[READM]E

docs/swarms/structs/agent_registry.md

@@ -143,6 +143,90 @@ Finds an agent by its name.
   agent = registry.find_agent_by_name("Agent1")
   ```
 
+
+### Full Example
+
+```python
+from swarms.structs.agent_registry import AgentRegistry
+from swarms import Agent, OpenAIChat, Anthropic
+
+# Initialize the agents
+growth_agent1 = Agent(
+  agent_name="Marketing Specialist",
+  system_prompt="You're the marketing specialist, your purpose is to help companies grow by improving their marketing strategies!",
+  agent_description="Improve a company's marketing strategies!",
+  llm=OpenAIChat(),
+  max_loops="auto",
+  autosave=True,
+  dashboard=False,
+  verbose=True,
+  streaming_on=True,
+  saved_state_path="marketing_specialist.json",
+  stopping_token="Stop!",
+  interactive=True,
+  context_length=1000,
+)
+
+growth_agent2 = Agent(
+  agent_name="Sales Specialist",
+  system_prompt="You're the sales specialist, your purpose is to help companies grow by improving their sales strategies!",
+  agent_description="Improve a company's sales strategies!",
+  llm=Anthropic(),
+  max_loops="auto",
+  autosave=True,
+  dashboard=False,
+  verbose=True,
+  streaming_on=True,
+  saved_state_path="sales_specialist.json",
+  stopping_token="Stop!",
+  interactive=True,
+  context_length=1000,
+)
+
+growth_agent3 = Agent(
+  agent_name="Product Development Specialist",
+  system_prompt="You're the product development specialist, your purpose is to help companies grow by improving their product development strategies!",
+  agent_description="Improve a company's product development strategies!",
+  llm=Anthropic(),
+  max_loops="auto",
+  autosave=True,
+  dashboard=False,
+  verbose=True,
+  streaming_on=True,
+  saved_state_path="product_development_specialist.json",
+  stopping_token="Stop!",
+  interactive=True,
+  context_length=1000,
+)
+
+growth_agent4 = Agent(
+  agent_name="Customer Service Specialist",
+  system_prompt="You're the customer service specialist, your purpose is to help companies grow by improving their customer service strategies!",
+  agent_description="Improve a company's customer service strategies!",
+  llm=OpenAIChat(),
+  max_loops="auto",
+  autosave=True,
+  dashboard=False,
+  verbose=True,
+  streaming_on=True,
+  saved_state_path="customer_service_specialist.json",
+  stopping_token="Stop!",
+  interactive=True,
+  context_length=1000,
+)
+
+
+# Register the agents\
+registry = AgentRegistry()
+
+# Register the agents
+registry.add("Marketing Specialist", growth_agent1)
+registry.add("Sales Specialist", growth_agent2)
+registry.add("Product Development Specialist", growth_agent3)
+registry.add("Customer Service Specialist", growth_agent4)
+
+```
+
 ## Logging and Error Handling
 
 Each method in the `AgentRegistry` class includes logging to track the execution flow and captures errors to provide detailed information in case of failures. This is crucial for debugging and ensuring smooth operation of the registry. The `report_error` function is used for reporting exceptions that occur during method execution.

example.py

@@ -30,16 +30,6 @@ def generate_report(company_name: str, profit: float):
     return f"The profit for {company_name} is ${profit}."
 
 
-EMAIL_DETECT_APPOINT = """
-
-if the user gives you an email address, then call the appointment function to schedule a meeting with the user.
-
-SCHEMA OF THE FUNCTION:
-
-
-"""
-
-
 def write_memory_to_rag(memory_name: str, memory: str):
     """
     Writes the memory to the RAG model for fine-tuning.
