1483 lines
65 KiB
Python
1483 lines
65 KiB
Python
import copy
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import json
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import logging
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import re
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from typing import Dict, List, Optional, Union
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import anthropic
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from anthropic import AsyncStream
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from anthropic.types.beta import BetaMessage as AnthropicMessage, BetaRawMessageStreamEvent
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from anthropic.types.beta.message_create_params import MessageCreateParamsNonStreaming
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from anthropic.types.beta.messages import BetaMessageBatch
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from anthropic.types.beta.messages.batch_create_params import Request
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from letta.constants import FUNC_FAILED_HEARTBEAT_MESSAGE, REQ_HEARTBEAT_MESSAGE, REQUEST_HEARTBEAT_PARAM
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from letta.errors import (
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ContextWindowExceededError,
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ErrorCode,
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LLMAuthenticationError,
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LLMBadRequestError,
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LLMConnectionError,
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LLMNotFoundError,
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LLMPermissionDeniedError,
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LLMProviderOverloaded,
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LLMRateLimitError,
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LLMServerError,
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LLMTimeoutError,
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LLMUnprocessableEntityError,
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)
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from letta.helpers.datetime_helpers import get_utc_time_int
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from letta.helpers.decorators import deprecated
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from letta.llm_api.anthropic_constants import ANTHROPIC_MAX_STRICT_TOOLS, ANTHROPIC_STRICT_MODE_ALLOWLIST
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from letta.llm_api.helpers import add_inner_thoughts_to_functions, unpack_all_inner_thoughts_from_kwargs
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from letta.llm_api.llm_client_base import LLMClientBase
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION
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from letta.log import get_logger
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from letta.otel.tracing import trace_method
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from letta.schemas.agent import AgentType
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.message import Message as PydanticMessage
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from letta.schemas.openai.chat_completion_request import Tool as OpenAITool
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from letta.schemas.openai.chat_completion_response import (
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ChatCompletionResponse,
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Choice,
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FunctionCall,
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Message as ChoiceMessage,
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ToolCall,
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UsageStatistics,
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)
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from letta.schemas.response_format import JsonSchemaResponseFormat
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from letta.settings import model_settings
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DUMMY_FIRST_USER_MESSAGE = "User initializing bootup sequence."
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logger = get_logger(__name__)
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class AnthropicClient(LLMClientBase):
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@trace_method
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@deprecated("Synchronous version of this is no longer valid. Will result in model_dump of coroutine")
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def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
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client = self._get_anthropic_client(llm_config, async_client=False)
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betas: list[str] = []
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# Interleaved thinking for reasoner (sync path parity)
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if llm_config.enable_reasoner:
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betas.append("interleaved-thinking-2025-05-14")
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# 1M context beta for Sonnet 4/4.5 when enabled
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try:
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from letta.settings import model_settings
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if model_settings.anthropic_sonnet_1m and (
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llm_config.model.startswith("claude-sonnet-4") or llm_config.model.startswith("claude-sonnet-4-5")
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):
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betas.append("context-1m-2025-08-07")
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except Exception:
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pass
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# Opus 4.5 effort parameter - to extend to other models, modify the model check
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if llm_config.model.startswith("claude-opus-4-5") and llm_config.effort is not None:
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betas.append("effort-2025-11-24")
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# Context management for Opus 4.5 to preserve thinking blocks (improves cache hits)
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if llm_config.model.startswith("claude-opus-4-5") and llm_config.enable_reasoner:
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betas.append("context-management-2025-06-27")
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# Structured outputs beta - only for supported models
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# Supported: Claude Sonnet 4.5, Opus 4.1, Opus 4.5, Haiku 4.5
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supports_structured_outputs = _supports_structured_outputs(llm_config.model)
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if supports_structured_outputs:
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# Always enable structured outputs beta on supported models.
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# NOTE: We do NOT send `strict` on tool schemas because the current Anthropic SDK
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# typed tool params reject unknown fields (e.g., `tools.0.custom.strict`).
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betas.append("structured-outputs-2025-11-13")
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if betas:
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response = client.beta.messages.create(**request_data, betas=betas)
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else:
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response = client.beta.messages.create(**request_data)
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return response.model_dump()
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@trace_method
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async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
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client = await self._get_anthropic_client_async(llm_config, async_client=True)
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betas: list[str] = []
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# interleaved thinking for reasoner
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if llm_config.enable_reasoner:
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betas.append("interleaved-thinking-2025-05-14")
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# 1M context beta for Sonnet 4/4.5 when enabled
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try:
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from letta.settings import model_settings
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if model_settings.anthropic_sonnet_1m and (
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llm_config.model.startswith("claude-sonnet-4") or llm_config.model.startswith("claude-sonnet-4-5")
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):
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betas.append("context-1m-2025-08-07")
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except Exception:
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pass
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# Opus 4.5 effort parameter - to extend to other models, modify the model check
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if llm_config.model.startswith("claude-opus-4-5") and llm_config.effort is not None:
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betas.append("effort-2025-11-24")
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# Context management for Opus 4.5 to preserve thinking blocks (improves cache hits)
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if llm_config.model.startswith("claude-opus-4-5") and llm_config.enable_reasoner:
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betas.append("context-management-2025-06-27")
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# Structured outputs beta - only for supported models
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supports_structured_outputs = _supports_structured_outputs(llm_config.model)
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if supports_structured_outputs:
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betas.append("structured-outputs-2025-11-13")
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if betas:
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response = await client.beta.messages.create(**request_data, betas=betas)
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else:
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response = await client.beta.messages.create(**request_data)
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return response.model_dump()
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@trace_method
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async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[BetaRawMessageStreamEvent]:
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client = await self._get_anthropic_client_async(llm_config, async_client=True)
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request_data["stream"] = True
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# Add fine-grained tool streaming beta header for better streaming performance
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# This helps reduce buffering when streaming tool call parameters
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# See: https://docs.anthropic.com/en/docs/build-with-claude/tool-use/fine-grained-streaming
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betas = ["fine-grained-tool-streaming-2025-05-14"]
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# If extended thinking, turn on interleaved header
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# https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking#interleaved-thinking
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if llm_config.enable_reasoner:
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betas.append("interleaved-thinking-2025-05-14")
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# 1M context beta for Sonnet 4/4.5 when enabled
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try:
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from letta.settings import model_settings
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if model_settings.anthropic_sonnet_1m and (
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llm_config.model.startswith("claude-sonnet-4") or llm_config.model.startswith("claude-sonnet-4-5")
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):
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betas.append("context-1m-2025-08-07")
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except Exception:
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pass
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# Opus 4.5 effort parameter - to extend to other models, modify the model check
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if llm_config.model.startswith("claude-opus-4-5") and llm_config.effort is not None:
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betas.append("effort-2025-11-24")
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# Context management for Opus 4.5 to preserve thinking blocks (improves cache hits)
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if llm_config.model.startswith("claude-opus-4-5") and llm_config.enable_reasoner:
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betas.append("context-management-2025-06-27")
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# Structured outputs beta - only for supported models
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supports_structured_outputs = _supports_structured_outputs(llm_config.model)
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if supports_structured_outputs:
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betas.append("structured-outputs-2025-11-13")
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# log failed requests
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try:
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return await client.beta.messages.create(**request_data, betas=betas)
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except Exception as e:
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logger.error(f"Error streaming Anthropic request: {e} with request data: {json.dumps(request_data)}")
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raise e
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@trace_method
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async def send_llm_batch_request_async(
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self,
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agent_type: AgentType,
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agent_messages_mapping: Dict[str, List[PydanticMessage]],
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agent_tools_mapping: Dict[str, List[dict]],
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agent_llm_config_mapping: Dict[str, LLMConfig],
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) -> BetaMessageBatch:
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"""
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Sends a batch request to the Anthropic API using the provided agent messages and tools mappings.
