swarms api docs

.gitignore

@@ -9,6 +9,8 @@ video/
 artifacts_three
 dataframe/
 .ruff_cache
+target/
+Cargo.lock
 .pytest_cache
 static/generated
 runs

docs/mkdocs.yml

@@ -275,6 +275,7 @@ nav:
     - Deploying Swarms on Google Cloud Run: "swarms_cloud/cloud_run.md"
     # - Swarms Cloud CLI: "swarms_cloud/cli.md"
     - Swarm APIs:
+      - Swarms API: "swarms_cloud/swarms_api.md"
       - MCS API: "swarms_cloud/mcs_api.md"
       - CreateNow API: "swarms_cloud/create_api.md"
   - Swarms Memory:

docs/swarms_cloud/swarms_api.md

@@ -0,0 +1,675 @@
+# Swarms API Documentation
+
+The Swarms API is a powerful REST API designed to help you create, manage, and execute various types of swarms efficiently. Whether you need to run tasks sequentially, concurrently, or in a custom workflow, the Swarms API has you covered.
+
+### Key Features:
+- **Sequential Swarms**: Execute tasks one after another in a defined order.
+- **Concurrent Swarms**: Run multiple tasks simultaneously to save time and resources.
+- **Custom Workflows**: Design your own swarm workflows to fit your specific needs.
+
+To get started, find your API key in the Swarms Cloud dashboard. [Get your API key here](https://swarms.world/platform/api-keys)
+
+## Base URL
+```
+https://swarms-api-285321057562.us-east1.run.app
+```
+
+## Authentication
+All API requests (except `/health`) require authentication using an API key passed in the `x-api-key` header:
+
+```http
+x-api-key: your_api_key_here
+```
+
+## Endpoints
+
+### Health Check
+Check if the API is operational.
+
+**Endpoint:** `GET /health`  
+**Authentication Required:** No  
+**Response:**
+```json
+{
+    "status": "ok"
+}
+```
+
+### Single Swarm Completion
+Run a single swarm with specified agents and tasks.
+
+**Endpoint:** `POST /v1/swarm/completions`  
+**Authentication Required:** Yes
+
+#### Request Parameters
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| name | string | Optional | Name of the swarm (max 100 chars) |
+| description | string | Optional | Description of the swarm (max 500 chars) |
+| agents | array | Required | Array of agent configurations |
+| max_loops | integer | Optional | Maximum number of iterations |
+| swarm_type | string | Optional | Type of swarm workflow |
+| task | string | Required | The task to be performed |
+| img | string | Optional | Image URL if relevant |
+
+#### Agent Configuration Parameters
+
+| Parameter | Type | Required | Default | Description |
+|-----------|------|----------|---------|-------------|
+| agent_name | string | Required | - | Name of the agent (max 100 chars) |
+| description | string | Optional | - | Description of the agent (max 500 chars) |
+| system_prompt | string | Optional | - | System prompt for the agent (max 500 chars) |
+| model_name | string | Optional | "gpt-4o" | Model to be used by the agent |
+| auto_generate_prompt | boolean | Optional | false | Whether to auto-generate prompts |
+| max_tokens | integer | Optional | - | Maximum tokens for response |
+| temperature | float | Optional | 0.5 | Temperature for response generation |
+| role | string | Optional | "worker" | Role of the agent |
+| max_loops | integer | Optional | 1 | Maximum iterations for this agent |
+
+#### Example Request
+```json
+{
+    "name": "Test Swarm",
+    "description": "A test swarm",
+    "agents": [
+        {
+            "agent_name": "Research Agent",
+            "description": "Conducts research",
+            "system_prompt": "You are a research assistant.",
+            "model_name": "gpt-4o",
+            "role": "worker",
+            "max_loops": 1
+        }
+    ],
+    "max_loops": 1,
+    "swarm_type": "ConcurrentWorkflow",
+    "task": "Write a short blog post about AI agents."
+}
+```
+
+#### Response Structure
+
+| Field | Type | Description |
+|-------|------|-------------|
+| status | string | Status of the swarm execution |
+| swarm_name | string | Name of the executed swarm |
+| description | string | Description of the swarm |
+| task | string | Original task description |
+| metadata | object | Execution metadata |
+| output | object/array | Results from the swarm execution |
+
+### Batch Swarm Completion
+Run multiple swarms in a single request.
+
+**Endpoint:** `POST /v1/swarm/batch/completions`  
+**Authentication Required:** Yes
+
+#### Request Format
+Array of swarm configurations, each following the same format as single swarm completion.
+
+#### Example Batch Request
+```json
+[
+    {
+        "name": "Batch Swarm 1",
+        "description": "First swarm in batch",
+        "agents": [...],
+        "task": "Task 1"
+    },
+    {
+        "name": "Batch Swarm 2",
+        "description": "Second swarm in batch",
+        "agents": [...],
+        "task": "Task 2"
+    }
+]
+```
+# Swarms API Implementation Examples
+
+## Python
+### Using requests
+
+```python
+import requests
+import json
+
+API_KEY = "your_api_key_here"
+BASE_URL = "https://swarms-api-285321057562.us-east1.run.app"
+
+headers = {
+    "x-api-key": API_KEY,
+    "Content-Type": "application/json"
+}
+
+def run_single_swarm():
+    payload = {
+        "name": "Financial Analysis Swarm",
+        "description": "Market analysis swarm",
+        "agents": [
+            {
+                "agent_name": "Market Analyst",
+                "description": "Analyzes market trends",
+                "system_prompt": "You are a financial analyst expert.",
+                "model_name": "gpt-4o",
+                "role": "worker",
+                "max_loops": 1
+            }
+        ],
+        "max_loops": 1,
+        "swarm_type": "SequentialWorkflow",
+        "task": "Analyze current market trends in tech sector"
+    }
+    
+    response = requests.post(
+        f"{BASE_URL}/v1/swarm/completions",
+        headers=headers,
+        json=payload
+    )
+    
+    return response.json()
+
+def run_batch_swarms():
+    payload = [
+        {
+            "name": "Market Analysis",
+            "description": "First swarm",
+            "agents": [
+                {
+                    "agent_name": "Analyst",
+                    "system_prompt": "You are a market analyst.",
+                    "model_name": "gpt-4o",
+                    "role": "worker"
+                }
+            ],
+            "task": "Analyze tech trends"
+        },
+        {
+            "name": "Risk Assessment",
+            "description": "Second swarm",
+            "agents": [
+                {
+                    "agent_name": "Risk Analyst",
+                    "system_prompt": "You are a risk analyst.",
+                    "model_name": "gpt-4o",
+                    "role": "worker"
+                }
+            ],
+            "task": "Assess market risks"
+        }
+    ]
+    
+    response = requests.post(
+        f"{BASE_URL}/v1/swarm/batch/completions",
+        headers=headers,
+        json=payload
+    )
+    
+    return response.json()
+
+# Using async/await with aiohttp
+import aiohttp
+import asyncio
+
+async def run_swarm_async():
+    async with aiohttp.ClientSession() as session:
+        async with session.post(
+            f"{BASE_URL}/v1/swarm/completions",
+            headers=headers,
+            json=payload
+        ) as response:
+            return await response.json()
+```
+
+## Node.js
+### Using Fetch API
+
+```javascript
+const API_KEY = 'your_api_key_here';
+const BASE_URL = 'https://swarms-api-285321057562.us-east1.run.app';
+
+const headers = {
+  'x-api-key': API_KEY,
+  'Content-Type': 'application/json'
+};
+
+// Single swarm execution
+async function runSingleSwarm() {
+  const payload = {
+    name: 'Financial Analysis',
+    description: 'Market analysis swarm',
+    agents: [
+      {
+        agent_name: 'Market Analyst',
+        description: 'Analyzes market trends',
+        system_prompt: 'You are a financial analyst expert.',
+        model_name: 'gpt-4o',
+        role: 'worker',
+        max_loops: 1
+      }
+    ],
+    max_loops: 1,
+    swarm_type: 'SequentialWorkflow',
+    task: 'Analyze current market trends'
+  };
+
+  try {
+    const response = await fetch(`${BASE_URL}/v1/swarm/completions`, {
+      method: 'POST',
+      headers,
+      body: JSON.stringify(payload)
+    });
+    
+    return await response.json();
+  } catch (error) {
+    console.error('Error:', error);
+    throw error;
+  }
+}
+
+// Batch swarm execution
+async function runBatchSwarms() {
+  const payload = [
+    {
+      name: 'Market Analysis',
+      agents: [{
+        agent_name: 'Analyst',
+        system_prompt: 'You are a market analyst.',
+        model_name: 'gpt-4o',
+        role: 'worker'
+      }],
+      task: 'Analyze tech trends'
+    },
+    {
+      name: 'Risk Assessment',
+      agents: [{
+        agent_name: 'Risk Analyst',
+        system_prompt: 'You are a risk analyst.',
+        model_name: 'gpt-4o',
+        role: 'worker'
+      }],
+      task: 'Assess market risks'
+    }
+  ];
+
+  try {
+    const response = await fetch(`${BASE_URL}/v1/swarm/batch/completions`, {
+      method: 'POST',
+      headers,
+      body: JSON.stringify(payload)
+    });
+    
+    return await response.json();
+  } catch (error) {
+    console.error('Error:', error);
+    throw error;
+  }
+}
+```
+
+### Using Axios
+
+```javascript
+const axios = require('axios');
+
+const api = axios.create({
+  baseURL: BASE_URL,
+  headers: {
+    'x-api-key': API_KEY,