@@ -66,7 +56,6 @@ agent = Agent(
     llm=Anthropic(),
     max_loops="auto",
     autosave=True,
-    sop_list=[EMAIL_DETECT_APPOINT],
     # dynamic_temperature_enabled=True,
     dashboard=False,
     verbose=True,

playground/structs/agent_registry.py

@@ -0,0 +1,79 @@
+from swarms.structs.agent_registry import AgentRegistry
+from swarms import Agent
+from swarms.models import Anthropic
+
+
+# Initialize the agents
+growth_agent1 = Agent(
+    agent_name="Marketing Specialist",
+    system_prompt="You're the marketing specialist, your purpose is to help companies grow by improving their marketing strategies!",
+    agent_description="Improve a company's marketing strategies!",
+    llm=Anthropic(),
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    streaming_on=True,
+    saved_state_path="marketing_specialist.json",
+    stopping_token="Stop!",
+    interactive=True,
+    context_length=1000,
+)
+
+growth_agent2 = Agent(
+    agent_name="Sales Specialist",
+    system_prompt="You're the sales specialist, your purpose is to help companies grow by improving their sales strategies!",
+    agent_description="Improve a company's sales strategies!",
+    llm=Anthropic(),
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    streaming_on=True,
+    saved_state_path="sales_specialist.json",
+    stopping_token="Stop!",
+    interactive=True,
+    context_length=1000,
+)
+
+growth_agent3 = Agent(
+    agent_name="Product Development Specialist",
+    system_prompt="You're the product development specialist, your purpose is to help companies grow by improving their product development strategies!",
+    agent_description="Improve a company's product development strategies!",
+    llm=Anthropic(),
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    streaming_on=True,
+    saved_state_path="product_development_specialist.json",
+    stopping_token="Stop!",
+    interactive=True,
+    context_length=1000,
+)
+
+growth_agent4 = Agent(
+    agent_name="Customer Service Specialist",
+    system_prompt="You're the customer service specialist, your purpose is to help companies grow by improving their customer service strategies!",
+    agent_description="Improve a company's customer service strategies!",
+    llm=Anthropic(),
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    streaming_on=True,
+    saved_state_path="customer_service_specialist.json",
+    stopping_token="Stop!",
+    interactive=True,
+    context_length=1000,
+)
+
+
+# Register the agents\
+registry = AgentRegistry()
+
+# Register the agents
+registry.add("Marketing Specialist", growth_agent1)
+registry.add("Sales Specialist", growth_agent2)
+registry.add("Product Development Specialist", growth_agent3)
+registry.add("Customer Service Specialist", growth_agent4)

swarms/structs/Untitled-1.py

@@ -0,0 +1,250 @@
+# ! pip install ai21
+# ! pip install swarms
+import os
+from typing import List
+
+from ai21 import AI21Client
+from ai21.models.chat import ChatMessage
+from dotenv import load_dotenv
+
+from swarms import Agent, BaseLLM
+from swarms.utils.loguru_logger import logger
+
+load_dotenv()
+
+
+class Jamba(BaseLLM):
+    def __init__(
+        self,
+        api_key: str = os.getenv("AI21_API_KEY"),
+        temperature: int = 0.8,
+        max_tokens: int = 200,
+    ):
+        """
+        Initializes the Jamba class with the provided API key.
+
+        Args:
+            api_key (str): The API key for the AI21Client.
+        """
+        os.environ["AI21_API_KEY"] = api_key
+        self.api_key = api_key
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self.client = AI21Client()
+
+    def run(self, prompt: str, *args, **kwargs) -> str:
+        """
+        Generates a response for the given prompt using the AI21 model.
+
+        Args:
+            prompt (str): The prompt for generating the response.
+
+        Returns:
+            str: The generated response.
+
+        Raises:
+            Exception: If there is an issue with the API request.
+        """
+        try:
+            response = self.client.chat.completions.create(
+                model="jamba-instruct-preview",  # Latest model
+                messages=[ChatMessage(role="user", content=prompt)],
+                temperature=self.temperature,
+                max_tokens=self.max_tokens,
+                *args,
+                **kwargs,
+            )
+            return response.choices[0].message.content
+        except Exception as e:
+            print(f"Error: {e}")
+            raise
+
+
+model = Jamba(
+    max_tokens=4000,
+)
+
+
+BOSS_PLANNER = """
+You're the swarm orchestrator agent
+
+**Objective:** Your task is to intake a business problem or activity and create a swarm of specialized LLM agents that can efficiently solve or automate the given problem. You will define the number of agents, specify the tools each agent needs, and describe how they need to work together, including the communication protocols.
+
+**Instructions:**
+
+1. **Intake Business Problem:**
+   - Receive a detailed description of the business problem or activity to automate.
+   - Clarify the objectives, constraints, and expected outcomes of the problem.
+   - Identify key components and sub-tasks within the problem.
+
+2. **Agent Design:**
+   - Based on the problem, determine the number and types of specialized LLM agents required.
+   - For each agent, specify:
+     - The specific task or role it will perform.
+     - The tools and resources it needs to perform its task.
+     - Any prerequisite knowledge or data it must have access to.
+   - Ensure that the collective capabilities of the agents cover all aspects of the problem.
+
+3. **Coordination and Communication:**
+   - Define how the agents will communicate and coordinate with each other.
+   - Choose the type of communication (e.g., synchronous, asynchronous, broadcast, direct messaging).
+   - Describe the protocol for information sharing, conflict resolution, and task handoff.
+
+4. **Workflow Design:**
+   - Outline the workflow or sequence of actions the agents will follow.
+   - Define the input and output for each agent.
+   - Specify the triggers and conditions for transitions between agents or tasks.
+   - Ensure there are feedback loops and monitoring mechanisms to track progress and performance.
+
+5. **Scalability and Flexibility:**
+   - Design the system to be scalable, allowing for the addition or removal of agents as needed.
+   - Ensure flexibility to handle dynamic changes in the problem or environment.
+
+6. **Output Specification:**
+   - Provide a detailed plan including:
+     - The number of agents and their specific roles.
+     - The tools and resources each agent will use.
+     - The communication and coordination strategy.
+     - The workflow and sequence of actions.
+   - Include a diagram or flowchart if necessary to visualize the system.
