mock_http_client(monkeypatch, handler)
+
+    async def run():
+        client = _make_client()
+        return await _collect(
+            client._stream_openai_responses(
+                messages = [{"role": "user", "content": "hi"}],
+                model = "gpt-5.5",
+                temperature = 0.7,
+                top_p = 0.95,
+                max_tokens = 4096,
+                enable_thinking = None,
+                reasoning_effort = None,
+                enabled_tools = ["code_execution"],
+                openai_code_exec_container_id = "cntr_stale_999",
+            )
+        )
+
+    lines = _drive(run())
+    events = _tool_events(lines)
+
+    # Two outbound HTTP calls were made: the expired-container attempt
+    # then the retry without the container field.
+    assert len(calls) == 2
+    shell_types = []
+    for body in calls:
+        for tool in body.get("tools", []) or []:
+            if tool.get("type") == "shell":
+                shell_types.append(tool.get("environment", {}).get("type"))
+    assert shell_types == ["container_reference", "container_auto"]
+
+    # container_invalidated emitted (frontend will null its stored id).
+    assert any(e.get("type") == "container_invalidated" for e in events)
+    # container_ready emitted from the retry stream with the fresh id.
+    assert any(
+        e.get("type") == "container_ready" and e.get("container_id") == "cntr_fresh_111"
+        for e in events
+    )
+    # CRUCIALLY: no SSE error line surfaced to the chat — only completion.
+    error_lines = [
+        line
+        for line in lines
+        if line.startswith("data:") and '"error"' in line and '"_toolEvent"' not in line
+    ]
+    assert error_lines == [], f"unexpected error line(s): {error_lines}"
+
+
+def test_expired_container_retries_only_once(monkeypatch):
+    """If the retry ALSO fails (any 4xx, expired or otherwise), the
+    error is surfaced normally — no infinite retry loop.
+    """
+    call_count = {"n": 0}
+
+    def handler(request: httpx.Request) -> httpx.Response:
+        call_count["n"] += 1
+        return httpx.Response(
+            400,
+            content = json.dumps(
+                {
+                    "error": {
+                        "message": "Container is expired.",
+                        "type": "invalid_request_error",
+                    }
+                }
+            ).encode("utf-8"),
+            headers = {"content-type": "application/json"},
+        )
+
+    _mock_http_client(monkeypatch, handler)
+
+    async def run():
+        client = _make_client()
+        return await _collect(
+            client._stream_openai_responses(
+                messages = [{"role": "user", "content": "hi"}],
+                model = "gpt-5.5",
+                temperature = 0.7,
+                top_p = 0.95,
+                max_tokens = 4096,
+                enable_thinking = None,
+                reasoning_effort = None,
+                enabled_tools = ["code_execution"],
+                openai_code_exec_container_id = "cntr_stale_999",
+            )
+        )
+
+    lines = _drive(run())
+
+    # Exactly two calls (first + one retry). Third would mean an
+    # infinite loop.
+    assert call_count["n"] == 2
+    # The second failure surfaces normally as an error SSE line.
+    error_lines = [
+        line for line in lines if '"error"' in line and "_toolEvent" not in line
+    ]
+    assert len(error_lines) >= 1
diff --git a/studio/frontend/src/features/chat/api/chat-adapter.ts b/studio/frontend/src/features/chat/api/chat-adapter.ts
index bbe299199c..61d71b641a 100644
--- a/studio/frontend/src/features/chat/api/chat-adapter.ts
+++ b/studio/frontend/src/features/chat/api/chat-adapter.ts
@@ -16,7 +16,10 @@ import {
   validateModel,
 } from "./chat-api";
 import { pickFriendlyContainerName } from "../lib/friendly-names";
-import { createOpenAIContainer } from "./openai-containers";
+import {
+  createOpenAIContainer,
+  listOpenAIContainers,
+} from "./openai-containers";
 import {
   encryptProviderApiKey,
   isProviderKeyRotationError,
@@ -1046,6 +1049,41 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
                 openaiCodeExecContainerId = null;
                 anthropicCodeExecContainerId = null;
               }
+              // Pre-send container validation (OpenAI only). The list
+              // endpoint already filters status==="expired" server-side
+              // (studio/backend/routes/inference.py — list_openai_containers),
+              // so membership in this set means "OpenAI will accept it
+              // as container_reference". A stale id silently dropped here
+              // falls through to the inheritance + lazy-create logic
+              // below, so the user never sees "Container is expired" in
+              // the chat thread. On list-call failure we leave
+              // activeContainerIds null and skip validation — the
+              // backend's transparent retry path is the safety net for
+              // that case.
+              let activeContainerIds: Set | null = null;
+              if (externalProvider.providerType === "openai") {
+                try {
+                  const list = await listOpenAIContainers({
+                    apiKey: externalApiKey,
+                    baseUrl: externalProvider.baseUrl || null,
+                  });
+                  activeContainerIds = new Set(list.map((c) => c.id));
+                } catch {
+                  activeContainerIds = null;
+                }
+                if (
+                  activeContainerIds &&
+                  openaiCodeExecContainerId &&
+                  !activeContainerIds.has(openaiCodeExecContainerId)
+                ) {
+                  void db.threads
+                    .update(resolvedThreadId, {
+                      openaiCodeExecContainerId: null,
+                    })
+                    .catch(() => {});
+                  openaiCodeExecContainerId = null;
+                }
+              }
               // Cross-thread inheritance: when the active thread has
               // no container yet, default to the one most recently
               // used on *any* other thread (provider-scoped).
@@ -1066,15 +1104,27 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
                     .toArray();
                   for (const t of others) {
                     if (t.id === resolvedThreadId) continue;
-                    if (t.openaiCodeExecContainerId) {
-                      openaiCodeExecContainerId = t.openaiCodeExecContainerId;
+                    if (!t.openaiCodeExecContainerId) continue;
+                    // Skip inherited ids that are not in the active
+                    // container set — they would 400 on send. Also
+                    // null them on the source thread so the next
+                    // inheritance pass doesn't re-pick the same dead id.
+                    if (
+                      activeContainerIds &&
+                      !activeContainerIds.has(t.openaiCodeExecContainerId)
+                    ) {
                       void db.threads
-                        .update(resolvedThreadId, {
-                          openaiCodeExecContainerId,
-                        })
+                        .update(t.id, { openaiCodeExecContainerId: null })
                         .catch(() => {});
-                      break;
+                      continue;
                     }
+                    openaiCodeExecContainerId = t.openaiCodeExecContainerId;
+                    void db.threads
+                      .update(resolvedThreadId, {
+                        openaiCodeExecContainerId,
+                      })
+                      .catch(() => {});
+                    break;
                   }
                 } catch {
                   /* fall through to lazy-create below */