AGENTOPS FIX

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"),
 )
 

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
 

example.py

@@ -35,7 +35,6 @@
     context_length=200000,
     # tool_schema=
     # tools
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 

multi_agent_collab_demo.py

@@ -34,7 +34,6 @@
     # 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 @@
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 

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"
+        )

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"),
 )
 

playground/agents/estate_planning_agent.py

@@ -114,7 +114,6 @@
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 

playground/agents/financial_agent_gpt4o_mini.py

@@ -40,7 +40,6 @@
     # tool_schema = dict
     context_length=200000,
     interactive=True,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 

playground/agents/first_agent_example.py

@@ -34,7 +34,6 @@
     # context_length=1000,
     # tool_schema = dict
     context_length=200000,
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 

playground/agents/llama_3_agent/llama_3_1_agent.py

@@ -39,7 +39,6 @@
     context_length=200000,
     # tool_schema=
     # tools
-    # agent_ops_on=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )
 

playground/demos/society_of_agents/accountant_team.py

@@ -31,8 +31,7 @@
     # # 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 @@
     # # 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,
 )

playground/demos/society_of_agents/hallucination_swarm.py

@@ -32,7 +32,6 @@
     # 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 @@
     user_name="Human",
     # tool_schema = dict
     context_length=1000,
-    # agent_ops_on=True,
     # tree_of_thoughts=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )

playground/demos/society_of_agents/probate_agent.py

@@ -158,7 +158,6 @@
     # context_length=1000,
     # tool_schema = dict
     context_length=1000,
-    # agent_ops_on=True,
     interactive=True,
     # long_term_memory=ChromaDB(docs_folder="artifacts"),
 )

playground/structs/multi_agent_collaboration/mixture_of_agents/agent_ops_moa.py

@@ -302,7 +302,6 @@
     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 @@
     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 @@
     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 @@
     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 @@
     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 @@
     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 @@
     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 @@
     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 @@
     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 @@
     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 @@
     agent_name="BudgetPlanner",
     system_prompt="Plans budgets",
     llm=llm,
-    agent_ops_on=True,
     max_loops=1,
     dashboard=False,
     state_save_file_type="json",

playground/structs/multi_agent_collaboration/swarm_of_ba_agents/agents.py

@@ -653,7 +653,6 @@ def call_with_summary(summary: str = None):
     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 @@ def call_with_summary(summary: str = None):
     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 @@ def call_with_summary(summary: str = None):
     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 @@ def call_with_summary(summary: str = None):
     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,
 )

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()")