+
+**Example Structure:**
+
+**Business Problem:** Automate customer support for an e-commerce platform.
+
+**Agents and Roles:**
+1. **Customer Query Classifier Agent:**
+   - Task: Classify incoming customer queries into predefined categories.
+   - Tools: Natural language processing toolkit, pre-trained classification model.
+   - Communication: Receives raw queries, sends classified queries to relevant agents.
+
+2. **Order Status Agent:**
+   - Task: Provide order status updates to customers.
+   - Tools: Access to order database, query processing toolkit.
+   - Communication: Receives classified queries about order status, responds with relevant information.
+
+3. **Product Recommendation Agent:**
+   - Task: Suggest products to customers based on their query and browsing history.
+   - Tools: Recommendation engine, access to product database.
+   - Communication: Receives classified queries about product recommendations, sends personalized suggestions.
+
+4. **Technical Support Agent:**
+   - Task: Assist customers with technical issues.
+   - Tools: Access to technical support database, troubleshooting toolkit.
+   - Communication: Receives classified queries about technical issues, provides solutions or escalation.
+
+**Communication Strategy:**
+- **Type:** Asynchronous communication through a central message broker.
+- **Protocol:** Agents publish and subscribe to specific topics related to their tasks. 
+- **Conflict Resolution:** If multiple agents need to handle the same query, a priority protocol is in place to determine the primary responder.
+
+**Workflow:**
+1. Customer Query Classifier Agent receives and classifies the query.
+2. Classified query is routed to the appropriate specialized agent.
+3. Specialized agent processes the query and sends a response.
+4. If needed, the response triggers further actions from other agents.
+
+**Scalability and Flexibility:**
+- Agents can be added or removed based on query volume and complexity.
+- System adapts to changes in query types and business needs.
+
+**Output Plan:**
+- Diagram illustrating agent roles and communication flow.
+- Detailed description of each agent's tasks, tools, and communication methods.
+- Workflow sequence from query intake to resolution.
+
+
+"""
+
+
+# Initialize the agent
+planning_agent = Agent(
+    agent_name="Boss Director",
+    system_prompt=BOSS_PLANNER,
+    agent_description="Generates a spec of agents for the problem at hand.",
+    llm=model,
+    max_loops=1,
+    autosave=True,
+    dynamic_temperature_enabled=True,
+    dashboard=False,
+    verbose=True,
+    streaming_on=True,
+    # interactive=True, # Set to False to disable interactive mode
+    saved_state_path="boss_planner.json",
+    # tools=[calculate_profit, generate_report],
+    # docs_folder="docs",
+    # pdf_path="docs/accounting_agent.pdf",
+    # tools=[browser_automation],
+)
+
+
+# Name, system prompt,
+def create_worker_agent(name: str, system_prompt: str) -> List[Agent]:
+    """
+    Creates a worker agent with the specified name, system prompt, and description.
+
+    Args:
+        name (List[str]): The name of the worker agent.
+        system_prompt List(str): The system prompt for the worker agent.
+
+    Returns:
+        List[Agent]: A list of worker agents created based on the input.
+    """
+    # return agents
+    name = Agent(
+        agent_name=name,
+        system_prompt=system_prompt,
+        llm=model,
+        max_loops=1,
+        autosave=True,
+        dynamic_temperature_enabled=True,
+        dashboard=False,
+        verbose=True,
+        streaming_on=True,
+        # interactive=True, # Set to False to disable interactive mode
+        saved_state_path=f"{name.lower().replace(' ', '_')}_agent.json",
+        # tools=[calculate_profit, generate_report],
+        # docs_folder="docs",
+        # pdf_path="docs/accounting_agent.pdf",
+        # tools=[browser_automation],
+    )
+
+    out = name.run(system_prompt)
+    return out
+
+
+# Boss Agent creator
+boss_agent_creator = Agent(
+    agent_name="Boss Agent Creator",
+    system_prompt="Create the worker agents for the problem at hand using the specified names and system prompt tools provided.",
+    agent_description="Generates a spec of agents for the problem at hand.",
+    llm=model,
+    max_loops=1,
+    autosave=True,
+    dynamic_temperature_enabled=True,
+    dashboard=False,
+    verbose=True,
+    streaming_on=True,
+    # interactive=True, # Set to False to disable interactive mode
+    saved_state_path="boss_director_agent.json",
+    # tools=[calculate_profit, generate_report],
+    # docs_folder="docs",
+    # pdf_path="docs/accounting_agent.pdf",
+    tools=[create_worker_agent],
+)
+
+
+def run_jamba_swarm(task: str = None):
+    logger.info(f"Making plan for the task: {task}")
+    out = planning_agent.run(task)
+
+    memory = planning_agent.short_memory.return_history_as_string()
+
+    # Boss agent
+    return boss_agent_creator.run(memory)
+
+
+# Example usage
+run_jamba_swarm(
+    "Create a swarm of agents for automating customer support for an e-commerce platform."
+)