Compare commits

...

10 Commits

Author SHA1 Message Date
Eduardo Chiarotti
c37a75797a Merge branch 'main' into feat/trace-ui-execution 2025-03-26 16:47:19 -03:00
Lucas Gomide
73701fda1e Merge pull request #2476 from crewAIInc/devin/1742990927-fix-issue-2475
Fix multimodal agent validation errors with image processing
2025-03-26 16:40:23 -03:00
lucasgomide
3deeba4cab test: adding missing test to ensure multimodal content structures 2025-03-26 16:30:17 -03:00
Devin AI
e3dde17af0 docs: improve LLMCallStartedEvent docstring to clarify multimodal support
Co-Authored-By: Joe Moura <joao@crewai.com>
2025-03-26 16:29:24 -03:00
Devin AI
49b8cc95ae fix: update LLMCallStartedEvent message type to support multimodal content (#2475)
fix: sort imports in test file to fix linting

fix: properly sort imports with ruff

Co-Authored-By: Joe Moura <joao@crewai.com>
2025-03-26 16:29:15 -03:00
Eduardo Chiarotti
f71aae97e0 feat: add tests 2025-03-26 13:52:17 -03:00
Eduardo Chiarotti
161f552c77 Merge branch 'main' into feat/trace-ui-execution 2025-03-26 13:39:06 -03:00
Tony Kipkemboi
0b58911153 Merge pull request #2482 from crewAIInc/docs/improve-observability
docs: update theme to mint and modify opik observability doc
2025-03-26 11:40:45 -04:00
Eduardo Chiarotti
7c5160bc92 feat: add type ignore 2025-03-26 10:07:35 -03:00
Eduardo Chiarotti
fbd9d832ef feat: add output to ToolUsageFinishedEvent 2025-03-26 09:58:37 -03:00
7 changed files with 650 additions and 9 deletions

View File

@@ -117,7 +117,10 @@ class ToolUsage:
self._printer.print(content=f"\n\n{error}\n", color="red")
return error
if isinstance(tool, CrewStructuredTool) and tool.name == self._i18n.tools("add_image")["name"]: # type: ignore
if (
isinstance(tool, CrewStructuredTool)
and tool.name == self._i18n.tools("add_image")["name"] # type: ignore
):
try:
result = self._use(tool_string=tool_string, tool=tool, calling=calling)
return result
@@ -181,7 +184,9 @@ class ToolUsage:
if calling.arguments:
try:
acceptable_args = tool.args_schema.model_json_schema()["properties"].keys() # type: ignore
acceptable_args = tool.args_schema.model_json_schema()[
"properties"
].keys() # type: ignore
arguments = {
k: v
for k, v in calling.arguments.items()
@@ -202,7 +207,7 @@ class ToolUsage:
error=e, tool=tool.name, tool_inputs=tool.description
)
error = ToolUsageErrorException(
f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
).message
self.task.increment_tools_errors()
if self.agent.verbose:
@@ -244,6 +249,7 @@ class ToolUsage:
tool_calling=calling,
from_cache=from_cache,
started_at=started_at,
result=result,
)
if (
@@ -380,7 +386,7 @@ class ToolUsage:
raise
else:
return ToolUsageErrorException(
f'{self._i18n.errors("tool_arguments_error")}'
f"{self._i18n.errors('tool_arguments_error')}"
)
if not isinstance(arguments, dict):
@@ -388,7 +394,7 @@ class ToolUsage:
raise
else:
return ToolUsageErrorException(
f'{self._i18n.errors("tool_arguments_error")}'
f"{self._i18n.errors('tool_arguments_error')}"
)
return ToolCalling(
@@ -416,7 +422,7 @@ class ToolUsage:
if self.agent.verbose:
self._printer.print(content=f"\n\n{e}\n", color="red")
return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
f"{self._i18n.errors('tool_usage_error').format(error=e)}\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
)
return self._tool_calling(tool_string)
@@ -492,7 +498,12 @@ class ToolUsage:
crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e}))
def on_tool_use_finished(
self, tool: Any, tool_calling: ToolCalling, from_cache: bool, started_at: float
self,
tool: Any,
tool_calling: ToolCalling,
from_cache: bool,
started_at: float,
result: Any,
) -> None:
finished_at = time.time()
event_data = self._prepare_event_data(tool, tool_calling)
@@ -501,6 +512,7 @@ class ToolUsage:
"started_at": datetime.datetime.fromtimestamp(started_at),
"finished_at": datetime.datetime.fromtimestamp(finished_at),
"from_cache": from_cache,
"output": result,
}
)
crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))

