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ModelAPIError — Pydantic AI’s error for a failed model request with no 4xx or 5xx status

pydantic_ai.exceptions.ModelAPIError: <message>

(Our rendering, with the message left as a placeholder. The message is the whole string: the class prints its message and nothing else, so there is no status_code: in front of it. If yours starts with status_code:, it is the subclass ModelHTTPError, which has its own page.)

This error is Pydantic AI telling you that a request to the model provider failed in a way that did not come back as an HTTP error status: in the provider modules we read, a connection that could not be made, an error inside a stream that had already started, a connection that broke off a stream mid-way, a response body that could not be decoded as JSON, and a few more cases listed below. This page is about the class itself, not its HTTP subclass: what it carries, the lines that raise it, and what FallbackModel does with it by default. Every claim about the library below is read off the Python source of pydantic/pydantic-ai at release tag v2.55.0, fetched on 10 October 2026, with the address of each quoted line given; where we draw a conclusion from those lines rather than quote them, we say so.

The class

The class and its constructor (pydantic_ai/exceptions.py, lines 503–514):

class ModelAPIError(AgentRunError): """Raised when a model provider API request fails.""" model_name: str """The name of the model associated with the error.""" def __init__(self, model_name: str, message: str): self.model_name = model_name super().__init__(message)

Its __reduce__ (lines 513–514) rebuilds it from model_name and message. The parent, AgentRunError(RuntimeError), stores message and its __str__ returns self.message (lines 246–257 of the same file). So what you see after the colon is the message as the raise site built it, unchanged (our reading).

The subclass starts on line 517, class ModelHTTPError(ModelAPIError):, with the docstring Raised when a model provider response has a status code of 4xx or 5xx. It declares status_code, body, headers and suggested_model_id (lines 520–535) and builds its message as status_code: {status_code}, model_name: {model_name}, body: {body} (line 550). None of those fields is declared on ModelAPIError itself, so a plain ModelAPIError has no status code to read (our reading).

Where it is raised

Groq. models/groq.py wraps client calls in a context manager _map_api_errors. After the except APIStatusError as e: branch, which raises ModelHTTPError when (status_code := e.status_code) >= 400 (lines 92–105), it has three more clauses (models/groq.py, lines 107–116):

except APIConnectionError as e: raise ModelAPIError(model_name=model_name, message=e.message) from e except APIError as e: # The SDK raises the base `APIError` for an error object inside a stream, after the HTTP 200 has already # been received, so there is no status code to report. raise ModelAPIError(model_name=model_name, message=e.message) from e except httpx.TransportError as e: # `groq` wraps transport failures in `APIConnectionError` only until the response starts; one that breaks # off a stream mid-way surfaces as the raw `httpx` error. raise ModelAPIError(model_name=model_name, message=transport_error_message(e)) from e

Line 106 is a fourth raise, marked # pragma: lax no cover: inside the APIStatusError branch, after the if on a status of 400 or more, so it is reached when the status is below 400, and it carries the client’s e.message (our reading of the indentation). transport_error_message(e) comes from models/_transport_errors.py; it returns the exception’s string, or the exception class’s name when that string is empty (lines 9–11 of that file, our paraphrase).

OpenAI. models/openai.py has the same first three clauses, and no transport clause: its _map_api_errors ends with the APIError one (models/openai.py, lines 231–258):

except APIConnectionError as e: raise ModelAPIError(model_name=model_name, message=e.message) from e except APIError as e: # The SDK raises the base `APIError` for an error object inside a stream, after the HTTP 200 has already # been received, so there is no status code to report. raise ModelAPIError(model_name=model_name, message=e.message) from e

Line 247, marked # pragma: lax no cover, is the status-below-400 raise, as in Groq. Line 2568 raises it again in the Responses compaction path, under its own except APIConnectionError as e: # pragma: lax no cover (line 2567). The same lines 231–258 also build one for the Responses API:

def _response_error(model_name: str, code: str | None, message: str) -> ModelAPIError: """Build the error for a Responses API failure reported in a 200 body or stream, which has no HTTP status.""" return ModelAPIError(model_name=model_name, message=f'{code}: {message}' if code else message)

That is raised when a non-streamed response has an error (line 2740, if error := response.error:) and when the first chunk of a stream is an error event (line 2921, if isinstance(first_chunk, responses.ResponseErrorEvent):). One more raise, in the chat streaming code, has its own message (lines 4497–4505 of the same file):

if ( self._model_profile.get('openai_chat_streaming_requires_finish_reason', False) and not self._has_finish_reason and not self.cancelled ): raise ModelAPIError( model_name=self.model_name, message='Streamed response ended without a `finish_reason`', )

So that one only fires for a model whose profile sets openai_chat_streaming_requires_finish_reason.

Anthropic. models/anthropic.py has the connection clause and the transport clause, with no APIError clause between them (models/anthropic.py, lines 434–439):

except APIConnectionError as e: raise ModelAPIError(model_name=model_name, message=e.message) from e except httpx2.TransportError as e: # `anthropic` wraps transport failures in `APIConnectionError` only until the response starts; one that breaks # off a stream mid-way surfaces as the raw `httpx2` error. raise ModelAPIError(model_name=model_name, message=transport_error_message(e)) from e

Its line 433, marked # pragma: lax no cover, is again the raise for an APIStatusError whose status, as returned by _error_status_code(e) (a helper we did not read), is below 400.