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Args:
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agent_messages_mapping: A dict mapping agent_id to their list of PydanticMessages.
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agent_tools_mapping: A dict mapping agent_id to their list of tool dicts.
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agent_llm_config_mapping: A dict mapping agent_id to their LLM config
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Returns:
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BetaMessageBatch: The batch response from the Anthropic API.
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Raises:
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ValueError: If the sets of agent_ids in the two mappings do not match.
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Exception: Transformed errors from the underlying API call.
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"""
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# Validate that both mappings use the same set of agent_ids.
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if set(agent_messages_mapping.keys()) != set(agent_tools_mapping.keys()):
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raise ValueError("Agent mappings for messages and tools must use the same agent_ids.")
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try:
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requests = {
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agent_id: self.build_request_data(
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agent_type=agent_type,
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messages=agent_messages_mapping[agent_id],
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llm_config=agent_llm_config_mapping[agent_id],
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tools=agent_tools_mapping[agent_id],
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)
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for agent_id in agent_messages_mapping
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}
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client = await self._get_anthropic_client_async(list(agent_llm_config_mapping.values())[0], async_client=True)
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anthropic_requests = [
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Request(custom_id=agent_id, params=MessageCreateParamsNonStreaming(**params)) for agent_id, params in requests.items()
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]
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batch_response = await client.beta.messages.batches.create(requests=anthropic_requests)
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return batch_response
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except Exception as e:
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# Enhance logging here if additional context is needed
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logger.error("Error during send_llm_batch_request_async.", exc_info=True)
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raise self.handle_llm_error(e)
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@trace_method
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def _get_anthropic_client(
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self, llm_config: LLMConfig, async_client: bool = False
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) -> Union[anthropic.AsyncAnthropic, anthropic.Anthropic]:
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api_key, _, _ = self.get_byok_overrides(llm_config)
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# For claude-pro-max provider, use OAuth Bearer token instead of api_key
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is_oauth_provider = llm_config.provider_name == "claude-pro-max"
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if async_client:
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if api_key:
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if is_oauth_provider:
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return anthropic.AsyncAnthropic(
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max_retries=model_settings.anthropic_max_retries,
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default_headers={
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"Authorization": f"Bearer {api_key}",
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"anthropic-version": "2023-06-01",
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"anthropic-beta": "oauth-2025-04-20",
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},
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)
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return anthropic.AsyncAnthropic(api_key=api_key, max_retries=model_settings.anthropic_max_retries)
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return anthropic.AsyncAnthropic(max_retries=model_settings.anthropic_max_retries)
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if api_key:
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if is_oauth_provider:
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return anthropic.Anthropic(
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max_retries=model_settings.anthropic_max_retries,
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default_headers={
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"Authorization": f"Bearer {api_key}",
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"anthropic-version": "2023-06-01",
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"anthropic-beta": "oauth-2025-04-20",
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},
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)
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return anthropic.Anthropic(api_key=api_key, max_retries=model_settings.anthropic_max_retries)
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return anthropic.Anthropic(max_retries=model_settings.anthropic_max_retries)
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@trace_method
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async def _get_anthropic_client_async(
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self, llm_config: LLMConfig, async_client: bool = False
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) -> Union[anthropic.AsyncAnthropic, anthropic.Anthropic]:
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api_key, _, _ = await self.get_byok_overrides_async(llm_config)
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# For claude-pro-max provider, use OAuth Bearer token instead of api_key
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is_oauth_provider = llm_config.provider_name == "claude-pro-max"
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if async_client:
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if api_key:
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if is_oauth_provider:
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return anthropic.AsyncAnthropic(
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max_retries=model_settings.anthropic_max_retries,
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default_headers={
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"Authorization": f"Bearer {api_key}",
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"anthropic-version": "2023-06-01",
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"anthropic-beta": "oauth-2025-04-20",
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},
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)
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return anthropic.AsyncAnthropic(api_key=api_key, max_retries=model_settings.anthropic_max_retries)
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return anthropic.AsyncAnthropic(max_retries=model_settings.anthropic_max_retries)
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if api_key:
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if is_oauth_provider:
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return anthropic.Anthropic(
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max_retries=model_settings.anthropic_max_retries,
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default_headers={
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"Authorization": f"Bearer {api_key}",
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"anthropic-version": "2023-06-01",
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"anthropic-beta": "oauth-2025-04-20",
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},
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)
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return anthropic.Anthropic(api_key=api_key, max_retries=model_settings.anthropic_max_retries)
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return anthropic.Anthropic(max_retries=model_settings.anthropic_max_retries)
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|
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@trace_method
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def build_request_data(
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self,
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agent_type: AgentType, # if react, use native content + strip heartbeats
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messages: List[PydanticMessage],
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llm_config: LLMConfig,
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tools: Optional[List[dict]] = None,
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force_tool_call: Optional[str] = None,
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requires_subsequent_tool_call: bool = False,
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tool_return_truncation_chars: Optional[int] = None,
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) -> dict:
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# TODO: This needs to get cleaned up. The logic here is pretty confusing.
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# TODO: I really want to get rid of prefixing, it's a recipe for disaster code maintenance wise
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prefix_fill = True if agent_type != AgentType.letta_v1_agent else False
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is_v1 = agent_type == AgentType.letta_v1_agent
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# Determine local behavior for putting inner thoughts in kwargs without mutating llm_config
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put_kwargs = bool(llm_config.put_inner_thoughts_in_kwargs) and not is_v1
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if not self.use_tool_naming:
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raise NotImplementedError("Only tool calling supported on Anthropic API requests")
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if not llm_config.max_tokens:
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# TODO strip this default once we add provider-specific defaults
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max_output_tokens = 4096 # the minimum max tokens (for Haiku 3)
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else:
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max_output_tokens = llm_config.max_tokens
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data = {
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"model": llm_config.model,
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"max_tokens": max_output_tokens,
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"temperature": llm_config.temperature,
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}
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# Extended Thinking
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if self.is_reasoning_model(llm_config) and llm_config.enable_reasoner:
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thinking_budget = max(llm_config.max_reasoning_tokens, 1024)
|
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if thinking_budget != llm_config.max_reasoning_tokens:
|
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logger.warning(
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f"Max reasoning tokens must be at least 1024 for Claude. Setting max_reasoning_tokens to 1024 for model {llm_config.model}."
