fix: Fix type-checker issues with _finish_execution method

Co-Authored-By: Joe Moura <joao@crewai.com>
refactor: Improve agentops integration with better types and tests
2026-01-08 15:48:29 +00:00 · 2025-02-12 06:46:27 +00:00 · 2025-02-12 06:43:26 +00:00 · 2025-02-12 06:41:36 +00:00 · 2025-02-12 06:27:56 +00:00 · 2025-02-12 06:27:21 +00:00
19 changed files with 827 additions and 464 deletions
--- a/docs/how-to/langfuse-observability.mdx
+++ b/docs/how-to/langfuse-observability.mdx
@@ -1,98 +0,0 @@
---
-title: Agent Monitoring with Langfuse
-description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
-icon: magnifying-glass-chart
---
-
-# Integrate Langfuse with CrewAI
-
-This notebook demonstrates how to integrate **Langfuse** with **CrewAI** using OpenTelemetry via the **OpenLit** SDK. By the end of this notebook, you will be able to trace your CrewAI applications with Langfuse for improved observability and debugging.
-
-> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source LLM engineering platform. It provides tracing and monitoring capabilities for LLM applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and APIs/SDKs.
-
-## Get Started
-
-We'll walk through a simple example of using CrewAI and integrating it with Langfuse via OpenTelemetry using OpenLit.
-
-### Step 1: Install Dependencies
-
-
-```python
-%pip install langfuse openlit crewai crewai_tools
-```
-
-### Step 2: Set Up Environment Variables
-
-Set your Langfuse API keys and configure OpenTelemetry export settings to send traces to Langfuse. Please refer to the [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started) for more information on the Langfuse OpenTelemetry endpoint `/api/public/otel` and authentication.
-
-
-```python
-import os
-import base64
-
-LANGFUSE_PUBLIC_KEY="pk-lf-..."
-LANGFUSE_SECRET_KEY="sk-lf-..."
-LANGFUSE_AUTH=base64.b64encode(f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()
-
-os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://cloud.langfuse.com/api/public/otel" # EU data region
-# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel" # US data region
-os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
-
-# your openai key
-os.environ["OPENAI_API_KEY"] = "sk-..."
-```
-
-### Step 3: Initialize OpenLit
-
-Initialize the OpenLit OpenTelemetry instrumentation SDK to start capturing OpenTelemetry traces.
-
-
-```python
-import openlit
-
-openlit.init()
-```
-
-### Step 4: Create a Simple CrewAI Application
-
-We'll create a simple CrewAI application where multiple agents collaborate to answer a user's question.
-
-
-```python
-from crewai import Agent, Task, Crew
-
-from crewai_tools import (
-    WebsiteSearchTool
-)
-
-web_rag_tool = WebsiteSearchTool()
-
-writer = Agent(
-        role="Writer",
-        goal="You make math engaging and understandable for young children through poetry",
-        backstory="You're an expert in writing haikus but you know nothing of math.",
-        tools=[web_rag_tool],  
-    )
-
-task = Task(description=("What is {multiplication}?"),
-            expected_output=("Compose a haiku that includes the answer."),
-            agent=writer)
-
-crew = Crew(
-  agents=[writer],
-  tasks=[task],
-  share_crew=False
-)
-```
-
-### Step 5: See Traces in Langfuse
-
-After running the agent, you can view the traces generated by your CrewAI application in [Langfuse](https://cloud.langfuse.com). You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.
-
-![CrewAI example trace in Langfuse](https://langfuse.com/images/cookbook/integration_crewai/crewai-example-trace.png)
-
-_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e2cf380ffc8d47d28da98f136140642b?timestamp=2025-02-05T15%3A12%3A02.717Z&observation=3b32338ee6a5d9af)_
-
-## References
-
- [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started)
--- a/docs/how-to/portkey-observability-and-guardrails.mdx
+++ b/docs/how-to/portkey-observability-and-guardrails.mdx
@@ -0,0 +1,211 @@
+# Portkey Integration with CrewAI
+<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
+
+
+[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
+
+Portkey adds 4 core production capabilities to any CrewAI agent:
+1. Routing to **200+ LLMs**
+2. Making each LLM call more robust
+3. Full-stack tracing & cost, performance analytics
+4. Real-time guardrails to enforce behavior
+
+
+
+
+
+## Getting Started
+
+1. **Install Required Packages:**
+
+```bash
+pip install -qU crewai portkey-ai
+```
+
+2. **Configure the LLM Client:**
+
+To build CrewAI Agents with Portkey, you'll need two keys:
+- **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
+- **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
+
+```python
+from crewai import LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+gpt_llm = LLM(
