cleanup

swarms/swarms/README.md

@@ -0,0 +1,97 @@
+Modularizing the provided framework for scalability and reliability will involve breaking down the overall architecture into smaller, more manageable pieces, as well as introducing additional features and capabilities to enhance reliability. Here's a list of ideas to achieve this:
+
+### 1. Dynamic Agent Management
+
+To ensure the swarm is both cost-effective and efficient, dynamically creating and destroying agents depending on the workload can be a game changer:
+
+**Idea**: Instead of having a fixed number of agents, allow the `AutoScaler` to both instantiate and destroy agents as necessary.
+
+**Example**: 
+```python
+class AutoScaler:
+    # ...
+    def remove_agent(self):
+        with self.lock:
+            if self.agents_pool:
+                agent_to_remove = self.agents_pool.pop()
+                del agent_to_remove
+```
+
+### 2. Task Segmentation & Aggregation
+
+Breaking down tasks into sub-tasks and then aggregating results ensures scalability:
+
+**Idea**: Create a method in the `Orchestrator` to break down larger tasks into smaller tasks and another method to aggregate results from sub-tasks.
+
+**Example**:
+```python
+class Orchestrator(ABC):
+    # ...
+    def segment_task(self, main_task: str) -> List[str]:
+        # Break down main_task into smaller tasks
+        # ...
+        return sub_tasks
+    
+    def aggregate_results(self, sub_results: List[Any]) -> Any:
+        # Combine results from sub-tasks into a cohesive output
+        # ...
+        return main_result
+```
+
+### 3. Enhanced Task Queuing
+
+**Idea**: Prioritize tasks based on importance or deadlines.
+
+**Example**: Use a priority queue for the `task_queue`, ensuring tasks of higher importance are tackled first.
+
+### 4. Error Recovery & Retry Mechanisms
+
+**Idea**: Introduce a retry mechanism for tasks that fail due to transient errors.
+
+**Example**:
+```python
+class Orchestrator(ABC):
+    MAX_RETRIES = 3
+    retry_counts = defaultdict(int)
+    # ...
+    def assign_task(self, agent_id, task):
+        # ...
+        except Exception as error:
+            if self.retry_counts[task] < self.MAX_RETRIES:
+                self.retry_counts[task] += 1
+                self.task_queue.put(task)
+```
+
+### 5. Swarm Communication & Collaboration
+
+**Idea**: Allow agents to communicate or request help from their peers.
+
+**Example**: Implement a `request_assistance` method within agents where, upon facing a challenging task, they can ask for help from other agents.
+
+### 6. Database Management
+
+**Idea**: Periodically clean, optimize, and back up the vector database to ensure data integrity and optimal performance.
+
+### 7. Logging & Monitoring
+
+**Idea**: Implement advanced logging and monitoring capabilities to provide insights into swarm performance, potential bottlenecks, and failures.
+
+**Example**: Use tools like Elasticsearch, Logstash, and Kibana (ELK stack) to monitor logs in real-time.
+
+### 8. Load Balancing
+
+**Idea**: Distribute incoming tasks among agents evenly, ensuring no single agent is overloaded.
+
+**Example**: Use algorithms or tools that assign tasks based on current agent workloads.
+
+### 9. Feedback Loop
+
+**Idea**: Allow the system to learn from its mistakes or inefficiencies. Agents can rate the difficulty of their tasks and this information can be used to adjust future task assignments.
+
+### 10. Agent Specialization
+
+**Idea**: Not all agents are equal. Some might be better suited to certain tasks. 
+
+**Example**: Maintain a performance profile for each agent, categorizing them based on their strengths. Assign tasks to agents based on their specialization for optimal performance.
+
+By implementing these ideas and constantly iterating based on real-world usage and performance metrics, it's possible to create a robust and scalable multi-agent collaboration framework.

swarms/swarms/autoscaler.py

@@ -5,18 +5,25 @@ from time import sleep
 from swarms.utils.decorators import error_decorator, log_decorator, timing_decorator
 from swarms.workers.worker import Worker
 
-# TODO Handle task assignment and task delegation
-# TODO: User task => decomposed into very small sub tasks => sub tasks assigned to workers => workers complete and update the swarm, can ask for help from other agents. 
-# TODO: Missing, Task Assignment, Task delegation, Task completion, Swarm level communication with vector db
-
 class AutoScaler:
     """
+
+    The AutoScaler is like a kubernetes pod, that autoscales an agent or worker or boss!
+
+    # TODO Handle task assignment and task delegation
+    # TODO: User task => decomposed into very small sub tasks => sub tasks assigned to workers => workers complete and update the swarm, can ask for help from other agents. 
+    # TODO: Missing, Task Assignment, Task delegation, Task completion, Swarm level communication with vector db
+
+    
+    Example
+    ```
     # usage of usage
     auto_scaler = AutoScaler(agent=YourCustomAgent)
     auto_scaler.start()
 
     for i in range(100):
     auto_scaler.add_task9f"task {I}})
+    ```
 
     """
     @log_decorator
@@ -81,3 +88,10 @@ class AutoScaler:
                 available_agent = next((agent for agent in self.agents_pool))
                 if available_agent:
                     available_agent.run(task)
+
+    def del_agent(self):
+        with self.lock:
+            if self.agents_pool:
+                agent_to_remove = self.agents_poo.pop()
+                del agent_to_remove
+

swarms/swarms/orchestrate.py

@@ -11,7 +11,6 @@ from chromadb.utils import embedding_functions
 
 class Orchestrator(ABC):
     """
-
     The Orchestrator takes in an agent, worker, or boss as input 
     then handles all the logic for
     - task creation,
@@ -125,6 +124,7 @@ class Orchestrator(ABC):
                     documents=[str(id(task))],
                     ids=[str(id(task))]
                 )
+
                 logging.info(f"Task {id(str)} has been processed by agent {id(agent)} with")
             
             except Exception as error: