feat(aws-bedrock/eu.anthropic.claude-fable-5): add new models [bot]#1317
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/test-models |
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Gateway test results
Failures (12)
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
tool_config = {
"tools": [
{
"toolSpec": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
}
},
}
}
],
"toolChoice": {"auto": {}},
}
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."}]},
]
system = [{"text": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
toolConfig=tool_config,
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "toolUse" in _start:
print(f"Tool: {_start['toolUse'].get('name', '')}", flush=True)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "toolUse" in _delta:
print(_delta["toolUse"].get("input", ""), end="", flush=True)
if "text" in _delta:
print(_delta["text"], end="", flush=True)
_tool_use_detected = False
for _event in _events:
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "toolUse" in _start:
_tool_use_detected = True
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "toolUse" in _delta:
_tool_use_detected = True
if not _tool_use_detected:
raise Exception("VALIDATION FAILED: tool-call stream - no tool uses in Bedrock stream")
print("\nVALIDATION: tool-call stream SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "What is the capital of France?"}]},
]
system = [{"text": "You are a helpful assistant."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
inferenceConfig={
"maxTokens": 512,
"temperature": 0.5,
},
)
_content = response["output"]["message"]["content"]
for _block in _content:
if "text" in _block:
print(_block["text"])
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "How to calculate 3^3^3^3? Think step by step and show all reasoning."}]},
]
system = [{"text": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
additionalModelRequestFields={
"thinking": {"type":"adaptive"},
},
)
_content = response["output"]["message"]["content"]
for _block in _content:
if "reasoningContent" in _block:
print(_block["reasoningContent"]["reasoningText"]["text"])
if "text" in _block:
print(_block["text"])
_content = response["output"]["message"]["content"]
_reasoning_detected = False
for _block in _content:
if "reasoningContent" in _block:
_reasoning_detected = True
_usage = response.get("usage", {})
if _usage.get("reasoning_tokens") or _usage.get("reasoningTokens"):
_reasoning_detected = True
if not _reasoning_detected:
print("Response: ", response)
raise Exception("VALIDATION FAILED: reasoning - no reasoning information in Bedrock response")
print("VALIDATION: reasoning SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "How to calculate 3^3^3^3? Think step by step and show all reasoning."}]},
]
system = [{"text": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
additionalModelRequestFields={
"thinking": {"type":"adaptive"},
},
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "reasoningContent" in _delta:
print(_delta["reasoningContent"].get("text", ""), end="", flush=True)
if "text" in _delta:
print(_delta["text"], end="", flush=True)
_reasoning_detected = False
for _event in _events:
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "reasoningContent" in _delta:
_reasoning_detected = True
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "reasoningContent" in _start:
_reasoning_detected = True
if "metadata" in _event:
_usage = _event["metadata"].get("usage", {})
if _usage.get("reasoning_tokens") or _usage.get("reasoningTokens"):
_reasoning_detected = True
if not _reasoning_detected:
raise Exception("VALIDATION FAILED: reasoning stream - no reasoning information in Bedrock stream")
print("\nVALIDATION: reasoning stream SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "What is the capital of France?"}]},
]
system = [{"text": "You are a helpful assistant."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
inferenceConfig={
"maxTokens": 512,
"temperature": 0.5,
},
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "text" in _delta:
print(_delta["text"], end="", flush=True)
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
tool_config = {
"tools": [
{
"toolSpec": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
}
},
}
}
],
"toolChoice": {"auto": {}},
}
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."}]},
]
system = [{"text": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
toolConfig=tool_config,
)
_content = response["output"]["message"]["content"]
_tool_uses = [block for block in _content if "toolUse" in block]
if _tool_uses:
for _tu in _tool_uses:
print(f"Tool: {_tu['toolUse']['name']}")
print(f"Input: {_tu['toolUse']['input']}")
else:
_text_blocks = [block["text"] for block in _content if "text" in block]
print("\n".join(_text_blocks))
_content = response["output"]["message"]["content"]