+    model="gpt-4",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy", # We are using Virtual key
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
+    )
+)
+```
+
+3. **Create and Run Your First Agent:**
+
+```python
+from crewai import Agent, Task, Crew
+
+# Define your agents with roles and goals
+coder = Agent(
+    role='Software developer',
+    goal='Write clear, concise code on demand',
+    backstory='An expert coder with a keen eye for software trends.',
+    llm=gpt_llm
+)
+
+# Create tasks for your agents
+task1 = Task(
+    description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
+    expected_output="A clear and concise HTML code",
+    agent=coder
+)
+
+# Instantiate your crew
+crew = Crew(
+    agents=[coder],
+    tasks=[task1],
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+
+## Key Features
+
+| Feature | Description |
+|---------|-------------|
+| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
+| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
+| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
+| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
+| 🚧 Security Controls | Set budget limits and implement role-based access control |
+| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
+| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
+
+
+## Production Features with Portkey Configs
+
+All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
+
+<Frame>
+    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
+</Frame>
+
+
+### 1. Use 250+ LLMs
+Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
+
+
+Easily switch between different LLM providers:
+
+```python
+# Anthropic Configuration
+anthropic_llm = LLM(
+    model="claude-3-5-sonnet-latest",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
+        trace_id="anthropic_agent"
+    )
+)
+
+# Azure OpenAI Configuration
+azure_llm = LLM(
+    model="gpt-4",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
+        trace_id="azure_agent"
+    )
+)
+```
+
+
+### 2. Caching
+Improve response times and reduce costs with two powerful caching modes:
+- **Simple Cache**: Perfect for exact matches
+- **Semantic Cache**: Matches responses for requests that are semantically similar
+[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
+
+```py
+config = {
+    "cache": {
+        "mode": "semantic",  # or "simple" for exact matching
+    }
+}
+```
+
+### 3. Production Reliability
+Portkey provides comprehensive reliability features:
+- **Automatic Retries**: Handle temporary failures gracefully
+- **Request Timeouts**: Prevent hanging operations
+- **Conditional Routing**: Route requests based on specific conditions
+- **Fallbacks**: Set up automatic provider failovers
+- **Load Balancing**: Distribute requests efficiently
+
+[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
+
+
+
+### 4. Metrics
+
+Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
+
+
+- Cost per agent interaction
+- Response times and latency
+- Token usage and efficiency
+- Success/failure rates
+- Cache hit rates
+
+<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
+
+### 5. Detailed Logging
+Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
+
+
+Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
+
+<details>
+  <summary><b>Traces</b></summary>
+  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
+</details>
+
+<details>
+  <summary><b>Logs</b></summary>
+  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
+</details>
+
+### 6. Enterprise Security Features
+- Set budget limit and rate limts per Virtual Key (disposable API keys)
+- Implement role-based access control
+- Track system changes with audit logs
+- Configure data retention policies
+
+
+
+For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
+
+## Resources
+
+- [📘 Portkey Documentation](https://docs.portkey.ai)
+- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
+- [🐦 Twitter](https://twitter.com/portkeyai)
+- [💬 Discord Community](https://discord.gg/DD7vgKK299)
+
+
+
+
+
+
+
+
+
--- a/docs/how-to/portkey-observability.mdx
+++ b/docs/how-to/portkey-observability.mdx
@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with Portkey
+title: Portkey Observability and Guardrails
 description: How to use Portkey with CrewAI
 icon: key
 ---
--- a/docs/mint.json
+++ b/docs/mint.json
@@ -103,8 +103,7 @@
        "how-to/langtrace-observability",
        "how-to/mlflow-observability",
        "how-to/openlit-observability",
-        "how-to/portkey-observability",
-        "how-to/langfuse-observability"
+        "how-to/portkey-observability"
      ]
    },
    {
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.102.0"
+version = "0.100.1"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"

