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feat: add streaming tool call events; fix provider id tracking; add tests and cassettes

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Adds support for streaming tool call events with test coverage, fixes tool-stream ID tracking (including OpenAI-style tracking for Azure), improves Gemini tool calling + streaming tests, adds Anthropic tests, generates Azure cassettes, and fixes Azure cassette URIs.
This commit is contained in:
Greyson LaLonde
2026-01-05 14:33:36 -05:00
committed by GitHub
parent f3c17a249b
commit f8deb0fd18
15 changed files with 1798 additions and 60 deletions
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12
lib/crewai/src/crewai/llms/base_llm.py
View File

@@ -354,8 +354,17 @@ class BaseLLM(ABC):
from_task: Task | None = None,
from_agent: Agent | None = None,
tool_call: dict[str, Any] | None = None,
call_type: LLMCallType | None = None,
) -> None:
"""Emit stream chunk event."""
"""Emit stream chunk event.
Args:
chunk: The text content of the chunk.
from_task: The task that initiated the call.
from_agent: The agent that initiated the call.
tool_call: Tool call information if this is a tool call chunk.
call_type: The type of LLM call (LLM_CALL or TOOL_CALL).
"""
if not hasattr(crewai_event_bus, "emit"):
raise ValueError("crewai_event_bus does not have an emit method") from None
@@ -366,6 +375,7 @@ class BaseLLM(ABC):
tool_call=tool_call,
from_task=from_task,
from_agent=from_agent,
call_type=call_type,
),
)

102
lib/crewai/src/crewai/llms/providers/anthropic/completion.py
View File

@@ -598,6 +598,8 @@ class AnthropicCompletion(BaseLLM):
# (the SDK sets it internally)
stream_params = {k: v for k, v in params.items() if k != "stream"}
current_tool_calls: dict[int, dict[str, Any]] = {}
# Make streaming API call
with self.client.messages.stream(**stream_params) as stream:
for event in stream:
@@ -610,6 +612,55 @@ class AnthropicCompletion(BaseLLM):
from_agent=from_agent,
)
if event.type == "content_block_start":
block = event.content_block
if block.type == "tool_use":
block_index = event.index
current_tool_calls[block_index] = {
"id": block.id,
"name": block.name,
"arguments": "",
"index": block_index,
}
self._emit_stream_chunk_event(
chunk="",
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": block.id,
"function": {
"name": block.name,
"arguments": "",
},
"type": "function",
"index": block_index,
},
call_type=LLMCallType.TOOL_CALL,
)
elif event.type == "content_block_delta":
if event.delta.type == "input_json_delta":
block_index = event.index
partial_json = event.delta.partial_json
if block_index in current_tool_calls and partial_json:
current_tool_calls[block_index]["arguments"] += partial_json
self._emit_stream_chunk_event(
chunk=partial_json,
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": current_tool_calls[block_index]["id"],
"function": {
"name": current_tool_calls[block_index]["name"],
"arguments": current_tool_calls[block_index][
"arguments"
],
},
"type": "function",
"index": block_index,
},
call_type=LLMCallType.TOOL_CALL,
)
final_message: Message = stream.get_final_message()
thinking_blocks: list[ThinkingBlock] = []
@@ -941,6 +992,8 @@ class AnthropicCompletion(BaseLLM):
stream_params = {k: v for k, v in params.items() if k != "stream"}
current_tool_calls: dict[int, dict[str, Any]] = {}
async with self.async_client.messages.stream(**stream_params) as stream:
async for event in stream:
if hasattr(event, "delta") and hasattr(event.delta, "text"):
@@ -952,6 +1005,55 @@ class AnthropicCompletion(BaseLLM):
from_agent=from_agent,
)
if event.type == "content_block_start":
block = event.content_block
if block.type == "tool_use":
block_index = event.index
current_tool_calls[block_index] = {
"id": block.id,
"name": block.name,
"arguments": "",
"index": block_index,
}
self._emit_stream_chunk_event(
chunk="",
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": block.id,
"function": {
"name": block.name,
"arguments": "",
},
"type": "function",
"index": block_index,
},
call_type=LLMCallType.TOOL_CALL,
)
elif event.type == "content_block_delta":
if event.delta.type == "input_json_delta":