+    'Content-Type': 'application/json'
+  }
+});
+
+async function runSwarm() {
+  try {
+    const response = await api.post('/v1/swarm/completions', payload);
+    return response.data;
+  } catch (error) {
+    console.error('Error:', error.response?.data || error.message);
+    throw error;
+  }
+}
+```
+
+## Go
+
+```go
+package main
+
+import (
+    "bytes"
+    "encoding/json"
+    "fmt"
+    "io/ioutil"
+    "net/http"
+)
+
+const (
+    baseURL = "https://swarms-api-285321057562.us-east1.run.app"
+    apiKey  = "your_api_key_here"
+)
+
+type Agent struct {
+    AgentName    string `json:"agent_name"`
+    Description  string `json:"description"`
+    SystemPrompt string `json:"system_prompt"`
+    ModelName    string `json:"model_name"`
+    Role         string `json:"role"`
+    MaxLoops     int    `json:"max_loops"`
+}
+
+type SwarmRequest struct {
+    Name        string   `json:"name"`
+    Description string   `json:"description"`
+    Agents      []Agent  `json:"agents"`
+    MaxLoops    int      `json:"max_loops"`
+    SwarmType   string   `json:"swarm_type"`
+    Task        string   `json:"task"`
+}
+
+func runSingleSwarm() ([]byte, error) {
+    payload := SwarmRequest{
+        Name:        "Financial Analysis",
+        Description: "Market analysis swarm",
+        Agents: []Agent{
+            {
+                AgentName:    "Market Analyst",
+                Description:  "Analyzes market trends",
+                SystemPrompt: "You are a financial analyst expert.",
+                ModelName:    "gpt-4o",
+                Role:         "worker",
+                MaxLoops:     1,
+            },
+        },
+        MaxLoops:  1,
+        SwarmType: "SequentialWorkflow",
+        Task:      "Analyze current market trends",
+    }
+
+    jsonPayload, err := json.Marshal(payload)
+    if err != nil {
+        return nil, err
+    }
+
+    client := &http.Client{}
+    req, err := http.NewRequest("POST", baseURL+"/v1/swarm/completions", bytes.NewBuffer(jsonPayload))
+    if err != nil {
+        return nil, err
+    }
+
+    req.Header.Set("x-api-key", apiKey)
+    req.Header.Set("Content-Type", "application/json")
+
+    resp, err := client.Do(req)
+    if err != nil {
+        return nil, err
+    }
+    defer resp.Body.Close()
+
+    return ioutil.ReadAll(resp.Body)
+}
+
+func main() {
+    response, err := runSingleSwarm()
+    if err != nil {
+        fmt.Printf("Error: %v\n", err)
+        return
+    }
+    
+    fmt.Printf("Response: %s\n", response)
+}
+```
+
+## Rust
+
+```rust
+use reqwest::{Client, header};
+use serde::{Deserialize, Serialize};
+use serde_json::json;
+
+const BASE_URL: &str = "https://swarms-api-285321057562.us-east1.run.app";
+const API_KEY: &str = "your_api_key_here";
+
+#[derive(Serialize, Deserialize)]
+struct Agent {
+    agent_name: String,
+    description: String,
+    system_prompt: String,
+    model_name: String,
+    role: String,
+    max_loops: i32,
+}
+
+#[derive(Serialize, Deserialize)]
+struct SwarmRequest {
+    name: String,
+    description: String,
+    agents: Vec<Agent>,
+    max_loops: i32,
+    swarm_type: String,
+    task: String,
+}
+
+async fn run_single_swarm() -> Result<String, Box<dyn std::error::Error>> {
+    let client = Client::new();
+    
+    let payload = SwarmRequest {
+        name: "Financial Analysis".to_string(),
+        description: "Market analysis swarm".to_string(),
+        agents: vec![Agent {
+            agent_name: "Market Analyst".to_string(),
+            description: "Analyzes market trends".to_string(),
+            system_prompt: "You are a financial analyst expert.".to_string(),
+            model_name: "gpt-4o".to_string(),
+            role: "worker".to_string(),
+            max_loops: 1,
+        }],
+        max_loops: 1,
+        swarm_type: "SequentialWorkflow".to_string(),
+        task: "Analyze current market trends".to_string(),
+    };
+
+    let response = client
+        .post(format!("{}/v1/swarm/completions", BASE_URL))
+        .header("x-api-key", API_KEY)
+        .header("Content-Type", "application/json")
+        .json(&payload)
+        .send()
+        .await?;
+
+    let result = response.text().await?;
+    Ok(result)
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let response = run_single_swarm().await?;
+    println!("Response: {}", response);
+    Ok(())
+}
+```
+
+## C#
+
+```csharp
+using System;
+using System.Net.Http;
+using System.Text;
+using System.Text.Json;
+using System.Threading.Tasks;
+
+public class SwarmClient
+{
+    private readonly HttpClient _client;
+    private const string BaseUrl = "https://swarms-api-285321057562.us-east1.run.app";
+    private readonly string _apiKey;
+
+    public SwarmClient(string apiKey)
+    {
+        _apiKey = apiKey;
+        _client = new HttpClient();
+        _client.DefaultRequestHeaders.Add("x-api-key", apiKey);
+    }
+
+    public class Agent
+    {
+        public string AgentName { get; set; }
+        public string Description { get; set; }
+        public string SystemPrompt { get; set; }
+        public string ModelName { get; set; }
+        public string Role { get; set; }
+        public int MaxLoops { get; set; }
+    }
+
+    public class SwarmRequest
+    {
+        public string Name { get; set; }
+        public string Description { get; set; }
+        public List<Agent> Agents { get; set; }
+        public int MaxLoops { get; set; }
+        public string SwarmType { get; set; }
+        public string Task { get; set; }
+    }
+
+    public async Task<string> RunSingleSwarm()
+    {
+        var payload = new SwarmRequest
+        {
+            Name = "Financial Analysis",
+            Description = "Market analysis swarm",
+            Agents = new List<Agent>
+            {
+                new Agent
+                {
+                    AgentName = "Market Analyst",
+                    Description = "Analyzes market trends",
+                    SystemPrompt = "You are a financial analyst expert.",
+                    ModelName = "gpt-4o",
+                    Role = "worker",
+                    MaxLoops = 1
+                }
+            },
+            MaxLoops = 1,
+            SwarmType = "SequentialWorkflow",
+            Task = "Analyze current market trends"
+        };
+
+        var content = new StringContent(
+            JsonSerializer.Serialize(payload),
+            Encoding.UTF8,
+            "application/json"
+        );
+
+        var response = await _client.PostAsync(
+            $"{BaseUrl}/v1/swarm/completions",
+            content
+        );
+
+        return await response.Content.ReadAsStringAsync();
+    }
+}
+
+// Usage
+class Program
+{
+    static async Task Main(string[] args)
+    {
+        var client = new SwarmClient("your_api_key_here");
+        var response = await client.RunSingleSwarm();
+        Console.WriteLine($"Response: {response}");
+    }
+}
+```
+
+## Shell (cURL)
+
+```bash
+# Single swarm execution
+curl -X POST "https://swarms-api-285321057562.us-east1.run.app/v1/swarm/completions" \
+  -H "x-api-key: your_api_key_here" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "name": "Financial Analysis",
+    "description": "Market analysis swarm",
+    "agents": [
+      {
+        "agent_name": "Market Analyst",
+        "description": "Analyzes market trends",
+        "system_prompt": "You are a financial analyst expert.",
+        "model_name": "gpt-4o",
+        "role": "worker",
+        "max_loops": 1
+      }
+    ],
+    "max_loops": 1,
+    "swarm_type": "SequentialWorkflow",
+    "task": "Analyze current market trends"
+  }'
+
+# Batch swarm execution
+curl -X POST "https://swarms-api-285321057562.us-east1.run.app/v1/swarm/batch/completions" \
+  -H "x-api-key: your_api_key_here" \
+  -H "Content-Type: application/json" \
+  -d '[
+    {
+      "name": "Market Analysis",
+      "agents": [{
+        "agent_name": "Analyst",
+        "system_prompt": "You are a market analyst.",
+        "model_name": "gpt-4o",
+        "role": "worker"
+      }],
+      "task": "Analyze tech trends"
+    },
+    {
+      "name": "Risk Assessment",
+      "agents": [{
+        "agent_name": "Risk Analyst",
+        "system_prompt": "You are a risk analyst.",
+        "model_name": "gpt-4o",
+        "role": "worker"
+      }],
+      "task": "Assess market risks"
+    }
+  ]'
+```
+
+
+## Billing and Credits
+
+The API uses a credit-based billing system with the following components:
+
+### Cost Calculation
+
+| Component | Cost |
+|-----------|------|
+| Base cost per agent | $0.01 |
+| Input tokens (per 1M) | $5.00 |
+| Output tokens (per 1M) | $15.50 |
+
+Credits are deducted based on:
+- Number of agents used
+- Total input tokens (including system prompts and agent memory)
+- Total output tokens generated
+- Execution time
+
+### Credit Types
+- Free credits: Used first
+- Regular credits: Used after free credits are exhausted
+
+## Error Handling
+
+| HTTP Status Code | Description |
+|-----------------|-------------|
+| 402 | Insufficient credits |
+| 403 | Invalid API key |
+| 404 | Resource not found |
+| 500 | Internal server error |
+
+## Best Practices
+
+1. Start with small swarms and gradually increase complexity
+2. Monitor credit usage and token counts
+3. Use appropriate max_loops values to control execution
+4. Implement proper error handling for API responses
+5. Consider using batch completions for multiple related tasks