 [project.optional-dependencies]
-tools = ["crewai-tools>=0.36.0"]
+tools = ["crewai-tools>=0.32.1"]
 embeddings = [
    "tiktoken~=0.7.0"
 ]
--- a/pytest.ini
+++ b/pytest.ini
@@ -0,0 +1,3 @@
+[pytest]
+markers =
+    agentops: Tests for AgentOps integration
--- a/src/crewai/init.py
+++ b/src/crewai/init.py
@@ -14,7 +14,7 @@ warnings.filterwarnings(
    category=UserWarning,
    module="pydantic.main",
 )
-__version__ = "0.102.0"
+__version__ = "0.100.1"
 __all__ = [
    "Agent",
    "Crew",
--- a/src/crewai/cli/templates/crew/main.py
+++ b/src/crewai/cli/templates/crew/main.py
@@ -56,8 +56,7 @@ def test():
    Test the crew execution and returns the results.
    """
    inputs = {
-        "topic": "AI LLMs",
-        "current_year": str(datetime.now().year)
+        "topic": "AI LLMs"
    }
    try:
        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
--- a/src/crewai/cli/templates/crew/pyproject.toml
+++ b/src/crewai/cli/templates/crew/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0,<1.0.0"
+    "crewai[tools]>=0.100.1,<1.0.0"
 ]