block_index = event.index
partial_json = event.delta.partial_json
if block_index in current_tool_calls and partial_json:
current_tool_calls[block_index]["arguments"] += partial_json
self._emit_stream_chunk_event(
chunk=partial_json,
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": current_tool_calls[block_index]["id"],
"function": {
"name": current_tool_calls[block_index]["name"],
"arguments": current_tool_calls[block_index][
"arguments"
],
},
"type": "function",
"index": block_index,
},
call_type=LLMCallType.TOOL_CALL,
)
final_message: Message = await stream.get_final_message()
usage = self._extract_anthropic_token_usage(final_message)

40
lib/crewai/src/crewai/llms/providers/azure/completion.py
View File

@@ -674,7 +674,7 @@ class AzureCompletion(BaseLLM):
self,
update: StreamingChatCompletionsUpdate,
full_response: str,
tool_calls: dict[str, dict[str, str]],
tool_calls: dict[int, dict[str, Any]],
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str:
@@ -702,25 +702,45 @@ class AzureCompletion(BaseLLM):
)
if choice.delta and choice.delta.tool_calls:
for tool_call in choice.delta.tool_calls:
call_id = tool_call.id or "default"
if call_id not in tool_calls:
tool_calls[call_id] = {
for idx, tool_call in enumerate(choice.delta.tool_calls):
if idx not in tool_calls:
tool_calls[idx] = {
"id": tool_call.id,
"name": "",
"arguments": "",
}
elif tool_call.id and not tool_calls[idx]["id"]:
tool_calls[idx]["id"] = tool_call.id
if tool_call.function and tool_call.function.name:
tool_calls[call_id]["name"] = tool_call.function.name
tool_calls[idx]["name"] = tool_call.function.name
if tool_call.function and tool_call.function.arguments:
tool_calls[call_id]["arguments"] += tool_call.function.arguments
tool_calls[idx]["arguments"] += tool_call.function.arguments
self._emit_stream_chunk_event(
chunk=tool_call.function.arguments
if tool_call.function and tool_call.function.arguments
else "",
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": tool_calls[idx]["id"],
"function": {
"name": tool_calls[idx]["name"],
"arguments": tool_calls[idx]["arguments"],
},
"type": "function",
"index": idx,
},
call_type=LLMCallType.TOOL_CALL,
)
return full_response
def _finalize_streaming_response(
self,
full_response: str,
tool_calls: dict[str, dict[str, str]],
tool_calls: dict[int, dict[str, Any]],
usage_data: dict[str, int],
params: AzureCompletionParams,
available_functions: dict[str, Any] | None = None,
@@ -804,7 +824,7 @@ class AzureCompletion(BaseLLM):
) -> str | Any:
"""Handle streaming chat completion."""
full_response = ""
tool_calls: dict[str, dict[str, Any]] = {}
tool_calls: dict[int, dict[str, Any]] = {}
usage_data = {"total_tokens": 0}
for update in self.client.complete(**params): # type: ignore[arg-type]
@@ -870,7 +890,7 @@ class AzureCompletion(BaseLLM):
) -> str | Any:
"""Handle streaming chat completion asynchronously."""
full_response = ""
tool_calls: dict[str, dict[str, Any]] = {}
tool_calls: dict[int, dict[str, Any]] = {}
usage_data = {"total_tokens": 0}

108
lib/crewai/src/crewai/llms/providers/bedrock/completion.py
View File

@@ -315,9 +315,7 @@ class BedrockCompletion(BaseLLM):
messages
)
if not self._invoke_before_llm_call_hooks(
cast(list[LLMMessage], formatted_messages), from_agent
):
if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
raise ValueError("LLM call blocked by before_llm_call hook")
# Prepare request body
@@ -361,7 +359,7 @@ class BedrockCompletion(BaseLLM):
if self.stream:
return self._handle_streaming_converse(
cast(list[LLMMessage], formatted_messages),
formatted_messages,
body,
available_functions,
from_task,
@@ -369,7 +367,7 @@ class BedrockCompletion(BaseLLM):
)
return self._handle_converse(
cast(list[LLMMessage], formatted_messages),
formatted_messages,
body,
available_functions,
from_task,
@@ -433,7 +431,7 @@ class BedrockCompletion(BaseLLM):
)
formatted_messages, system_message = self._format_messages_for_converse(
messages # type: ignore[arg-type]
messages
)
body: BedrockConverseRequestBody = {
@@ -687,8 +685,10 @@ class BedrockCompletion(BaseLLM):
) -> str:
"""Handle streaming converse API call with comprehensive event handling."""