example.py

@@ -24,7 +24,7 @@ agent = Agent(
     max_tokens=4000,  # max output tokens
     saved_state_path="agent_00.json",
     interactive=False,
-    roles="director",
+    role="director",
 )
 
 agent.run(

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "7.2.7"
+version = "7.4.0"
 description = "Swarms - TGSC"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -75,6 +75,7 @@ aiofiles = "*"
 rich = "*"
 numpy = "*"
 litellm = "*"
+torch = "*"
 
 [tool.poetry.scripts]
 swarms = "swarms.cli.main:main"
@@ -114,3 +115,10 @@ exclude = '''
 )/
 '''
 
+
+
+[tool.maturin]
+module-name = "swarms_rust"
+
+[tool.maturin.build]
+features = ["extension-module"]

swarm_router.py

@@ -0,0 +1,27 @@
+from swarms import Agent, SwarmRouter
+
+
+agents = [
+    Agent(
+        agent_name="test_agent_1",
+        agent_description="test_agent_1_description",
+        system_prompt="test_agent_1_system_prompt",
+        model_name="gpt-4o",
+    ),
+    Agent(
+        agent_name="test_agent_2",
+        agent_description="test_agent_2_description",
+        system_prompt="test_agent_2_system_prompt",
+        model_name="gpt-4o",
+    ),
+    Agent(
+        agent_name="test_agent_3",
+        agent_description="test_agent_3_description",
+        system_prompt="test_agent_3_system_prompt",
+        model_name="gpt-4o",
+    ),
+]
+
+router = SwarmRouter(agents=agents, swarm_type="SequentialWorkflow")
+
+print(router.run("How are you doing?"))

swarms/agents/__init__.py

@@ -10,13 +10,13 @@ from swarms.structs.stopping_conditions import (
     check_stopped,
     check_success,
 )
-from swarms.agents.tool_agent import ToolAgent
+# from swarms.agents.tool_agent import ToolAgent
 from swarms.agents.create_agents_from_yaml import (
     create_agents_from_yaml,
 )
 
 __all__ = [
-    "ToolAgent",
+    # "ToolAgent",
     "check_done",
     "check_finished",
     "check_complete",

swarms/structs/Talk_Hier.py

@@ -31,7 +31,8 @@ class AgentRole(Enum):
 @dataclass
 class CommunicationEvent:
     """Represents a structured communication event between agents."""
-    message: str 
+
+    message: str
     background: Optional[str] = None
     intermediate_output: Optional[Dict[str, Any]] = None
     sender: str = ""
@@ -96,7 +97,7 @@ class TalkHier:
                 import re
 
                 json_match = re.search(r"\{.*\}", json_str, re.DOTALL)
-                if (json_match):
+                if json_match:
                     return json.loads(json_match.group())
                 # Try extracting from markdown code blocks
                 code_block_match = re.search(
@@ -104,7 +105,7 @@ class TalkHier:
                     json_str,
                     re.DOTALL,
                 )
-                if (code_block_match):
+                if code_block_match:
                     return json.loads(code_block_match.group(1))
             except Exception as e:
                 logger.warning(f"Failed to extract JSON: {str(e)}")
@@ -173,7 +174,9 @@ Output all responses in strict JSON format:
             system_prompt=self._get_criteria_generator_prompt(),
             model_name=self.model_name,
             max_loops=1,
-            saved_state_path=str(self.base_path / "criteria_generator.json"),
+            saved_state_path=str(
+                self.base_path / "criteria_generator.json"
+            ),
             verbose=True,
         )
 
@@ -350,77 +353,97 @@ Output all responses in strict JSON format:
     }
 }"""
 
-    def _generate_criteria_for_task(self, task: str) -> Dict[str, Any]:
+    def _generate_criteria_for_task(
+        self, task: str
+    ) -> Dict[str, Any]:
         """Generate evaluation criteria for the given task."""
         try:
             criteria_input = {
                 "task": task,
-                "instruction": "Generate specific evaluation criteria for this task."
+                "instruction": "Generate specific evaluation criteria for this task.",
             }
-            
-            criteria_response = self.criteria_generator.run(json.dumps(criteria_input))
+
+            criteria_response = self.criteria_generator.run(
+                json.dumps(criteria_input)
+            )
             self.conversation.add(
-                role="Criteria-Generator",
-                content=criteria_response
+                role="Criteria-Generator", content=criteria_response
             )
-            
+
             return self._safely_parse_json(criteria_response)
         except Exception as e:
             logger.error(f"Error generating criteria: {str(e)}")
             return {"criteria": {}}
 
-    def _create_comm_event(self, sender: Agent, receiver: Agent, response: Dict) -> CommunicationEvent:
+    def _create_comm_event(
+        self, sender: Agent, receiver: Agent, response: Dict
+    ) -> CommunicationEvent:
         """Create a structured communication event between agents."""
         return CommunicationEvent(
             message=response.get("message", ""),
             background=response.get("background", ""),
-            intermediate_output=response.get("intermediate_output", {}),
+            intermediate_output=response.get(
+                "intermediate_output", {}
+            ),
             sender=sender.agent_name,
             receiver=receiver.agent_name,
         )
 
-    def _evaluate_content(self, content: Union[str, Dict], task: str) -> Dict[str, Any]:
+    def _evaluate_content(
+        self, content: Union[str, Dict], task: str
+    ) -> Dict[str, Any]:
         """Coordinate evaluation process with parallel evaluator execution."""
         try:
-            content_dict = self._safely_parse_json(content) if isinstance(content, str) else content
+            content_dict = (
+                self._safely_parse_json(content)
+                if isinstance(content, str)
+                else content
+            )
             criteria_data = self._generate_criteria_for_task(task)
 
             def run_evaluator(evaluator, eval_input):
                 response = evaluator.run(json.dumps(eval_input))
                 return {
-                    "evaluator_id": evaluator.agent_name, 
-                    "evaluation": self._safely_parse_json(response)
+                    "evaluator_id": evaluator.agent_name,
+                    "evaluation": self._safely_parse_json(response),
                 }
 
-            eval_inputs = [{
-                "task": task,
-                "content": content_dict,
-                "criteria": criteria_data.get("criteria", {})
-            } for _ in self.evaluators]
+            eval_inputs = [
+                {
+                    "task": task,
+                    "content": content_dict,
+                    "criteria": criteria_data.get("criteria", {}),
+                }
+                for _ in self.evaluators
+            ]
 
             with ThreadPoolExecutor() as executor:
-                evaluations = list(executor.map(
-                    lambda x: run_evaluator(*x), 
-                    zip(self.evaluators, eval_inputs)
-                ))
+                evaluations = list(
+                    executor.map(
+                        lambda x: run_evaluator(*x),
+                        zip(self.evaluators, eval_inputs),
+                    )
+                )
 
             supervisor_input = {
                 "evaluations": evaluations,
                 "task": task,
-                "instruction": "Synthesize feedback"
+                "instruction": "Synthesize feedback",
             }
-            supervisor_response = self.main_supervisor.run(json.dumps(supervisor_input))
-            aggregated_eval = self._safely_parse_json(supervisor_response)
+            supervisor_response = self.main_supervisor.run(
+                json.dumps(supervisor_input)
+            )
+            aggregated_eval = self._safely_parse_json(
+                supervisor_response
+            )
 
             # Track communication
             comm_event = self._create_comm_event(
-                self.main_supervisor, 
-                self.revisor, 
-                aggregated_eval
+                self.main_supervisor, self.revisor, aggregated_eval
             )
             self.conversation.add(
                 role="Communication",
-                content=json.dumps(asdict(comm_event))
+                content=json.dumps(asdict(comm_event)),
             )
 
             return aggregated_eval
@@ -455,11 +478,15 @@ Output all responses in strict JSON format:
             # Collect all unique criteria from evaluators
             all_criteria = set()
             for eval_data in evaluations:
-                categories = eval_data.get("scores", {}).get("categories", {})
+                categories = eval_data.get("scores", {}).get(
+                    "categories", {}
+                )
                 all_criteria.update(categories.keys())
 
