From b2f8721f85e73a47e0ddac2698f247069d3f56d5 Mon Sep 17 00:00:00 2001
From: Nicolas Nahas <45595586+nicorne@users.noreply.github.com>
Date: Sun, 28 Jul 2024 18:12:36 +0200
Subject: [PATCH] AGENTOPS FIX

---
 README.md                                     |  2 -
 docs/swarms/structs/agent.md                  |  1 -
 example.py                                    |  1 -
 multi_agent_collab_demo.py                    |  2 -
 playground/agents/agent_ops.py                | 95 ++++++++++++++++---
 .../finance_agent_with_memory                 |  1 -
 playground/agents/estate_planning_agent.py    |  1 -
 .../agents/financial_agent_gpt4o_mini.py      |  1 -
 playground/agents/first_agent_example.py      |  1 -
 .../agents/llama_3_agent/llama_3_1_agent.py   |  1 -
 .../society_of_agents/accountant_team.py      |  6 +-
 .../society_of_agents/hallucination_swarm.py  |  2 -
 .../demos/society_of_agents/probate_agent.py  |  1 -
 .../mixture_of_agents/agent_ops_moa.py        | 11 ---
 .../swarm_of_ba_agents/agents.py              |  4 -
 swarms/schemas/agent_input_schema.py          |  1 -
 swarms/structs/agent.py                       | 41 --------
 swarms/structs/base_swarm.py                  | 15 ---
 swarms/utils/agent_ops_check.py               | 28 ------
 19 files changed, 82 insertions(+), 133 deletions(-)
 delete mode 100644 swarms/utils/agent_ops_check.py

diff --git a/README.md b/README.md
index 24df5941..ffa4135b 100644
--- a/README.md
+++ b/README.md
@@ -109,7 +109,6 @@ agent = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
@@ -172,7 +171,6 @@ agent = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
diff --git a/docs/swarms/structs/agent.md b/docs/swarms/structs/agent.md
index 39076a5e..eb4a2865 100644
--- a/docs/swarms/structs/agent.md
+++ b/docs/swarms/structs/agent.md
@@ -88,7 +88,6 @@ Swarm Agent is a powerful autonomous agent framework designed to connect Languag
 | `device` | A string representing the device on which the agent should run. |
 | `custom_planning_prompt` | A string representing a custom prompt for planning. |
 | `memory_chunk_size` | An integer representing the maximum size of memory chunks for long-term memory retrieval. |
-| `agent_ops_on` | A boolean indicating whether agent operations should be enabled. |
 
 ### `Agent` Methods
 
diff --git a/example.py b/example.py
index c647556d..9ddc549b 100644
--- a/example.py
+++ b/example.py
@@ -35,7 +35,6 @@ agent = Agent(
     context_length=200000,
     # tool_schema=
     # tools
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
diff --git a/multi_agent_collab_demo.py b/multi_agent_collab_demo.py
index 468ebc59..bc9387e8 100644
--- a/multi_agent_collab_demo.py
+++ b/multi_agent_collab_demo.py
@@ -34,7 +34,6 @@ fiancial_analyst = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=160000,
-    # agent_ops_on=True,
     interactive=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
@@ -68,7 +67,6 @@ fiancial_director = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
diff --git a/playground/agents/agent_ops.py b/playground/agents/agent_ops.py
index f2dd1489..49cfdd6f 100644
--- a/playground/agents/agent_ops.py
+++ b/playground/agents/agent_ops.py
@@ -1,26 +1,91 @@
-from swarms import Agent, OpenAIChat
+"""
+* WORKING
+
+What this script does:
+Simple agent run to test AgentOps (https://www.agentops.ai/)
+
+Requirements:
+1. Create an account on https://www.agentops.ai/ and run pip install agentops
+2. Add the folowing API key(s) in your .env file:
+   - OPENAI_API_KEY
+   - AGENTOPS_API_KEY
+3. Go to your agentops dashboard to observe your activity
+
+"""
+
+################ Adding project root to PYTHONPATH ################################
+# If you are running playground examples in the project files directly, use this: 
+
+import sys
+import os
+
+sys.path.insert(0, os.getcwd())
+
+################ Adding project root to PYTHONPATH ################################
+
+
+from swarms import Agent, OpenAIChat, AgentRearrange
+import agentops
+
+# Initialize agentops
+agentops.start_session()
 
