swarms api examples

docs/mkdocs.yml

@@ -207,6 +207,7 @@ nav:
 
     - Swarms API Examples:
       - Medical Swarm: "swarms/examples/swarms_api_medical.md"
+      - Finance Swarm: "swarms/examples/swarms_api_finance.md"
       - ML Model Code Generation Swarm: "swarms/examples/swarms_api_ml_model.md"
     - Various Model Providers:
       - OpenAI: "swarms/examples/openai_example.md"

docs/swarms/examples/swarms_api_finance.md

@@ -0,0 +1,127 @@
+
+# Finance Swarm Example
+
+1. Get your API key from the Swarms API dashboard [HERE](https://swarms.world/platform/api-keys)
+2. Create a `.env` file in the root directory and add your API key:
+
+```bash
+SWARMS_API_KEY=<your-api-key>
+```
+
+3. Create a Python script to create and trigger the financial swarm:
+
+
+```python
+import os
+import requests
+from dotenv import load_dotenv
+import json
+
+load_dotenv()
+
+# Retrieve API key securely from .env
+API_KEY = os.getenv("SWARMS_API_KEY")
+BASE_URL = "https://swarms-api-285321057562.us-east1.run.app"
+
+# Headers for secure API communication
+headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}
+
+def create_financial_swarm(equity_data: str):
+    """
+    Constructs and triggers a full-stack financial swarm consisting of three agents:
+    Equity Analyst, Risk Assessor, and Market Advisor.
+    Each agent is provided with a comprehensive, detailed system prompt to ensure high reliability.
+    """
+
+    payload = {
+        "swarm_name": "Enhanced Financial Analysis Swarm",
+        "description": "A swarm of agents specialized in performing comprehensive financial analysis, risk assessment, and market recommendations.",
+        "agents": [
+            {
+                "agent_name": "Equity Analyst",
+                "description": "Agent specialized in analyzing equities data to provide insights on stock performance and valuation.",
+                "system_prompt": (
+                    "You are an experienced equity analyst with expertise in financial markets and stock valuation. "
+                    "Your role is to analyze the provided equities data, including historical performance, financial statements, and market trends. "
+                    "Provide a detailed analysis of the stock's potential, including valuation metrics and growth prospects. "
+                    "Consider macroeconomic factors, industry trends, and company-specific news. Your analysis should be clear, actionable, and well-supported by data."
+                ),
+                "model_name": "openai/gpt-4o",
+                "role": "worker",
+                "max_loops": 1,
+                "max_tokens": 4000,
+                "temperature": 0.3,
+                "auto_generate_prompt": False
+            },
+            {
+                "agent_name": "Risk Assessor",
+                "description": "Agent responsible for evaluating the risks associated with equity investments.",
+                "system_prompt": (
+                    "You are a certified risk management professional with expertise in financial risk assessment. "
+                    "Your task is to evaluate the risks associated with the provided equities data, including market risk, credit risk, and operational risk. "
+                    "Provide a comprehensive risk analysis, including potential scenarios and their impact on investment performance. "
+                    "Your output should be detailed, reliable, and compliant with current risk management standards."
+                ),
+                "model_name": "openai/gpt-4o",
+                "role": "worker",
+                "max_loops": 1,
+                "max_tokens": 3000,
+                "temperature": 0.2,
+                "auto_generate_prompt": False
+            },
+            {
+                "agent_name": "Market Advisor",
+                "description": "Agent dedicated to suggesting investment strategies based on market conditions and equity analysis.",
+                "system_prompt": (
+                    "You are a knowledgeable market advisor with expertise in investment strategies and portfolio management. "
+                    "Based on the analysis provided by the Equity Analyst and the risk assessment, your task is to recommend a comprehensive investment strategy. "
+                    "Your suggestions should include asset allocation, diversification strategies, and considerations for market conditions. "
+                    "Explain the rationale behind each recommendation and reference relevant market data where applicable. "
+                    "Your recommendations should be reliable, detailed, and clearly prioritized based on risk and return."
+                ),
+                "model_name": "openai/gpt-4o",
+                "role": "worker",
+                "max_loops": 1,
+                "max_tokens": 5000,
+                "temperature": 0.3,
+                "auto_generate_prompt": False
+            }
+        ],
+        "max_loops": 1,
+        "swarm_type": "SequentialWorkflow",
+        "task": equity_data,
+    }
+
+    # Payload includes the equity data as the task to be processed by the swarm
+
+    response = requests.post(
+        f"{BASE_URL}/v1/swarm/completions",
+        headers=headers,
+        json=payload,
+    )
+
+    if response.status_code == 200:
+        print("Swarm successfully executed!")
+        return json.dumps(response.json(), indent=4)
+    else:
+        print(f"Error {response.status_code}: {response.text}")
+        return None
+
+
+# Example Equity Data for the Swarm to analyze
+if __name__ == "__main__":
+    equity_data = (
+        "Analyze the equity data for Company XYZ, which has shown a 15% increase in revenue over the last quarter, "
+        "with a P/E ratio of 20 and a market cap of $1 billion. Consider the current market conditions and potential risks."
+    )
+
+    financial_output = create_financial_swarm(equity_data)
+    print(financial_output)
+```
+
+4. Run the script:
+
+```bash
+python financial_swarm.py
+```
+

