[DOCS]

docs/mkdocs.yml

@@ -152,6 +152,7 @@ nav:
         - HuggingFaceLLM: "swarms/models/huggingface.md"
         - Anthropic: "swarms/models/anthropic.md"
         - OpenAIChat: "swarms/models/openai.md"
+        - OpenAIFunctionCaller: "swarms/models/openai_function_caller.md"
         # - TogetherAI: "swarms/models/togetherai.md"
       - MultiModal Models:
         - BaseMultiModalModel: "swarms/models/base_multimodal_model.md"

playground/agents/monitoring/agent_ops.py

@@ -14,7 +14,7 @@ Requirements:
 """
 
 ################ Adding project root to PYTHONPATH ################################
-# If you are running playground examples in the project files directly, use this: 
+# If you are running playground examples in the project files directly, use this:
 
 import sys
 import os
@@ -29,9 +29,9 @@ Treasurer = Agent(
     agent_name="Treasurer",
     system_prompt="Give your opinion on the cash management.",
     agent_description=(
-        "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 " 
+        "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(),
@@ -44,14 +44,14 @@ CFO = 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"
+        "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,
@@ -63,23 +63,24 @@ swarm = AgentRearrange(
     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"
-        )
+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/monitoring/agent_ops_tools.py

@@ -14,7 +14,7 @@ Requirements:
 """
 
 ################ Adding project root to PYTHONPATH ################################
-# If you are running playground examples in the project files directly, use this: 
+# If you are running playground examples in the project files directly, use this:
 
 import sys
 import os
@@ -25,7 +25,8 @@ sys.path.insert(0, os.getcwd())
 
 
 from swarms import Agent, OpenAIChat
-from agentops import record_function 
+from agentops import record_function
+
 
 # Add agentops decorator on your tools
 @record_function("length_checker")
@@ -41,6 +42,7 @@ def length_checker(string: str) -> int:
     """
     return len(string)
 
+
 agent1 = Agent(
     agent_name="lengther",
     system_prompt="return the length of the string",
@@ -55,4 +57,4 @@ agent1 = Agent(
 )
 
 
-agent1.run("hello")
+agent1.run("hello")

swarms/structs/agent.py

@@ -91,6 +91,7 @@ def step_id():
 agent_output_type = Union[BaseModel, dict, str]
 ToolUsageType = Union[BaseModel, Dict[str, Any]]
 
+
 # [FEAT][AGENT]
 @agentops.track_agent()
 class Agent(BaseStructure):

swarms/structs/rearrange.py

@@ -224,7 +224,11 @@ class AgentRearrange(BaseSwarm):
                             else:
                                 agent = self.agents[agent_name]
                                 result = agent.run(
-                                    current_task, img, is_last, *args, **kwargs
+                                    current_task,
+                                    img,
+                                    is_last,
+                                    *args,
+                                    **kwargs,
                                 )
                                 results.append(result)
 
@@ -324,13 +328,22 @@ class AgentRearrange(BaseSwarm):
                 results = []
                 for agent_name in agent_names:
                     result = self.process_agent_or_swarm(
-                        agent_name, current_task, img, is_last*args, **kwargs
+                        agent_name,
+                        current_task,
+                        img,
+                        is_last * args,
+                        **kwargs,
                     )
                     results.append(result)
                 current_task = "; ".join(results)
             else:
                 current_task = self.process_agent_or_swarm(
-                    agent_names[0], current_task, is_last, img, *args, **kwargs
+                    agent_names[0],
+                    current_task,
+                    is_last,
+                    img,
+                    *args,
+                    **kwargs,
                 )
         return current_task
 

swarms/tools/tool_parse_exec.py

@@ -30,8 +30,12 @@ def parse_and_execute_json(
         function_dict = {func.__name__: func for func in functions}
 
         data = json.loads(json_string)
-        function_list = data.get("functions", []) if data.get("functions") else [data.get("function", [])]
-        
+        function_list = (
+            data.get("functions", [])
+            if data.get("functions")
+            else [data.get("function", [])]
+        )
+
         results = {}
         for function_data in function_list:
             function_name = function_data.get("name")