View File

@@ -12,10 +12,15 @@ class LLMCallType(Enum):
class LLMCallStartedEvent(CrewEvent):
"""Event emitted when a LLM call starts"""
"""Event emitted when a LLM call starts
Attributes:
messages: Content can be either a string or a list of dictionaries that support
multimodal content (text, images, etc.)
"""
type: str = "llm_call_started"
messages: Union[str, List[Dict[str, str]]]
messages: Union[str, List[Dict[str, Any]]]
tools: Optional[List[dict]] = None
callbacks: Optional[List[Any]] = None
available_functions: Optional[Dict[str, Any]] = None

View File

@@ -30,6 +30,7 @@ class ToolUsageFinishedEvent(ToolUsageEvent):
started_at: datetime
finished_at: datetime
from_cache: bool = False
output: Any
type: str = "tool_usage_finished"

View File

@@ -0,0 +1,378 @@
interactions:
- request:
body: !!binary |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=
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate, zstd
Connection:
- keep-alive
Content-Length:
- '1301'
Content-Type:
- application/x-protobuf
User-Agent:
- OTel-OTLP-Exporter-Python/1.31.1
method: POST
uri: https://telemetry.crewai.com:4319/v1/traces
response:
body:
string: "\n\0"
headers:
Content-Length:
- '2'
Content-Type:
- application/x-protobuf
Date:
- Wed, 26 Mar 2025 19:24:52 GMT
status:
code: 200
message: OK
- request:
body: '{"messages": [{"role": "system", "content": "You are Visual Quality Inspector.
Senior quality control expert with expertise in visual inspection\nYour personal
goal is: Perform detailed quality analysis of product images\nYou ONLY have
access to the following tools, and should NEVER make up tools that are not listed
here:\n\nTool Name: Add image to content\nTool Arguments: {''image_url'': {''description'':
''The URL or path of the image to add'', ''type'': ''str''}, ''action'': {''description'':
''Optional context or question about the image'', ''type'': ''Union[str, NoneType]''}}\nTool
Description: See image to understand its content, you can optionally ask a question
about the image\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
you should always think about what to do\nAction: the action to take, only one
name of [Add image to content], just the name, exactly as it''s written.\nAction
Input: the input to the action, just a simple JSON object, enclosed in curly
braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce
all necessary information is gathered, return the following format:\n\n```\nThought:
I now know the final answer\nFinal Answer: the final answer to the original
input question\n```"}, {"role": "user", "content": "\nCurrent Task: \n Analyze
the product image at https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244
with focus on:\n 1. Quality of materials\n 2. Manufacturing defects\n 3.
Compliance with standards\n Provide a detailed report highlighting any
issues found.\n \n\nThis is the expected criteria for your final answer:
A detailed report highlighting any issues found\nyou MUST return the actual
complete content as the final answer, not a summary.\n\nBegin! This is VERY
important to you, use the tools available and give your best Final Answer, your
job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"],
"temperature": 0.7}'
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '2033'
content-type:
- application/json
host:
- api.openai.com
user-agent:
- OpenAI/Python 1.68.2
x-stainless-arch:
- x64
x-stainless-async:
- 'false'
x-stainless-lang:
- python
x-stainless-os:
- MacOS
x-stainless-package-version:
- 1.68.2
x-stainless-raw-response:
- 'true'
x-stainless-read-timeout:
- '600.0'
x-stainless-retry-count:
- '0'
x-stainless-runtime:
- CPython
x-stainless-runtime-version:
- 3.12.9
method: POST
uri: https://api.openai.com/v1/chat/completions
response:
content: "{\n \"id\": \"chatcmpl-BFQepLwSYYzdKLylSFsgcJeg6GTqS\",\n \"object\":
\"chat.completion\",\n \"created\": 1743017091,\n \"model\": \"gpt-4o-2024-08-06\",\n
\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\":
\"assistant\",\n \"content\": \"Thought: I need to examine the product
image to assess the quality of materials, look for any manufacturing defects,
and check compliance with standards.\\n\\nAction: Add image to content\\nAction
Input: {\\\"image_url\\\": \\\"https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244\\\",
\\\"action\\\": \\\"Analyze the quality of materials, manufacturing defects,
and compliance with standards.\\\"}\",\n \"refusal\": null,\n \"annotations\":
[]\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n