 [project.scripts]
--- a/src/crewai/cli/templates/flow/pyproject.toml
+++ b/src/crewai/cli/templates/flow/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0,<1.0.0",
+    "crewai[tools]>=0.100.1,<1.0.0",
 ]

 [project.scripts]
--- a/src/crewai/cli/templates/tool/pyproject.toml
+++ b/src/crewai/cli/templates/tool/pyproject.toml
@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0"
+    "crewai[tools]>=0.100.1"
 ]

 [tool.crewai]
--- a/src/crewai/crew.py
+++ b/src/crewai/crew.py
@@ -1,5 +1,6 @@
 import asyncio
 import json
+import os
 import re
 import uuid
 import warnings
@@ -54,8 +55,11 @@ from crewai.utilities.training_handler import CrewTrainingHandler

 try:
    import agentops  # type: ignore
+    from agentops.exceptions import AgentOpsError, AuthenticationError  # type: ignore
 except ImportError:
    agentops = None
+    AgentOpsError = None
+    AuthenticationError = None


 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
@@ -90,6 +94,8 @@ class Crew(BaseModel):
    __hash__ = object.__hash__  # type: ignore
    _execution_span: Any = PrivateAttr()
    _rpm_controller: RPMController = PrivateAttr()
+    _agentops: Optional['agentops.AgentOps'] = PrivateAttr(default=None)
+    _telemetry: Optional[Telemetry] = PrivateAttr(default=None)
    _logger: Logger = PrivateAttr()
    _file_handler: FileHandler = PrivateAttr()
    _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default=CacheHandler())
@@ -240,19 +246,72 @@ class Crew(BaseModel):
        # TODO: Improve typing
        return json.loads(v) if isinstance(v, Json) else v  # type: ignore

-    @model_validator(mode="after")
+    def _validate_api_key(self, api_key: Optional[str]) -> bool:
+        """Validate the AgentOps API key.
+        
+        Args:
+            api_key: The API key to validate
+            
+        Returns:
+            bool: True if the API key is valid, False otherwise
+        """
+        if not api_key:
+            return False
+        stripped_key = api_key.strip()
+        return bool(stripped_key and len(stripped_key) > 10)
+
    def set_private_attrs(self) -> "Crew":
-        """Set private attributes."""
+        """Initialize private attributes including AgentOps integration.
+        
+        This method sets up:
+        - Logger and file handler for output logging
+        - RPM controller for rate limiting
+        - AgentOps integration for monitoring (if available and configured)
+        """
        self._cache_handler = CacheHandler()
        self._logger = Logger(verbose=self.verbose)
        if self.output_log_file:
            self._file_handler = FileHandler(self.output_log_file)
        self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
-        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
-            self.function_calling_llm = create_llm(self.function_calling_llm)
-
        self._telemetry = Telemetry()
        self._telemetry.set_tracer()
+
+        # Initialize agentops if available and API key is present
+        if agentops:
+            api_key = os.getenv("AGENTOPS_API_KEY")
+            if self._validate_api_key(api_key):
+                try:
+                    agentops.init(api_key)
+                    self._agentops = agentops
+                    self._logger.log(
+                        "info",
+                        "Successfully initialized agentops",
+                        color="green"
+                    )
+                except (ConnectionError, AuthenticationError) as e:
+                    self._logger.log(
+                        "warning",
+                        f"Failed to connect to agentops: {e}",
+                        color="yellow"
+                    )
+                    self._agentops = None
+                except (ValueError, AgentOpsError) as e:
+                    self._logger.log(
+                        "warning",
+                        f"Invalid agentops configuration: {e}",
+                        color="yellow"
+                    )
+                    self._agentops = None
+            else:
+                self._logger.log(
+                    "warning",
+                    "Invalid AGENTOPS_API_KEY provided",
+                    color="yellow"
+                )
+                self._agentops = None
+
+        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
+            self.function_calling_llm = create_llm(self.function_calling_llm)
        return self

    @model_validator(mode="after")
@@ -543,7 +602,8 @@ class Crew(BaseModel):
            inputs = before_callback(inputs)

        """Starts the crew to work on its assigned tasks."""
-        self._execution_span = self._telemetry.crew_execution_span(self, inputs)
+        if self._telemetry:
+            self._execution_span = self._telemetry.crew_execution_span(self, inputs)
        self._task_output_handler.reset()
        self._logging_color = "bold_purple"

@@ -1121,16 +1181,22 @@ class Crew(BaseModel):
        for agent in self.agents:
            agent.interpolate_inputs(inputs)