_tool_uses = [block for block in _content if "toolUse" in block]
if not _tool_uses:
raise Exception("VALIDATION FAILED: tool-call - no tool uses in Bedrock response")
print("VALIDATION: tool-call SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "How to calculate 3^3^3^3? Think step by step and show all reasoning."},
],
reasoning_effort="medium",
stream=False,
)
_usage = getattr(response, "usage", None)
_reasoning_detected = False
_choices = getattr(response, "choices", None)
if _choices and len(_choices) > 0:
_message = getattr(_choices[0], "message", None)
else:
_message = None
if _message and getattr(_message, "content", None) is not None:
print(_message.content)
if _usage is not None:
_output_token_details = getattr(_usage, "completion_tokens_details", None)
if _output_token_details and getattr(_output_token_details, "reasoning_tokens", 0) > 0:
_reasoning_detected = True
elif getattr(_usage, "reasoning", None) is not None:
_reasoning_detected = True
if getattr(_message, "reasoning_content", None) is not None:
_reasoning_detected = True
elif getattr(_message, "reasoning", None) is not None:
_reasoning_detected = True
if not _reasoning_detected:
print("Response: ", response)
raise Exception("VALIDATION FAILED: reasoning - no reasoning information in response")
print("VALIDATION: reasoning SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
"additionalProperties": False,
},
"strict": True,
},
},
]
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."},
],
tools=tools,
tool_choice="auto",
stream=True,
)
_tool_calls_made = False
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True)
if delta.tool_calls:
_tool_calls_made = True
for _tc in delta.tool_calls:
if _tc.function:
print(_tc.function.arguments or "", end="", flush=True)
if not _tool_calls_made:
raise Exception("VALIDATION FAILED: tool-call stream - no tool calls received")
print("\nVALIDATION: tool-call stream SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "What is the capital of France?"},
],
max_tokens=512,
temperature=0.5,
stream=True,
)
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True)
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "What is the capital of France?"},
],
max_tokens=512,
temperature=0.5,
stream=False,
)
print(response.choices[0].message.content)
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
"additionalProperties": False,
},
"strict": True,
},
},
]
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."},
],
tools=tools,
tool_choice="auto",
stream=False,
)
_message = response.choices[0].message
if _message.tool_calls:
for _tc in _message.tool_calls:
print(f"Function: {_tc.function.name}")
print(f"Arguments: {_tc.function.arguments}")
else:
print(_message.content)
if not _message.tool_calls or len(_message.tool_calls) == 0:
raise Exception("VALIDATION FAILED: tool-call - no tool calls in response")
print("VALIDATION: tool-call SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "How to calculate 3^3^3^3? Think step by step and show all reasoning."},
],
reasoning_effort="medium",
stream=True,
)
_reasoning_detected = False
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True)
if getattr(delta, "reasoning_content", None) is not None:
_reasoning_detected = True
if getattr(delta, "reasoning", None) is not None:
_reasoning_detected = True
_usage = getattr(chunk, "usage", None)
if _usage is not None:
_details = getattr(_usage, "completion_tokens_details", None)
if _details and getattr(_details, "reasoning_tokens", 0) > 0:
_reasoning_detected = True
if not _reasoning_detected:
raise Exception("VALIDATION FAILED: reasoning stream - no reasoning information in stream")
print("\nVALIDATION: reasoning stream SUCCESS") |
![cursor[bot]](https://avatars.githubusercontent.com/in/1210556?s=60&v=4){kind=small}
| - cache_read_input_token_cost: 0.0000011 | ||
| input_cost_per_token: 0.000011 | ||
| output_cost_per_token: 0.000055 | ||
| region: eu-central-1 |
There was a problem hiding this comment.
Missing cache write pricing
Medium Severity
Each regional costs entry lists cache_read_input_token_cost and advertises prompt_caching, but omits cache_creation_input_token_cost and cache_creation_input_token_cost_per_hour. Peer EU Anthropic Bedrock models include both, so cache-write usage can be priced incorrectly or not at all.
Reviewed by Cursor Bugbot for commit 6a2dab4. Configure here.
| status: active | ||
| supportedModes: | ||
| - chat | ||
| thinking: true |
There was a problem hiding this comment.
Missing token limits metadata
High Severity
The model sets thinking: true and mythos-class capabilities but has no limits or params for max_tokens. aws-bedrock defaults cap max_tokens at 2048, so registry consumers can treat Fable as a short-context model instead of its much larger window and output.
Reviewed by Cursor Bugbot for commit 6a2dab4. Configure here.