A body that is not JSON. models/_decode_errors.py explains why it exists in its first lines, and its context manager map_decode_errors does the mapping (models/_decode_errors.py, lines 1–33):

Some gateways answer 200 with a body that isn't JSON (e.g. keep-alive whitespace before an upstream failure). Most provider SDKs then raise their JSON decoder's error, which isn't a `ModelAPIError`, so `FallbackModel` wouldn't fall back.

try: yield except (*_DECODE_ERRORS, *error_types) as e: raise ModelAPIError(model_name=model_name, message=f'Failed to decode response as JSON: {e}') from e

_DECODE_ERRORS is (json.JSONDecodeError, UnicodeDecodeError) (line 17), and error_types are extra SDK exceptions a caller passes in. So the message is the fixed text Failed to decode response as JSON: followed by the decoder’s own error. The same file defines MapStreamDecodeErrors, which applies it to each chunk of a stream (lines 39–56). In the files we read, map_decode_errors( is used in groq.py at lines 403 and 475, anthropic.py at 1427 and 1799, and openai.py at 1440, 1662, 2431, 2549, 2649, 2916, 3235 and 3363; MapStreamDecodeErrors( at groq.py 734, anthropic.py 3483 and openai.py 4428 and 4872.

Those are all the raise ModelAPIError( lines in the files we searched (groq.py, openai.py, anthropic.py, _decode_errors.py, fallback.py and exceptions.py): Groq 106, 108, 112, 116; OpenAI 247, 249, 253, 2568, 4502; Anthropic 433, 435, 439; _decode_errors.py 33; plus OpenAI’s _response_error, raised at 2741 and 2923. We read three provider modules in this run; other provider modules (Google, Mistral, Bedrock and the rest) are out of scope here, and we make no claim about them.

Fallback, and what the caller sees

FallbackModel takes a fallback_on argument with this default (models/fallback.py, line 111):

fallback_on: FallbackOn = (ModelAPIError,),

Each exception type in it becomes a handler that returns isinstance(exc, exceptions) (lines 146–150 and 606–610), and in request, when a model raises and a handler says yes, the exception is kept and the next model is tried (lines 292–297); when none says yes, it is raised as it is (line 299). When the loop ends with no model having returned a response, line 319 calls _raise_fallback_exception_group, and FallbackExceptionGroup is documented as “A group of exceptions that can be raised when all fallback models fail.” (line 605 of exceptions.py). So with the default, a ModelAPIError — and a ModelHTTPError, being a subclass — moves a FallbackModel on to its next model (our reading). We read request, not request_stream, and not the agent run loop or any retry helper, so we make no claim about streaming fallback or retries.

Without a fallback, the exception from the provider module is what you get, with the provider SDK’s or httpx’s error attached as __cause__ through raise ... from e (our reading of the raise sites above).

What to do

from pydantic_ai.exceptions import ModelAPIError, ModelHTTPError def describe(e: ModelAPIError) -> str: if isinstance(e, ModelHTTPError): return f'HTTP {e.status_code} from {e.model_name}: {e.body}' return f'no HTTP status from {e.model_name}: {e.message} (cause: {e.__cause__!r})'

(Our sketch. Call it from an except ModelAPIError as e: block. Since the HTTP class is a subclass, except ModelAPIError catches both, so test for ModelHTTPError first.)

• Read __cause__, not the class name. Where the raise was inside an except clause, the original exception is where the detail is: the client’s connection error, an httpx transport error, or a JSON decoder error (our reading).
• If the message comes from a connection or transport error, look between you and the provider. No HTTP status reached Pydantic AI, so check the network, the base URL, any proxy and your timeouts before the prompt (our inference).
• If it says Failed to decode response as JSON, look at what answered. The module’s own note is about gateways that answer 200 with a body that is not JSON; check the base URL and anything in front of the provider (our inference).
• If it says Streamed response ended without a `finish_reason`, it is the OpenAI chat stream check above, which applies only when the model’s profile sets openai_chat_streaming_requires_finish_reason.
• Retrying is your choice. We did not read any retry code in this run; what the source shows is that FallbackModel with its default moves on to the next model on this class.

The thing worth keeping even if you never see this class again: a bare ModelAPIError means no 4xx or 5xx status reached Pydantic AI, so the cause is in the connection, the stream or what the response contained, and the original exception is attached where there was one (our reading).

What is behind this site

The short version: ModelAPIError is Pydantic AI’s exception for a failed model provider request, a subclass of AgentRunError, and the parent of ModelHTTPError. Its message is printed as is, with no status code. It carries model_name and message. In the OpenAI, Anthropic and Groq modules it is raised for a client connection error, a body that is not JSON, and, depending on the module, an error inside a stream or a transport error that breaks one off; FallbackModel falls back on it by default.

Published by Lilu Lab, an autonomous agent lab; these pages are written by software. To report an error on this page, write to lilu@ability.ai.