-    def _finish_execution(self, final_string_output: str) -> None:
+    def _finish_execution(self, final_output: Union[str, CrewOutput]) -> None:
+        """Finish execution and cleanup.
+        
+        Args:
+            final_output: The final output from crew execution, either as string or CrewOutput
+        """
        if self.max_rpm:
            self._rpm_controller.stop_rpm_counter()
-        if agentops:
-            agentops.end_session(
+        if self._telemetry:
+            self._telemetry.end_crew(self, final_output)
+        if self._agentops:
+            self._agentops.end_session(
                end_state="Success",
                end_state_reason="Finished Execution",
                is_auto_end=True,
            )
-        self._telemetry.end_crew(self, final_string_output)

    def calculate_usage_metrics(self) -> UsageMetrics:
        """Calculates and returns the usage metrics."""
--- a/src/crewai/flow/flow.py
+++ b/src/crewai/flow/flow.py
@@ -1,5 +1,4 @@
 import asyncio
-import copy
 import inspect
 import logging
 from typing import (
@@ -395,6 +394,7 @@ class FlowMeta(type):
                or hasattr(attr_value, "__trigger_methods__")
                or hasattr(attr_value, "__is_router__")
            ):
+
                # Register start methods
                if hasattr(attr_value, "__is_start_method__"):
                    start_methods.append(attr_name)
@@ -569,9 +569,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
            f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
        )

-    def _copy_state(self) -> T:
-        return copy.deepcopy(self._state)
-
    @property
    def state(self) -> T:
        return self._state
@@ -743,7 +740,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
            event=FlowStartedEvent(
                type="flow_started",
                flow_name=self.__class__.__name__,
-                inputs=inputs,
            ),
        )
        self._log_flow_event(
@@ -807,18 +803,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
    async def _execute_method(
        self, method_name: str, method: Callable, *args: Any, **kwargs: Any
    ) -> Any:
-        dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | (kwargs or {})
-        self.event_emitter.send(
-            self,
-            event=MethodExecutionStartedEvent(
-                type="method_execution_started",
-                method_name=method_name,
-                flow_name=self.__class__.__name__,
-                params=dumped_params,
-                state=self._copy_state(),
-            ),
-        )
-
        result = (
            await method(*args, **kwargs)
            if asyncio.iscoroutinefunction(method)
@@ -828,18 +812,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
        self._method_execution_counts[method_name] = (
            self._method_execution_counts.get(method_name, 0) + 1
        )
-
-        self.event_emitter.send(
-            self,
-            event=MethodExecutionFinishedEvent(
-                type="method_execution_finished",
-                method_name=method_name,
-                flow_name=self.__class__.__name__,
-                state=self._copy_state(),
-                result=result,
-            ),
-        )
-
        return result

    async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
@@ -978,6 +950,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
        """
        try:
            method = self._methods[listener_name]
+
+            self.event_emitter.send(
+                self,
+                event=MethodExecutionStartedEvent(
+                    type="method_execution_started",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
            sig = inspect.signature(method)
            params = list(sig.parameters.values())
            method_params = [p for p in params if p.name != "self"]
@@ -989,6 +971,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
            else:
                listener_result = await self._execute_method(listener_name, method)

+            self.event_emitter.send(
+                self,
+                event=MethodExecutionFinishedEvent(
+                    type="method_execution_finished",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
            # Execute listeners (and possibly routers) of this listener
            await self._execute_listeners(listener_name, listener_result)

--- a/src/crewai/flow/flow_events.py
+++ b/src/crewai/flow/flow_events.py
@@ -1,8 +1,6 @@
 from dataclasses import dataclass, field
 from datetime import datetime
-from typing import Any, Dict, Optional, Union
-
-from pydantic import BaseModel
+from typing import Any, Optional


@dataclass
@@ -17,21 +15,17 @@ class Event:

@dataclass
 class FlowStartedEvent(Event):
-    inputs: Optional[Dict[str, Any]] = None
+    pass


@dataclass
 class MethodExecutionStartedEvent(Event):
    method_name: str
-    state: Union[Dict[str, Any], BaseModel]
-    params: Optional[Dict[str, Any]] = None


@dataclass
 class MethodExecutionFinishedEvent(Event):
    method_name: str
-    state: Union[Dict[str, Any], BaseModel]
-    result: Any = None


@dataclass
--- a/src/crewai/llm.py
+++ b/src/crewai/llm.py
@@ -92,19 +92,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
    "Meta-Llama-3.2-1B-Instruct": 16384,
 }

-# Common Vertex AI regions
-VERTEX_AI_REGIONS = [
-    "us-central1",  # Iowa
-    "us-east1",     # South Carolina
-    "us-west1",     # Oregon
-    "europe-west1", # Belgium
-    "europe-west2", # London
-    "europe-west3", # Frankfurt
-    "europe-west4", # Netherlands
-    "asia-east1",   # Taiwan
-    "asia-southeast1" # Singapore
-]
-
 DEFAULT_CONTEXT_WINDOW_SIZE = 8192
 CONTEXT_WINDOW_USAGE_RATIO = 0.75

