[BUFG][Majority Voting]

README.md

@@ -599,6 +599,54 @@ print(inference)
 ```
 
 
+## Majority Voting
+Multiple-agents will evaluate an idea based off of an parsing or evaluation function. From papers like "[More agents is all you need](https://arxiv.org/pdf/2402.05120.pdf)
+
+```python
+from swarms import Agent, MajorityVoting, ChromaDB, Anthropic
+
+# Initialize the llm
+llm = Anthropic()
+
+# Agents
+agent1 = Agent(
+    llm = llm,
+    system_prompt="You are the leader of the Progressive Party. What is your stance on healthcare?",
+    agent_name="Progressive Leader",
+    agent_description="Leader of the Progressive Party",
+    long_term_memory=ChromaDB(),
+    max_steps=1,
+)
+
+agent2 = Agent(
+    llm=llm,
+    agent_name="Conservative Leader",
+    agent_description="Leader of the Conservative Party",
+    long_term_memory=ChromaDB(),
+    max_steps=1,
+)
+
+agent3 = Agent(
+    llm=llm,
+    agent_name="Libertarian Leader",
+    agent_description="Leader of the Libertarian Party",
+    long_term_memory=ChromaDB(),
+    max_steps=1,
+)
+
+# Initialize the majority voting
+mv = MajorityVoting(
+    agents=[agent1, agent2, agent3],
+    output_parser=llm.majority_voting,
+    autosave=False,
+    verbose=True,
+)
+
+
+# Start the majority voting
+mv.run("What is your stance on healthcare?")
+```
+
 ## Real-World Deployment
 
 ### Multi-Agent Swarm for Logistics

majority_voting.py

@@ -0,0 +1,42 @@
+from swarms import Agent, MajorityVoting, ChromaDB, Anthropic
+
+# Initialize the llm
+llm = Anthropic()
+
+# Agents
+agent1 = Agent(
+    llm = llm,
+    system_prompt="You are the leader of the Progressive Party. What is your stance on healthcare?",
+    agent_name="Progressive Leader",
+    agent_description="Leader of the Progressive Party",
+    long_term_memory=ChromaDB(),
+    max_steps=1,
+)
+
+agent2 = Agent(
+    llm=llm,
+    agent_name="Conservative Leader",
+    agent_description="Leader of the Conservative Party",
+    long_term_memory=ChromaDB(),
+    max_steps=1,
+)
+
+agent3 = Agent(
+    llm=llm,
+    agent_name="Libertarian Leader",
+    agent_description="Leader of the Libertarian Party",
+    long_term_memory=ChromaDB(),
+    max_steps=1,
+)
+
+# Initialize the majority voting
+mv = MajorityVoting(
+    agents=[agent1, agent2, agent3],
+    output_parser=llm.majority_voting,
+    autosave=False,
+    verbose=True,
+)
+
+
+# Start the majority voting
+mv.run("What is your stance on healthcare?")

playground/structs/majority_voting.py

@@ -1,21 +0,0 @@
-from swarms import Agent, MajorityVoting, OpenAIChat
-
-# Initialize the llm
-llm = OpenAIChat()
-
-# Initialize the agents
-agent1 = Agent(agent_name="worker-1", llm=llm, max_loops=1)
-agent2 = Agent(agent_name="worker-2", llm=llm, max_loops=1)
-agent3 = Agent(agent_name="worker3", llm=llm, max_loops=1)
-
-
-# Initialize the majority voting
-mv = MajorityVoting(
-    agents=[agent1, agent2, agent3],
-    concurrent=True,
-    multithreaded=True,
-)
-
-
-# Start the majority voting
-mv.run("What is the capital of France?")

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "4.2.7"
+version = "4.2.8"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]

swarms/__init__.py

@@ -17,3 +17,4 @@ from swarms.telemetry import *  # noqa: E402, F403
 from swarms.tokenizers import *  # noqa: E402, F403
 from swarms.tools import *  # noqa: E402, F403
 from swarms.utils import *  # noqa: E402, F403
+from swarms.memory import *  # noqa: E402, F403

swarms/models/gemini.py

@@ -33,7 +33,8 @@ def get_gemini_api_key_env():
     key = os.getenv("GEMINI_API_KEY")
     if key is None:
         raise ValueError("Please provide a Gemini API key")
-    return key
+    return str(key)
+
 
 
 # Main class

swarms/structs/majority_voting.py

@@ -1,15 +1,14 @@
-import asyncio
 import concurrent.futures
 import re
 import sys
 from collections import Counter
-from multiprocessing import Pool
+from typing import Any, Callable, List, Optional
-from typing import Any, List
 
 from loguru import logger
 
 from swarms.structs.agent import Agent
 from swarms.structs.conversation import Conversation
+from swarms.utils.file_processing import create_file
 
 # Configure loguru logger with advanced settings
 logger.remove()
@@ -96,7 +95,6 @@ def most_frequent(
 
     for i in clist:
         current_frequency = sum(cmp_func(i, item) for item in clist)
-        print(current_frequency)
         if current_frequency > counter:
             counter = current_frequency
             num = i
@@ -104,7 +102,7 @@ def most_frequent(
     return num, counter
 
 
-def majority_voting(answers: list):
+def majority_voting(answers: List[str]):
     """
     Performs majority voting on a list of answers and returns the most common answer.