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)
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data["thinking"] = {
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"type": "enabled",
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"budget_tokens": thinking_budget,
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}
|
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# `temperature` may only be set to 1 when thinking is enabled. Please consult our documentation at https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking#important-considerations-when-using-extended-thinking'
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data["temperature"] = 1.0
|
|
|
|
# Silently disable prefix_fill for now
|
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prefix_fill = False
|
|
|
|
# Effort configuration for Opus 4.5 (controls token spending)
|
|
# To extend to other models, modify the model check
|
|
if llm_config.model.startswith("claude-opus-4-5") and llm_config.effort is not None:
|
|
data["output_config"] = {"effort": llm_config.effort}
|
|
|
|
# Context management for Opus 4.5 to preserve thinking blocks and improve cache hits
|
|
# See: https://docs.anthropic.com/en/docs/build-with-claude/context-editing
|
|
if llm_config.model.startswith("claude-opus-4-5") and llm_config.enable_reasoner:
|
|
data["context_management"] = {
|
|
"edits": [
|
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{
|
|
"type": "clear_thinking_20251015",
|
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"keep": "all", # Preserve all thinking blocks for maximum cache performance
|
|
}
|
|
]
|
|
}
|
|
|
|
# Structured outputs via response_format
|
|
if hasattr(llm_config, "response_format") and isinstance(llm_config.response_format, JsonSchemaResponseFormat):
|
|
data["output_format"] = {
|
|
"type": "json_schema",
|
|
"schema": llm_config.response_format.json_schema["schema"],
|
|
}
|
|
|
|
# Tools
|
|
# For an overview on tool choice:
|
|
# https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview
|
|
if not tools:
|
|
# Special case for summarization path
|
|
tools_for_request = None
|
|
tool_choice = None
|
|
elif self.is_reasoning_model(llm_config) and llm_config.enable_reasoner or agent_type == AgentType.letta_v1_agent:
|
|
# NOTE: reasoning models currently do not allow for `any`
|
|
# NOTE: react agents should always have auto on, since the precense/absense of tool calls controls chaining
|
|
tool_choice = {"type": "auto", "disable_parallel_tool_use": True}
|
|
tools_for_request = [OpenAITool(function=f) for f in tools]
|
|
elif force_tool_call is not None:
|
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tool_choice = {"type": "tool", "name": force_tool_call, "disable_parallel_tool_use": True}
|
|
tools_for_request = [OpenAITool(function=f) for f in tools if f["name"] == force_tool_call]
|
|
|
|
# need to have this setting to be able to put inner thoughts in kwargs
|
|
if not put_kwargs:
|
|
if is_v1:
|
|
# For v1 agents, native content is used and kwargs must remain disabled to avoid conflicts
|
|
logger.warning(
|
|
"Forced tool call requested but inner_thoughts_in_kwargs is disabled for v1 agent; proceeding without inner thoughts in kwargs."
|
|
)
|
|
else:
|
|
logger.warning(
|
|
f"Force enabling inner thoughts in kwargs for Claude due to forced tool call: {force_tool_call} (local override only)"
|
|
)
|
|
put_kwargs = True
|
|
else:
|
|
tool_choice = {"type": "any", "disable_parallel_tool_use": True}
|
|
tools_for_request = [OpenAITool(function=f) for f in tools] if tools is not None else None
|
|
|
|
# Add tool choice
|
|
if tool_choice:
|
|
data["tool_choice"] = tool_choice
|
|
|
|
# Add inner thoughts kwarg
|
|
# TODO: Can probably make this more efficient
|
|
if tools_for_request and len(tools_for_request) > 0 and put_kwargs:
|
|
tools_with_inner_thoughts = add_inner_thoughts_to_functions(
|
|
functions=[t.function.model_dump() for t in tools_for_request],
|
|
inner_thoughts_key=INNER_THOUGHTS_KWARG,
|
|
inner_thoughts_description=INNER_THOUGHTS_KWARG_DESCRIPTION,
|
|
)
|
|
tools_for_request = [OpenAITool(function=f) for f in tools_with_inner_thoughts]
|
|
|
|
if tools_for_request and len(tools_for_request) > 0:
|
|
# TODO eventually enable parallel tool use
|
|
data["tools"] = convert_tools_to_anthropic_format(
|
|
tools_for_request,
|
|
use_strict=_supports_structured_outputs(llm_config.model),
|
|
)
|
|
# Add cache control to the last tool for caching tool definitions
|
|
if len(data["tools"]) > 0:
|
|
data["tools"][-1]["cache_control"] = {"type": "ephemeral"}
|
|
|
|
# Messages
|
|
inner_thoughts_xml_tag = "thinking"
|
|
|
|
# Move 'system' to the top level
|
|
if messages[0].role != "system":
|
|
raise RuntimeError(f"First message is not a system message, instead has role {messages[0].role}")
|
|
system_content = messages[0].content if isinstance(messages[0].content, str) else messages[0].content[0].text
|
|
data["system"] = self._add_cache_control_to_system_message(system_content)
|
|
data["messages"] = PydanticMessage.to_anthropic_dicts_from_list(
|
|
messages=messages[1:],
|
|
current_model=llm_config.model,
|
|
inner_thoughts_xml_tag=inner_thoughts_xml_tag,
|
|
put_inner_thoughts_in_kwargs=put_kwargs,
|
|
# if react, use native content + strip heartbeats
|
|
native_content=is_v1,
|
|
strip_request_heartbeat=is_v1,
|
|
tool_return_truncation_chars=tool_return_truncation_chars,
|
|
)
|
|
|
|
# Ensure first message is user
|
|
if data["messages"][0]["role"] != "user":
|
|
data["messages"] = [{"role": "user", "content": DUMMY_FIRST_USER_MESSAGE}] + data["messages"]
|
|
|
|
# Handle alternating messages
|
|
data["messages"] = merge_tool_results_into_user_messages(data["messages"])
|
|
|
|
if agent_type == AgentType.letta_v1_agent:
|
|
# Both drop heartbeats in the payload
|
|
data["messages"] = drop_heartbeats(data["messages"])
|
|
# And drop heartbeats in the tools
|
|
if "tools" in data:
|
|
for tool in data["tools"]:
|
|
tool["input_schema"]["properties"].pop(REQUEST_HEARTBEAT_PARAM, None)
|
|
if "required" in tool["input_schema"] and REQUEST_HEARTBEAT_PARAM in tool["input_schema"]["required"]:
|
|
# NOTE: required is not always present
|
|
tool["input_schema"]["required"].remove(REQUEST_HEARTBEAT_PARAM)
|
|
|
|
else:
|
|
# Strip heartbeat pings if extended thinking
|
|
if llm_config.enable_reasoner:
|
|
data["messages"] = merge_heartbeats_into_tool_responses(data["messages"])
|
|
|
|
# Deduplicate tool_result blocks that reference the same tool_use_id within a single user message
|
|
# Anthropic requires a single result per tool_use. Merging consecutive user messages can accidentally
|
|
# produce multiple tool_result blocks with the same id; consolidate them here.
|
|
data["messages"] = dedupe_tool_results_in_user_messages(data["messages"])
|
|
|
|
# Add cache control to final message for incremental conversation caching
|
|
# Per Anthropic docs: "During each turn, we mark the final block of the final message with
|
|
# cache_control so the conversation can be incrementally cached."
|
|
data["messages"] = self._add_cache_control_to_messages(data["messages"])
|
|
|
|
# Debug: Log cache control placement
|
|
logger.debug(f"Anthropic request has {len(data.get('messages', []))} messages")
|
|
if data.get("messages") and len(data["messages"]) > 0:
|
|
last_msg = data["messages"][-1]
|
|
logger.debug(f"Last message role: {last_msg.get('role')}, content type: {type(last_msg.get('content'))}")
|
|
if isinstance(last_msg.get("content"), list) and len(last_msg["content"]) > 0:
|
|
last_block = last_msg["content"][-1]
|
|
logger.debug(f"Last content block type: {last_block.get('type')}, has cache_control: {'cache_control' in last_block}")
|
|
if "cache_control" in last_block:
|
|
logger.debug(f"Cache control value: {last_block['cache_control']}")
|
|
|
|
# Prefix fill
|
|
# https://docs.anthropic.com/en/api/messages#body-messages
|
|
# NOTE: cannot prefill with tools for opus:
|
|
# Your API request included an `assistant` message in the final position, which would pre-fill the `assistant` response. When using tools with "claude-3-opus-20240229"
|
|
if prefix_fill and not put_kwargs and "opus" not in data["model"]:
|
|
data["messages"].append(
|
|
# Start the thinking process for the assistant
|
|
{"role": "assistant", "content": f"<{inner_thoughts_xml_tag}>"},
|
|
)