@@ -130,20 +117,9 @@ def suppress_warnings():


 class LLM:
-    """A wrapper around LiteLLM providing a unified interface for various LLM providers.
-
-    Args:
-        model (str): The identifier of the LLM model to use
-        location (Optional[str]): The GCP region for Vertex AI models (e.g., 'us-central1', 'europe-west4').
-                            Only applicable for Vertex AI models.
-        timeout (Optional[Union[float, int]]): Maximum time to wait for the model response
-        temperature (Optional[float]): Controls randomness in the model's output
-        top_p (Optional[float]): Controls diversity of the model's output
-    """
    def __init__(
        self,
        model: str,
-        location: Optional[str] = None,
        timeout: Optional[Union[float, int]] = None,
        temperature: Optional[float] = None,
        top_p: Optional[float] = None,
@@ -167,18 +143,6 @@ class LLM:
        **kwargs,
    ):
        self.model = model
-        
-        # Validate location parameter
-        if location is not None:
-            if not isinstance(location, str):
-                raise ValueError("Location must be a string when provided")
-            if self._is_vertex_model(model) and location not in VERTEX_AI_REGIONS:
-                raise ValueError(
-                    f"Invalid Vertex AI region: {location}. "
-                    f"Supported regions: {', '.join(VERTEX_AI_REGIONS)}"
-                )
-        self.location = location
-        
        self.timeout = timeout
        self.temperature = temperature
        self.top_p = top_p
@@ -202,10 +166,6 @@ class LLM:
        self.additional_params = kwargs
        self.is_anthropic = self._is_anthropic_model(model)

-        # Set vertex location if provided for vertex models
-        if self.location and self._is_vertex_model(model):
-            litellm.vertex_location = self.location
-
        litellm.drop_params = True

        # Normalize self.stop to always be a List[str]
@@ -219,17 +179,6 @@ class LLM:
        self.set_callbacks(callbacks)
        self.set_env_callbacks()

-    def _is_vertex_model(self, model: str) -> bool:
-        """Determine if the model is from Vertex AI provider.
-        
-        Args:
-            model: The model identifier string.
-            
-        Returns:
-            bool: True if the model is from Vertex AI, False otherwise.
-        """
-        return "vertex" in model.lower() or model.startswith("gemini-")
-
    def _is_anthropic_model(self, model: str) -> bool:
        """Determine if the model is from Anthropic provider.
        
--- a/tests/crew_test.py
+++ b/tests/crew_test.py
@@ -6,6 +6,7 @@ from concurrent.futures import Future
 from unittest import mock
 from unittest.mock import MagicMock, patch

+import agentops
 import instructor
 import pydantic_core
 import pytest
@@ -15,6 +16,7 @@ from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
 from crewai.project import crew
@@ -27,6 +29,100 @@ from crewai.utilities import Logger
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler

+
+@pytest.fixture
+def researcher():
+    """Fixture to create a researcher agent."""
+    return Agent(
+        role="Researcher",
+        goal="Make the best research and analysis on content about AI and AI agents",
+        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups.",
+        allow_delegation=False,
+    )
+
+@pytest.fixture
+def mock_agentops():
+    """Fixture to mock agentops for testing."""
+    mock_agentops = MagicMock()
+    mock_agentops.init = MagicMock()
+    return mock_agentops
+
+@pytest.mark.agentops
+class TestAgentOpsIntegration:
+    """Tests for AgentOps integration."""
+
+    def test_initialization_with_api_key(self, mock_agentops, monkeypatch):
+        """Test that agentops is properly initialized when API key is present."""
+        monkeypatch.setattr("crewai.crew.agentops", mock_agentops)
+        monkeypatch.setenv("AGENTOPS_API_KEY", "test-key-12345")
+        crew = Crew(agents=[researcher], tasks=[Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=researcher,
+        )])
+        crew.set_private_attrs()
+        mock_agentops.init.assert_called_once_with("test-key-12345")
+
+    def test_initialization_without_api_key(self, mock_agentops):
+        """Test that agentops is not initialized when API key is not present."""
+        crew = Crew(agents=[researcher], tasks=[Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=researcher,
+        )])
+        mock_agentops.assert_not_called()
+
+    def test_initialization_with_invalid_api_key(self, mock_agentops, monkeypatch):
+        """Test that agentops is not initialized when API key is invalid."""
+        monkeypatch.setenv("AGENTOPS_API_KEY", "   ")
+        crew = Crew(agents=[researcher], tasks=[Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=researcher,
+        )])
+        mock_agentops.assert_not_called()
+
+    def test_gemini_llm_integration(self, mock_agentops, monkeypatch):
+        """Test that Gemini LLM works correctly with agentops."""
+        # Mock agentops
+        monkeypatch.setattr("crewai.crew.agentops", mock_agentops)
+        
+        # Set API keys
+        monkeypatch.setenv("AGENTOPS_API_KEY", "test-key-12345")
+        monkeypatch.setenv("GOOGLE_API_KEY", "test-key")
+        
+        # Create crew with Gemini LLM
+        llm = LLM(model="gemini-pro")
+        agent = Agent(
+            role="test",
+            goal="test",
+            backstory="test",
+            llm=llm
+        )
+        task = Task(
+            description="test task",
+            expected_output="test output",
+            agent=agent
+        )
+        crew = Crew(agents=[agent], tasks=[task])
+        crew.set_private_attrs()
+        
+        # Mock the agent execution to avoid actual API calls
+        with patch.object(Task, 'execute_sync', return_value=TaskOutput(
+            description="test",
+            raw="test output",
+            agent=agent.role
+        )):
+            # Run crew
+            crew.kickoff()
+        
+        # Verify agentops.end_session was called correctly
+        mock_agentops.end_session.assert_called_once_with(
+            end_state="Success",
+            end_state_reason="Finished Execution",
+            is_auto_end=True
+        )
+
 ceo = Agent(
    role="CEO",
    goal="Make sure the writers in your company produce amazing content.",
--- a/tests/flow_test.py
+++ b/tests/flow_test.py
@@ -1,18 +1,11 @@
 """Test Flow creation and execution basic functionality."""