full_response = ""
current_tool_use = None
tool_use_id = None
current_tool_use: dict[str, Any] | None = None
tool_use_id: str | None = None
tool_use_index = 0
accumulated_tool_input = ""
try:
response = self.client.converse_stream(
@@ -709,9 +709,30 @@ class BedrockCompletion(BaseLLM):
elif "contentBlockStart" in event:
start = event["contentBlockStart"].get("start", {})
content_block_index = event["contentBlockStart"].get(
"contentBlockIndex", 0
)
if "toolUse" in start:
current_tool_use = start["toolUse"]
tool_use_block = start["toolUse"]
current_tool_use = cast(dict[str, Any], tool_use_block)
tool_use_id = current_tool_use.get("toolUseId")
tool_use_index = content_block_index
accumulated_tool_input = ""
self._emit_stream_chunk_event(
chunk="",
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": tool_use_id or "",
"function": {
"name": current_tool_use.get("name", ""),
"arguments": "",
},
"type": "function",
"index": tool_use_index,
},
call_type=LLMCallType.TOOL_CALL,
)
logging.debug(
f"Tool use started in stream: {json.dumps(current_tool_use)} (ID: {tool_use_id})"
)
@@ -730,7 +751,23 @@ class BedrockCompletion(BaseLLM):
elif "toolUse" in delta and current_tool_use:
tool_input = delta["toolUse"].get("input", "")
if tool_input:
accumulated_tool_input += tool_input
logging.debug(f"Tool input delta: {tool_input}")
self._emit_stream_chunk_event(
chunk=tool_input,
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": tool_use_id or "",
"function": {
"name": current_tool_use.get("name", ""),
"arguments": accumulated_tool_input,
},
"type": "function",
"index": tool_use_index,
},
call_type=LLMCallType.TOOL_CALL,
)
elif "contentBlockStop" in event:
logging.debug("Content block stopped in stream")
if current_tool_use and available_functions:
@@ -848,7 +885,7 @@ class BedrockCompletion(BaseLLM):
async def _ahandle_converse(
self,
messages: list[dict[str, Any]],
messages: list[LLMMessage],
body: BedrockConverseRequestBody,
available_functions: Mapping[str, Any] | None = None,
from_task: Any | None = None,
@@ -1013,7 +1050,7 @@ class BedrockCompletion(BaseLLM):
async def _ahandle_streaming_converse(
self,
messages: list[dict[str, Any]],
messages: list[LLMMessage],
body: BedrockConverseRequestBody,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
@@ -1021,8 +1058,10 @@ class BedrockCompletion(BaseLLM):
) -> str:
"""Handle async streaming converse API call."""
