swarms
5.3.7
企業級生產的多代理編排框架
？ Twitter • ？不和諧•群平台• ？文件
| 類別 | 特徵 | 好處 |
|---|---|---|
| ？企業體系結構 | •準備生產的基礎設施 •高可靠性系統 •模塊化設計 •全面的記錄 | •減少停機時間 •更容易維護 •更好的調試 •增強的監視 |
| ？代理編排 | •分層群 •並行處理 •順序工作流程 •基於圖的工作流程 •動態代理重排 | •複雜的任務處理 •提高性能 •靈活的工作流程 •優化執行 |
| 集成功能 | •多模型支持 •定制代理創建 •廣泛的工具庫 •多個內存系統 | •提供商的靈活性 •自定義解決方案 •擴展功能 •增強的內存管理 |
| ？可伸縮性 | •並發處理 •資源管理 •負載平衡 •水平縮放 | •更高的吞吐量 •有效的資源使用 •表現更好 •易於縮放 |
| 開發人員工具 | •簡單的API •廣泛的文檔 •活躍的社區 •CLI工具 | •更快的發展 •易於學習曲線 •社區支持 •快速部署 |
| ？安全功能 | •錯誤處理 •費率限制 •監視集成 •審核記錄 | •提高可靠性 •API保護 •更好的監視 •增強的跟踪 |
| 高級功能 | •電子表格工程 •小組聊天 •代理註冊表 •代理的混合物 | •群眾代理管理 •協作AI •集中控制 •複雜的解決方案 |
| ？提供者支持 | •Openai •人類 •Chromadb •自定義提供商 | •提供商的靈活性 •存儲選項 •自定義集成 •供應商獨立性 |
| ？生產功能 | •自動檢索 •異步支持 •環境管理 •鍵入安全性 | •更好的可靠性 •提高性能 •簡單配置 •更安全的代碼 |
| 用例支持 | •特定於任務的代理 •自定義工作流程 •行業解決方案 •可擴展的框架 | •快速部署 •靈活的解決方案 •行業準備就緒 •簡單自定義 |
python3.10或更高！$ pip install -U swarms ，別忘了安裝群！.env.env變量： WORKSPACE_DIR="agent_workspace"或使用export WORKSPACE_DIR="agent_workspace"在終端中進行操作swarms onboarding讓您入門。 有關生產等級實施詳細信息，請參閱我們的文檔。
| 部分 | 鏈接 |
|---|---|
| 安裝 | 安裝 |
| Quickstart | 開始 |
| 代理內部機制 | 代理體系結構 |
| 代理API | 代理API |
| 整合外部代理，grippape，autogen等 | 集成外部API |
| 用yaml創建代理商 | 用yaml創建代理商 |
| 為什麼需要群 | 為什麼需要多種合作 |
| 群體系結構分析 | 群結構 |
| 為您的業務問題選擇合適的群 | 點擊這裡 |
| AgentRearrange文檔 | 點擊這裡 |
$ pip3 install -U swarms現在，您已經下載了使用pip3 install -U swarms群，我們可以訪問CLI 。現在就加入CLI：
swarms onboarding您還可以運行此命令尋求幫助：
swarms help有關CLI的更多文檔，請單擊此處
以下是一些示例腳本可以讓您入門。有關更全面的文檔，請訪問我們的文檔。
| 示例名稱 | 描述 | 示例類型 | 關聯 |
|---|---|---|---|
| 群例 | 簡單示例的集合來證明群體能力。 | 基本用法 | https://github.com/the-swarm-corporation/swarms-examples？tab = ReadMe-ov-file |
| 食譜 | 綜合指南，其中包含各種用例和場景的食譜。 | 高級用法 | https://github.com/the-swarm-corporation/cookbook |
Agent類Agent類是Swarms框架的基本組成部分，旨在自主執行任務。它融合了LLM，工具和長期內存功能，以創建完整的堆棧代理。 Agent類是高度可定制的，可以對其行為和相互作用進行細粒度的控制。
run方法run方法是使用Agent實例執行任務的主要入口點。它接受任務字符串作為主要輸入任務，並根據代理的配置對其進行處理。並且，它也可以接受img參數，例如img="image_filepath.png如果您有VLM，請處理圖像
Agent類提供了一系列設置，以根據特定需求量身定制其行為。一些關鍵設置包括：
| 環境 | 描述 | 預設值 |
|---|---|---|
agent_name | 代理的名稱。 | “違約” |
system_prompt | 系統提示用於代理。 | “默認系統提示。” |
llm | 用於處理任務的語言模型。 | OpenAIChat實例 |
max_loops | 任務執行的最大循環數。 | 1 |
autosave | 啟用或禁用代理狀態的自動保存。 | 錯誤的 |
dashboard | 啟用或禁用代理的儀表板。 | 錯誤的 |
verbose | 控制代理輸出的詳細性。 | 錯誤的 |
dynamic_temperature_enabled | 啟用或禁用語言模型的動態溫度調整。 | 錯誤的 |
saved_state_path | 保存代理狀態的途徑。 | “ agent_state.json” |
user_name | 與代理相關的用戶名。 | “ default_user” |
retry_attempts | 試圖失敗任務的重試嘗試。 | 1 |
context_length | 要考慮任務的上下文的最大長度。 | 200000 |
return_step_meta | 控制是否要在輸出中返回步驟元數據。 | 錯誤的 |
output_type | 要返回的輸出類型（例如“ JSON”，“ String”）。 | “細繩” |
import os
from swarms import Agent
from swarm_models import OpenAIChat
from swarms . prompts . finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT ,
)
from dotenv import load_dotenv
load_dotenv ()
# Get the OpenAI API key from the environment variable
api_key = os . getenv ( "OPENAI_API_KEY" )
# Create an instance of the OpenAIChat class
model = OpenAIChat (
openai_api_key = api_key , model_name = "gpt-4o-mini" , temperature = 0.1
)
# Initialize the agent
agent = Agent (
agent_name = "Financial-Analysis-Agent" ,
system_prompt = FINANCIAL_AGENT_SYS_PROMPT ,
llm = model ,
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 ,
)
agent . run (
"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
)使用抹布（關係代理圖）配備了準儀式長期內存的Agent ，以理解，分析和檢索功能。
抹布整合的美人魚圖
圖TD
a [用抹布初始化代理] - > b [接收任務]
b-> c [查詢長期記憶]
c-> d [使用上下文的過程任務]
d-> e [生成響應]
e-> f [更新長期內存]
f-> g [返回輸出]
步驟1：初始化Chromadb客戶端
import os
from swarms_memory import ChromaDB
# Initialize the ChromaDB client for long-term memory management
chromadb = ChromaDB (
metric = "cosine" , # Metric for similarity measurement
output_dir = "finance_agent_rag" , # Directory for storing RAG data