-# Initialize the agent
 agent = Agent(
-    agent_name="Accounting Agent",
-    system_prompt="Generate a financial report for the company's quarterly earnings.",
+    agent_name="Treasurer",
+    system_prompt="Give your opinion on the cash management.",
     agent_description=(
-        "Generate a financial report for the company's quarterly earnings."
+        "responsible for managing an organization's financial assets and liquidity. They oversee cash management, " 
+        "investment strategies, and financial risk. Key duties include monitoring cash flow, managing bank relationships, " 
+        "ensuring sufficient funds for operations, and optimizing returns on short-term investments. Treasurers also often " 
+        "handle debt management and may be involved in capital raising activities."
     ),
     llm=OpenAIChat(),
     max_loops=1,
-    autosave=True,
-    dashboard=False,
     streaming_on=True,
     verbose=True,
-    stopping_token="<DONE>",
-    interactive=False,
-    state_save_file_type="json",
-    saved_state_path="accounting_agent.json",
-    agent_ops_on=True,
 )
 
-# Run the Agent on a task
-agent.run(
-    "Generate a financial report for the company's quarterly earnings!"
+agent2 = Agent(
+    agent_name="CFO",
+    system_prompt="Give your opinion on the financial performance of the company.",
+    agent_description=(
+    "the top financial executive in an organization, overseeing all financial operations and strategy. Their role is broader than a treasurer's and includes:\n"
+    "Financial planning and analysis\n"
+    "Accounting and financial reporting\n"
+    "Budgeting and forecasting\n"
+    "Strategic financial decision-making\n"
+    "Compliance and risk management\n"
+    "Investor relations (in public companies)\n"
+    "Overseeing the finance and accounting departments"
+    ),
+    llm=OpenAIChat(),
+    max_loops=1,
+    streaming_on=True,
+    verbose=True,
+)
+
+swarm = AgentRearrange(
+    agents=[agent, agent2],
+    flow="Treasurer -> CFO",
 )
+
+results = swarm.run("Date,Revenue,Expenses,Profit,Cash_Flow,Inventory,Customer_Acquisition_Cost,Customer_Retention_Rate,Marketing_Spend,R&D_Spend,Debt,Assets\n"
+        "2023-01-01,1000000,800000,200000,150000,500000,100,0.85,50000,100000,2000000,5000000\n"
+        "2023-02-01,1050000,820000,230000,180000,520000,95,0.87,55000,110000,1950000,5100000\n"
+        "2023-03-01,1100000,850000,250000,200000,530000,90,0.88,60000,120000,1900000,5200000\n"
+        "2023-04-01,1200000,900000,300000,250000,550000,85,0.90,70000,130000,1850000,5400000\n"
+        "2023-05-01,1300000,950000,350000,300000,580000,80,0.92,80000,140000,1800000,5600000\n"
+        "2023-06-01,1400000,1000000,400000,350000,600000,75,0.93,90000,150000,1750000,5800000\n"
+        "2023-07-01,1450000,1050000,400000,320000,620000,78,0.91,95000,160000,1700000,5900000\n"
+        "2023-08-01,1500000,1100000,400000,300000,650000,80,0.90,100000,170000,1650000,6000000\n"
+        "2023-09-01,1550000,1150000,400000,280000,680000,82,0.89,105000,180000,1600000,6100000\n"
+        "2023-10-01,1600000,1200000,400000,260000,700000,85,0.88,110000,190000,1550000,6200000\n"
+        "2023-11-01,1650000,1250000,400000,240000,720000,88,0.87,115000,200000,1500000,6300000\n"
+        "2023-12-01,1700000,1300000,400000,220000,750000,90,0.86,120000,210000,1450000,6400000\n"
+        "2024-01-01,1500000,1200000,300000,180000,780000,95,0.84,100000,180000,1500000,6300000\n"
+        "2024-02-01,1550000,1220000,330000,200000,760000,92,0.85,105000,185000,1480000,6350000\n"
+        "2024-03-01,1600000,1240000,360000,220000,740000,89,0.86,110000,190000,1460000,6400000\n"