full_response = ""
current_tool_use = None
tool_use_id = None
current_tool_use: dict[str, Any] | None = None
tool_use_id: str | None = None
tool_use_index = 0
accumulated_tool_input = ""
try:
async_client = await self._ensure_async_client()
@@ -1044,9 +1083,30 @@ class BedrockCompletion(BaseLLM):
elif "contentBlockStart" in event:
start = event["contentBlockStart"].get("start", {})
content_block_index = event["contentBlockStart"].get(
"contentBlockIndex", 0
)
if "toolUse" in start:
current_tool_use = start["toolUse"]
tool_use_block = start["toolUse"]
current_tool_use = cast(dict[str, Any], tool_use_block)
tool_use_id = current_tool_use.get("toolUseId")
tool_use_index = content_block_index
accumulated_tool_input = ""
self._emit_stream_chunk_event(
chunk="",
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": tool_use_id or "",
"function": {
"name": current_tool_use.get("name", ""),
"arguments": "",
},
"type": "function",
"index": tool_use_index,
},
call_type=LLMCallType.TOOL_CALL,
)
logging.debug(
f"Tool use started in stream: {current_tool_use.get('name')} (ID: {tool_use_id})"
)
@@ -1065,7 +1125,23 @@ class BedrockCompletion(BaseLLM):
elif "toolUse" in delta and current_tool_use:
tool_input = delta["toolUse"].get("input", "")
if tool_input:
accumulated_tool_input += tool_input
logging.debug(f"Tool input delta: {tool_input}")
self._emit_stream_chunk_event(
chunk=tool_input,
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": tool_use_id or "",
"function": {
"name": current_tool_use.get("name", ""),
"arguments": accumulated_tool_input,
},
"type": "function",
"index": tool_use_index,
},
call_type=LLMCallType.TOOL_CALL,
)
elif "contentBlockStop" in event:
logging.debug("Content block stopped in stream")
@@ -1174,7 +1250,7 @@ class BedrockCompletion(BaseLLM):
def _format_messages_for_converse(
self, messages: str | list[LLMMessage]
) -> tuple[list[dict[str, Any]], str | None]:
) -> tuple[list[LLMMessage], str | None]:
"""Format messages for Converse API following AWS documentation.
Note: Returns dict[str, Any] instead of LLMMessage because Bedrock uses
@@ -1184,7 +1260,7 @@ class BedrockCompletion(BaseLLM):
# Use base class formatting first
formatted_messages = self._format_messages(messages)
converse_messages: list[dict[str, Any]] = []
converse_messages: list[LLMMessage] = []
system_message: str | None = None
for message in formatted_messages:

62
lib/crewai/src/crewai/llms/providers/gemini/completion.py
View File

@@ -1,5 +1,6 @@
from __future__ import annotations
import json
import logging
import os
import re
@@ -24,7 +25,7 @@ try:
from google import genai
from google.genai import types
from google.genai.errors import APIError
from google.genai.types import GenerateContentResponse, Schema
from google.genai.types import GenerateContentResponse
except ImportError:
raise ImportError(
'Google Gen AI native provider not available, to install: uv add "crewai[google-genai]"'
@@ -434,12 +435,9 @@ class GeminiCompletion(BaseLLM):
function_declaration = types.FunctionDeclaration(
name=name,
description=description,
parameters=parameters if parameters else None,
)
# Add parameters if present - ensure parameters is a dict
if parameters and isinstance(parameters, Schema):
function_declaration.parameters = parameters
gemini_tool = types.Tool(function_declarations=[function_declaration])
gemini_tools.append(gemini_tool)
@@ -609,7 +607,7 @@ class GeminiCompletion(BaseLLM):
candidate = response.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
if hasattr(part, "function_call") and part.function_call:
if part.function_call:
function_name = part.function_call.name
if function_name is None:
continue
@@ -645,17 +643,17 @@ class GeminiCompletion(BaseLLM):
self,
chunk: GenerateContentResponse,
full_response: str,
function_calls: dict[str, dict[str, Any]],
function_calls: dict[int, dict[str, Any]],
usage_data: dict[str, int],
from_task: Any | None = None,
from_agent: Any | None = None,
) -> tuple[str, dict[str, dict[str, Any]], dict[str, int]]:
) -> tuple[str, dict[int, dict[str, Any]], dict[str, int]]:
"""Process a single streaming chunk.