|
|
|
|
# As a final safeguard for request payloads: drop empty messages (instead of inserting placeholders)
|
|
# to avoid changing conversational meaning. Preserve an optional final assistant prefill if present.
|
|
if data.get("messages"):
|
|
sanitized_messages = []
|
|
dropped_messages = []
|
|
empty_blocks_removed = 0
|
|
total = len(data["messages"])
|
|
for i, msg in enumerate(data["messages"]):
|
|
role = msg.get("role")
|
|
content = msg.get("content")
|
|
is_final_assistant = i == total - 1 and role == "assistant"
|
|
|
|
# If content is a list, drop empty text blocks but keep non-text blocks
|
|
if isinstance(content, list) and len(content) > 0:
|
|
new_blocks = []
|
|
for block in content:
|
|
if isinstance(block, dict) and block.get("type") == "text":
|
|
if block.get("text", "").strip():
|
|
new_blocks.append(block)
|
|
else:
|
|
empty_blocks_removed += 1
|
|
else:
|
|
new_blocks.append(block)
|
|
msg["content"] = new_blocks
|
|
content = new_blocks
|
|
|
|
# Determine emptiness after trimming blocks
|
|
is_empty = (
|
|
content is None
|
|
or (isinstance(content, str) and not content.strip())
|
|
or (isinstance(content, list) and len(content) == 0)
|
|
)
|
|
|
|
# Drop empty messages except an allowed final assistant prefill
|
|
if is_empty and not is_final_assistant:
|
|
dropped_messages.append({"index": i, "role": role})
|
|
continue
|
|
sanitized_messages.append(msg)
|
|
|
|
data["messages"] = sanitized_messages
|
|
|
|
# Log unexpected sanitation events for visibility
|
|
if dropped_messages or empty_blocks_removed > 0:
|
|
logger.error(
|
|
"[Anthropic] Sanitized request messages: dropped=%s, empty_text_blocks_removed=%s, model=%s",
|
|
dropped_messages,
|
|
empty_blocks_removed,
|
|
data.get("model"),
|
|
)
|
|
|
|
# Ensure first message is user after sanitation
|
|
if not data["messages"] or data["messages"][0].get("role") != "user":
|
|
logger.error("[Anthropic] Inserting dummy first user message after sanitation to satisfy API constraints")
|
|
data["messages"] = [{"role": "user", "content": DUMMY_FIRST_USER_MESSAGE}] + data["messages"]
|
|
|
|
return data
|
|
|
|
async def count_tokens(self, messages: List[dict] = None, model: str = None, tools: List[OpenAITool] = None) -> int:
|
|
logging.getLogger("httpx").setLevel(logging.WARNING)
|
|
# Use the default client; token counting is lightweight and does not require BYOK overrides
|
|
client = anthropic.AsyncAnthropic()
|
|
if messages and len(messages) == 0:
|
|
messages = None
|
|
if tools and len(tools) > 0:
|
|
# Token counting endpoint requires additionalProperties: false (use_strict=True)
|
|
# but does NOT support the `strict` field on tools (add_strict_field=False)
|
|
anthropic_tools = convert_tools_to_anthropic_format(
|
|
tools,
|
|
use_strict=True,
|
|
add_strict_field=False,
|
|
)
|
|
else:
|
|
anthropic_tools = None
|
|
|
|
# Convert final thinking blocks to text to work around token counting endpoint limitation.
|
|
# The token counting endpoint rejects messages where the final content block is thinking,
|
|
# even though the main API supports this with the interleaved-thinking beta.
|
|
# We convert (not strip) to preserve accurate token counts.
|
|
# TODO: Remove this workaround if Anthropic fixes the token counting endpoint.
|
|
thinking_enabled = False
|
|
messages_for_counting = messages
|
|
|
|
if messages and len(messages) > 0:
|
|
messages_for_counting = copy.deepcopy(messages)
|
|
|
|
# Scan all assistant messages and convert any final thinking blocks to text
|
|
for message in messages_for_counting:
|
|
if message.get("role") == "assistant":
|
|
content = message.get("content")
|
|
|
|
# Check for thinking in any format
|
|
if isinstance(content, list) and len(content) > 0:
|
|
# Check if message has any thinking blocks (to enable thinking mode)
|
|
has_thinking = any(
|
|
isinstance(part, dict) and part.get("type") in {"thinking", "redacted_thinking"} for part in content
|
|
)
|
|
if has_thinking:
|
|
thinking_enabled = True
|
|
|
|
# If final block is thinking, handle it
|
|
last_block = content[-1]
|
|
if isinstance(last_block, dict) and last_block.get("type") in {"thinking", "redacted_thinking"}:
|
|
if len(content) == 1:
|
|
# Thinking-only message: add text at end (don't convert the thinking)
|
|
# API requires first block to be thinking when thinking is enabled
|
|
content.append({"type": "text", "text": "."})
|
|
else:
|
|
# Multiple blocks: convert final thinking to text
|
|
if last_block["type"] == "thinking":
|
|
content[-1] = {"type": "text", "text": last_block.get("thinking", "")}
|
|
elif last_block["type"] == "redacted_thinking":
|
|
content[-1] = {"type": "text", "text": last_block.get("data", "[redacted]")}
|
|
|
|
elif isinstance(content, str) and "<thinking>" in content:
|
|
# Handle XML-style thinking in string content
|
|
thinking_enabled = True
|
|
|
|
# Replace empty content with placeholder (Anthropic requires non-empty content except for final assistant message)
|
|
if messages_for_counting:
|
|
for i, msg in enumerate(messages_for_counting):
|
|
content = msg.get("content")
|
|
is_final_assistant = i == len(messages_for_counting) - 1 and msg.get("role") == "assistant"
|
|
|
|
# Check if content is empty and needs replacement
|
|
if content is None:
|
|
if not is_final_assistant:
|
|
msg["content"] = "."
|
|
elif isinstance(content, str) and not content.strip():
|
|
if not is_final_assistant:
|
|
msg["content"] = "."
|
|
elif isinstance(content, list):
|
|
if len(content) == 0:
|
|
# Preserve truly empty list for final assistant message
|
|
if not is_final_assistant:
|
|
msg["content"] = [{"type": "text", "text": "."}]
|
|
else:
|
|
# Always fix empty text blocks within lists, even for final assistant message
|
|
# The API exemption is for truly empty content (empty string or empty list),
|
|
# not for lists with explicit empty text blocks
|
|
for block in content:
|
|
if isinstance(block, dict) and block.get("type") == "text":
|
|
if not block.get("text", "").strip():
|
|
block["text"] = "."