             # Initialize score aggregation
-            aggregated_scores = {criterion: [] for criterion in all_criteria}
+            aggregated_scores = {
+                criterion: [] for criterion in all_criteria
+            }
             overall_scores = []
             all_feedback = []
 
@@ -467,7 +494,7 @@ Output all responses in strict JSON format:
             for eval_data in evaluations:
                 scores = eval_data.get("scores", {})
                 overall_scores.append(scores.get("overall", 0.5))
-                
+
                 categories = scores.get("categories", {})
                 for criterion in all_criteria:
                     if criterion in categories:
@@ -508,32 +535,40 @@ Output all responses in strict JSON format:
         try:
             # Get evaluations and supervisor selection
             evaluation_result = self._evaluate_content(content, task)
-            
+
             # Extract selected evaluation and supervisor reasoning
-            selected_evaluation = evaluation_result.get("selected_evaluation", {})
-            supervisor_reasoning = evaluation_result.get("supervisor_reasoning", {})
-            
+            selected_evaluation = evaluation_result.get(
+                "selected_evaluation", {}
+            )
+            supervisor_reasoning = evaluation_result.get(
+                "supervisor_reasoning", {}
+            )
+
             # Prepare revision input with selected evaluation
             revision_input = {
                 "content": content,
                 "evaluation": selected_evaluation,
                 "supervisor_feedback": supervisor_reasoning,
-                "instruction": "Revise the content based on the selected evaluation feedback"
+                "instruction": "Revise the content based on the selected evaluation feedback",
             }
 
             # Get revision from content generator
-            revision_response = self.generator.run(json.dumps(revision_input))
-            revised_content = self._safely_parse_json(revision_response)
+            revision_response = self.generator.run(
+                json.dumps(revision_input)
+            )
+            revised_content = self._safely_parse_json(
+                revision_response
+            )
 
             return {
                 "content": revised_content,
-                "evaluation": evaluation_result
+                "evaluation": evaluation_result,
             }
         except Exception as e:
             logger.error(f"Evaluation and revision error: {str(e)}")
             return {
                 "content": content,
-                "evaluation": self._get_fallback_evaluation()
+                "evaluation": self._get_fallback_evaluation(),
             }
 
     def run(self, task: str) -> Dict[str, Any]:
@@ -579,22 +614,34 @@ Output all responses in strict JSON format:
                 logger.info(f"Starting iteration {iteration + 1}")
 
                 # Evaluate and revise content
-                result = self._evaluate_and_revise(current_content, task)
+                result = self._evaluate_and_revise(
+                    current_content, task
+                )
                 evaluation = result["evaluation"]
                 current_content = result["content"]
 
                 # Check if quality threshold is met
-                selected_eval = evaluation.get("selected_evaluation", {})
-                overall_score = selected_eval.get("scores", {}).get("overall", 0.0)
-                
+                selected_eval = evaluation.get(
+                    "selected_evaluation", {}
+                )
+                overall_score = selected_eval.get("scores", {}).get(
+                    "overall", 0.0
+                )
+
                 if overall_score >= self.quality_threshold:
-                    logger.info("Quality threshold met, returning content")
+                    logger.info(
+                        "Quality threshold met, returning content"
+                    )
                     return {
-                        "content": current_content.get("content", {}).get("main_body", ""),
+                        "content": current_content.get(
+                            "content", {}
+                        ).get("main_body", ""),
                         "final_score": overall_score,
                         "iterations": iteration + 1,
                         "metadata": {
-                            "content_metadata": current_content.get("content", {}).get("metadata", {}),
+                            "content_metadata": current_content.get(
+                                "content", {}
+                            ).get("metadata", {}),
                             "evaluation": evaluation,
                         },
                     }
@@ -665,18 +712,18 @@ Output all responses in strict JSON format:
                 )
 
 
-if __name__ == "__main__":
-    try:
-        talkhier = TalkHier(
-            max_iterations=1,
-            quality_threshold=0.8,
-            model_name="gpt-4o",
-            return_string=False,
-        )
+# if __name__ == "__main__":
+#     try:
+#         talkhier = TalkHier(
+#             max_iterations=1,
+#             quality_threshold=0.8,
+#             model_name="gpt-4o",
+#             return_string=False,
+#         )
 
-        # Ask for user input
-        task = input("Enter the content generation task description: ")
-        result = talkhier.run(task)
+#         # Ask for user input
+#         task = input("Enter the content generation task description: ")
+#         result = talkhier.run(task)
 
-    except Exception as e:
-        logger.error(f"Error in main execution: {str(e)}")
+#     except Exception as e:
+#         logger.error(f"Error in main execution: {str(e)}")

swarms/structs/agent.py

@@ -340,6 +340,7 @@ class Agent:
         llm_args: dict = None,
         load_state_path: str = None,
         role: agent_roles = "worker",
+        no_print: bool = False,
         *args,
         **kwargs,
     ):
@@ -457,6 +458,7 @@ class Agent:
         self.llm_args = llm_args
         self.load_state_path = load_state_path
         self.role = role
+        self.no_print = no_print
 
         # Initialize the short term memory
         self.short_memory = Conversation(
@@ -867,18 +869,19 @@ class Agent:
                         #         # break
 
                         # Print
-                        if self.streaming_on is True:
-                            # self.stream_response(response)
-                            formatter.print_panel_token_by_token(
-                                f"{self.agent_name}: {response}",
-                                title=f"Agent Name: {self.agent_name} [Max Loops: {loop_count}]",
-                            )
-                        else:
-                            # logger.info(f"Response: {response}")
-                            formatter.print_panel(
-                                f"{self.agent_name}: {response}",
-                                f"Agent Name {self.agent_name} [Max Loops: {loop_count} ]",
-                            )
+                        if self.no_print is False:
+                            if self.streaming_on is True:
+                                # self.stream_response(response)
+                                formatter.print_panel_token_by_token(
+                                    f"{self.agent_name}: {response}",
+                                    title=f"Agent Name: {self.agent_name} [Max Loops: {loop_count}]",
+                                )
+                            else:
+                                # logger.info(f"Response: {response}")
+                                formatter.print_panel(
+                                    f"{self.agent_name}: {response}",
+                                    f"Agent Name {self.agent_name} [Max Loops: {loop_count} ]",
+                                )
 
                         # Check if response is a dictionary and has 'choices' key
                         if (
@@ -2606,3 +2609,10 @@ class Agent:
         Get the role of the agent.
         """
         return self.role
+
+    # def __getstate__(self):
+    #     state = self.__dict__.copy()
+    #     # Remove or replace unpicklable attributes.
+    #     if '_queue' in state:
+    #         del state['_queue']
+    #     return state

swarms/structs/agent_registry.py

@@ -1,3 +1,4 @@
+import json
 import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from threading import Lock
@@ -5,7 +6,7 @@ from typing import Any, Callable, Dict, List, Optional
 
 from pydantic import BaseModel, Field, ValidationError
 
-from swarms import Agent
+from swarms.structs.agent import Agent
 from swarms.utils.loguru_logger import logger
 
 
@@ -302,6 +303,25 @@ class AgentRegistry:
             logger.error(f"Error: {e}")
             raise e
 
+    def find_agent_by_id(self, agent_id: str) -> Optional[Agent]:
+        """
+        Find an agent by its ID.
+        """
+        return self.agents.get(agent_id)
+
+    def agents_to_json(self) -> str:
+        """
+        Converts all agents in the registry to a JSON string.
+
+        Returns:
+            str: A JSON string representation of all agents, keyed by their names.
+        """
+        agents_dict = {
+            name: agent.to_dict()
+            for name, agent in self.agents.items()
+        }
+        return json.dumps(agents_dict, indent=4)
+
     def agent_to_py_model(self, agent: Agent):
         """
         Converts an agent to a Pydantic model.
@@ -328,3 +348,30 @@ class AgentRegistry:
         )
 
         self.agent_registry.agents.append(schema)
+
+
+# if __name__ == "__main__":
+#     from swarms import Agent
+
+#     agent1 = Agent(agent_name="test_agent_1")
+#     agent2 = Agent(agent_name="test_agent_2")
+#     agent3 = Agent(agent_name="test_agent_3")
+#     print(f"Created agents: {agent1}, {agent2}, {agent3}")
+
+#     registry = AgentRegistry()
+#     print(f"Created agent registry: {registry}")
+
+#     registry.add(agent1)
+#     registry.add(agent2)
+#     registry.add(agent3)
+#     print(f"Added agents to registry: {agent1}, {agent2}, {agent3}")
+
+#     all_agents = registry.return_all_agents()
+#     print(f"All agents in registry: {all_agents}")
+
+#     found_agent1 = registry.find_agent_by_name("test_agent_1")
+#     found_agent2 = registry.find_agent_by_name("test_agent_2")
+#     found_agent3 = registry.find_agent_by_name("test_agent_3")
+#     print(f"Found agents by name: {found_agent1}, {found_agent2}, {found_agent3}")
+
+#     print(registry.agents_to_json())

swarms/structs/concurrent_workflow.py

@@ -109,6 +109,7 @@ class ConcurrentWorkflow(BaseSwarm):
         agent_responses: list = [],
         auto_generate_prompts: bool = False,
         max_workers: int = None,
+        user_interface: bool = True,
         *args,
         **kwargs,
     ):
@@ -130,10 +131,15 @@ class ConcurrentWorkflow(BaseSwarm):
         self.agent_responses = agent_responses
         self.auto_generate_prompts = auto_generate_prompts
         self.max_workers = max_workers or os.cpu_count()
+        self.user_interface = user_interface
         self.tasks = []  # Initialize tasks list
 
         self.reliability_check()
 