Args:
chunk: The streaming chunk response
full_response: Accumulated response text
function_calls: Accumulated function calls
function_calls: Accumulated function calls keyed by sequential index
usage_data: Accumulated usage data
from_task: Task that initiated the call
from_agent: Agent that initiated the call
@@ -678,22 +676,44 @@ class GeminiCompletion(BaseLLM):
candidate = chunk.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
if hasattr(part, "function_call") and part.function_call:
call_id = part.function_call.name or "default"
if call_id not in function_calls:
function_calls[call_id] = {
"name": part.function_call.name,
"args": dict(part.function_call.args)
if part.function_call.args
else {},
}
if part.function_call:
call_index = len(function_calls)
call_id = f"call_{call_index}"
args_dict = (
dict(part.function_call.args)
if part.function_call.args
else {}
)
args_json = json.dumps(args_dict)
function_calls[call_index] = {
"id": call_id,
"name": part.function_call.name,
"args": args_dict,
}
self._emit_stream_chunk_event(
chunk=args_json,
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": call_id,
"function": {
"name": part.function_call.name or "",
"arguments": args_json,
},
"type": "function",
"index": call_index,
},
call_type=LLMCallType.TOOL_CALL,
)
return full_response, function_calls, usage_data
def _finalize_streaming_response(
self,
full_response: str,
function_calls: dict[str, dict[str, Any]],
function_calls: dict[int, dict[str, Any]],
usage_data: dict[str, int],
contents: list[types.Content],
available_functions: dict[str, Any] | None = None,
@@ -800,7 +820,7 @@ class GeminiCompletion(BaseLLM):
) -> str:
"""Handle streaming content generation."""
full_response = ""
function_calls: dict[str, dict[str, Any]] = {}
function_calls: dict[int, dict[str, Any]] = {}
usage_data = {"total_tokens": 0}
# The API accepts list[Content] but mypy is overly strict about variance
@@ -878,7 +898,7 @@ class GeminiCompletion(BaseLLM):
) -> str:
"""Handle async streaming content generation."""
full_response = ""
function_calls: dict[str, dict[str, Any]] = {}
function_calls: dict[int, dict[str, Any]] = {}
usage_data = {"total_tokens": 0}
# The API accepts list[Content] but mypy is overly strict about variance

72
lib/crewai/src/crewai/llms/providers/openai/completion.py
View File

@@ -521,7 +521,7 @@ class OpenAICompletion(BaseLLM):
) -> str:
"""Handle streaming chat completion."""
full_response = ""
tool_calls = {}
tool_calls: dict[int, dict[str, Any]] = {}
if response_model:
parse_params = {
@@ -591,17 +591,41 @@ class OpenAICompletion(BaseLLM):
if chunk_delta.tool_calls:
for tool_call in chunk_delta.tool_calls:
call_id = tool_call.id or "default"
if call_id not in tool_calls:
tool_calls[call_id] = {
tool_index = tool_call.index if tool_call.index is not None else 0
if tool_index not in tool_calls:
tool_calls[tool_index] = {
"id": tool_call.id,
"name": "",
"arguments": "",
"index": tool_index,
}
elif tool_call.id and not tool_calls[tool_index]["id"]:
tool_calls[tool_index]["id"] = tool_call.id
if tool_call.function and tool_call.function.name:
tool_calls[call_id]["name"] = tool_call.function.name
tool_calls[tool_index]["name"] = tool_call.function.name
if tool_call.function and tool_call.function.arguments:
tool_calls[call_id]["arguments"] += tool_call.function.arguments
tool_calls[tool_index]["arguments"] += (
tool_call.function.arguments
)
self._emit_stream_chunk_event(
chunk=tool_call.function.arguments
if tool_call.function and tool_call.function.arguments
else "",
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": tool_calls[tool_index]["id"],
"function": {
"name": tool_calls[tool_index]["name"],
"arguments": tool_calls[tool_index]["arguments"],
},
"type": "function",
"index": tool_calls[tool_index]["index"],
},
call_type=LLMCallType.TOOL_CALL,
)
self._track_token_usage_internal(usage_data)
@@ -789,7 +813,7 @@ class OpenAICompletion(BaseLLM):
) -> str:
"""Handle async streaming chat completion."""