+        "2024-04-01,1650000,1260000,390000,240000,720000,86,0.87,115000,195000,1440000,6450000\n"
+        "2024-05-01,1700000,1280000,420000,260000,700000,83,0.88,120000,200000,1420000,6500000\n"
+        "2024-06-01,1750000,1300000,450000,280000,680000,80,0.89,125000,205000,1400000,6550000"
+        )
\ No newline at end of file
diff --git a/playground/agents/agents_and_memory/finance_agent_with_memory b/playground/agents/agents_and_memory/finance_agent_with_memory
index 4064b303..98255293 100644
--- a/playground/agents/agents_and_memory/finance_agent_with_memory
+++ b/playground/agents/agents_and_memory/finance_agent_with_memory
@@ -45,7 +45,6 @@ agent = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
diff --git a/playground/agents/estate_planning_agent.py b/playground/agents/estate_planning_agent.py
index 878c7f78..3aaf896d 100644
--- a/playground/agents/estate_planning_agent.py
+++ b/playground/agents/estate_planning_agent.py
@@ -114,7 +114,6 @@ agent = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
diff --git a/playground/agents/financial_agent_gpt4o_mini.py b/playground/agents/financial_agent_gpt4o_mini.py
index 52d8329c..d32bd098 100644
--- a/playground/agents/financial_agent_gpt4o_mini.py
+++ b/playground/agents/financial_agent_gpt4o_mini.py
@@ -40,7 +40,6 @@ agent = Agent(
     # tool_schema = dict
     context_length=200000,
     interactive=True,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
diff --git a/playground/agents/first_agent_example.py b/playground/agents/first_agent_example.py
index efd4310f..f2e18f33 100644
--- a/playground/agents/first_agent_example.py
+++ b/playground/agents/first_agent_example.py
@@ -34,7 +34,6 @@ agent = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
diff --git a/playground/agents/llama_3_agent/llama_3_1_agent.py b/playground/agents/llama_3_agent/llama_3_1_agent.py
index 653b9ada..dccfb732 100644
--- a/playground/agents/llama_3_agent/llama_3_1_agent.py
+++ b/playground/agents/llama_3_agent/llama_3_1_agent.py
@@ -39,7 +39,6 @@ agent = Agent(
     context_length=200000,
     # tool_schema=
     # tools
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 
diff --git a/playground/demos/society_of_agents/accountant_team.py b/playground/demos/society_of_agents/accountant_team.py
index 882890b1..510475a5 100644
--- a/playground/demos/society_of_agents/accountant_team.py
+++ b/playground/demos/society_of_agents/accountant_team.py
@@ -31,8 +31,7 @@ receipt_analyzer_agent = Agent(
     # # docs=
     # # docs_folder="docs",
     retry_attempts=3,
-    # tool_schema = dict
-    # agent_ops_on=True,
+    # tool_schema = dict,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
     # multi_modal=True
 )
@@ -63,8 +62,7 @@ analyst_agent = Agent(
     # # docs=
     # # docs_folder="docs",
     retry_attempts=3,
-    # tool_schema = dict
-    # agent_ops_on=True,
+    # tool_schema = dict,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
     # multi_modal=True,
 )
diff --git a/playground/demos/society_of_agents/hallucination_swarm.py b/playground/demos/society_of_agents/hallucination_swarm.py
index 3f6764ba..1a00fb06 100644
--- a/playground/demos/society_of_agents/hallucination_swarm.py
+++ b/playground/demos/society_of_agents/hallucination_swarm.py
@@ -32,7 +32,6 @@ hallucinator = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=1000,
-    # agent_ops_on=True,
     # tree_of_thoughts=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
@@ -213,7 +212,6 @@ agent_evaluator = Agent(
     user_name="Human",
     # tool_schema = dict
     context_length=1000,
-    # agent_ops_on=True,
     # tree_of_thoughts=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