\ }\n ],\n \"usage\": {\n \"prompt_tokens\": 413,\n \"completion_tokens\":
101,\n \"total_tokens\": 514,\n \"prompt_tokens_details\": {\n \"cached_tokens\":
0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n
\ \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\":
0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\":
\"default\",\n \"system_fingerprint\": \"fp_7e8d90e604\"\n}\n"
headers:
CF-RAY:
- 926907d79dcff1e7-GRU
Connection:
- keep-alive
Content-Encoding:
- gzip
Content-Type:
- application/json
Date:
- Wed, 26 Mar 2025 19:24:53 GMT
Server:
- cloudflare
Set-Cookie:
- __cf_bm=WK433.4kW8cr9rwvOlk4EZ2SfRYK9lAPwXCBYEvLcmU-1743017093-1.0.1.1-kVZyUew5rUbMk.2koGJF_rmX.fTseqN241n2M40n8KvBGoKgy6KM6xBmvFbIVWxUs2Y5ZAz8mWy9CrGjaNKSfCzxmv4.pq78z_DGHr37PgI;
path=/; expires=Wed, 26-Mar-25 19:54:53 GMT; domain=.api.openai.com; HttpOnly;
Secure; SameSite=None
- _cfuvid=T77PMcuNYeyzK0tQyDOe7EScjVBVzW_7DpD3YQBqmUc-1743017093675-0.0.1.1-604800000;
path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
Transfer-Encoding:
- chunked
X-Content-Type-Options:
- nosniff
access-control-expose-headers:
- X-Request-ID
alt-svc:
- h3=":443"; ma=86400
cf-cache-status:
- DYNAMIC
openai-organization:
- crewai-iuxna1
openai-processing-ms:
- '1729'
openai-version:
- '2020-10-01'
strict-transport-security:
- max-age=31536000; includeSubDomains; preload
x-ratelimit-limit-requests:
- '50000'
x-ratelimit-limit-tokens:
- '150000000'
x-ratelimit-remaining-requests:
- '49999'
x-ratelimit-remaining-tokens:
- '149999534'
x-ratelimit-reset-requests:
- 1ms
x-ratelimit-reset-tokens:
- 0s
x-request-id:
- req_2399c3355adf16734907c73611a7d330
http_version: HTTP/1.1
status_code: 200
- request:
body: !!binary |
CtgBCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkSrwEKEgoQY3Jld2FpLnRl
bGVtZXRyeRKYAQoQp2ACB2xRGve4HGtU2RdWCBIIlQcsbhK22ykqClRvb2wgVXNhZ2UwATlACEXG
z3AwGEHAjGPGz3AwGEobCg5jcmV3YWlfdmVyc2lvbhIJCgcwLjEwOC4wSiMKCXRvb2xfbmFtZRIW
ChRBZGQgaW1hZ2UgdG8gY29udGVudEoOCghhdHRlbXB0cxICGAF6AhgBhQEAAQAA
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate, zstd
Connection:
- keep-alive
Content-Length:
- '219'
Content-Type:
- application/x-protobuf
User-Agent:
- OTel-OTLP-Exporter-Python/1.31.1
method: POST
uri: https://telemetry.crewai.com:4319/v1/traces
response:
body:
string: "\n\0"
headers:
Content-Length:
- '2'
Content-Type:
- application/x-protobuf
Date:
- Wed, 26 Mar 2025 19:24:57 GMT
status:
code: 200
message: OK
- request:
body: '{"messages": [{"role": "system", "content": "You are Visual Quality Inspector.
Senior quality control expert with expertise in visual inspection\nYour personal
goal is: Perform detailed quality analysis of product images\nYou ONLY have
access to the following tools, and should NEVER make up tools that are not listed
here:\n\nTool Name: Add image to content\nTool Arguments: {''image_url'': {''description'':
''The URL or path of the image to add'', ''type'': ''str''}, ''action'': {''description'':
''Optional context or question about the image'', ''type'': ''Union[str, NoneType]''}}\nTool
Description: See image to understand its content, you can optionally ask a question
about the image\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
you should always think about what to do\nAction: the action to take, only one
name of [Add image to content], just the name, exactly as it''s written.\nAction
Input: the input to the action, just a simple JSON object, enclosed in curly
braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce
all necessary information is gathered, return the following format:\n\n```\nThought:
I now know the final answer\nFinal Answer: the final answer to the original
input question\n```"}, {"role": "user", "content": "\nCurrent Task: \n Analyze
the product image at https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244
with focus on:\n 1. Quality of materials\n 2. Manufacturing defects\n 3.
Compliance with standards\n Provide a detailed report highlighting any
issues found.\n \n\nThis is the expected criteria for your final answer:
A detailed report highlighting any issues found\nyou MUST return the actual
complete content as the final answer, not a summary.\n\nBegin! This is VERY
important to you, use the tools available and give your best Final Answer, your
job depends on it!\n\nThought:"}, {"role": "user", "content": [{"type": "text",
"text": "Analyze the quality of materials, manufacturing defects, and compliance