full_response = ""
tool_calls = {}
tool_calls: dict[int, dict[str, Any]] = {}
if response_model:
completion_stream: AsyncIterator[
@@ -870,17 +894,41 @@ class OpenAICompletion(BaseLLM):
if chunk_delta.tool_calls:
for tool_call in chunk_delta.tool_calls:
call_id = tool_call.id or "default"
if call_id not in tool_calls:
tool_calls[call_id] = {
tool_index = tool_call.index if tool_call.index is not None else 0
if tool_index not in tool_calls:
tool_calls[tool_index] = {
"id": tool_call.id,
"name": "",
"arguments": "",
"index": tool_index,
}
elif tool_call.id and not tool_calls[tool_index]["id"]:
tool_calls[tool_index]["id"] = tool_call.id
if tool_call.function and tool_call.function.name:
tool_calls[call_id]["name"] = tool_call.function.name
tool_calls[tool_index]["name"] = tool_call.function.name
if tool_call.function and tool_call.function.arguments:
tool_calls[call_id]["arguments"] += tool_call.function.arguments
tool_calls[tool_index]["arguments"] += (
tool_call.function.arguments
)
self._emit_stream_chunk_event(
chunk=tool_call.function.arguments
if tool_call.function and tool_call.function.arguments
else "",
from_task=from_task,
from_agent=from_agent,
tool_call={
"id": tool_calls[tool_index]["id"],
"function": {
"name": tool_calls[tool_index]["name"],
"arguments": tool_calls[tool_index]["arguments"],
},
"type": "function",
"index": tool_calls[tool_index]["index"],
},
call_type=LLMCallType.TOOL_CALL,
)
self._track_token_usage_internal(usage_data)

371
lib/crewai/tests/cassettes/llms/TestAnthropicToolCallStreaming.test_anthropic_streaming_emits_tool_call_events.yaml Normal file
View File

@@ -0,0 +1,371 @@
interactions:
- request:
body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the temperature
in San Francisco?"}],"model":"claude-3-5-haiku-latest","tools":[{"name":"get_current_temperature","description":"Get
the current temperature in a city.","input_schema":{"type":"object","properties":{"city":{"type":"string","description":"The
name of the city to get the temperature for."}},"required":["city"]}}],"stream":true}'
headers:
User-Agent:
- X-USER-AGENT-XXX
accept:
- application/json
accept-encoding:
- ACCEPT-ENCODING-XXX
anthropic-version:
- '2023-06-01'
connection:
- keep-alive
content-length:
- '408'
content-type:
- application/json
host:
- api.anthropic.com
x-api-key:
- X-API-KEY-XXX
x-stainless-arch:
- X-STAINLESS-ARCH-XXX
x-stainless-async:
- 'false'
x-stainless-lang:
- python
x-stainless-os:
- X-STAINLESS-OS-XXX
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324
lib/crewai/tests/llms/test_tool_call_streaming.py Normal file
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"""Tests for tool call streaming events across LLM providers.
These tests verify that when streaming is enabled and the LLM makes a tool call,
the stream chunk events include proper tool call information with
call_type=LLMCallType.TOOL_CALL.
"""
from typing import Any
from unittest.mock import MagicMock, patch
import pytest
from crewai.events.types.llm_events import LLMCallType, LLMStreamChunkEvent, ToolCall
from crewai.llm import LLM
@pytest.fixture
def get_temperature_tool_schema() -> dict[str, Any]:
"""Create a temperature tool schema for native function calling."""
return {
"type": "function",
"function": {
"name": "get_current_temperature",
"description": "Get the current temperature in a city.",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The name of the city to get the temperature for.",
}
},
"required": ["city"],
},
},
}
@pytest.fixture
def mock_emit() -> MagicMock:
"""Mock the event bus emit function."""
from crewai.events.event_bus import CrewAIEventsBus
with patch.object(CrewAIEventsBus, "emit") as mock:
yield mock
def get_tool_call_events(mock_emit: MagicMock) -> list[LLMStreamChunkEvent]:
"""Extract tool call streaming events from mock emit calls."""
tool_call_events = []
for call in mock_emit.call_args_list:
event = call[1].get("event") if len(call) > 1 else None
if isinstance(event, LLMStreamChunkEvent) and event.call_type == LLMCallType.TOOL_CALL:
tool_call_events.append(event)
return tool_call_events
def get_all_stream_events(mock_emit: MagicMock) -> list[LLMStreamChunkEvent]:
"""Extract all streaming events from mock emit calls."""
stream_events = []
for call in mock_emit.call_args_list:
event = call[1].get("event") if len(call) > 1 else None
if isinstance(event, LLMStreamChunkEvent):
stream_events.append(event)
return stream_events
class TestOpenAIToolCallStreaming:
"""Tests for OpenAI provider tool call streaming events."""