diff --git a/playground/demos/society_of_agents/probate_agent.py b/playground/demos/society_of_agents/probate_agent.py
index 04660860..74a8d4a9 100644
--- a/playground/demos/society_of_agents/probate_agent.py
+++ b/playground/demos/society_of_agents/probate_agent.py
@@ -158,7 +158,6 @@ agent = Agent(
     # context_length=1000,
     # tool_schema = dict
     context_length=1000,
-    # agent_ops_on=True,
     interactive=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
diff --git a/playground/structs/multi_agent_collaboration/mixture_of_agents/agent_ops_moa.py b/playground/structs/multi_agent_collaboration/mixture_of_agents/agent_ops_moa.py
index 2274f956..528e010d 100644
--- a/playground/structs/multi_agent_collaboration/mixture_of_agents/agent_ops_moa.py
+++ b/playground/structs/multi_agent_collaboration/mixture_of_agents/agent_ops_moa.py
@@ -302,7 +302,6 @@ director = Agent(
     agent_name="Director",
     system_prompt="Directs the tasks for the accountants",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -314,7 +313,6 @@ accountant1 = Agent(
     agent_name="Accountant1",
     system_prompt="Prepares financial statements",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -326,7 +324,6 @@ accountant2 = Agent(
     agent_name="Accountant2",
     system_prompt="Audits financial records",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -338,7 +335,6 @@ balance_sheet_analyzer = Agent(
     agent_name="BalanceSheetAnalyzer",
     system_prompt="Analyzes balance sheets",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -349,7 +345,6 @@ income_statement_analyzer = Agent(
     agent_name="IncomeStatementAnalyzer",
     system_prompt="Analyzes income statements",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -360,7 +355,6 @@ cash_flow_analyzer = Agent(
     agent_name="CashFlowAnalyzer",
     system_prompt="Analyzes cash flow statements",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -371,7 +365,6 @@ financial_ratio_calculator = Agent(
     agent_name="FinancialRatioCalculator",
     system_prompt="Calculates financial ratios",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -382,7 +375,6 @@ tax_preparer = Agent(
     agent_name="TaxPreparer",
     system_prompt="Prepares tax returns",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -393,7 +385,6 @@ payroll_processor = Agent(
     agent_name="PayrollProcessor",
     system_prompt="Processes payroll",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -404,7 +395,6 @@ inventory_manager = Agent(
     agent_name="InventoryManager",
     system_prompt="Manages inventory",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
@@ -415,7 +405,6 @@ budget_planner = Agent(
     agent_name="BudgetPlanner",
     system_prompt="Plans budgets",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",
diff --git a/playground/structs/multi_agent_collaboration/swarm_of_ba_agents/agents.py b/playground/structs/multi_agent_collaboration/swarm_of_ba_agents/agents.py
index b01b9982..bbaa2fb3 100644
--- a/playground/structs/multi_agent_collaboration/swarm_of_ba_agents/agents.py
+++ b/playground/structs/multi_agent_collaboration/swarm_of_ba_agents/agents.py
@@ -653,7 +653,6 @@ revenue_maximalist_agent = Agent(
     dashboard=False,
     state_save_file_type="json",
     saved_state_path="revenue_maximalist_analyst.json",
-    # agent_ops_on=True,
     # # long_term_memory=memory,
     # context_length=10000,
 )
@@ -666,7 +665,6 @@ cost_control_agent = Agent(
     dashboard=False,
     state_save_file_type="json",
     saved_state_path="operational_efficiency.json",
-    # agent_ops_on=True,
     # # long_term_memory=memory,
 )
 