with standards."}, {"type": "image_url", "image_url": {"url": "https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244"}}]},
{"role": "assistant", "content": "Thought: I need to examine the product image
to assess the quality of materials, look for any manufacturing defects, and
check compliance with standards.\n\nAction: Add image to content\nAction Input:
{\"image_url\": \"https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244\",
\"action\": \"Analyze the quality of materials, manufacturing defects, and compliance
with standards.\"}"}], "model": "gpt-4o", "stop": ["\nObservation:"], "temperature":
0.7}'
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '2797'
content-type:
- application/json
cookie:
- __cf_bm=WK433.4kW8cr9rwvOlk4EZ2SfRYK9lAPwXCBYEvLcmU-1743017093-1.0.1.1-kVZyUew5rUbMk.2koGJF_rmX.fTseqN241n2M40n8KvBGoKgy6KM6xBmvFbIVWxUs2Y5ZAz8mWy9CrGjaNKSfCzxmv4.pq78z_DGHr37PgI;
_cfuvid=T77PMcuNYeyzK0tQyDOe7EScjVBVzW_7DpD3YQBqmUc-1743017093675-0.0.1.1-604800000
host:
- api.openai.com
user-agent:
- OpenAI/Python 1.68.2
x-stainless-arch:
- x64
x-stainless-async:
- 'false'
x-stainless-lang:
- python
x-stainless-os:
- MacOS
x-stainless-package-version:
- 1.68.2
x-stainless-raw-response:
- 'true'
x-stainless-read-timeout:
- '600.0'
x-stainless-retry-count:
- '0'
x-stainless-runtime:
- CPython
x-stainless-runtime-version:
- 3.12.9
method: POST
uri: https://api.openai.com/v1/chat/completions
response:
content: "{\n \"id\": \"chatcmpl-BFQetNNvmPgPxhzaKiHYsPqm8aN0i\",\n \"object\":
\"chat.completion\",\n \"created\": 1743017095,\n \"model\": \"gpt-4o-2024-08-06\",\n
\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\":
\"assistant\",\n \"content\": \"Observation: The image displays a black
leather boot with a pointed toe and a low heel. \\n\\nQuality of Materials:\\n1.
The leather appears to be of good quality, displaying a consistent texture and
finish, which suggests durability.\\n2. The material has a slight sheen, indicating
a possible finishing treatment that enhances the appearance and may offer some
protection.\\n\\nManufacturing Defects:\\n1. There are no visible stitching
errors; the seams appear straight and clean.\\n2. No apparent glue marks or
uneven edges, which indicates good craftsmanship.\\n3. There is a slight distressed
effect, but it appears intentional as part of the design rather than a defect.\\n\\nCompliance
with Standards:\\n1. The shoe design seems to comply with typical fashion standards,
showing a balance of aesthetics and functionality.\\n2. The heel height and
shape appear to provide stability, aligning with safety standards for footwear.\\n\\nFinal
Answer: The analysis of the product image reveals that the black leather boot
is made of high-quality materials with no visible manufacturing defects. The
craftsmanship is precise, with clean seams and a well-executed design. The distressed
effect appears intentional and part of the aesthetic. The boot seems to comply
with fashion and safety standards, offering both style and functionality. No
significant issues were found.\",\n \"refusal\": null,\n \"annotations\":
[]\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n
\ }\n ],\n \"usage\": {\n \"prompt_tokens\": 1300,\n \"completion_tokens\":
250,\n \"total_tokens\": 1550,\n \"prompt_tokens_details\": {\n \"cached_tokens\":
0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n
\ \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\":
0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\":
\"default\",\n \"system_fingerprint\": \"fp_3a5b33c01a\"\n}\n"
headers:
CF-RAY:
- 926907e45f33f1e7-GRU
Connection:
- keep-alive
Content-Encoding:
- gzip
Content-Type:
- application/json
Date:
- Wed, 26 Mar 2025 19:25:01 GMT
Server:
- cloudflare
Transfer-Encoding:
- chunked
X-Content-Type-Options:
- nosniff
access-control-expose-headers:
- X-Request-ID
alt-svc:
- h3=":443"; ma=86400
cf-cache-status:
- DYNAMIC
openai-organization:
- crewai-iuxna1
openai-processing-ms:
- '7242'
openai-version:
- '2020-10-01'
strict-transport-security:
- max-age=31536000; includeSubDomains; preload
x-ratelimit-limit-input-images:
- '250000'
x-ratelimit-limit-requests:
- '50000'
x-ratelimit-limit-tokens:
- '150000000'
x-ratelimit-remaining-input-images:
- '249999'
x-ratelimit-remaining-requests:
- '49999'
x-ratelimit-remaining-tokens:
- '149998641'
x-ratelimit-reset-input-images:
- 0s
x-ratelimit-reset-requests:
- 1ms
x-ratelimit-reset-tokens:
- 0s
x-request-id:
- req_c5dd144c8ac1bb3bd96ffbba40707b2d
http_version: HTTP/1.1
status_code: 200
version: 1