docs/swarms/examples/swarms_api_medical.md

@@ -48,7 +48,7 @@ def create_medical_swarm(patient_case: str):
                 ),
                 "model_name": "openai/gpt-4o",
                 "role": "worker",
-                "max_loops": 2,
+                "max_loops": 1,
                 "max_tokens": 4000,
                 "temperature": 0.3,
                 "auto_generate_prompt": False
@@ -88,18 +88,15 @@ def create_medical_swarm(patient_case: str):
                 "auto_generate_prompt": False
             }
         ],
-        "max_loops": 3,
-        "swarm_type": "SequentialWorkflow" 
-    }
-
-    # Payload includes the patient case as the task to be processed by the swarm
-    payload = {
+        "max_loops": 1,
+        "swarm_type": "SequentialWorkflow",
         "task": patient_case,
-        "swarm": payload
     }
 
+    # Payload includes the patient case as the task to be processed by the swar
+
     response = requests.post(
-        f"{BASE_URL}/swarm/completion",
+        f"{BASE_URL}/v1/swarm/completions",
         headers=headers,
         json=payload,
     )
@@ -122,4 +119,10 @@ if __name__ == "__main__":
 
     diagnostic_output = create_medical_swarm(patient_case)
     print(diagnostic_output)
+```
+
+4. Run the script:
+
+```bash
+python medical_swarm.py
 ```

medical_swarm.py

@@ -0,0 +1,105 @@
+import os
+import requests
+from dotenv import load_dotenv
+import json
+
+load_dotenv()
+
+# Retrieve API key securely from .env
+API_KEY = os.getenv("SWARMS_API_KEY")
+BASE_URL = "https://swarms-api-285321057562.us-east1.run.app"
+
+# Headers for secure API communication
+headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}
+
+def create_financial_swarm(equity_data: str):
+    """
+    Constructs and triggers a full-stack financial swarm consisting of three agents:
+    Equity Analyst, Risk Assessor, and Market Advisor.
+    Each agent is provided with a comprehensive, detailed system prompt to ensure high reliability.
+    """
+
+    payload = {
+        "swarm_name": "Enhanced Financial Analysis Swarm",
+        "description": "A swarm of agents specialized in performing comprehensive financial analysis, risk assessment, and market recommendations.",
+        "agents": [
+            {
+                "agent_name": "Equity Analyst",
+                "description": "Agent specialized in analyzing equities data to provide insights on stock performance and valuation.",
+                "system_prompt": (
+                    "You are an experienced equity analyst with expertise in financial markets and stock valuation. "
+                    "Your role is to analyze the provided equities data, including historical performance, financial statements, and market trends. "
+                    "Provide a detailed analysis of the stock's potential, including valuation metrics and growth prospects. "
+                    "Consider macroeconomic factors, industry trends, and company-specific news. Your analysis should be clear, actionable, and well-supported by data."
+                ),
+                "model_name": "openai/gpt-4o",
+                "role": "worker",
+                "max_loops": 1,
+                "max_tokens": 4000,
+                "temperature": 0.3,
+                "auto_generate_prompt": False
+            },
+            {
+                "agent_name": "Risk Assessor",
+                "description": "Agent responsible for evaluating the risks associated with equity investments.",
+                "system_prompt": (
+                    "You are a certified risk management professional with expertise in financial risk assessment. "
+                    "Your task is to evaluate the risks associated with the provided equities data, including market risk, credit risk, and operational risk. "
+                    "Provide a comprehensive risk analysis, including potential scenarios and their impact on investment performance. "
+                    "Your output should be detailed, reliable, and compliant with current risk management standards."
+                ),
+                "model_name": "openai/gpt-4o",
+                "role": "worker",
+                "max_loops": 1,
+                "max_tokens": 3000,
+                "temperature": 0.2,
+                "auto_generate_prompt": False
+            },
+            {
+                "agent_name": "Market Advisor",
+                "description": "Agent dedicated to suggesting investment strategies based on market conditions and equity analysis.",
+                "system_prompt": (
+                    "You are a knowledgeable market advisor with expertise in investment strategies and portfolio management. "
+                    "Based on the analysis provided by the Equity Analyst and the risk assessment, your task is to recommend a comprehensive investment strategy. "
+                    "Your suggestions should include asset allocation, diversification strategies, and considerations for market conditions. "
+                    "Explain the rationale behind each recommendation and reference relevant market data where applicable. "
+                    "Your recommendations should be reliable, detailed, and clearly prioritized based on risk and return."
+                ),
+                "model_name": "openai/gpt-4o",
+                "role": "worker",
+                "max_loops": 1,
+                "max_tokens": 5000,
+                "temperature": 0.3,