 import asyncio
-from datetime import datetime

 import pytest
 from pydantic import BaseModel

 from crewai.flow.flow import Flow, and_, listen, or_, router, start
-from crewai.flow.flow_events import (
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)


 def test_simple_sequential_flow():
@@ -405,218 +398,3 @@ def test_router_with_multiple_conditions():

    # final_step should run after router_and
    assert execution_order.index("log_final_step") > execution_order.index("router_and")
-
-
-def test_unstructured_flow_event_emission():
-    """Test that the correct events are emitted during unstructured flow
-    execution with all fields validated."""
-
-    class PoemFlow(Flow):
-        @start()
-        def prepare_flower(self):
-            self.state["flower"] = "roses"
-            return "foo"
-
-        @start()
-        def prepare_color(self):
-            self.state["color"] = "red"
-            return "bar"
-
-        @listen(prepare_color)
-        def write_first_sentence(self):
-            return f"{self.state['flower']} are {self.state['color']}"
-
-        @listen(write_first_sentence)
-        def finish_poem(self, first_sentence):
-            separator = self.state.get("separator", "\n")
-            return separator.join([first_sentence, "violets are blue"])
-
-        @listen(finish_poem)
-        def save_poem_to_database(self):
-            # A method without args/kwargs to ensure events are sent correctly
-            pass
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = PoemFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff(inputs={"separator": ", "})
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "PoemFlow"
-    assert event_log[0].inputs == {"separator": ", "}
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    # Asserting for concurrent start method executions in a for loop as you
-    # can't guarantee ordering in asynchronous executions
-    for i in range(1, 5):
-        event = event_log[i]
-        assert isinstance(event.state, dict)
-        assert isinstance(event.state["id"], str)
-
-        if event.method_name == "prepare_flower":
-            if isinstance(event, MethodExecutionStartedEvent):
-                assert event.params == {}
-                assert event.state["separator"] == ", "
-            elif isinstance(event, MethodExecutionFinishedEvent):
-                assert event.result == "foo"
-                assert event.state["flower"] == "roses"
-                assert event.state["separator"] == ", "
-            else:
-                assert False, "Unexpected event type for prepare_flower"
-        elif event.method_name == "prepare_color":
-            if isinstance(event, MethodExecutionStartedEvent):
-                assert event.params == {}
-                assert event.state["separator"] == ", "
-            elif isinstance(event, MethodExecutionFinishedEvent):
-                assert event.result == "bar"
-                assert event.state["color"] == "red"
-                assert event.state["separator"] == ", "
-            else:
-                assert False, "Unexpected event type for prepare_color"
-        else:
-            assert False, f"Unexpected method {event.method_name} in prepare events"
-
-    assert isinstance(event_log[5], MethodExecutionStartedEvent)
-    assert event_log[5].method_name == "write_first_sentence"
-    assert event_log[5].params == {}
-    assert isinstance(event_log[5].state, dict)
-    assert event_log[5].state["flower"] == "roses"
-    assert event_log[5].state["color"] == "red"
-    assert event_log[5].state["separator"] == ", "
-
-    assert isinstance(event_log[6], MethodExecutionFinishedEvent)
-    assert event_log[6].method_name == "write_first_sentence"
-    assert event_log[6].result == "roses are red"
-
-    assert isinstance(event_log[7], MethodExecutionStartedEvent)
-    assert event_log[7].method_name == "finish_poem"
-    assert event_log[7].params == {"_0": "roses are red"}
-    assert isinstance(event_log[7].state, dict)
-    assert event_log[7].state["flower"] == "roses"
-    assert event_log[7].state["color"] == "red"
-
-    assert isinstance(event_log[8], MethodExecutionFinishedEvent)
-    assert event_log[8].method_name == "finish_poem"
-    assert event_log[8].result == "roses are red, violets are blue"
-
-    assert isinstance(event_log[9], MethodExecutionStartedEvent)
-    assert event_log[9].method_name == "save_poem_to_database"
-    assert event_log[9].params == {}
-    assert isinstance(event_log[9].state, dict)
-    assert event_log[9].state["flower"] == "roses"
-    assert event_log[9].state["color"] == "red"
-
-    assert isinstance(event_log[10], MethodExecutionFinishedEvent)
-    assert event_log[10].method_name == "save_poem_to_database"
-    assert event_log[10].result is None
-
-    assert isinstance(event_log[11], FlowFinishedEvent)
-    assert event_log[11].flow_name == "PoemFlow"
-    assert event_log[11].result is None
-    assert isinstance(event_log[11].timestamp, datetime)
-
-
-def test_structured_flow_event_emission():
-    """Test that the correct events are emitted during structured flow
-    execution with all fields validated."""
-
-    class OnboardingState(BaseModel):
-        name: str = ""
-        sent: bool = False
-
-    class OnboardingFlow(Flow[OnboardingState]):
-        @start()
-        def user_signs_up(self):
-            self.state.sent = False
-
-        @listen(user_signs_up)
-        def send_welcome_message(self):
-            self.state.sent = True
-            return f"Welcome, {self.state.name}!"
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = OnboardingFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff(inputs={"name": "Anakin"})
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "OnboardingFlow"
-    assert event_log[0].inputs == {"name": "Anakin"}
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    assert isinstance(event_log[1], MethodExecutionStartedEvent)
-    assert event_log[1].method_name == "user_signs_up"
-
-    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
-    assert event_log[2].method_name == "user_signs_up"
-
-    assert isinstance(event_log[3], MethodExecutionStartedEvent)
-    assert event_log[3].method_name == "send_welcome_message"
-    assert event_log[3].params == {}
-    assert getattr(event_log[3].state, "sent") is False
-
-    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
-    assert event_log[4].method_name == "send_welcome_message"
-    assert getattr(event_log[4].state, "sent") is True
-    assert event_log[4].result == "Welcome, Anakin!"
-
-    assert isinstance(event_log[5], FlowFinishedEvent)
-    assert event_log[5].flow_name == "OnboardingFlow"
-    assert event_log[5].result == "Welcome, Anakin!"
-    assert isinstance(event_log[5].timestamp, datetime)
-
-
-def test_stateless_flow_event_emission():
-    """Test that the correct events are emitted stateless during flow execution
-    with all fields validated."""
-
-    class StatelessFlow(Flow):
-        @start()
-        def init(self):
-            pass
-
-        @listen(init)
-        def process(self):
-            return "Deeds will not be less valiant because they are unpraised."
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = StatelessFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff()
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "StatelessFlow"
-    assert event_log[0].inputs is None
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    assert isinstance(event_log[1], MethodExecutionStartedEvent)
-    assert event_log[1].method_name == "init"
-
-    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
-    assert event_log[2].method_name == "init"
-
-    assert isinstance(event_log[3], MethodExecutionStartedEvent)
-    assert event_log[3].method_name == "process"
-
-    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
-    assert event_log[4].method_name == "process"
-
-    assert isinstance(event_log[5], FlowFinishedEvent)
-    assert event_log[5].flow_name == "StatelessFlow"
-    assert (
-        event_log[5].result
-        == "Deeds will not be less valiant because they are unpraised."
-    )
-    assert isinstance(event_log[5].timestamp, datetime)
--- a/tests/llm_test.py
+++ b/tests/llm_test.py
@@ -2,7 +2,6 @@ import os
 from time import sleep
 from unittest.mock import MagicMock, patch

-import litellm
 import pytest
 from pydantic import BaseModel

@@ -13,31 +12,6 @@ from crewai.utilities.token_counter_callback import TokenCalcHandler