+    def disable_agent_prints(self):
+        for agent in self.agents:
+            agent.no_print = False
+
     def reliability_check(self):
         try:
             logger.info("Starting reliability checks")
@@ -176,62 +182,6 @@ class ConcurrentWorkflow(BaseSwarm):
                 agent.auto_generate_prompt = True
 
     # @retry(wait=wait_exponential(min=2), stop=stop_after_attempt(3))
-    async def _run_agent(
-        self,
-        agent: Agent,
-        task: str,
-        executor: ThreadPoolExecutor,
-    ) -> AgentOutputSchema:
-        """
-        Runs a single agent with the given task and tracks its output and metadata with retry logic.
-
-        Args:
-            agent (Agent): The agent instance to run.
-            task (str): The task or query to give to the agent.
-            img (str): The image to be processed by the agent.
-            executor (ThreadPoolExecutor): The thread pool executor to use for running the agent task.
-
-        Returns:
-            AgentOutputSchema: The metadata and output from the agent's execution.
-
-        Example:
-            >>> async def run_agent_example():
-            >>>     executor = ThreadPoolExecutor()
-            >>>     agent_output = await workflow._run_agent(agent=Agent(), task="Example task", img="example.jpg", executor=executor)
-            >>>     print(agent_output)
-        """
-        start_time = datetime.now()
-        try:
-            loop = asyncio.get_running_loop()
-            output = await loop.run_in_executor(
-                executor,
-                agent.run,
-                task,
-            )
-        except Exception as e:
-            logger.error(
-                f"Error running agent {agent.agent_name}: {e}"
-            )
-            raise
-
-        end_time = datetime.now()
-        duration = (end_time - start_time).total_seconds()
-
-        agent_output = AgentOutputSchema(
-            run_id=uuid.uuid4().hex,
-            agent_name=agent.agent_name,
-            task=task,
-            output=output,
-            start_time=start_time,
-            end_time=end_time,
-            duration=duration,
-        )
-
-        logger.info(
-            f"Agent {agent.agent_name} completed task: {task} in {duration:.2f} seconds."
-        )
-
-        return agent_output
 
     def transform_metadata_schema_to_str(
         self, schema: MetadataSchema
@@ -258,47 +208,6 @@ class ConcurrentWorkflow(BaseSwarm):
         # Return the agent responses as a string
         return "\n".join(self.agent_responses)
 
-    # @retry(wait=wait_exponential(min=2), stop=stop_after_attempt(3))
-    async def _execute_agents_concurrently(
-        self, task: str, img: str = None, *args, **kwargs
-    ) -> MetadataSchema:
-        """
-        Executes multiple agents concurrently with the same task, incorporating retry logic for failed executions.
-
-        Args:
-            task (str): The task or query to give to all agents.
-            img (str): The image to be processed by the agents.
-
-        Returns:
-            MetadataSchema: The aggregated metadata and outputs from all agents.
-
-        Example:
-            >>> async def execute_agents_example():
-            >>>     metadata_schema = await workflow._execute_agents_concurrently(task="Example task", img="example.jpg")
-            >>>     print(metadata_schema)
-        """
-        with ThreadPoolExecutor(
-            max_workers=os.cpu_count()
-        ) as executor:
-            tasks_to_run = [
-                self._run_agent(
-                    agent=agent,
-                    task=task,
-                    executor=executor,
-                    *args,
-                    **kwargs,
-                )
-                for agent in self.agents
-            ]
-
-            agent_outputs = await asyncio.gather(*tasks_to_run)
-        return MetadataSchema(
-            swarm_id=uuid.uuid4().hex,
-            task=task,
-            description=self.description,
-            agents=agent_outputs,
-        )
-
     def save_metadata(self):
         """
         Saves the metadata to a JSON file based on the auto_save flag.
@@ -322,7 +231,7 @@ class ConcurrentWorkflow(BaseSwarm):
         self, task: str, img: str = None, *args, **kwargs
     ) -> Union[Dict[str, Any], str]:
         """
-        Runs the workflow for the given task, executes agents concurrently, and saves metadata in a production-grade manner.
+        Runs the workflow for the given task, executes agents concurrently using ThreadPoolExecutor, and saves metadata.
 
         Args:
             task (str): The task or query to give to all agents.
@@ -339,10 +248,50 @@ class ConcurrentWorkflow(BaseSwarm):
         logger.info(
             f"Running concurrent workflow with {len(self.agents)} agents."
         )
-        self.output_schema = asyncio.run(
-            self._execute_agents_concurrently(
-                task, img, *args, **kwargs
+
+        def run_agent(agent: Agent, task: str) -> AgentOutputSchema:
+            start_time = datetime.now()
+            try:
+                output = agent.run(task)
+            except Exception as e:
+                logger.error(
+                    f"Error running agent {agent.agent_name}: {e}"
+                )
+                raise
+
+            end_time = datetime.now()
+            duration = (end_time - start_time).total_seconds()
+
+            agent_output = AgentOutputSchema(
+                run_id=uuid.uuid4().hex,
+                agent_name=agent.agent_name,
+                task=task,
+                output=output,
+                start_time=start_time,
+                end_time=end_time,
+                duration=duration,
             )
+
+            logger.info(
+                f"Agent {agent.agent_name} completed task: {task} in {duration:.2f} seconds."
+            )
+
+            return agent_output
+
+        with ThreadPoolExecutor(
+            max_workers=os.cpu_count()
+        ) as executor:
+            agent_outputs = list(
+                executor.map(
+                    lambda agent: run_agent(agent, task), self.agents
+                )
+            )
+
+        self.output_schema = MetadataSchema(
+            swarm_id=uuid.uuid4().hex,
+            task=task,
+            description=self.description,
+            agents=agent_outputs,
         )
 
         self.save_metadata()
@@ -351,9 +300,7 @@ class ConcurrentWorkflow(BaseSwarm):
             return self.transform_metadata_schema_to_str(
                 self.output_schema
             )
-
         else:
-            # Return metadata as a dictionary
             return self.output_schema.model_dump_json(indent=4)
 
     def run(
@@ -390,7 +337,8 @@ class ConcurrentWorkflow(BaseSwarm):
             self.tasks.append(task)
 
         try:
-            return self._run(task, img, *args, **kwargs)
+            outputs = self._run(task, img, *args, **kwargs)
+            return outputs
         except ValueError as e:
             logger.error(f"Invalid device specified: {e}")
             raise e
@@ -523,26 +471,12 @@ class ConcurrentWorkflow(BaseSwarm):
 
 # if __name__ == "__main__":
 #     # Assuming you've already initialized some agents outside of this class
-#     model = OpenAIChat(
-#         api_key=os.getenv("OPENAI_API_KEY"),
-#         model_name="gpt-4o-mini",
-#         temperature=0.1,
-#     )
 #     agents = [
 #         Agent(
 #             agent_name=f"Financial-Analysis-Agent-{i}",
 #             system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-#             llm=model,
+#             model_name="gpt-4o",
 #             max_loops=1,
-#             autosave=True,
-#             dashboard=False,
-#             verbose=True,
-#             dynamic_temperature_enabled=True,
-#             saved_state_path=f"finance_agent_{i}.json",
-#             user_name="swarms_corp",
-#             retry_attempts=1,
-#             context_length=200000,
-#             return_step_meta=False,
 #         )
 #         for i in range(3)  # Adjust number of agents as needed
 #     ]

swarms/structs/multi_agent_exec.py

@@ -1,7 +1,9 @@
 import asyncio
 import os
 import threading
-from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import (
+    ThreadPoolExecutor,
+)
 from dataclasses import dataclass
 from typing import Any, List
 
@@ -413,29 +415,141 @@ def run_agents_with_tasks_concurrently(
     )
 