+                "auto_generate_prompt": False
+            }
+        ],
+        "max_loops": 1,
+        "swarm_type": "SequentialWorkflow",
+        "task": equity_data,
+    }
+
+    # Payload includes the equity data as the task to be processed by the swarm
+
+    response = requests.post(
+        f"{BASE_URL}/v1/swarm/completions",
+        headers=headers,
+        json=payload,
+    )
+
+    if response.status_code == 200:
+        print("Swarm successfully executed!")
+        return json.dumps(response.json(), indent=4)
+    else:
+        print(f"Error {response.status_code}: {response.text}")
+        return None
+
+
+# Example Equity Data for the Swarm to analyze
+if __name__ == "__main__":
+    equity_data = (
+        "Analyze the equity data for Company XYZ, which has shown a 15% increase in revenue over the last quarter, "
+        "with a P/E ratio of 20 and a market cap of $1 billion. Consider the current market conditions and potential risks."
+    )
+
+    financial_output = create_financial_swarm(equity_data)
+    print(financial_output)

swarms/agents/consistency_agent.py

@@ -15,7 +15,9 @@ You are a reasoning agent designed for complex problem-solving and decision-maki
 
 
 def aggregation_agent(
-    responses: List[str], prompt: str = majority_voting_prompt
+    responses: List[str],
+    prompt: str = majority_voting_prompt,
+    model_name: str = "gpt-4o-mini",
 ) -> str:
     """
     Aggregates a list of responses into a single final answer.
@@ -25,7 +27,7 @@ def aggregation_agent(
     agent = Agent(
         agent_name="Aggregation-Agent",
         description="An agent that aggregates a list of responses into a single final answer.",
-        model_name="gpt-4o-mini",
+        model_name=model_name,
         system_prompt=prompt,
         max_loops=1,
     )
@@ -38,6 +40,8 @@ def aggregation_agent(
 class SelfConsistencyAgent(Agent):
     def __init__(
         self,
+        name: str = "Self-Consistency-Agent",
+        description: str = "An agent that uses self consistency to generate a final answer.",
         num_samples: int = 5,
         return_list: bool = False,
         max_loops: int = 1,
@@ -54,7 +58,10 @@ class SelfConsistencyAgent(Agent):
             **kwargs: Other keyword arguments passed to the base Agent.
         """
         super().__init__(
-            **kwargs, system_prompt=CONSISTENCY_SYSTEM_PROMPT
+            name=name,
+            description=description,
+            system_prompt=CONSISTENCY_SYSTEM_PROMPT,
+            **kwargs,
         )
         self.num_samples = num_samples
         self.conversation = Conversation()

swarms/agents/i_agent.py

@@ -43,11 +43,12 @@ class IterativeReflectiveExpansion:
 
     def __init__(
         self,
-        agent: Agent,
+        agent: Agent = None,
         max_iterations: int = 5,
         return_list: bool = False,
         return_dict: bool = False,
         prompt: str = GENERAL_REASONING_AGENT_SYS_PROMPT,
+        model_name: str = "gpt-4o-mini",
     ) -> None:
         """
         Initialize the Iterative Reflective Expansion engine.
@@ -57,30 +58,30 @@ class IterativeReflectiveExpansion:
         """
         self.agent = agent
         self.max_iterations = max_iterations
-        self.conversation = Conversation()
         self.return_list = return_list
         self.return_dict = return_dict
 
+        self.conversation = Conversation()
+
         self.agent = Agent(
             agent_name="General-Reasoning-Agent",
             system_prompt=prompt,
-            model_name="gpt-4o-mini",
+            model_name=model_name,
             max_loops=1,
+            dynamic_temperature_enabled=True,
         )
 
-    def generate_initial_hypotheses(
-        self, problem_input: str
-    ) -> List[str]:
+    def generate_initial_hypotheses(self, task: str) -> List[str]:
         """
         Generate an initial set of reasoning hypotheses based on the problem input.
 
-        :param problem_input: The problem statement.
+        :param task: The problem statement.
         :return: A list of candidate reasoning paths/hypotheses.
         """
         logger.info("Generating initial hypotheses for the problem.")
         prompt = (
             f"Given the following problem:\n\n"
-            f"'{problem_input}'\n\n"
+            f"'{task}'\n\n"
             "Generate a list of possible approaches and strategies to solve it. "
             "Present each approach on a new line."
         )
@@ -235,19 +236,17 @@ class IterativeReflectiveExpansion:
         logger.debug(f"Synthesized solution: {solution}")
         return solution
 
-    def run(self, problem_input: str) -> str:
+    def run(self, task: str) -> str:
         """
         Execute the Iterative Reflective Expansion process on the provided problem.
 
-        :param problem_input: The problem statement.
+        :param task: The problem statement.
         :return: The final solution generated after iterative reasoning.
         """
         logger.info(
-            f"Starting iterative reflective expansion for problem: {problem_input}"
-        )
-        candidate_paths = self.generate_initial_hypotheses(
-            problem_input
+            f"Starting iterative reflective expansion for problem: {task}"
         )
+        candidate_paths = self.generate_initial_hypotheses(task)
         memory_pool: List[str] = []
 
         for iteration in range(self.max_iterations):