View File

@@ -3731,6 +3731,44 @@ def test_multimodal_agent_image_tool_handling():
assert result["content"][1]["type"] == "image_url"
@pytest.mark.vcr(filter_headers=["authorization"])
def test_multimodal_agent_describing_image_successfully():
"""
Test that a multimodal agent can process images without validation errors.
This test reproduces the scenario from issue #2475.
"""
llm = LLM(model="openai/gpt-4o", temperature=0.7) # model with vision capabilities
expert_analyst = Agent(
role="Visual Quality Inspector",
goal="Perform detailed quality analysis of product images",
backstory="Senior quality control expert with expertise in visual inspection",
llm=llm,
verbose=True,
allow_delegation=False,
multimodal=True,
)
inspection_task = Task(
description="""
Analyze the product image at https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244 with focus on:
1. Quality of materials
2. Manufacturing defects
3. Compliance with standards
Provide a detailed report highlighting any issues found.
""",
expected_output="A detailed report highlighting any issues found",
agent=expert_analyst,
)
crew = Crew(agents=[expert_analyst], tasks=[inspection_task])
result = crew.kickoff()
task_output = result.tasks_output[0]
assert isinstance(task_output, TaskOutput)
assert task_output.raw == result.raw
@pytest.mark.vcr(filter_headers=["authorization"])
def test_multimodal_agent_live_image_analysis():
"""

View File

@@ -0,0 +1,46 @@
import os
import pytest
from crewai import LLM, Agent, Crew, Task
@pytest.mark.skip(reason="Only run manually with valid API keys")
def test_multimodal_agent_with_image_url():
"""
Test that a multimodal agent can process images without validation errors.
This test reproduces the scenario from issue #2475.
"""
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
pytest.skip("OPENAI_API_KEY environment variable not set")
llm = LLM(
model="openai/gpt-4o", # model with vision capabilities
api_key=OPENAI_API_KEY,
temperature=0.7
)
expert_analyst = Agent(
role="Visual Quality Inspector",
goal="Perform detailed quality analysis of product images",
backstory="Senior quality control expert with expertise in visual inspection",
llm=llm,
verbose=True,
allow_delegation=False,
multimodal=True
)
inspection_task = Task(
description="""
Analyze the product image at https://www.us.maguireshoes.com/collections/spring-25/products/lucena-black-boot with focus on:
1. Quality of materials
2. Manufacturing defects
3. Compliance with standards
Provide a detailed report highlighting any issues found.
""",
expected_output="A detailed report highlighting any issues found",
agent=expert_analyst
)
crew = Crew(agents=[expert_analyst], tasks=[inspection_task])

