Given the complexity of the topic, please note that these simplified markdown documents are quite abstract and high level. They can be used as a starting point for further detailed design and implementation:

### Document 1: Hierarchical Swarms

#### Overall Architecture

1. Leader Agent (LA): This agent has the authority to manage and distribute tasks to the Worker Agents (WA).
2. Worker Agents (WAs): These agents perform the tasks assigned by the LA.

#### Simplified Requirements

1. LA should be able to distribute tasks to WAs.
2. WAs should be able to execute tasks and return results to LA.
3. LA should be able to consolidate and process results.

#### Pseudocode

```
create LA
create WAs

for each task in tasks:
  LA.distribute_task(WAs, task)
  
  for each WA in WAs:
    WA.execute_task()
    
  LA.collect_results(WAs)
  
LA.process_results()
```

#### General Classes

```python
class LeaderAgent:
  def distribute_task(self, WAs, task):
    pass
  
  def collect_results(self, WAs):
    pass

  def process_results(self):
    pass

class WorkerAgent:
  def execute_task(self):
    pass
```

### Document 2: Collaborative Swarms

#### Overall Architecture

1. Collaborative Agents (CAs): These agents work in parallel on different aspects of a task and then collectively determine the best output.

#### Simplified Requirements

1. CAs should be able to work on tasks in parallel.
2. CAs should be able to collaborate to determine the best result.

#### Pseudocode

```
create CAs

for each task in tasks:
  for each CA in CAs:
    CA.execute_task(task)
  
  CA.collaborate()
```

#### General Classes

```python
class CollaborativeAgent:
  def execute_task(self, task):
    pass

  def collaborate(self):
    pass
```

### Document 3: Competitive Swarms

#### Overall Architecture

1. Competitive Agents (CompAs): These agents work independently on the same tasks, and the best result is selected.

#### Simplified Requirements

1. CompAs should be able to work independently on tasks.
2. An evaluation method should be used to select the best result.

#### Pseudocode

```
create CompAs

for each task in tasks:
  for each CompA in CompAs:
    CompA.execute_task(task)

evaluate_results(CompAs)
```

#### General Classes

```python
class CompetitiveAgent:
  def execute_task(self, task):
    pass

def evaluate_results(CompAs):
  pass
```

Note: In the real world, the complexity of the architecture and requirements will significantly exceed what is presented here. These examples provide a basic starting point but should be expanded upon based on the specifics of the task or problem you're trying to solve.



# Swarms

BabyAGI -> Autogpt's -> tools -> other agents
 