@pytest.mark.vcr()
def test_openai_streaming_emits_tool_call_events(
self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
) -> None:
"""Test that OpenAI streaming emits tool call events with correct call_type."""
llm = LLM(model="openai/gpt-4o-mini", stream=True)
llm.call(
messages=[
{"role": "user", "content": "What is the temperature in San Francisco?"},
],
tools=[get_temperature_tool_schema],
available_functions={
"get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
},
)
tool_call_events = get_tool_call_events(mock_emit)
assert len(tool_call_events) > 0, "Should receive tool call streaming events"
first_tool_call_event = tool_call_events[0]
assert first_tool_call_event.call_type == LLMCallType.TOOL_CALL
assert first_tool_call_event.tool_call is not None
assert isinstance(first_tool_call_event.tool_call, ToolCall)
assert first_tool_call_event.tool_call.function is not None
assert first_tool_call_event.tool_call.function.name == "get_current_temperature"
assert first_tool_call_event.tool_call.type == "function"
assert first_tool_call_event.tool_call.index >= 0
class TestToolCallStreamingEventStructure:
"""Tests for the structure and content of tool call streaming events."""
@pytest.mark.vcr()
def test_tool_call_event_accumulates_arguments(
self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
) -> None:
"""Test that tool call events accumulate arguments progressively."""
llm = LLM(model="openai/gpt-4o-mini", stream=True)
llm.call(
messages=[
{"role": "user", "content": "What is the temperature in San Francisco?"},
],
tools=[get_temperature_tool_schema],
available_functions={
"get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
},
)
tool_call_events = get_tool_call_events(mock_emit)
assert len(tool_call_events) >= 2, "Should receive multiple tool call streaming events"
for evt in tool_call_events:
assert evt.tool_call is not None
assert evt.tool_call.function is not None
@pytest.mark.vcr()
def test_tool_call_events_have_consistent_tool_id(
self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
) -> None:
"""Test that all events for the same tool call have the same tool ID."""
llm = LLM(model="openai/gpt-4o-mini", stream=True)
llm.call(
messages=[
{"role": "user", "content": "What is the temperature in San Francisco?"},
],
tools=[get_temperature_tool_schema],
available_functions={
"get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
},
)
tool_call_events = get_tool_call_events(mock_emit)
assert len(tool_call_events) >= 1, "Should receive tool call streaming events"
if len(tool_call_events) > 1:
events_by_index: dict[int, list[LLMStreamChunkEvent]] = {}
for evt in tool_call_events:
if evt.tool_call is not None:
idx = evt.tool_call.index
if idx not in events_by_index:
events_by_index[idx] = []
events_by_index[idx].append(evt)
for idx, evts in events_by_index.items():
ids = [
e.tool_call.id
for e in evts
if e.tool_call is not None and e.tool_call.id
]
if ids:
assert len(set(ids)) == 1, f"Tool call ID should be consistent for index {idx}"
class TestMixedStreamingEvents:
"""Tests for scenarios with both text and tool call streaming events."""
@pytest.mark.vcr()
def test_streaming_distinguishes_text_and_tool_calls(
self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
) -> None:
"""Test that streaming correctly distinguishes between text chunks and tool calls."""
llm = LLM(model="openai/gpt-4o-mini", stream=True)
llm.call(
messages=[
{"role": "user", "content": "What is the temperature in San Francisco?"},
],
tools=[get_temperature_tool_schema],
available_functions={
"get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
},
)
all_events = get_all_stream_events(mock_emit)
tool_call_events = get_tool_call_events(mock_emit)
assert len(all_events) >= 1, "Should receive streaming events"
for event in tool_call_events:
assert event.call_type == LLMCallType.TOOL_CALL
assert event.tool_call is not None
class TestGeminiToolCallStreaming:
"""Tests for Gemini provider tool call streaming events."""