View File

@@ -1,5 +1,7 @@
import datetime
import json
import random
import time
from unittest.mock import MagicMock, patch
import pytest
@@ -11,6 +13,7 @@ from crewai.tools.tool_usage import ToolUsage
from crewai.utilities.events import crewai_event_bus
from crewai.utilities.events.tool_usage_events import (
ToolSelectionErrorEvent,
ToolUsageFinishedEvent,
ToolValidateInputErrorEvent,
)
@@ -624,3 +627,161 @@ def test_tool_validate_input_error_event():
assert event.agent_role == "test_role"
assert event.tool_name == "test_tool"
assert "must be a valid dictionary" in event.error
def test_tool_usage_finished_event_with_result():
"""Test that ToolUsageFinishedEvent is emitted with correct result attributes."""
# Create mock agent with proper string values
mock_agent = MagicMock()
mock_agent.key = "test_agent_key"
mock_agent.role = "test_agent_role"
mock_agent._original_role = "test_agent_role"
mock_agent.i18n = MagicMock()
mock_agent.verbose = False
# Create mock task
mock_task = MagicMock()
mock_task.delegations = 0
# Create mock tool
class TestTool(BaseTool):
name: str = "Test Tool"
description: str = "A test tool"
def _run(self, input: dict) -> str:
return "test result"
test_tool = TestTool()
# Create mock tool calling
mock_tool_calling = MagicMock()
mock_tool_calling.arguments = {"arg1": "value1"}
# Create ToolUsage instance
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[test_tool],
original_tools=[test_tool],
tools_description="Test Tool Description",
tools_names="Test Tool",
task=mock_task,
function_calling_llm=None,
agent=mock_agent,
action=MagicMock(),
)
# Track received events
received_events = []
@crewai_event_bus.on(ToolUsageFinishedEvent)
def event_handler(source, event):
received_events.append(event)
# Call on_tool_use_finished with test data
started_at = time.time()
result = "test output result"
tool_usage.on_tool_use_finished(
tool=test_tool,
tool_calling=mock_tool_calling,
from_cache=False,
started_at=started_at,
result=result,
)
# Verify event was emitted
assert len(received_events) == 1, "Expected one event to be emitted"
event = received_events[0]
assert isinstance(event, ToolUsageFinishedEvent)
# Verify event attributes
assert event.agent_key == "test_agent_key"
assert event.agent_role == "test_agent_role"
assert event.tool_name == "Test Tool"
assert event.tool_args == {"arg1": "value1"}
assert event.tool_class == "TestTool"
assert event.run_attempts == 1 # Default value from ToolUsage
assert event.delegations == 0
assert event.from_cache is False
assert event.output == "test output result"
assert isinstance(event.started_at, datetime.datetime)
assert isinstance(event.finished_at, datetime.datetime)
assert event.type == "tool_usage_finished"
def test_tool_usage_finished_event_with_cached_result():
"""Test that ToolUsageFinishedEvent is emitted with correct result attributes when using cached result."""
# Create mock agent with proper string values
mock_agent = MagicMock()
mock_agent.key = "test_agent_key"
mock_agent.role = "test_agent_role"
mock_agent._original_role = "test_agent_role"
mock_agent.i18n = MagicMock()
mock_agent.verbose = False
# Create mock task
mock_task = MagicMock()
mock_task.delegations = 0
# Create mock tool
class TestTool(BaseTool):
name: str = "Test Tool"
description: str = "A test tool"
def _run(self, input: dict) -> str:
return "test result"
test_tool = TestTool()
# Create mock tool calling
mock_tool_calling = MagicMock()
mock_tool_calling.arguments = {"arg1": "value1"}
# Create ToolUsage instance
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[test_tool],
original_tools=[test_tool],
tools_description="Test Tool Description",
tools_names="Test Tool",
task=mock_task,
function_calling_llm=None,
agent=mock_agent,
action=MagicMock(),
)
# Track received events
received_events = []
@crewai_event_bus.on(ToolUsageFinishedEvent)
def event_handler(source, event):
received_events.append(event)
# Call on_tool_use_finished with test data and from_cache=True
started_at = time.time()
result = "cached test output result"
tool_usage.on_tool_use_finished(
tool=test_tool,
tool_calling=mock_tool_calling,
from_cache=True,
started_at=started_at,
result=result,
)
# Verify event was emitted
assert len(received_events) == 1, "Expected one event to be emitted"
event = received_events[0]
assert isinstance(event, ToolUsageFinishedEvent)
# Verify event attributes
assert event.agent_key == "test_agent_key"
assert event.agent_role == "test_agent_role"
assert event.tool_name == "Test Tool"
assert event.tool_args == {"arg1": "value1"}
assert event.tool_class == "TestTool"
assert event.run_attempts == 1 # Default value from ToolUsage
assert event.delegations == 0
assert event.from_cache is True
assert event.output == "cached test output result"
assert isinstance(event.started_at, datetime.datetime)
assert isinstance(event.finished_at, datetime.datetime)
assert event.type == "tool_usage_finished"