"""
Copyright 2026 OÜ KAVAL AI (registry code 17393877)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import os
import time
from typing import Any, Dict, List, Optional, Tuple, Type
from anthropic import AsyncAnthropic
from loguru import logger
from pydantic import BaseModel
from kavalai.llm_clients.base_client import (
ensure_user_turn,
BaseLlmClient,
ChatHistory,
LlmClientException,
LlmClientParameters,
ModelStatsReceiver,
)
from kavalai.llm_clients.common import walk_json_schema
from kavalai.llm_clients.streamer import Streamer
# The Messages API requires max_tokens on every request, so this is sent when
# `max_output_tokens` is unset. It is only an output ceiling (nothing is
# pre-allocated) and responses stream, so a generous default is safe.
DEFAULT_MAX_TOKENS = 64000
[docs]
class AnthropicClient(BaseLlmClient):
"""
Anthropic (Claude) LLM client implementation using the Messages API and
Streamer.
"""
provider = "anthropic"
def __init__(
self,
model: str,
llm_client_parameters: Optional[LlmClientParameters] = None,
model_stats_receiver: Optional[ModelStatsReceiver] = None,
api_key: Optional[str] = None,
base_url: Optional[str] = None,
):
"""
Initialize the Anthropic client.
Args:
model: The Anthropic model name (e.g., 'claude-opus-5').
llm_client_parameters: Optional parameters like temperature, top_p, etc.
model_stats_receiver: Optional receiver for model call statistics.
api_key: Optional API key (falls back to ANTHROPIC_API_KEY env var).
base_url: Optional base URL for the API.
"""
super().__init__(llm_client_parameters, model_stats_receiver)
self.model = model
self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
self.base_url = base_url
self.client = AsyncAnthropic(
api_key=self.api_key, base_url=self.base_url, timeout=self.timeout_seconds
)
async def _run_chat_completions(
self,
chat_history: ChatHistory,
response_model: Optional[Type[BaseModel]],
streamer: Streamer,
):
"""
Background task to handle the actual Anthropic API call and stream results.
"""
start_time = time.perf_counter()
value_streamer = streamer.get_value_streamer(
"response", response_model=response_model
)
system_prompt, messages = convert_messages(
[msg.model_dump() for msg in ensure_user_turn(chat_history.messages)]
)
params = self.parameters
call_kwargs: Dict[str, Any] = {
"model": self.model,
"max_tokens": (
DEFAULT_MAX_TOKENS
if params.max_output_tokens is None
else params.max_output_tokens
),
"messages": messages,
}
if system_prompt:
call_kwargs["system"] = system_prompt
output_config: Dict[str, Any] = {}
if params.temperature is not None:
call_kwargs["temperature"] = params.temperature
if params.top_p is not None:
# The Messages API rejects a request carrying both sampling
# parameters, so send only temperature when both are set.
if "temperature" in call_kwargs:
logger.warning(
f"Anthropic model '{self.model}' accepts only one of "
"temperature and top_p; sending temperature and "
"ignoring top_p."
)
else:
call_kwargs["top_p"] = params.top_p
if params.service_tier is not None:
call_kwargs["service_tier"] = params.service_tier
if params.reasoning_effort is not None:
output_config["effort"] = params.reasoning_effort
if response_model:
schema = response_model.model_json_schema()
forbid_additional_properties(schema)
output_config["format"] = {"type": "json_schema", "schema": schema}
if output_config:
call_kwargs["output_config"] = output_config
prompt_tokens = 0
completion_tokens = 0
full_response = ""
async with self.client.messages.stream(**call_kwargs) as stream:
async for event in stream:
if event.type == "text":
full_response += event.text
await value_streamer.stream_partial(event.text)
final_message = await stream.get_final_message()
if final_message.stop_reason == "refusal":
raise LlmClientException(
f"Anthropic model '{self.model}' refused to generate a response."
)
cached_prompt_tokens = None
if final_message.usage:
usage = final_message.usage
prompt_tokens = usage.input_tokens
completion_tokens = usage.output_tokens
# Cache reads are billed at a fraction of fresh input; cache writes
# cost a premium. Both are input tokens, so record them together.
cached_prompt_tokens = (
getattr(usage, "cache_read_input_tokens", 0) or 0
) + (getattr(usage, "cache_creation_input_tokens", 0) or 0) or None
if final_message.stop_reason == "max_tokens":
raise self._output_truncated(
"max_tokens",
full_response,
max_output_tokens=call_kwargs["max_tokens"],
request_data=call_kwargs,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
cached_prompt_tokens=cached_prompt_tokens,
)
if final_message.stop_reason == "model_context_window_exceeded":
raise LlmClientException(
f"Anthropic model '{self.model}' stopped because its context "
"window was exhausted; the partial output is not returned."
)
await value_streamer.stream_complete()
await self._record_completed_call(
request_data=call_kwargs,
response_data=full_response,
started=start_time,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
cached_prompt_tokens=cached_prompt_tokens,
)
[docs]
def convert_messages(
messages: List[Dict[str, Any]],
) -> Tuple[Optional[str], List[Dict[str, Any]]]:
"""Split chat messages into the system prompt and Anthropic message turns.
The Messages API takes the system prompt as a top-level ``system``
parameter and requires a non-empty ``messages`` list, so a system-only
history (e.g. from :meth:`BaseLlmClient.prompt`) is sent as the single
user turn instead.
"""
system_prompt = None
converted: List[Dict[str, Any]] = []
for msg in messages:
role = msg.get("role")
content = msg.get("content")
if content is None:
continue
if role == "system":
if system_prompt:
system_prompt += "\n" + content
else:
system_prompt = content
continue
anthropic_role = "user" if role == "user" else "assistant"
converted.append({"role": anthropic_role, "content": content})
if not converted:
converted.append({"role": "user", "content": system_prompt or "..."})
system_prompt = None
return system_prompt, converted
[docs]
def forbid_additional_properties(schema: Dict[str, Any]) -> None:
"""Set ``additionalProperties: false`` on every object schema, in place.
Anthropic structured outputs require closed object schemas.
"""
def close_object(node: dict) -> None:
if node.get("type") == "object" or "properties" in node:
node["additionalProperties"] = False
walk_json_schema(schema, close_object)