|
|
|
|
try:
|
|
count_params = {
|
|
"model": model or "claude-3-7-sonnet-20250219",
|
|
"messages": messages_for_counting or [{"role": "user", "content": "hi"}],
|
|
"tools": anthropic_tools or [],
|
|
}
|
|
|
|
betas: list[str] = []
|
|
if thinking_enabled:
|
|
# Match interleaved thinking behavior so token accounting is consistent
|
|
count_params["thinking"] = {"type": "enabled", "budget_tokens": 16000}
|
|
betas.append("interleaved-thinking-2025-05-14")
|
|
|
|
# Opt-in to 1M context if enabled for this model in settings
|
|
try:
|
|
if (
|
|
model
|
|
and model_settings.anthropic_sonnet_1m
|
|
and (model.startswith("claude-sonnet-4") or model.startswith("claude-sonnet-4-5"))
|
|
):
|
|
betas.append("context-1m-2025-08-07")
|
|
except Exception:
|
|
pass
|
|
|
|
# Opus 4.5 beta flags for effort and context management
|
|
# Note: effort beta is added if model is kevlar (actual effort value is in count_params)
|
|
# Context management beta is added for consistency with main requests
|
|
if model and model.startswith("claude-opus-4-5"):
|
|
# Add effort beta if output_config is present in count_params
|
|
if "output_config" in count_params:
|
|
betas.append("effort-2025-11-24")
|
|
# Add context management beta if thinking is enabled
|
|
if thinking_enabled:
|
|
betas.append("context-management-2025-06-27")
|
|
|
|
# Structured outputs beta - only for supported models
|
|
if model and _supports_structured_outputs(model):
|
|
betas.append("structured-outputs-2025-11-13")
|
|
|
|
if betas:
|
|
result = await client.beta.messages.count_tokens(**count_params, betas=betas)
|
|
else:
|
|
result = await client.beta.messages.count_tokens(**count_params)
|
|
except Exception as e:
|
|
raise self.handle_llm_error(e)
|
|
|
|
token_count = result.input_tokens
|
|
if messages is None:
|
|
token_count -= 8
|
|
return token_count
|
|
|
|
def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
|
|
return (
|
|
llm_config.model.startswith("claude-3-7-sonnet")
|
|
or llm_config.model.startswith("claude-sonnet-4")
|
|
or llm_config.model.startswith("claude-opus-4")
|
|
or llm_config.model.startswith("claude-haiku-4-5")
|
|
# Opus 4.5 support - to extend effort parameter to other models, modify this check
|
|
or llm_config.model.startswith("claude-opus-4-5")
|
|
)
|
|
|
|
@trace_method
|
|
def handle_llm_error(self, e: Exception) -> Exception:
|
|
# make sure to check for overflow errors, regardless of error type
|
|
error_str = str(e).lower()
|
|
if (
|
|
"prompt is too long" in error_str
|
|
or "exceed context limit" in error_str
|
|
or "exceeds context" in error_str
|
|
or "too many total text bytes" in error_str
|
|
or "total text bytes" in error_str
|
|
):
|
|
logger.warning(f"[Anthropic] Context window exceeded: {str(e)}")
|
|
return ContextWindowExceededError(
|
|
message=f"Context window exceeded for Anthropic: {str(e)}",
|
|
)
|
|
|
|
if isinstance(e, anthropic.APITimeoutError):
|
|
logger.warning(f"[Anthropic] Request timeout: {e}")
|
|
return LLMTimeoutError(
|
|
message=f"Request to Anthropic timed out: {str(e)}",
|
|
code=ErrorCode.TIMEOUT,
|
|
details={"cause": str(e.__cause__) if e.__cause__ else None},
|
|
)
|
|
|
|
if isinstance(e, anthropic.APIConnectionError):
|
|
logger.warning(f"[Anthropic] API connection error: {e.__cause__}")
|
|
return LLMConnectionError(
|
|
message=f"Failed to connect to Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
details={"cause": str(e.__cause__) if e.__cause__ else None},
|
|
)
|
|
|
|
if isinstance(e, anthropic.RateLimitError):
|
|
logger.warning("[Anthropic] Rate limited (429). Consider backoff.")
|
|
return LLMRateLimitError(
|
|
message=f"Rate limited by Anthropic: {str(e)}",
|
|
code=ErrorCode.RATE_LIMIT_EXCEEDED,
|
|
)
|
|
|
|
if isinstance(e, anthropic.BadRequestError):
|
|
logger.warning(f"[Anthropic] Bad request: {str(e)}")
|
|
error_str = str(e).lower()
|
|
if (
|
|
"prompt is too long" in error_str
|
|
or "exceed context limit" in error_str
|
|
or "exceeds context" in error_str
|
|
or "too many total text bytes" in error_str
|
|
or "total text bytes" in error_str
|
|
):
|
|
# If the context window is too large, we expect to receive either:
|
|
# 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'prompt is too long: 200758 tokens > 200000 maximum'}}
|
|
# 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'input length and `max_tokens` exceed context limit: 173298 + 32000 > 200000, decrease input length or `max_tokens` and try again'}}
|
|
return ContextWindowExceededError(
|
|
message=f"Bad request to Anthropic (context window exceeded): {str(e)}",
|
|
)
|
|
else:
|
|
return LLMBadRequestError(
|
|
message=f"Bad request to Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.AuthenticationError):
|
|
logger.warning(f"[Anthropic] Authentication error: {str(e)}")
|
|
return LLMAuthenticationError(
|
|
message=f"Authentication failed with Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.PermissionDeniedError):
|
|
logger.warning(f"[Anthropic] Permission denied: {str(e)}")
|
|
return LLMPermissionDeniedError(
|
|
message=f"Permission denied by Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.NotFoundError):
|
|
logger.warning(f"[Anthropic] Resource not found: {str(e)}")
|
|
return LLMNotFoundError(
|
|
message=f"Resource not found in Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.UnprocessableEntityError):
|
|
logger.warning(f"[Anthropic] Unprocessable entity: {str(e)}")
|
|
return LLMUnprocessableEntityError(
|
|
message=f"Invalid request content for Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.APIStatusError):
|
|
logger.warning(f"[Anthropic] API status error: {str(e)}")
|
|
if "overloaded" in str(e).lower():
|
|
return LLMProviderOverloaded(
|
|
message=f"Anthropic API is overloaded: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
return LLMServerError(
|
|
message=f"Anthropic API error: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
details={
|
|
"status_code": e.status_code if hasattr(e, "status_code") else None,
|
|
"response": str(e.response) if hasattr(e, "response") else None,
|
|
},
|
|
)
|
|
|
|
return super().handle_llm_error(e)
|
|
|
|
# TODO: Input messages doesn't get used here
|
|
# TODO: Clean up this interface
|
|
@trace_method
|
|
async def convert_response_to_chat_completion(
|
|
self,
|
|
response_data: dict,
|
|
input_messages: List[PydanticMessage],
|
|
llm_config: LLMConfig,
|
|
) -> ChatCompletionResponse:
|
|
"""
|
|
Example response from Claude 3:
|
|
response.json = {
|
|
'id': 'msg_01W1xg9hdRzbeN2CfZM7zD2w',
|
|
'type': 'message',
|
|
'role': 'assistant',
|
|
'content': [
|
|
{
|
|
'type': 'text',
|
|
'text': "<thinking>Analyzing user login event. This is Chad's first
|
|
interaction with me. I will adjust my personality and rapport accordingly.</thinking>"
|
|
},
|
|
{
|
|
'type':
|
|
'tool_use',
|
|
'id': 'toolu_01Ka4AuCmfvxiidnBZuNfP1u',
|
|
'name': 'core_memory_append',
|
|
'input': {
|
|
'name': 'human',
|
|
'content': 'Chad is logging in for the first time. I will aim to build a warm
|
|
and welcoming rapport.',
|
|
'request_heartbeat': True
|
|
}
|
|
}
|
|
],
|
|
'model': 'claude-3-haiku-20240307',
|
|
'stop_reason': 'tool_use',
|
|
'stop_sequence': None,
|
|
'usage': {
|
|
'input_tokens': 3305,
|
|
'output_tokens': 141