 
+# from joblib import Parallel, delayed
+
+
+# def run_agents_joblib(
+#     agents: List[Any],
+#     tasks: List[str] = [],
+#     img: List[str] = None,
+#     max_workers: int = None,
+#     max_loops: int = 1,
+#     prefer: str = "threads",
+# ) -> List[Any]:
+#     """
+#     Executes a list of agents with their corresponding tasks concurrently using joblib.
+
+#     Each agent is expected to have a .run() method that accepts at least:
+#         - task: A string indicating the task to execute.
+#         - img: (Optional) A string representing image input.
+
+#     Args:
+#         agents (List[Any]): A list of agent instances.
+#         tasks (List[str], optional): A list of task strings. If provided, each agent gets a task.
+#                                      If fewer tasks than agents, the first task is reused.
+#         img (List[str], optional): A list of image strings. If provided, each agent gets an image.
+#                                    If fewer images than agents, the first image is reused.
+#         max_workers (int, optional): The maximum number of processes to use.
+#                                      Defaults to all available CPU cores.
+#         max_loops (int, optional): Number of times to execute the whole batch.
+
+#     Returns:
+#         List[Any]: The list of results returned by each agent’s run() method.
+#     """
+#     max_workers = max_workers or os.cpu_count()
+#     results = []
+
+#     for _ in range(max_loops):
+#         results.extend(
+#             Parallel(n_jobs=max_workers, prefer=prefer)(
+#                 delayed(lambda a, t, i: a.run(task=t, img=i))(
+#                     agent,
+#                     (
+#                         tasks[idx]
+#                         if tasks and idx < len(tasks)
+#                         else (tasks[0] if tasks else "")
+#                     ),
+#                     (
+#                         img[idx]
+#                         if img and idx < len(img)
+#                         else (img[0] if img else None)
+#                     ),
+#                 )
+#                 for idx, agent in enumerate(agents)
+#             )
+#         )
+
+#     return results
+
+
+# # Example usage:
+# if __name__ == '__main__':
+#     # Dummy Agent class for demonstration.
+#     class Agent:
+#         def __init__(self, agent_name, max_loops, model_name):
+#             self.agent_name = agent_name
+#             self.max_loops = max_loops
+#             self.model_name = model_name
+
+#         def run(self, task: str, img: str = None) -> str:
+#             img_info = f" with image '{img}'" if img else ""
+#             return (f"{self.agent_name} using model '{self.model_name}' processed task: '{task}'{img_info}")
+
+#     # Create a few Agent instances.
+#     agents = [
+#         Agent(
+#             agent_name=f"Financial-Analysis-Agent_parallel_swarm{i}",
+#             max_loops=1,
+#             model_name="gpt-4o-mini",
+#         )
+#         for i in range(3)
+#     ]
+
+#     task = "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
+#     outputs = run_agents_process_pool(agents, tasks=[task])
+
+#     for i, output in enumerate(outputs):
+#         print(f"Output from agent {i+1}:\n{output}")
+
 # # Example usage:
-# # Initialize your agents with the same model to avoid re-creating it
-# agents = [
-#     Agent(
-#         agent_name=f"Financial-Analysis-Agent_parallel_swarm{i}",
-#         system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-#         llm=model,
-#         max_loops=1,
-#         autosave=True,
-#         dashboard=False,
-#         verbose=False,
-#         dynamic_temperature_enabled=False,
-#         saved_state_path=f"finance_agent_{i}.json",
-#         user_name="swarms_corp",
-#         retry_attempts=1,
-#         context_length=200000,
-#         return_step_meta=False,
+# if __name__ == '__main__':
+#     # A sample agent class with a run method.
+#     class SampleAgent:
+#         def __init__(self, name):
+#             self.name = name
+
+#         def run(self, task, device, device_id, no_clusterops):
+#             # Simulate some processing.
+#             return (f"Agent {self.name} processed task '{task}' on {device} "
+#                     f"(device_id={device_id}), no_clusterops={no_clusterops}")
+
+#     # Create a list of sample agents.
+#     agents = [SampleAgent(f"Agent_{i}") for i in range(5)]
+#     # Define tasks; if fewer tasks than agents, the first task will be reused.
+#     tasks = ["task1", "task2", "task3"]
+
+#     outputs = run_agents_with_tasks_concurrently(
+#         agents=agents,
+#         tasks=tasks,
+#         max_workers=4,
+#         device="cpu",
+#         device_id=1,
+#         all_cores=True,
+#         no_clusterops=False
 #     )
-#     for i in range(5)  # Assuming you want 10 agents
-# ]
 
-# task = "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
-# outputs = run_agents_concurrently(agents, task)
+#     for output in outputs:
+#         print(output)
+
 
-# for i, output in enumerate(outputs):
-#     print(f"Output from agent {i+1}:\n{output}")
+# # Example usage:
+# if __name__ == "__main__":
+#     # Initialize your agents (for example, 3 agents)
+#     agents = [
+#         Agent(
+#             agent_name=f"Financial-Analysis-Agent_parallel_swarm{i}",
+#             max_loops=1,
+#             model_name="gpt-4o-mini",
+#         )
+#         for i in range(3)
+#     ]
+
+#     # Generate a list of tasks.
+#     tasks = [
+#         "How can I establish a ROTH IRA to buy stocks and get a tax break?",
+#         "What are the criteria for establishing a ROTH IRA?",
+#         "What are the tax benefits of a ROTH IRA?",
+#         "How to buy stocks using a ROTH IRA?",
+#         "What are the limitations of a ROTH IRA?",
+#     ]
+#     outputs = run_agents_joblib(agents, tasks)

swarms/structs/rearrange.py

@@ -113,6 +113,7 @@ class AgentRearrange(BaseSwarm):
         all_gpus: bool = True,
         no_use_clusterops: bool = True,
         autosave: bool = True,
+        return_entire_history: bool = False,
         *args,
         **kwargs,
     ):
@@ -141,7 +142,7 @@ class AgentRearrange(BaseSwarm):
         self.all_gpus = all_gpus
         self.no_use_clusterops = no_use_clusterops
         self.autosave = autosave
-
+        self.return_entire_history = return_entire_history
         self.output_schema = AgentRearrangeOutput(
             input=AgentRearrangeInput(
                 swarm_id=id,
@@ -465,6 +466,9 @@ class AgentRearrange(BaseSwarm):
             if self.return_json:
                 return self.output_schema.model_dump_json(indent=4)
 
+            if self.return_entire_history:
+                return self.output_schema.model_dump_json(indent=4)
+
             # Handle different output types
             if self.output_type == "all":
                 output = " ".join(all_responses)

swarms/structs/sequential_workflow.py

@@ -33,6 +33,7 @@ class SequentialWorkflow:
         output_type: OutputType = "all",
         return_json: bool = False,
         shared_memory_system: callable = None,
+        return_entire_history: bool = False,
         *args,
         **kwargs,
     ):
@@ -43,6 +44,7 @@ class SequentialWorkflow:
         self.output_type = output_type
         self.return_json = return_json
         self.shared_memory_system = shared_memory_system
+        self.return_entire_history = return_entire_history
 
         self.reliability_check()
         self.flow = self.sequential_flow()

swarms/structs/swarm_router.py

@@ -4,7 +4,6 @@ from datetime import datetime
 from typing import Any, Callable, Dict, List, Literal, Union
 
 from pydantic import BaseModel, Field
-from tenacity import retry, stop_after_attempt, wait_fixed
 
 from swarms.prompts.ag_prompt import aggregator_system_prompt
 from swarms.structs.agent import Agent
@@ -138,11 +137,12 @@ class SwarmRouter:
         shared_memory_system: Any = None,
         rules: str = None,
         documents: List[str] = [],  # A list of docs file paths
-        output_type: str = "string",  # Md, PDF, Txt, csv
+        output_type: str = "all",
         no_cluster_ops: bool = False,
         speaker_fn: callable = None,
         load_agents_from_csv: bool = False,
         csv_file_path: str = None,
+        return_entire_history: bool = False,
         *args,
         **kwargs,
     ):
@@ -164,6 +164,7 @@ class SwarmRouter:
         self.logs = []
         self.load_agents_from_csv = load_agents_from_csv
         self.csv_file_path = csv_file_path
+        self.return_entire_history = return_entire_history
 
         if self.load_agents_from_csv:
             self.agents = AgentLoader(
@@ -233,7 +234,6 @@ class SwarmRouter:
             self._log("error", error_msg)
             raise RuntimeError(error_msg) from e
 
-    @retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
     def reliability_check(self):
         logger.info("Initializing reliability checks")
 
@@ -256,6 +256,8 @@ class SwarmRouter:
         SpreadSheetSwarm,
         SequentialWorkflow,
         ConcurrentWorkflow,
+        GroupChat,
+        MultiAgentRouter,
     ]:
         """
         Dynamically create and return the specified swarm type or automatically match the best swarm type for a given task.
@@ -286,6 +288,7 @@ class SwarmRouter:
                 flow=self.rearrange_flow,
                 return_json=self.return_json,
                 output_type=self.output_type,
+                return_entire_history=self.return_entire_history,
                 *args,
                 **kwargs,
             )
@@ -339,6 +342,7 @@ class SwarmRouter:
                 shared_memory_system=self.shared_memory_system,
                 output_type=self.output_type,
                 return_json=self.return_json,
+                return_entire_history=self.return_entire_history,
                 *args,
                 **kwargs,
             )
@@ -384,7 +388,6 @@ class SwarmRouter:
         self.logs.append(log_entry)
         logger.log(level.upper(), message)
 
-    @retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
     def _run(self, task: str, img: str, *args, **kwargs) -> Any:
         """
         Dynamically run the specified task on the selected or matched swarm type.