…e-5-20260609-182154
Contributor
|
/test-models |
Collaborator
Gateway test results
Failures (12)
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "How to calculate 3^3^3^3? Think step by step and show all reasoning."}]},
]
system = [{"text": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
additionalModelRequestFields={
"thinking": {"type":"adaptive"},
},
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "reasoningContent" in _delta:
print(_delta["reasoningContent"].get("text", ""), end="", flush=True)
if "text" in _delta:
print(_delta["text"], end="", flush=True)
_reasoning_detected = False
for _event in _events:
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "reasoningContent" in _delta:
_reasoning_detected = True
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "reasoningContent" in _start:
_reasoning_detected = True
if "metadata" in _event:
_usage = _event["metadata"].get("usage", {})
if _usage.get("reasoning_tokens") or _usage.get("reasoningTokens"):
_reasoning_detected = True
if not _reasoning_detected:
raise Exception("VALIDATION FAILED: reasoning stream - no reasoning information in Bedrock stream")
print("\nVALIDATION: reasoning stream SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "How to calculate 3^3^3^3? Think step by step and show all reasoning."}]},
]
system = [{"text": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
additionalModelRequestFields={
"thinking": {"type":"adaptive"},
},
)
_content = response["output"]["message"]["content"]
for _block in _content:
if "reasoningContent" in _block:
print(_block["reasoningContent"]["reasoningText"]["text"])
if "text" in _block:
print(_block["text"])
_content = response["output"]["message"]["content"]
_reasoning_detected = False
for _block in _content:
if "reasoningContent" in _block:
_reasoning_detected = True
_usage = response.get("usage", {})
if _usage.get("reasoning_tokens") or _usage.get("reasoningTokens"):
_reasoning_detected = True
if not _reasoning_detected:
print("Response: ", response)
raise Exception("VALIDATION FAILED: reasoning - no reasoning information in Bedrock response")
print("VALIDATION: reasoning SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "What is the capital of France?"}]},
]
system = [{"text": "You are a helpful assistant."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
inferenceConfig={
"maxTokens": 512,
"temperature": 0.5,
},
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "text" in _delta:
print(_delta["text"], end="", flush=True)
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
tool_config = {
"tools": [
{
"toolSpec": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
}
},
}
}
],
"toolChoice": {"auto": {}},
}
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."}]},
]
system = [{"text": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
toolConfig=tool_config,
)
_content = response["output"]["message"]["content"]
_tool_uses = [block for block in _content if "toolUse" in block]
if _tool_uses:
for _tu in _tool_uses:
print(f"Tool: {_tu['toolUse']['name']}")
print(f"Input: {_tu['toolUse']['input']}")
else:
_text_blocks = [block["text"] for block in _content if "text" in block]
print("\n".join(_text_blocks))
_content = response["output"]["message"]["content"]
_tool_uses = [block for block in _content if "toolUse" in block]
if not _tool_uses:
raise Exception("VALIDATION FAILED: tool-call - no tool uses in Bedrock response")
print("VALIDATION: tool-call SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
tool_config = {
"tools": [
{
"toolSpec": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
}
},
}
}
],
"toolChoice": {"auto": {}},
}
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."}]},
]
system = [{"text": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
toolConfig=tool_config,
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "toolUse" in _start:
print(f"Tool: {_start['toolUse'].get('name', '')}", flush=True)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "toolUse" in _delta:
print(_delta["toolUse"].get("input", ""), end="", flush=True)
if "text" in _delta:
print(_delta["text"], end="", flush=True)
_tool_use_detected = False
for _event in _events:
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "toolUse" in _start:
_tool_use_detected = True
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "toolUse" in _delta:
_tool_use_detected = True
if not _tool_use_detected:
raise Exception("VALIDATION FAILED: tool-call stream - no tool uses in Bedrock stream")
print("\nVALIDATION: tool-call stream SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-aws-bedrock/eu.anthropic.claude-fable-5"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "What is the capital of France?"}]},
]
system = [{"text": "You are a helpful assistant."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
inferenceConfig={
"maxTokens": 512,
"temperature": 0.5,
},
)
_content = response["output"]["message"]["content"]
for _block in _content:
if "text" in _block:
print(_block["text"])
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "What is the capital of France?"},
],
max_tokens=512,
temperature=0.5,
stream=True,
)
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True)
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "How to calculate 3^3^3^3? Think step by step and show all reasoning."},