|
|
}
|
|
}
|
|
"""
|
|
response = AnthropicMessage(**response_data)
|
|
prompt_tokens = response.usage.input_tokens
|
|
completion_tokens = response.usage.output_tokens
|
|
finish_reason = remap_finish_reason(str(response.stop_reason))
|
|
|
|
content = None
|
|
reasoning_content = None
|
|
reasoning_content_signature = None
|
|
redacted_reasoning_content = None
|
|
tool_calls: list[ToolCall] = []
|
|
|
|
if len(response.content) > 0:
|
|
for content_part in response.content:
|
|
if content_part.type == "text":
|
|
content = strip_xml_tags(string=content_part.text, tag="thinking")
|
|
if content_part.type == "tool_use":
|
|
# hack for incorrect tool format
|
|
tool_input = json.loads(json.dumps(content_part.input))
|
|
if "id" in tool_input and tool_input["id"].startswith("toolu_") and "function" in tool_input:
|
|
if isinstance(tool_input["function"], str):
|
|
tool_input["function"] = json.loads(tool_input["function"])
|
|
arguments = json.dumps(tool_input["function"]["arguments"], indent=2)
|
|
try:
|
|
args_json = json.loads(arguments)
|
|
if not isinstance(args_json, dict):
|
|
raise LLMServerError("Expected parseable json object for arguments")
|
|
except:
|
|
arguments = str(tool_input["function"]["arguments"])
|
|
else:
|
|
arguments = json.dumps(tool_input, indent=2)
|
|
tool_calls.append(
|
|
ToolCall(
|
|
id=content_part.id,
|
|
type="function",
|
|
function=FunctionCall(
|
|
name=content_part.name,
|
|
arguments=arguments,
|
|
),
|
|
)
|
|
)
|
|
if content_part.type == "thinking":
|
|
reasoning_content = content_part.thinking
|
|
reasoning_content_signature = content_part.signature
|
|
if content_part.type == "redacted_thinking":
|
|
redacted_reasoning_content = content_part.data
|
|
|
|
else:
|
|
raise RuntimeError("Unexpected empty content in response")
|
|
|
|
assert response.role == "assistant"
|
|
choice = Choice(
|
|
index=0,
|
|
finish_reason=finish_reason,
|
|
message=ChoiceMessage(
|
|
role=response.role,
|
|
content=content,
|
|
reasoning_content=reasoning_content,
|
|
reasoning_content_signature=reasoning_content_signature,
|
|
redacted_reasoning_content=redacted_reasoning_content,
|
|
tool_calls=tool_calls or None,
|
|
),
|
|
)
|
|
|
|
# Build prompt tokens details with cache data if available
|
|
prompt_tokens_details = None
|
|
cache_read_tokens = 0
|
|
cache_creation_tokens = 0
|
|
if hasattr(response.usage, "cache_read_input_tokens") or hasattr(response.usage, "cache_creation_input_tokens"):
|
|
from letta.schemas.openai.chat_completion_response import UsageStatisticsPromptTokenDetails
|
|
|
|
cache_read_tokens = getattr(response.usage, "cache_read_input_tokens", 0) or 0
|
|
cache_creation_tokens = getattr(response.usage, "cache_creation_input_tokens", 0) or 0
|
|
prompt_tokens_details = UsageStatisticsPromptTokenDetails(
|
|
cache_read_tokens=cache_read_tokens,
|
|
cache_creation_tokens=cache_creation_tokens,
|
|
)
|
|
|
|
# Per Anthropic docs: "Total input tokens in a request is the summation of
|
|
# input_tokens, cache_creation_input_tokens, and cache_read_input_tokens."
|
|
actual_input_tokens = prompt_tokens + cache_read_tokens + cache_creation_tokens
|
|
|
|
chat_completion_response = ChatCompletionResponse(
|
|
id=response.id,
|
|
choices=[choice],
|
|
created=get_utc_time_int(),
|
|
model=response.model,
|
|
usage=UsageStatistics(
|
|
prompt_tokens=actual_input_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=actual_input_tokens + completion_tokens,
|
|
prompt_tokens_details=prompt_tokens_details,
|
|
),
|
|
)
|
|
if llm_config.put_inner_thoughts_in_kwargs:
|
|
chat_completion_response = unpack_all_inner_thoughts_from_kwargs(
|
|
response=chat_completion_response, inner_thoughts_key=INNER_THOUGHTS_KWARG
|
|
)
|
|
|
|
return chat_completion_response
|
|
|
|
def _add_cache_control_to_system_message(self, system_content):
|
|
"""Add cache control to system message content."""
|
|
if isinstance(system_content, str):
|
|
# For string content, convert to list format with cache control
|
|
return [{"type": "text", "text": system_content, "cache_control": {"type": "ephemeral"}}]
|
|
elif isinstance(system_content, list):
|
|
# For list content, add cache control to the last text block
|
|
cached_content = system_content.copy()
|
|
for i in range(len(cached_content) - 1, -1, -1):
|
|
if cached_content[i].get("type") == "text":
|
|
cached_content[i]["cache_control"] = {"type": "ephemeral"}
|
|
break
|
|
return cached_content
|
|
|
|
return system_content
|
|
|
|
def _add_cache_control_to_messages(self, messages):
|
|
"""
|
|
Add cache control to the final content block of the final message.
|
|
|
|
This enables incremental conversation caching per Anthropic docs:
|
|
"During each turn, we mark the final block of the final message with cache_control
|
|
so the conversation can be incrementally cached."
|
|
|
|
Args:
|
|
messages: List of Anthropic-formatted message dicts
|
|
|
|
Returns:
|
|
Modified messages list with cache_control on final block
|
|
"""
|
|
if not messages or len(messages) == 0:
|
|
return messages
|
|
|
|
# Work backwards to find the last message with content
|
|
for i in range(len(messages) - 1, -1, -1):
|
|
message = messages[i]
|
|
content = message.get("content")
|
|
|
|
if not content:
|
|
continue
|
|
|
|
# Handle string content
|
|
if isinstance(content, str):
|
|
messages[i]["content"] = [{"type": "text", "text": content, "cache_control": {"type": "ephemeral"}}]
|
|
return messages
|
|
|
|
# Handle list content - add cache_control to the last block
|
|
if isinstance(content, list) and len(content) > 0:
|
|
# Add cache_control to the last content block
|
|
messages[i]["content"][-1]["cache_control"] = {"type": "ephemeral"}
|
|
return messages
|
|
|
|
return messages
|
|
|
|
|
|
def _supports_structured_outputs(model: str) -> bool:
|
|
"""Check if the model supports structured outputs (strict mode).
|
|
|
|
Only these 4 models are supported:
|
|
- Claude Sonnet 4.5
|
|
- Claude Opus 4.1
|
|
- Claude Opus 4.5
|
|
- Claude Haiku 4.5
|
|
"""
|
|
model_lower = model.lower()
|
|
|
|
if "sonnet-4-5" in model_lower:
|
|
return True
|
|
elif "opus-4-1" in model_lower:
|
|
return True
|
|
elif "opus-4-5" in model_lower:
|
|
return True
|
|
elif "haiku-4-5" in model_lower:
|
|
return True
|
|
|
|
return False
|
|
|
|
|
|
def convert_tools_to_anthropic_format(
|
|
tools: List[OpenAITool],
|
|
use_strict: bool = False,
|
|
add_strict_field: bool = True,
|
|
) -> List[dict]:
|
|
"""See: https://docs.anthropic.com/claude/docs/tool-use
|
|
|
|
OpenAI style:
|
|
"tools": [{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "find_movies",
|
|
"description": "find ....",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {...},
|
|
"required": List[str],
|
|
}
|
|
}
|
|
}]
|
|
|
|
Anthropic style:
|
|
"tools": [{
|
|
"name": "find_movies",
|
|
"description": "find ....",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {...},
|
|
"required": List[str],
|
|
},
|
|
}]
|
|
|
|
Args:
|
|
tools: List of OpenAI-style tools to convert
|
|
use_strict: If True, add additionalProperties: false to all object schemas
|
|
add_strict_field: If True (and use_strict=True), add strict: true to allowlisted tools.
|
|
Set to False for token counting endpoint which doesn't support this field.
