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fix: use beta structured output for new models

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This commit is contained in:
Greyson LaLonde
2026-01-26 10:11:32 -05:00
parent f5d0fd1a62
commit 67e4818703
2 changed files with 94 additions and 46 deletions
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124
lib/crewai/src/crewai/llms/providers/anthropic/completion.py
View File

@@ -3,7 +3,7 @@ from __future__ import annotations
import json
import logging
import os
from typing import TYPE_CHECKING, Any, Final, Literal, cast
from typing import TYPE_CHECKING, Any, Final, Literal, TypeGuard, cast
from pydantic import BaseModel
@@ -21,7 +21,7 @@ if TYPE_CHECKING:
from crewai.llms.hooks.base import BaseInterceptor
try:
from anthropic import Anthropic, AsyncAnthropic
from anthropic import Anthropic, AsyncAnthropic, transform_schema
from anthropic.types import Message, TextBlock, ThinkingBlock, ToolUseBlock
from anthropic.types.beta import BetaMessage
import httpx
@@ -36,22 +36,33 @@ ANTHROPIC_STRUCTURED_OUTPUTS_BETA: Final = "structured-outputs-2025-11-13"
NATIVE_STRUCTURED_OUTPUT_MODELS: Final[
tuple[
Literal["claude-sonnet-4"],
Literal["claude-opus-4"],
Literal["claude-haiku-4"],
Literal["claude-sonnet-4-5"],
Literal["claude-sonnet-4.5"],
Literal["claude-opus-4-5"],
Literal["claude-opus-4.5"],
Literal["claude-opus-4-1"],
Literal["claude-opus-4.1"],
Literal["claude-haiku-4-5"],
Literal["claude-haiku-4.5"],
]
] = (
"claude-sonnet-4",
"claude-opus-4",
"claude-haiku-4",
"claude-sonnet-4-5",
"claude-sonnet-4.5",
"claude-opus-4-5",
"claude-opus-4.5",
"claude-opus-4-1",
"claude-opus-4.1",
"claude-haiku-4-5",
"claude-haiku-4.5",
)
def _supports_native_structured_outputs(model: str) -> bool:
"""Check if the model supports native structured outputs.
Native structured outputs are only available for Claude 4.x models.
Claude 3.x models require the tool-based fallback approach.
Native structured outputs are only available for Claude 4.5 models
(Sonnet 4.5, Opus 4.5, Opus 4.1, Haiku 4.5).
Other models require the tool-based fallback approach.
Args:
model: The model name/identifier.
@@ -63,6 +74,21 @@ def _supports_native_structured_outputs(model: str) -> bool:
return any(prefix in model_lower for prefix in NATIVE_STRUCTURED_OUTPUT_MODELS)
def _is_pydantic_model_class(obj: Any) -> TypeGuard[type[BaseModel]]:
"""Check if an object is a Pydantic model class.
This distinguishes between Pydantic model classes that support structured
outputs (have model_json_schema) and plain dicts like {"type": "json_object"}.
Args:
obj: The object to check.
Returns:
True if obj is a Pydantic model class.
"""
return isinstance(obj, type) and issubclass(obj, BaseModel)
def _contains_file_id_reference(messages: list[dict[str, Any]]) -> bool:
"""Check if any message content contains a file_id reference.