],
reasoning_effort="medium",
stream=False,
)
_usage = getattr(response, "usage", None)
_reasoning_detected = False
_choices = getattr(response, "choices", None)
if _choices and len(_choices) > 0:
_message = getattr(_choices[0], "message", None)
else:
_message = None
if _message and getattr(_message, "content", None) is not None:
print(_message.content)
if _usage is not None:
_output_token_details = getattr(_usage, "completion_tokens_details", None)
if _output_token_details and getattr(_output_token_details, "reasoning_tokens", 0) > 0:
_reasoning_detected = True
elif getattr(_usage, "reasoning", None) is not None:
_reasoning_detected = True
if getattr(_message, "reasoning_content", None) is not None:
_reasoning_detected = True
elif getattr(_message, "reasoning", None) is not None:
_reasoning_detected = True
if not _reasoning_detected:
print("Response: ", response)
raise Exception("VALIDATION FAILED: reasoning - no reasoning information in response")
print("VALIDATION: reasoning SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
"additionalProperties": False,
},
"strict": True,
},
},
]
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."},
],
tools=tools,
tool_choice="auto",
stream=False,
)
_message = response.choices[0].message
if _message.tool_calls:
for _tc in _message.tool_calls:
print(f"Function: {_tc.function.name}")
print(f"Arguments: {_tc.function.arguments}")
else:
print(_message.content)
if not _message.tool_calls or len(_message.tool_calls) == 0:
raise Exception("VALIDATION FAILED: tool-call - no tool calls in response")
print("VALIDATION: tool-call SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "What is the capital of France?"},
],
max_tokens=512,
temperature=0.5,
stream=False,
)
print(response.choices[0].message.content)
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
"additionalProperties": False,
},
"strict": True,
},
},
]
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."},
],
tools=tools,
tool_choice="auto",
stream=True,
)
_tool_calls_made = False
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True)
if delta.tool_calls:
_tool_calls_made = True
for _tc in delta.tool_calls:
if _tc.function:
print(_tc.function.arguments or "", end="", flush=True)
if not _tool_calls_made:
raise Exception("VALIDATION FAILED: tool-call stream - no tool calls received")
print("\nVALIDATION: tool-call stream SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-aws-bedrock/eu.anthropic.claude-fable-5",
messages=[
{"role": "system", "content": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "How to calculate 3^3^3^3? Think step by step and show all reasoning."},
],
reasoning_effort="medium",
stream=True,
)
_reasoning_detected = False
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True)
if getattr(delta, "reasoning_content", None) is not None:
_reasoning_detected = True
if getattr(delta, "reasoning", None) is not None:
_reasoning_detected = True
_usage = getattr(chunk, "usage", None)
if _usage is not None:
_details = getattr(_usage, "completion_tokens_details", None)
if _details and getattr(_details, "reasoning_tokens", 0) > 0:
_reasoning_detected = True
if not _reasoning_detected:
raise Exception("VALIDATION FAILED: reasoning stream - no reasoning information in stream")
print("\nVALIDATION: reasoning stream SUCCESS") |
There was a problem hiding this comment.
Cursor Bugbot has reviewed your changes and found 1 potential issue.
There are 3 total unresolved issues (including 2 from previous reviews).
❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.
Reviewed by Cursor Bugbot for commit 9b37d6a. Configure here.
| mode: chat | ||
| model: eu.anthropic.claude-fable-5 | ||
| provisioning: serverless | ||
| sources: |
There was a problem hiding this comment.
Missing unsupported param removal
High Severity
The config has no removeParams, so it inherits temperature and top_p from providers/aws-bedrock/default.yaml. The same model on the direct Anthropic provider explicitly removes temperature, top_p, and top_k, so merged gateway requests can send unsupported sampling params to Bedrock Fable.
Reviewed by Cursor Bugbot for commit 9b37d6a. Configure here.
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Auto-generated by model-addition-agent for
aws-bedrock/eu.anthropic.claude-fable-5.Note
Low Risk
Metadata-only addition with no runtime or application code changes; incorrect pricing or feature flags would only affect catalog consumers.
Overview
Adds a new AWS Bedrock provider definition for
eu.anthropic.claude-fable-5, registering Claude Fable 5 for EU cross-region inference.The entry marks the model active and serverless, with chat mode, extended thinking enabled, and capabilities including function calling, prompt caching, system messages, and tool choice. Input modalities cover text, image, and PDF; output is text-only.
Pricing is listed for eight EU regions (
eu-central-1/2,eu-north-1,eu-south-1/2,eu-west-1/2/3) using uniform per-token input, output, and cache-read rates. Documentation links point to Anthropic’s Fable page and AWS’s Bedrock availability announcement.Reviewed by Cursor Bugbot for commit 9b37d6a. Bugbot is set up for automated code reviews on this repo. Configure here.