@@ -678,7 +676,6 @@ investment_analyst_agent = Agent(
     dashboard=False,
     state_save_file_type="json",
     saved_state_path="strategic_investment_analyst.json",
-    # agent_ops_on=True,
     # # long_term_memory=memory,
 )
 
@@ -690,7 +687,6 @@ sales_marketing_agent = Agent(
     dashboard=False,
     state_save_file_type="json",
     saved_state_path="sales_marketing_agent.json",
-    # agent_ops_on=True,
     # # long_term_memory=memory,
     # context_length=8192,
 )
diff --git a/swarms/schemas/agent_input_schema.py b/swarms/schemas/agent_input_schema.py
index 1c34719f..29149f74 100644
--- a/swarms/schemas/agent_input_schema.py
+++ b/swarms/schemas/agent_input_schema.py
@@ -84,7 +84,6 @@ class AgentSchema(BaseModel):
     device: Optional[str] = Field(default=None)
     custom_planning_prompt: Optional[str] = Field(default=None)
     memory_chunk_size: Optional[int] = Field(default=2000, ge=0)
-    agent_ops_on: Optional[bool] = Field(default=False)
     log_directory: Optional[str] = Field(default=None)
     project_path: Optional[str] = Field(default=None)
     tool_system_prompt: Optional[str] = Field(default="tool_sop_prompt()")
diff --git a/swarms/structs/agent.py b/swarms/structs/agent.py
index 9e493ee4..91b4fbfc 100644
--- a/swarms/structs/agent.py
+++ b/swarms/structs/agent.py
@@ -257,7 +257,6 @@ class Agent(BaseStructure):
         device: str = None,
         custom_planning_prompt: str = None,
         memory_chunk_size: int = 2000,
-        agent_ops_on: bool = False,
         log_directory: str = None,
         project_path: str = None,
         tool_system_prompt: str = tool_sop_prompt(),
@@ -348,7 +347,6 @@ class Agent(BaseStructure):
         self.rules = rules
         self.custom_tools_prompt = custom_tools_prompt
         self.memory_chunk_size = memory_chunk_size
-        self.agent_ops_on = agent_ops_on
         self.log_directory = log_directory
         self.project_path = project_path
         self.tool_system_prompt = tool_system_prompt
@@ -473,10 +471,6 @@ class Agent(BaseStructure):
         if exists(self.sop):
             self.short_memory.add(role=self.user_name, content=self.sop)
 
-        # If agent_ops is on => activate agentops
-        if agent_ops_on is True:
-            self.activate_agentops()
-
     def set_system_prompt(self, system_prompt: str):
         """Set the system prompt"""
         self.system_prompt = system_prompt
@@ -883,8 +877,6 @@ class Agent(BaseStructure):
             #     print(f"Response after output model: {response}")
 
             # print(response)
-            if self.agent_ops_on is True:
-                self.check_end_session_agentops()
 
             # final_response = " ".join(all_responses)
             all_responses = [response for response in all_responses if response is not None]
@@ -1601,20 +1593,6 @@ class Agent(BaseStructure):
                 )
             )
 
-    def check_end_session_agentops(self):
-        if self.agent_ops_on is True:
-            try:
-                from swarms.utils.agent_ops_check import (
-                    end_session_agentops,
-                )
-
-                # Try ending the session
-                return end_session_agentops()
-            except ImportError:
-                logger.error(
-                    "Could not import agentops, try installing agentops: $ pip3 install agentops"
-                )
-
     def convert_tool_into_openai_schema(self):
         logger.info("Converting tools into OpenAI function calling schema")
 