|
|
"""
|
|
formatted_tools = []
|
|
strict_count = 0
|
|
|
|
for tool in tools:
|
|
# Get the input schema
|
|
input_schema = tool.function.parameters or {"type": "object", "properties": {}, "required": []}
|
|
|
|
# Use the older lightweight cleanup: remove defaults and simplify union-with-null.
|
|
# When using structured outputs (use_strict=True), also add additionalProperties: false to all object types.
|
|
cleaned_schema = (
|
|
_clean_property_schema(input_schema, add_additional_properties_false=use_strict)
|
|
if isinstance(input_schema, dict)
|
|
else input_schema
|
|
)
|
|
# Normalize to a safe "object" schema shape to avoid downstream assumptions failing.
|
|
if isinstance(cleaned_schema, dict):
|
|
if cleaned_schema.get("type") != "object":
|
|
cleaned_schema["type"] = "object"
|
|
if not isinstance(cleaned_schema.get("properties"), dict):
|
|
cleaned_schema["properties"] = {}
|
|
# Ensure additionalProperties: false for structured outputs on the top-level schema
|
|
# Must override any existing additionalProperties: true as well
|
|
if use_strict:
|
|
cleaned_schema["additionalProperties"] = False
|
|
formatted_tool: dict = {
|
|
"name": tool.function.name,
|
|
"description": tool.function.description if tool.function.description else "",
|
|
"input_schema": cleaned_schema,
|
|
}
|
|
|
|
# Structured outputs "strict" mode: always attach `strict` for allowlisted tools
|
|
# when we are using structured outputs models. Limit the number of strict tools
|
|
# to avoid exceeding Anthropic constraints.
|
|
# NOTE: The token counting endpoint does NOT support `strict` - only the messages endpoint does.
|
|
if (
|
|
use_strict
|
|
and add_strict_field
|
|
and tool.function.name in ANTHROPIC_STRICT_MODE_ALLOWLIST
|
|
and strict_count < ANTHROPIC_MAX_STRICT_TOOLS
|
|
):
|
|
formatted_tool["strict"] = True
|
|
strict_count += 1
|
|
|
|
formatted_tools.append(formatted_tool)
|
|
|
|
return formatted_tools
|
|
|
|
|
|
def _clean_property_schema(schema: dict, add_additional_properties_false: bool = False) -> dict:
|
|
"""Older schema cleanup used for Anthropic tools.
|
|
|
|
Removes / simplifies fields that commonly cause Anthropic tool schema issues:
|
|
- Remove `default` values
|
|
- Simplify nullable unions like {"type": ["null", "string"]} -> {"type": "string"}
|
|
- Recurse through nested schemas (properties/items/anyOf/oneOf/allOf/etc.)
|
|
- Optionally add additionalProperties: false to object types (required for structured outputs)
|
|
"""
|
|
if not isinstance(schema, dict):
|
|
return schema
|
|
|
|
cleaned: dict = {}
|
|
|
|
# Simplify union types like ["null", "string"] to "string"
|
|
if "type" in schema:
|
|
t = schema.get("type")
|
|
if isinstance(t, list):
|
|
non_null = [x for x in t if x != "null"]
|
|
if len(non_null) == 1:
|
|
cleaned["type"] = non_null[0]
|
|
elif len(non_null) > 1:
|
|
cleaned["type"] = non_null
|
|
else:
|
|
cleaned["type"] = "string"
|
|
else:
|
|
cleaned["type"] = t
|
|
|
|
for key, value in schema.items():
|
|
if key == "type":
|
|
continue
|
|
if key == "default":
|
|
continue
|
|
|
|
if key == "properties" and isinstance(value, dict):
|
|
cleaned["properties"] = {k: _clean_property_schema(v, add_additional_properties_false) for k, v in value.items()}
|
|
elif key == "items" and isinstance(value, dict):
|
|
cleaned["items"] = _clean_property_schema(value, add_additional_properties_false)
|
|
elif key in ("anyOf", "oneOf", "allOf") and isinstance(value, list):
|
|
cleaned[key] = [_clean_property_schema(v, add_additional_properties_false) if isinstance(v, dict) else v for v in value]
|
|
elif key in ("additionalProperties",) and isinstance(value, dict):
|
|
cleaned[key] = _clean_property_schema(value, add_additional_properties_false)
|
|
else:
|
|
cleaned[key] = value
|
|
|
|
# For structured outputs, Anthropic requires additionalProperties: false on all object types
|
|
# We must override any existing additionalProperties: true as well
|
|
if add_additional_properties_false and cleaned.get("type") == "object":
|
|
cleaned["additionalProperties"] = False
|
|
|
|
return cleaned
|
|
|
|
|
|
def is_heartbeat(message: dict, is_ping: bool = False) -> bool:
|
|
"""Check if the message is an automated heartbeat ping"""
|
|
|
|
if "role" not in message or message["role"] != "user" or "content" not in message:
|
|
return False
|
|
|
|
try:
|
|
message_json = json.loads(message["content"])
|
|
except:
|
|
return False
|
|
|
|
# Check if message_json is a dict (not int, str, list, etc.)
|
|
if not isinstance(message_json, dict):
|
|
return False
|
|
|
|
if "reason" not in message_json:
|
|
return False
|
|
|
|
if message_json.get("type") != "heartbeat":
|
|
return False
|
|
|
|
if not is_ping:
|
|
# Just checking if 'type': 'heartbeat'
|
|
return True
|
|
else:
|
|
# Also checking if it's specifically a 'ping' style message
|
|
# NOTE: this will not catch tool rule heartbeats
|
|
if REQ_HEARTBEAT_MESSAGE in message_json["reason"] or FUNC_FAILED_HEARTBEAT_MESSAGE in message_json["reason"]:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
def drop_heartbeats(messages: List[dict]):
|
|
cleaned_messages = []
|
|
|
|
# Loop through messages
|
|
# For messages with role 'user' and len(content) > 1,
|
|
# Check if content[0].type == 'tool_result'
|
|
# If so, iterate over content[1:] and while content.type == 'text' and is_heartbeat(content.text),
|
|
# merge into content[0].content
|
|
|
|
for message in messages:
|
|
if "role" in message and "content" in message and message["role"] == "user":
|
|
content_parts = message["content"]
|
|
|
|
if isinstance(content_parts, str):
|
|
if is_heartbeat({"role": "user", "content": content_parts}):
|
|
continue
|
|
elif isinstance(content_parts, list) and len(content_parts) == 1 and "text" in content_parts[0]:
|
|
if is_heartbeat({"role": "user", "content": content_parts[0]["text"]}):
|
|
continue # skip
|
|
else:
|
|
cleaned_parts = []
|
|
# Drop all the parts
|
|
for content_part in content_parts:
|
|
if "text" in content_part and is_heartbeat({"role": "user", "content": content_part["text"]}):
|
|
continue # skip
|
|
else:
|
|
cleaned_parts.append(content_part)
|
|
|
|
if len(cleaned_parts) == 0:
|
|
continue
|
|
else:
|
|
message["content"] = cleaned_parts
|
|
|
|
cleaned_messages.append(message)
|
|
|
|
return cleaned_messages
|
|
|
|
|
|
def merge_heartbeats_into_tool_responses(messages: List[dict]):
|
|
"""For extended thinking mode, we don't want anything other than tool responses in-between assistant actions
|
|
|
|
Otherwise, the thinking will silently get dropped.