 
@@ -115,7 +113,12 @@ def majority_voting(answers: list):
         The most common answer in the list.
     """
     counter = Counter(answers)
-    answer = counter.most_common(1)[0][0]
+    if counter:
+        
+        answer = counter.most_common(1)[0][0]
+    else: 
+        answer = "I don't know"
+        
     return answer
 
 
@@ -124,13 +127,11 @@ class MajorityVoting:
     Class representing a majority voting system for agents.
 
     Args:
-        agents (List[Agent]): A list of agents to use in the majority voting system.
+        agents (list): A list of agents to be used in the majority voting system.
-        concurrent (bool, optional): Whether to run the agents concurrently. Defaults to False.
+        output_parser (function, optional): A function used to parse the output of the agents.
-        multithreaded (bool, optional): Whether to run the agents using multithreading. Defaults to False.
+            If not provided, the default majority voting function is used.
-        multiprocess (bool, optional): Whether to run the agents using multiprocessing. Defaults to False.
+        autosave (bool, optional): A boolean indicating whether to autosave the conversation to a file.
-        asynchronous (bool, optional): Whether to run the agents asynchronously. Defaults to False.
+        verbose (bool, optional): A boolean indicating whether to enable verbose logging.
-        output_parser (callable, optional): A callable function to parse the output of the majority voting system. Defaults to None.
-
     Examples:
         >>> from swarms.structs.agent import Agent
         >>> from swarms.structs.majority_voting import MajorityVoting
@@ -148,21 +149,13 @@ class MajorityVoting:
     def __init__(
         self,
         agents: List[Agent],
-        concurrent: bool = False,
+        output_parser: Optional[Callable] = majority_voting,
-        multithreaded: bool = False,
-        multiprocess: bool = False,
-        asynchronous: bool = False,
-        output_parser: callable = None,
         autosave: bool = False,
         verbose: bool = False,
         *args,
         **kwargs,
     ):
         self.agents = agents
-        self.concurrent = concurrent
-        self.multithreaded = multithreaded
-        self.multiprocess = multiprocess
-        self.asynchronous = asynchronous
         self.output_parser = output_parser
         self.autosave = autosave
         self.verbose = verbose
@@ -173,7 +166,7 @@ class MajorityVoting:
 
         # If autosave is enabled, save the conversation to a file
         if self.autosave:
-            self.conversation.save()
+            create_file(str(self.conversation), "majority_voting.json")
 
         # Log the agents
         logger.info("Initializing majority voting system")
@@ -198,69 +191,37 @@ class MajorityVoting:
 
         """
         # Route to each agent
-        if self.concurrent:
+        with concurrent.futures.ThreadPoolExecutor() as executor:
-            with concurrent.futures.ThreadPoolExecutor() as executor:
+            logger.info("Running agents concurrently")
-                # Log the agents
+
-                logger.info("Running agents concurrently")
+            futures = [
-                futures = [
+                executor.submit(agent.run, task, *args)
-                    executor.submit(agent.run, task, *args)
-                    for agent in self.agents
-                ]
-                results = [
-                    future.result()
-                    for future in concurrent.futures.as_completed(
-                        futures
-                    )
-                ]
-        elif self.multithreaded:
-            logger.info("Running agents using multithreading")
-            with concurrent.futures.ThreadPoolExecutor() as executor:
-                results = [
-                    executor.submit(agent.run, task, *args)
-                    for agent in self.agents
-                ]
-                results = [future.result() for future in results]
-        elif self.multiprocess:
-            logger.info("Running agents using multiprocessing")
-            with Pool() as pool:
-                results = pool.starmap(
-                    Agent.run,
-                    [(agent, task, *args) for agent in self.agents],
-                )
-        elif self.asynchronous:
-            loop = asyncio.get_event_loop()
-            tasks = [
-                loop.run_in_executor(None, agent.run, task, *args)
                 for agent in self.agents
             ]
-            results = loop.run_until_complete(asyncio.gather(*tasks))
-            loop.close()
-        else:
             results = [
-                agent.run(task, *args) for agent in self.agents
+                future.result()
+                for future in concurrent.futures.as_completed(futures)
             ]
 
         # Add responses to conversation and log them
         for agent, response in zip(self.agents, results):
-            logger.info(f"[{agent.agent_id}][{response}]")
+            response = response if isinstance(response, list) else [response]
-
-            response = (
-                response if isinstance(response, list) else [response]
-            )
             self.conversation.add(agent.agent_name, response)
-            logger.info(f"[{agent.agent_id}][{response}]")
+            logger.info(f"[Agent][Name: {agent.agent_name}][Response: {response}]")
 
         # Perform majority voting on the conversation
-        majority_vote = majority_voting(self.conversation.responses)
+        responses = [
-
+            message["content"]
-        # Log the majority vote
+            for message in self.conversation.conversation_history
-        logger.info(f"Majority vote: {majority_vote}")
+            if message["role"] == "agent"
-
+        ]
-        # If an output parser is provided, parse the output
+
-        if self.output_parser:
+        # If an output parser is provided, parse the responses
-            majority_vote = self.output_parser(
+        if self.output_parser is not None:
-                majority_vote, *args, **kwargs
+            majority_vote = self.output_parser(responses, *args, **kwargs)
-            )
+        else:
+            majority_vote = majority_voting(responses)
+        
 
         # Return the majority vote
         return majority_vote