@pytest.mark.vcr()
def test_gemini_streaming_emits_tool_call_events(
self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
) -> None:
"""Test that Gemini streaming emits tool call events with correct call_type."""
llm = LLM(model="gemini/gemini-2.0-flash", stream=True)
llm.call(
messages=[
{"role": "user", "content": "What is the temperature in San Francisco?"},
],
tools=[get_temperature_tool_schema],
available_functions={
"get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
},
)
tool_call_events = get_tool_call_events(mock_emit)
assert len(tool_call_events) > 0, "Should receive tool call streaming events"
first_tool_call_event = tool_call_events[0]
assert first_tool_call_event.call_type == LLMCallType.TOOL_CALL
assert first_tool_call_event.tool_call is not None
assert isinstance(first_tool_call_event.tool_call, ToolCall)
assert first_tool_call_event.tool_call.function is not None
assert first_tool_call_event.tool_call.function.name == "get_current_temperature"
assert first_tool_call_event.tool_call.type == "function"
@pytest.mark.vcr()
def test_gemini_streaming_multiple_tool_calls_unique_ids(
self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
) -> None:
"""Test that Gemini streaming assigns unique IDs to multiple tool calls."""
llm = LLM(model="gemini/gemini-2.0-flash", stream=True)
llm.call(
messages=[
{"role": "user", "content": "What is the temperature in Paris and London?"},
],
tools=[get_temperature_tool_schema],
available_functions={
"get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
},
)
tool_call_events = get_tool_call_events(mock_emit)
assert len(tool_call_events) >= 2, "Should receive at least 2 tool call events"
tool_ids = [
evt.tool_call.id
for evt in tool_call_events
if evt.tool_call is not None and evt.tool_call.id
]
assert len(set(tool_ids)) >= 2, "Each tool call should have a unique ID"
class TestAzureToolCallStreaming:
"""Tests for Azure provider tool call streaming events."""
@pytest.mark.vcr()
def test_azure_streaming_emits_tool_call_events(
self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
) -> None:
"""Test that Azure streaming emits tool call events with correct call_type."""
llm = LLM(model="azure/gpt-4o-mini", stream=True)
llm.call(
messages=[
{"role": "user", "content": "What is the temperature in San Francisco?"},
],
tools=[get_temperature_tool_schema],
available_functions={
"get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
},
)
tool_call_events = get_tool_call_events(mock_emit)
assert len(tool_call_events) > 0, "Should receive tool call streaming events"
first_tool_call_event = tool_call_events[0]
assert first_tool_call_event.call_type == LLMCallType.TOOL_CALL
assert first_tool_call_event.tool_call is not None
assert isinstance(first_tool_call_event.tool_call, ToolCall)
assert first_tool_call_event.tool_call.function is not None
assert first_tool_call_event.tool_call.function.name == "get_current_temperature"
assert first_tool_call_event.tool_call.type == "function"
class TestAnthropicToolCallStreaming:
"""Tests for Anthropic provider tool call streaming events."""
@pytest.mark.vcr()
def test_anthropic_streaming_emits_tool_call_events(
self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
) -> None:
"""Test that Anthropic streaming emits tool call events with correct call_type."""
llm = LLM(model="anthropic/claude-3-5-haiku-latest", stream=True)
llm.call(
messages=[
{"role": "user", "content": "What is the temperature in San Francisco?"},
],
tools=[get_temperature_tool_schema],
available_functions={
"get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
},
)
tool_call_events = get_tool_call_events(mock_emit)
assert len(tool_call_events) > 0, "Should receive tool call streaming events"
first_tool_call_event = tool_call_events[0]
assert first_tool_call_event.call_type == LLMCallType.TOOL_CALL
assert first_tool_call_event.tool_call is not None
assert isinstance(first_tool_call_event.tool_call, ToolCall)
assert first_tool_call_event.tool_call.function is not None
assert first_tool_call_event.tool_call.function.name == "get_current_temperature"
assert first_tool_call_event.tool_call.type == "function"