swarms/telemetry/main.py

@@ -11,10 +11,6 @@ import psutil
 import requests
 import toml
 
-from swarms.utils.loguru_logger import initialize_logger
-
-logger = initialize_logger(log_folder="capture_sys_data")
-
 
 # Helper functions
 def generate_user_id():
@@ -262,7 +258,8 @@ def capture_system_data() -> Dict[str, str]:
 
         return system_data
     except Exception as e:
-        logger.error("Failed to capture system data: {}", e)
+        # logger.error("Failed to capture system data: {}", e)
+        print(f"Failed to capture system data: {e}")
         return {}
 
 

swarms/utils/__init__.py

@@ -16,6 +16,7 @@ from swarms.utils.parse_code import extract_code_from_markdown
 from swarms.utils.pdf_to_text import pdf_to_text
 from swarms.utils.try_except_wrapper import try_except_wrapper
 from swarms.utils.calculate_func_metrics import profile_func
+from swarms.utils.litellm_tokenizer import count_tokens
 
 
 __all__ = [
@@ -33,4 +34,5 @@ __all__ = [
     "pdf_to_text",
     "try_except_wrapper",
     "profile_func",
+    "count_tokens",
 ]

swarms/utils/loguru_logger.py

@@ -1,50 +1,26 @@
-import os
-import uuid
 import sys
 from loguru import logger
 
 
 def initialize_logger(log_folder: str = "logs"):
-    AGENT_WORKSPACE = "agent_workspace"
-
-    # Check if WORKSPACE_DIR is set, if not, set it to AGENT_WORKSPACE
-    if "WORKSPACE_DIR" not in os.environ:
-        os.environ["WORKSPACE_DIR"] = AGENT_WORKSPACE
-
-    # Create a folder within the agent_workspace
-    log_folder_path = os.path.join(
-        os.getenv("WORKSPACE_DIR"), log_folder
-    )
-    if not os.path.exists(log_folder_path):
-        os.makedirs(log_folder_path)
-
-    # Generate a unique identifier for the log file
-    uuid_for_log = str(uuid.uuid4())
-    log_file_path = os.path.join(
-        log_folder_path, f"{log_folder}_{uuid_for_log}.log"
-    )
-
-    # Remove default handler and add custom handlers
+    # Remove default handler and add a combined handler
     logger.remove()
 
-    # Add console handler with colors
+    # Add a combined console and file handler
     logger.add(
         sys.stdout,
         colorize=True,
         format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",
         level="INFO",
-    )
-
-    # Add file handler
-    logger.add(
-        log_file_path,
-        format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}",
-        level="INFO",
         backtrace=True,
         diagnose=True,
         enqueue=True,
-        retention="10 days",
-        # compression="zip",
+        # retention="10 days",  # Removed this line
     )
 
     return logger
+
+
+# logger = initialize_logger()
+
+# logger.info("Hello, world!")

swarms/utils/visualizer.py

@@ -1,7 +1,6 @@
 import asyncio
 from dataclasses import dataclass
 from datetime import datetime
-from queue import Queue
 from typing import Any, Callable, Dict, List, Optional
 
 import psutil
@@ -19,6 +18,8 @@ from rich.table import Table
 from rich.text import Text
 from rich.tree import Tree
 
+from swarms.structs.agent import Agent
+
 try:
     import pynvml
 
@@ -30,59 +31,47 @@ except ImportError:
 
 @dataclass
 class SwarmMetadata:
-    name: str
-    description: str
-    version: str
-    type: str  # hierarchical, parallel, sequential
-    created_at: datetime
-    author: str
-    tags: List[str]
-    primary_objective: str
-    secondary_objectives: List[str]
-
-
-@dataclass
-class Agent:
-    name: str
-    role: str
-    description: str
-    agent_type: str  # e.g., "LLM", "Neural", "Rule-based"
-    capabilities: List[str]
-    parameters: Dict[str, any]
-    metadata: Dict[str, str]
-    children: List["Agent"] = None
-    parent: Optional["Agent"] = None
-    output_stream: Queue = None
+    name: Optional[str] = None
+    description: Optional[str] = None
+    version: Optional[str] = None
+    type: Optional[str] = None  # hierarchical, parallel, sequential
+    created_at: Optional[datetime] = None
+    author: Optional[str] = None
+    tags: Optional[List[str]] = None
+    primary_objective: Optional[str] = None
+    secondary_objectives: Optional[List[str]] = None
 
     def __post_init__(self):
-        self.children = self.children or []
-        self.output_stream = Queue()
-
-    @property
-    def hierarchy_level(self) -> int:
-        level = 0
-        current = self
-        while current.parent:
-            level += 1
-            current = current.parent
-        return level
+        self.tags = self.tags or []
+        self.secondary_objectives = self.secondary_objectives or []
+        self.created_at = self.created_at or datetime.now()
 
 
 class SwarmVisualizationRich:
     def __init__(
         self,
         swarm_metadata: SwarmMetadata,
-        root_agent: Agent,
+        agents: List[Agent],
         update_resources: bool = True,
         refresh_rate: float = 0.1,
     ):
+        """
+        Initializes the visualizer with a list of agents.
+
+        Args:
+            swarm_metadata (SwarmMetadata): Metadata for the swarm.
+            agents (List[Agent]): List of root agents.
+            update_resources (bool): Whether to update system resource stats.
+            refresh_rate (float): Refresh rate for the live visualization.
+        """
         self.swarm_metadata = swarm_metadata
-        self.root_agent = root_agent
+        self.agents = agents
         self.update_resources = update_resources
         self.refresh_rate = refresh_rate
         self.console = Console()
         self.live = None
-        self.output_history = {}
+        # A dictionary mapping agent names to list of output messages
+        self.output_history: Dict[str, List[Dict[str, Any]]] = {}
 
         # System monitoring
         self.cores_available = 0
@@ -100,96 +89,112 @@ class SwarmVisualizationRich:
         minutes, seconds = divmod(remainder, 60)
         return f"{hours:02d}:{minutes:02d}:{seconds:02d}"
 
-    def _build_agent_tree(
-        self, agent: Agent, tree: Optional[Tree] = None
-    ) -> Tree:
-        """Builds a detailed tree visualization of the agent hierarchy."""
+    def _build_agent_tree(self, agents: List[Agent]) -> Tree:
+        """
+        Builds a detailed tree visualization for a list of agents.
+
+        Args:
+            agents (List[Agent]): The list of root agents.
+
+        Returns:
+            Tree: A rich Tree object displaying agent metadata.
+        """
+        tree = Tree("[bold underline]Agents[/bold underline]")
+        for agent in agents:
+            self._add_agent_to_tree(agent, tree)
+        return tree
+
+    def _add_agent_to_tree(self, agent: Agent, tree: Tree) -> None:
+        """
+        Recursively adds an agent and its children to the given tree.
+
+        Args:
+            agent (Agent): The agent to add.
+            tree (Tree): The tree to update.
+        """
         agent_info = [
             f"[bold cyan]{agent.name}[/bold cyan]",
             f"[yellow]Role:[/yellow] {agent.role}",
-            f"[green]Type:[/green] {agent.agent_type}",
-            f"[blue]Level:[/blue] {agent.hierarchy_level}",
-            f"[magenta]Capabilities:[/magenta] {', '.join(agent.capabilities)}",
         ]
 
-        # Add any custom metadata
-        for key, value in agent.metadata.items():
-            agent_info.append(f"[white]{key}:[/white] {value}")
+        # # Add any custom metadata from the agent (if available)
+        # for key, value in getattr(agent, "metadata", {}).items():
+        #     agent_info.append(f"[white]{key}:[/white] {value}")
 
-        # Parameters summary
-        param_summary = ", ".join(
-            f"{k}: {v}" for k, v in agent.parameters.items()
-        )
-        agent_info.append(
-            f"[white]Parameters:[/white] {param_summary}"
-        )
+        # # Parameters summary if available
+        # parameters = getattr(agent, "parameters", {})
+        # if parameters:
+        #     param_summary = ", ".join(f"{k}: {v}" for k, v in parameters.items())
+        #     agent_info.append(f"[white]Parameters:[/white] {param_summary}")
 
         node_text = "\n".join(agent_info)
+        branch = tree.add(node_text)
+        for child in getattr(agent, "children", []):
+            self._add_agent_to_tree(child, branch)
 
-        if tree is None:
-            tree = Tree(node_text)
-        else:
-            branch = tree.add(node_text)
-            tree = branch
-
-        for child in agent.children:
-            self._build_agent_tree(child, tree)
+    def _count_agents(self, agents: List[Agent]) -> int:
+        """
+        Recursively counts total number of agents from a list of root agents.
 
-        return tree
+        Args:
+            agents (List[Agent]): List of agents.
 