@@ -1955,25 +1933,6 @@ class Agent(BaseStructure):
                 "Error parsing and executing function call"
             ) from error
 
-    def activate_agentops(self):
-        if self.agent_ops_on is True:
-            try:
-                from swarms.utils.agent_ops_check import (
-                    try_import_agentops,
-                )
-
-                # Try importing agent ops
-                logger.info(
-                    "Agent Ops Initializing, ensure that you have the agentops API key and the pip package installed."
-                )
-                try_import_agentops()
-
-                logger.info("Agentops successfully activated!")
-            except ImportError:
-                logger.error(
-                    "Could not import agentops, try installing agentops: $ pip3 install agentops"
-                )
-
     def handle_multiple_base_models(self) -> None:
         try:
             # If a list of tool schemas is provided
diff --git a/swarms/structs/base_swarm.py b/swarms/structs/base_swarm.py
index 8a688b06..4bc355f2 100644
--- a/swarms/structs/base_swarm.py
+++ b/swarms/structs/base_swarm.py
@@ -89,7 +89,6 @@ class BaseSwarm(ABC):
         speaker_selection_func: Optional[Callable] = None,
         rules: Optional[str] = None,
         collective_memory_system: Optional[BaseVectorDatabase] = False,
-        agent_ops_on: bool = False,
         *args,
         **kwargs,
     ):
@@ -111,7 +110,6 @@ class BaseSwarm(ABC):
         self.speaker_selection_func = speaker_selection_func
         self.rules = rules
         self.collective_memory_system = collective_memory_system
-        self.agent_ops_on = agent_ops_on
 
         logger.info("Reliability checks activated.")
         # Ensure that agents is exists
@@ -161,11 +159,6 @@ class BaseSwarm(ABC):
             self.stopping_condition_args = stopping_condition_args
             self.stopping_condition = stopping_condition
 
-        # If agentops is enabled, try to import agentops
-        if agentops_on is True:
-            for agent in self.agents:
-                agent.agent_ops_on = True
-
         # Handle speaker selection function
         if speaker_selection_func is not None:
             if not callable(speaker_selection_func):
@@ -174,11 +167,6 @@ class BaseSwarm(ABC):
                 )
             self.speaker_selection_func = speaker_selection_func
 
-        # Add the check for all the agents to see if agent ops is on!
-        if agent_ops_on is True:
-            for agent in self.agents:
-                agent.agent_ops_on = True
-
         # Agents dictionary with agent name as key and agent object as value
         self.agents_dict = {
             agent.agent_name: agent for agent in self.agents
@@ -694,9 +682,6 @@ class BaseSwarm(ABC):
     def __contains__(self, value):
         return value in self.agents
 
-    def __eq__(self, other):
-        return self.__dict__ == other.__dict__
-
     def agent_error_handling_check(self):
         try:
             if self.agents is None:
diff --git a/swarms/utils/agent_ops_check.py b/swarms/utils/agent_ops_check.py
deleted file mode 100644
index 4e1df6e7..00000000
--- a/swarms/utils/agent_ops_check.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from swarms.utils.loguru_logger import logger
-import os
-from dotenv import load_dotenv
-
-
-def try_import_agentops(*args, **kwargs):
-    try:
-        load_dotenv()
-        logger.info("Trying to import agentops")
-        import agentops
-
-        agentops.init(os.getenv("AGENTOPS_API_KEY"), *args, **kwargs)
-
-        return "agentops imported successfully."
-    except ImportError:
-        logger.error("Could not import agentops")
-
-
-def end_session_agentops():
-    try:
-        logger.info("Trying to end session")
-        import agentops
-
-        agentops.end_session("Success")
-        return "Session ended successfully."
-    except ImportError:
-        logger.error("Could not import agentops")
-        return "Could not end session."