|
|
|
|
NOTE: assumes merge_tool_results_into_user_messages has already been called
|
|
"""
|
|
|
|
merged_messages = []
|
|
|
|
# Loop through messages
|
|
# For messages with role 'user' and len(content) > 1,
|
|
# Check if content[0].type == 'tool_result'
|
|
# If so, iterate over content[1:] and while content.type == 'text' and is_heartbeat(content.text),
|
|
# merge into content[0].content
|
|
|
|
for message in messages:
|
|
if "role" not in message or "content" not in message:
|
|
# Skip invalid messages
|
|
merged_messages.append(message)
|
|
continue
|
|
|
|
if message["role"] == "user" and len(message["content"]) > 1:
|
|
content_parts = message["content"]
|
|
|
|
# If the first content part is a tool result, merge the heartbeat content into index 0 of the content
|
|
# Two end cases:
|
|
# 1. It was [tool_result, heartbeat], in which case merged result is [tool_result+heartbeat] (len 1)
|
|
# 2. It was [tool_result, user_text], in which case it should be unchanged (len 2)
|
|
if "type" in content_parts[0] and "content" in content_parts[0] and content_parts[0]["type"] == "tool_result":
|
|
new_content_parts = [content_parts[0]]
|
|
|
|
# If the first content part is a tool result, merge the heartbeat content into index 0 of the content
|
|
for i, content_part in enumerate(content_parts[1:]):
|
|
# If it's a heartbeat, add it to the merge
|
|
if (
|
|
content_part["type"] == "text"
|
|
and "text" in content_part
|
|
and is_heartbeat({"role": "user", "content": content_part["text"]})
|
|
):
|
|
# NOTE: joining with a ','
|
|
new_content_parts[0]["content"] += ", " + content_part["text"]
|
|
|
|
# If it's not, break, and concat to finish
|
|
else:
|
|
# Append the rest directly, no merging of content strings
|
|
new_content_parts.extend(content_parts[i + 1 :])
|
|
break
|
|
|
|
# Set the content_parts
|
|
message["content"] = new_content_parts
|
|
merged_messages.append(message)
|
|
|
|
else:
|
|
# Skip invalid messages parts
|
|
merged_messages.append(message)
|
|
continue
|
|
else:
|
|
merged_messages.append(message)
|
|
|
|
return merged_messages
|
|
|
|
|
|
def merge_tool_results_into_user_messages(messages: List[dict]):
|
|
"""Anthropic API doesn't allow role 'tool'->'user' sequences
|
|
|
|
Example HTTP error:
|
|
messages: roles must alternate between "user" and "assistant", but found multiple "user" roles in a row
|
|
|
|
From: https://docs.anthropic.com/claude/docs/tool-use
|
|
You may be familiar with other APIs that return tool use as separate from the model's primary output,
|
|
or which use a special-purpose tool or function message role.
|
|
In contrast, Anthropic's models and API are built around alternating user and assistant messages,
|
|
where each message is an array of rich content blocks: text, image, tool_use, and tool_result.
|
|
"""
|
|
|
|
# TODO walk through the messages list
|
|
# When a dict (dict_A) with 'role' == 'user' is followed by a dict with 'role' == 'user' (dict B), do the following
|
|
# dict_A["content"] = dict_A["content"] + dict_B["content"]
|
|
|
|
# The result should be a new merged_messages list that doesn't have any back-to-back dicts with 'role' == 'user'
|
|
merged_messages = []
|
|
if not messages:
|
|
return merged_messages
|
|
|
|
# Start with the first message in the list
|
|
current_message = messages[0]
|
|
|
|
for next_message in messages[1:]:
|
|
if current_message["role"] == "user" and next_message["role"] == "user":
|
|
# Merge contents of the next user message into current one
|
|
current_content = (
|
|
current_message["content"]
|
|
if isinstance(current_message["content"], list)
|
|
else [{"type": "text", "text": current_message["content"]}]
|
|
)
|
|
next_content = (
|
|
next_message["content"]
|
|
if isinstance(next_message["content"], list)
|
|
else [{"type": "text", "text": next_message["content"]}]
|
|
)
|
|
merged_content: list = current_content + next_content
|
|
current_message["content"] = merged_content
|
|
else:
|
|
# Append the current message to result as it's complete
|
|
merged_messages.append(current_message)
|
|
# Move on to the next message
|
|
current_message = next_message
|
|
|
|
# Append the last processed message to the result
|
|
merged_messages.append(current_message)
|
|
|
|
return merged_messages
|
|
|
|
|
|
def dedupe_tool_results_in_user_messages(messages: List[dict]) -> List[dict]:
|
|
"""Ensure each tool_use has a single tool_result within a user message.
|
|
|
|
If multiple tool_result blocks with the same tool_use_id appear in the same user message
|
|
(e.g., after merging consecutive user messages), merge their content and keep only one block.
|
|
"""
|
|
any_deduped = False
|
|
dedup_counts: dict[str, int] = {}
|
|
|
|
for msg in messages:
|
|
if not isinstance(msg, dict):
|
|
continue
|
|
if msg.get("role") != "user":
|
|
continue
|
|
content = msg.get("content")
|
|
if not isinstance(content, list) or len(content) == 0:
|
|
continue
|
|
|
|
seen: dict[str, dict] = {}
|
|
new_content: list = []
|
|
for block in content:
|
|
if isinstance(block, dict) and block.get("type") == "tool_result" and "tool_use_id" in block:
|
|
tid = block.get("tool_use_id")
|
|
if tid in seen:
|
|
# Merge duplicate tool_result into the first occurrence
|
|
first = seen[tid]
|
|
extra = block.get("content")
|
|
if extra:
|
|
if isinstance(first.get("content"), str) and isinstance(extra, str):
|
|
sep = "\n" if first["content"] and extra else ""
|
|
first["content"] = f"{first['content']}{sep}{extra}"
|
|
else:
|
|
sep = "\n" if first.get("content") else ""
|
|
# Fallback: coerce to strings and concat
|
|
first["content"] = f"{first.get('content')}{sep}{extra}"
|
|
any_deduped = True
|
|
dedup_counts[tid] = dedup_counts.get(tid, 0) + 1
|
|
# Skip appending duplicate
|
|
continue
|
|
else:
|
|
new_content.append(block)
|
|
seen[tid] = block
|
|
else:
|
|
new_content.append(block)
|
|
|
|
# Replace content if we pruned/merged duplicates
|
|
if len(new_content) != len(content):
|
|
msg["content"] = new_content
|
|
|
|
if any_deduped:
|
|
logger.error("[Anthropic] Deduped tool_result blocks in user messages: %s", dedup_counts)
|
|
|
|
return messages
|
|
|
|
|
|
def remap_finish_reason(stop_reason: str) -> str:
|
|
"""Remap Anthropic's 'stop_reason' to OpenAI 'finish_reason'
|
|
|
|
OpenAI: 'stop', 'length', 'function_call', 'content_filter', null
|
|
see: https://platform.openai.com/docs/guides/text-generation/chat-completions-api
|
|
|
|
From: https://docs.anthropic.com/claude/reference/migrating-from-text-completions-to-messages#stop-reason
|
|
|
|
Messages have a stop_reason of one of the following values:
|
|
"end_turn": The conversational turn ended naturally.
|
|
"stop_sequence": One of your specified custom stop sequences was generated.
|
|
"max_tokens": (unchanged)
|
|
|
|
"""
|
|
if stop_reason == "end_turn":
|
|
return "stop"
|
|
elif stop_reason == "stop_sequence":
|
|
return "stop"
|
|
elif stop_reason == "max_tokens":
|
|
return "length"
|
|
elif stop_reason == "tool_use":
|
|
return "function_call"
|
|
else:
|
|
raise LLMServerError(f"Unexpected stop_reason: {stop_reason}")
|
|
|
|
|
|
def strip_xml_tags(string: str, tag: Optional[str]) -> str:
|
|
if tag is None:
|
|
return string
|
|
# Construct the regular expression pattern to find the start and end tags
|
|
tag_pattern = f"<{tag}.*?>|</{tag}>"
|
|
# Use the regular expression to replace the tags with an empty string
|
|
return re.sub(tag_pattern, "", string)
|
|
|
|
|
|
def strip_xml_tags_streaming(string: str, tag: Optional[str]) -> str:
|
|
if tag is None:
|
|
return string
|
|
|
|
# Handle common partial tag cases
|
|
parts_to_remove = [
|
|
"<", # Leftover start bracket
|
|
f"<{tag}", # Opening tag start
|
|
f"</{tag}", # Closing tag start
|
|
f"/{tag}>", # Closing tag end
|
|
f"{tag}>", # Opening tag end
|
|
f"/{tag}", # Partial closing tag without >
|
|
">", # Leftover end bracket
|
|
]
|
|
|
|
result = string
|
|
for part in parts_to_remove:
|
|
result = result.replace(part, "")
|
|
|
|
return result
|