-    def _count_agents(self, agent: Agent) -> int:
-        """Recursively counts total number of agents in the swarm."""
-        count = 1  # Count current agent
-        for child in agent.children or []:
-            count += self._count_agents(child)
-        return count
+        Returns:
+            int: Total count of agents including children.
+        """
+        return len(agents)
 
     def _create_unified_info_panel(self) -> Panel:
-        """Creates a unified panel showing both swarm metadata and architecture."""
-        # Create the main container
+        """
+        Creates a unified panel showing swarm metadata and agents' metadata.
+        """
         info_layout = Layout()
         info_layout.split_column(
-            Layout(name="metadata", size=13),
+            Layout(name="metadata", size=15),
             Layout(name="architecture"),
         )
 
-        # Calculate total agents
-        total_agents = self._count_agents(self.root_agent)
+        total_agents = self._count_agents(self.agents)
 
         # Metadata section
         metadata_table = Table.grid(padding=1, expand=True)
         metadata_table.add_column("Label", style="bold cyan")
         metadata_table.add_column("Value", style="white")
 
-        # System resources
+        # Update system resources if needed
         if self.update_resources:
             self._update_resource_stats()
 
-        # Add description with proper wrapping
+        # Wrap the description text properly
         description_text = Text(
-            self.swarm_metadata.description, style="italic"
+            self.swarm_metadata.description or "", style="italic"
         )
         description_text.wrap(self.console, width=60, overflow="fold")
 
-        metadata_table.add_row("Swarm Name", self.swarm_metadata.name)
+        metadata_table.add_row(
+            "Swarm Name", self.swarm_metadata.name or "N/A"
+        )
         metadata_table.add_row("Description", description_text)
-        metadata_table.add_row("Version", self.swarm_metadata.version)
+        metadata_table.add_row(
+            "Version", self.swarm_metadata.version or "N/A"
+        )
         metadata_table.add_row("Total Agents", str(total_agents))
-        metadata_table.add_row("Author", self.swarm_metadata.author)
+        metadata_table.add_row(
+            "Author", self.swarm_metadata.author or "N/A"
+        )
         metadata_table.add_row(
             "System",
             f"CPU: {self.cores_available} cores | Memory: {self.memory_usage}",
         )
         metadata_table.add_row(
-            "Primary Objective", self.swarm_metadata.primary_objective
+            "Primary Objective",
+            self.swarm_metadata.primary_objective or "N/A",
         )
 
         info_layout["metadata"].update(metadata_table)
 
-        info_layout["metadata"].update(metadata_table)
-
-        # Architecture section with tree visualization
-        architecture_tree = self._build_agent_tree(self.root_agent)
+        # Architecture section with the agent tree
+        architecture_tree = self._build_agent_tree(self.agents)
         info_layout["architecture"].update(architecture_tree)
 
         return Panel(
@@ -198,12 +203,13 @@ class SwarmVisualizationRich:
         )
 
     def _create_outputs_panel(self) -> Panel:
-        """Creates a panel that displays stacked message history for all agents."""
-        # Create a container for all messages across all agents
+        """
+        Creates a panel that displays stacked message history for all agents.
+        """
         all_messages = []
 
         def collect_agent_messages(agent: Agent):
-            """Recursively collect messages from all agents."""
+            """Recursively collect messages from an agent and its children."""
             messages = self.output_history.get(agent.name, [])
             for msg in messages:
                 all_messages.append(
@@ -214,37 +220,31 @@ class SwarmVisualizationRich:
                         "style": msg["style"],
                     }
                 )
-            for child in agent.children:
+            for child in getattr(agent, "children", []):
                 collect_agent_messages(child)
 
-        # Collect all messages
-        collect_agent_messages(self.root_agent)
+        # Collect messages from every root agent
+        for agent in self.agents:
+            collect_agent_messages(agent)
 
         # Sort messages by timestamp
         all_messages.sort(key=lambda x: x["time"])
 
-        # Create the stacked message display
-        Layout()
         messages_container = []
-
         for msg in all_messages:
-            # Create a panel for each message
             message_text = Text()
             message_text.append(f"[{msg['time']}] ", style="dim")
             message_text.append(
                 f"{msg['agent']}: ", style="bold cyan"
             )
             message_text.append(msg["content"], style=msg["style"])
-
             messages_container.append(message_text)
 
-        # Join all messages with line breaks
         if messages_container:
             final_text = Text("\n").join(messages_container)
         else:
             final_text = Text("No messages yet...", style="dim")
 
-        # Create scrollable panel for all messages
         return Panel(
             final_text,
             title="[bold]Agent Communication Log[/bold]",
@@ -286,36 +286,30 @@ class SwarmVisualizationRich:
         by_word: bool = False,
     ):
         """
-        Streams output for a specific agent with sophisticated token-by-token animation.
+        Streams output for a specific agent with token-by-token animation.
 
         Args:
-            agent (Agent): The agent whose output is being streamed
-            text (str): The text to stream
-            title (Optional[str]): Custom title for the output panel
-            style (str): Style for the output text
-            delay (float): Delay between tokens
-            by_word (bool): If True, streams word by word instead of character by character
+            agent (Agent): The agent whose output is being streamed.
+            text (str): The text to stream.
+            title (Optional[str]): Custom title for the output panel.
+            style (str): Style for the output text.
+            delay (float): Delay between tokens.
+            by_word (bool): If True, stream word by word instead of character by character.
         """
         display_text = Text(style=style)
         current_output = ""
 
-        # Split into words or characters
         tokens = text.split() if by_word else text
-
-        # Create a panel for this agent's output
         title = title or f"{agent.name} Output"
 
         for token in tokens:
-            # Add appropriate spacing
             token_with_space = token + (" " if by_word else "")
             current_output += token_with_space
             display_text.append(token_with_space)
 
-            # Initialize history list if it doesn't exist
             if agent.name not in self.output_history:
                 self.output_history[agent.name] = []
 
-            # Store the complete message when finished
             if token == tokens[-1]:
                 timestamp = datetime.now().strftime("%H:%M:%S")
                 self.output_history[agent.name].append(
@@ -326,11 +320,19 @@ class SwarmVisualizationRich:
                     }
                 )
 
-            # Update live display if active
             if self.live:
                 self.live.update(self._create_layout())
             await asyncio.sleep(delay)
 
+    def log_agent_output(self, agent: Agent, text: str):
+        asyncio.create_task(
+            self.stream_output(
+                agent=agent,
+                text=text,
+                title=f"{agent.name} Output {agent.max_loops}",
+            )
+        )
+
     async def print_progress(
         self,
         description: str,
@@ -342,13 +344,13 @@ class SwarmVisualizationRich:
         Displays a progress spinner while executing a task.
 
         Args:
-            description (str): Task description
-            task_fn (Callable): Function to execute
-            *args (Any): Arguments for task_fn
-            **kwargs (Any): Keyword arguments for task_fn
+            description (str): Task description.
+            task_fn (Callable): Function to execute.
+            *args (Any): Arguments for task_fn.
+            **kwargs (Any): Keyword arguments for task_fn.
 
         Returns:
-            Any: Result of task_fn
+            Any: The result of task_fn.
         """
         progress = Progress(
             SpinnerColumn(),
@@ -393,10 +395,12 @@ class SwarmVisualizationRich:
                         asyncio.create_task(
                             self.stream_output(agent, new_output)
                         )
-                    for child in agent.children:
+                    for child in getattr(agent, "children", []):
                         process_agent_streams(child)
 
-                process_agent_streams(self.root_agent)
+                # Process streams for each root agent
+                for agent in self.agents:
+                    process_agent_streams(agent)
                 await asyncio.sleep(self.refresh_rate)
 
 

visualizer_test.py

@@ -0,0 +1,67 @@
+import asyncio
+from swarms import Agent
+from swarms.prompts.finance_agent_sys_prompt import (
+    FINANCIAL_AGENT_SYS_PROMPT,
+)
+from swarms.utils.visualizer import (
+    SwarmVisualizationRich,
+    SwarmMetadata,
+)  # Replace with your actual module name
+
+# Create two example agents
+agent1 = Agent(
+    agent_name="Financial-Analysis-Agent",
+    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+    model_name="gpt-4o-mini",
+    max_loops=1,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="finance_agent.json",
+    user_name="swarms_corp",
+    retry_attempts=1,
+    context_length=200000,
+    return_step_meta=False,
+    output_type="string",
+    streaming_on=False,
+)
+
+# Create a second dummy agent for demonstration
+agent2 = Agent(
+    agent_name="Stock-Advisor-Agent",
+    system_prompt="Provide stock market insights and investment advice.",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="stock_agent.json",
+    user_name="swarms_corp",
+    retry_attempts=1,
+    context_length=200000,
+    return_step_meta=False,
+    output_type="string",
+    streaming_on=False,
+)
+
+# Create swarm metadata
+metadata = SwarmMetadata(
+    name="Financial Swarm",
+    description="A swarm of agents focused on financial analysis and stock market advice.",
+    version="1.0",
+    author="Your Name",
+    primary_objective="Provide comprehensive financial and investment analysis.",
+)
+
+# Instantiate the visualizer with a list of agents
+visualizer = SwarmVisualizationRich(
+    swarm_metadata=metadata,
+    agents=[agent1, agent2],
+    update_resources=True,
+    refresh_rate=0.1,
+)
+
+# Start the visualization
+asyncio.run(visualizer.start())