@@ -609,19 +635,23 @@ class AnthropicCompletion(BaseLLM):
if uses_file_api:
betas.append(ANTHROPIC_FILES_API_BETA)
if response_model:
extra_body: dict[str, Any] | None = None
if _is_pydantic_model_class(response_model):
schema = transform_schema(response_model.model_json_schema())
if _supports_native_structured_outputs(self.model):
use_native_structured_output = True
betas.append(ANTHROPIC_STRUCTURED_OUTPUTS_BETA)
params["output_format"] = {
"type": "json_schema",
"schema": response_model.model_json_schema(),
extra_body = {
"output_format": {
"type": "json_schema",
"schema": schema,
}
}
else:
structured_tool = {
"name": "structured_output",
"description": "Output the structured response",
"input_schema": response_model.model_json_schema(),
"input_schema": schema,
}
params["tools"] = [structured_tool]
params["tool_choice"] = {"type": "tool", "name": "structured_output"}
@@ -629,7 +659,9 @@ class AnthropicCompletion(BaseLLM):
try:
if betas:
params["betas"] = betas
response = self.client.beta.messages.create(**params)
response = self.client.beta.messages.create(
**params, extra_body=extra_body
)
else:
response = self.client.messages.create(**params)
@@ -642,7 +674,7 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(response)
self._track_token_usage_internal(usage)
if response_model and response.content:
if _is_pydantic_model_class(response_model) and response.content:
if use_native_structured_output:
for block in response.content:
if isinstance(block, TextBlock):
@@ -744,19 +776,23 @@ class AnthropicCompletion(BaseLLM):
betas: list[str] = []
use_native_structured_output = False
if response_model:
extra_body: dict[str, Any] | None = None
if _is_pydantic_model_class(response_model):
schema = transform_schema(response_model.model_json_schema())
if _supports_native_structured_outputs(self.model):
use_native_structured_output = True
betas.append(ANTHROPIC_STRUCTURED_OUTPUTS_BETA)
params["output_format"] = {
"type": "json_schema",
"schema": response_model.model_json_schema(),
extra_body = {
"output_format": {
"type": "json_schema",
"schema": schema,
}
}
else:
structured_tool = {
"name": "structured_output",
"description": "Output the structured response",
"input_schema": response_model.model_json_schema(),
"input_schema": schema,
}
params["tools"] = [structured_tool]
params["tool_choice"] = {"type": "tool", "name": "structured_output"}
@@ -773,7 +809,7 @@ class AnthropicCompletion(BaseLLM):
current_tool_calls: dict[int, dict[str, Any]] = {}
stream_context = (
self.client.beta.messages.stream(**stream_params)
self.client.beta.messages.stream(**stream_params, extra_body=extra_body)
if betas
else self.client.messages.stream(**stream_params)
)
@@ -859,7 +895,7 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(final_message)
self._track_token_usage_internal(usage)
if response_model:
if _is_pydantic_model_class(response_model):
if use_native_structured_output:
self._emit_call_completed_event(
response=full_response,
@@ -1088,19 +1124,23 @@ class AnthropicCompletion(BaseLLM):
if uses_file_api:
betas.append(ANTHROPIC_FILES_API_BETA)
if response_model:
extra_body: dict[str, Any] | None = None
if _is_pydantic_model_class(response_model):
schema = transform_schema(response_model.model_json_schema())
if _supports_native_structured_outputs(self.model):
use_native_structured_output = True
betas.append(ANTHROPIC_STRUCTURED_OUTPUTS_BETA)
params["output_format"] = {
"type": "json_schema",
"schema": response_model.model_json_schema(),
extra_body = {
"output_format": {
"type": "json_schema",
"schema": schema,
}
}
else:
structured_tool = {
"name": "structured_output",
"description": "Output the structured response",
"input_schema": response_model.model_json_schema(),
"input_schema": schema,
}
params["tools"] = [structured_tool]
params["tool_choice"] = {"type": "tool", "name": "structured_output"}
@@ -1108,7 +1148,9 @@ class AnthropicCompletion(BaseLLM):
try:
if betas:
params["betas"] = betas
response = await self.async_client.beta.messages.create(**params)
response = await self.async_client.beta.messages.create(
**params, extra_body=extra_body
)
else:
response = await self.async_client.messages.create(**params)
@@ -1121,7 +1163,7 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(response)
self._track_token_usage_internal(usage)
if response_model and response.content:
if _is_pydantic_model_class(response_model) and response.content:
if use_native_structured_output:
for block in response.content:
if isinstance(block, TextBlock):
@@ -1209,19 +1251,23 @@ class AnthropicCompletion(BaseLLM):
betas: list[str] = []
use_native_structured_output = False
if response_model:
extra_body: dict[str, Any] | None = None
if _is_pydantic_model_class(response_model):
schema = transform_schema(response_model.model_json_schema())
if _supports_native_structured_outputs(self.model):
use_native_structured_output = True
betas.append(ANTHROPIC_STRUCTURED_OUTPUTS_BETA)
params["output_format"] = {
"type": "json_schema",
"schema": response_model.model_json_schema(),
extra_body = {
"output_format": {
"type": "json_schema",
"schema": schema,
}
}
else:
structured_tool = {
"name": "structured_output",
"description": "Output the structured response",
"input_schema": response_model.model_json_schema(),
"input_schema": schema,
}
params["tools"] = [structured_tool]
params["tool_choice"] = {"type": "tool", "name": "structured_output"}
@@ -1236,7 +1282,9 @@ class AnthropicCompletion(BaseLLM):
current_tool_calls: dict[int, dict[str, Any]] = {}
stream_context = (
self.async_client.beta.messages.stream(**stream_params)
self.async_client.beta.messages.stream(
**stream_params, extra_body=extra_body
)
if betas
else self.async_client.messages.stream(**stream_params)
)
@@ -1312,7 +1360,7 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(final_message)
self._track_token_usage_internal(usage)
if response_model:
if _is_pydantic_model_class(response_model):
if use_native_structured_output:
self._emit_call_completed_event(
response=full_response,

16
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_model.yaml
View File

@@ -1,6 +1,8 @@
interactions:
- request:
body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say hello in French"}],"model":"claude-sonnet-4-0","stream":false,"tool_choice":{"type":"tool","name":"structured_output"},"tools":[{"name":"structured_output","description":"Returns structured data according to the schema","input_schema":{"description":"Response model for greeting test.","properties":{"greeting":{"title":"Greeting","type":"string"},"language":{"title":"Language","type":"string"}},"required":["greeting","language"],"title":"GreetingResponse","type":"object"}}]}'
body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say hello in French"}],"model":"claude-sonnet-4-0","stream":false,"tool_choice":{"type":"tool","name":"structured_output"},"tools":[{"name":"structured_output","description":"Output
the structured response","input_schema":{"type":"object","description":"Response
model for greeting test.","title":"GreetingResponse","properties":{"greeting":{"type":"string","title":"Greeting"},"language":{"type":"string","title":"Language"}},"additionalProperties":false,"required":["greeting","language"]}}]}'
headers:
User-Agent:
- X-USER-AGENT-XXX
@@ -13,7 +15,7 @@ interactions:
connection:
- keep-alive
content-length:
- '539'
- '551'
content-type:
- application/json
host:
@@ -29,7 +31,7 @@ interactions:
x-stainless-os:
- X-STAINLESS-OS-XXX
x-stainless-package-version:
- 0.75.0
- 0.76.0
x-stainless-retry-count:
- '0'
x-stainless-runtime:
@@ -42,7 +44,7 @@ interactions:
uri: https://api.anthropic.com/v1/messages
response:
body:
string: '{"model":"claude-sonnet-4-20250514","id":"msg_01XjvX2nCho1knuucbwwgCpw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019rfPRSDmBb7CyCTdGMv5rK","name":"structured_output","input":{"greeting":"Bonjour","language":"French"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":432,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":53,"service_tier":"standard"}}'
string: '{"model":"claude-sonnet-4-20250514","id":"msg_01CKTyVmak15L5oQ36mv4sL9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0174BYmn6xiSnUwVhFD8S7EW","name":"structured_output","input":{"greeting":"Bonjour","language":"French"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":436,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":53,"service_tier":"standard"}}'
headers:
CF-RAY:
- CF-RAY-XXX
@@ -51,7 +53,7 @@ interactions:
Content-Type:
- application/json
Date:
- Mon, 01 Dec 2025 11:19:38 GMT
- Mon, 26 Jan 2026 14:59:34 GMT
Server:
- cloudflare
Transfer-Encoding:
@@ -82,12 +84,10 @@ interactions:
- DYNAMIC
request-id:
- REQUEST-ID-XXX
retry-after:
- '24'
strict-transport-security:
- STS-XXX
x-envoy-upstream-service-time:
- '2101'
- '968'
status:
code: 200
message: OK