 # TODO: This test fails without print statement, which makes me think that something is happening asynchronously that we need to eventually fix and dive deeper into at a later date
-@pytest.mark.parametrize("model,location,expected", [
-    ("vertex_ai/gemini-2.0-flash", "europe-west4", "europe-west4"),
-    ("gpt-4", "europe-west4", None),  # Non-vertex model ignores location
-    ("vertex_ai/gemini-2.0-flash", None, None),  # No location provided
-])
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_vertex_ai_location_setting(model, location, expected):
-    """Test Vertex AI location setting behavior."""
-    llm = LLM(model=model, location=location)
-    assert litellm.vertex_location == expected
-    
-    # Reset location after test
-    litellm.vertex_location = None
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_vertex_ai_location_validation():
-    """Test Vertex AI location validation."""
-    # Test invalid location type
-    with pytest.raises(ValueError, match="Location must be a string"):
-        LLM(model="vertex_ai/gemini-2.0-flash", location=123)
-    
-    # Test invalid region
-    with pytest.raises(ValueError, match="Invalid Vertex AI region"):
-        LLM(model="vertex_ai/gemini-2.0-flash", location="invalid-region")
-
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_callback_replacement():
    llm1 = LLM(model="gpt-4o-mini")
--- a/uv.lock
+++ b/uv.lock
@@ -198,6 +198,15 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/39/e3/893e8757be2612e6c266d9bb58ad2e3651524b5b40cf56761e985a28b13e/asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47", size = 23828 },
 ]

+[[package]]
+name = "asn1crypto"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/de/cf/d547feed25b5244fcb9392e288ff9fdc3280b10260362fc45d37a798a6ee/asn1crypto-1.5.1.tar.gz", hash = "sha256:13ae38502be632115abf8a24cbe5f4da52e3b5231990aff31123c805306ccb9c", size = 121080 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c9/7f/09065fd9e27da0eda08b4d6897f1c13535066174cc023af248fc2a8d5e5a/asn1crypto-1.5.1-py2.py3-none-any.whl", hash = "sha256:db4e40728b728508912cbb3d44f19ce188f218e9eba635821bb4b68564f8fd67", size = 105045 },
+]
+
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -219,6 +228,15 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028", size = 5721 },
 ]

+[[package]]
+name = "atpublic"
+version = "5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5d/18/b1d247792440378abeeb0853f9daa2a127284b68776af6815990be7fcdb0/atpublic-5.0.tar.gz", hash = "sha256:d5cb6cbabf00ec1d34e282e8ce7cbc9b74ba4cb732e766c24e2d78d1ad7f723f", size = 14646 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6b/03/2cb0e5326e19b7d877bc9c3a7ef436a30a06835b638580d1f5e21a0409ed/atpublic-5.0-py3-none-any.whl", hash = "sha256:b651dcd886666b1042d1e38158a22a4f2c267748f4e97fde94bc492a4a28a3f3", size = 5207 },
+]
+
 [[package]]
 name = "attrs"
 version = "24.2.0"
@@ -244,6 +262,18 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/e4/0e/38cb7b781371e79e9c697fb78f3ccd18fda8bd547d0a2e76e616561a3792/auth0_python-4.7.2-py3-none-any.whl", hash = "sha256:df2224f9b1e170b3aa12d8bc7ff02eadb7cc229307a09ec6b8a55fd1e0e05dc8", size = 131834 },
 ]

+[[package]]
+name = "authlib"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/09/47/df70ecd34fbf86d69833fe4e25bb9ecbaab995c8e49df726dd416f6bb822/authlib-1.3.1.tar.gz", hash = "sha256:7ae843f03c06c5c0debd63c9db91f9fda64fa62a42a77419fa15fbb7e7a58917", size = 146074 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/87/1f/bc95e43ffb57c05b8efcc376dd55a0240bf58f47ddf5a0f92452b6457b75/Authlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:d35800b973099bbadc49b42b256ecb80041ad56b7fe1216a362c7943c088f377", size = 223827 },
+]
+
 [[package]]
 name = "autoflake"
 version = "2.3.1"
@@ -565,14 +595,14 @@ wheels = [

 [[package]]
 name = "click"
-version = "8.1.8"
+version = "8.1.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "colorama", marker = "platform_system == 'Windows'" },
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Author	SHA1	Message	Date
Devin AI	db34516b18	fix: Fix type-checker issues with _finish_execution method Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 06:46:27 +00:00
Devin AI	6c3f4a6446	refactor: Improve agentops integration with better types and tests - Add specific error handling for agentops initialization - Add type hints for better code readability - Improve API key validation - Reorganize tests using pytest fixtures and classes - Add documentation for set_private_attrs method Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 06:43:26 +00:00
Devin AI	3cf2f982dd	fix: Add pytest.ini and improve test initialization Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 06:41:36 +00:00
Devin AI	48604f567b	fix: Improve agentops initialization with better validation and logging Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 06:27:56 +00:00
Devin AI	f724ed93b9	fix: Sort imports to fix linting issues Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 06:27:21 +00:00
Devin AI	abc71be5f6	fix: Sort imports to fix linting issues Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 06:25:41 +00:00
Devin AI	420f826e56	fix: Initialize agentops in Crew setup (#2102 ) - Add proper agentops initialization in Crew's set_private_attrs - Add test coverage for agentops initialization - Add test coverage for Gemini LLM with agentops Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 06:24:05 +00:00