# docs_folder="artifacts", # Uncomment and specify the folder containing your documents
)步驟2：定義模型
from swarm_models import Anthropic
from swarms . prompts . finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT ,
)
# Define the Anthropic model for language processing
model = Anthropic ( anthropic_api_key = os . getenv ( "ANTHROPIC_API_KEY" ))步驟3：用抹布初始化代理
from swarms import Agent
# Initialize the agent with RAG capabilities
agent = Agent (
agent_name = "Financial-Analysis-Agent" ,
system_prompt = FINANCIAL_AGENT_SYS_PROMPT ,
agent_description = "Agent creates a comprehensive financial analysis" ,
llm = model ,
max_loops = "auto" , # Auto-adjusts loops based on task complexity
autosave = True , # Automatically saves agent state
dashboard = False , # Disables dashboard for this example
verbose = True , # Enables verbose mode for detailed output
streaming_on = True , # Enables streaming for real-time processing
dynamic_temperature_enabled = True , # Dynamically adjusts temperature for optimal performance
saved_state_path = "finance_agent.json" , # Path to save agent state
user_name = "swarms_corp" , # User name for the agent
retry_attempts = 3 , # Number of retry attempts for failed tasks
context_length = 200000 , # Maximum length of the context to consider
long_term_memory = chromadb , # Integrates ChromaDB for long-term memory management
return_step_meta = False ,
output_type = "string" ,
)
# Run the agent with a sample task
agent . run (
"What are the components of a startups stock incentive equity plan"
)我們提供各種功能，以使用JSON，YAML，TOML，上傳PDF，批處理作業等節省代理狀態！
方法表
| 方法 | 描述 |
|---|---|
to_dict() | 將代理對象轉換為字典。 |
to_toml() | 將代理對象轉換為toml字符串。 |
model_dump_json() | 將模型轉到JSON文件。 |
model_dump_yaml() | 將模型轉儲到YAML文件。 |
ingest_docs() | 將文件攝取到代理商的知識庫中。 |
receive_message() | 從用戶接收消息並處理。 |
send_agent_message() | 將代理商發送給用戶的消息。 |
filtered_run() | 使用過濾的系統提示運行代理。 |
bulk_run() | 使用多個系統提示運行代理。 |
add_memory() | 向代理添加內存。 |
check_available_tokens() | 檢查代理的可用令牌數量。 |
tokens_checks() | 執行代理的令牌檢查。 |
print_dashboard() | 打印代理的儀表板。 |
get_docs_from_doc_folders() | 從DOC文件夾中獲取所有文檔。 |
activate_agentops() | 激活代理操作。 |
check_end_session_agentops() | 檢查會話結束以進行代理操作。 |
# # Convert the agent object to a dictionary
print ( agent . to_dict ())
print ( agent . to_toml ())
print ( agent . model_dump_json ())
print ( agent . model_dump_yaml ())
# Ingest documents into the agent's knowledge base
agent . ingest_docs ( "your_pdf_path.pdf" )
# Receive a message from a user and process it
agent . receive_message ( name = "agent_name" , message = "message" )
# Send a message from the agent to a user
agent . send_agent_message ( agent_name = "agent_name" , message = "message" )
# Ingest multiple documents into the agent's knowledge base
agent . ingest_docs ( "your_pdf_path.pdf" , "your_csv_path.csv" )
# Run the agent with a filtered system prompt
agent . filtered_run (
"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?"
)
# Run the agent with multiple system prompts
agent . bulk_run (
[
"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?" ,
"Another system prompt" ,
]
)
# Add a memory to the agent
agent . add_memory ( "Add a memory to the agent" )
# Check the number of available tokens for the agent
agent . check_available_tokens ()
# Perform token checks for the agent
agent . tokens_checks ()
# Print the dashboard of the agent
agent . print_dashboard ()
# Fetch all the documents from the doc folders
agent . get_docs_from_doc_folders ()
# Activate agent ops
agent . activate_agentops ()
agent . check_end_session_agentops ()
# Dump the model to a JSON file
agent . model_dump_json ()
print ( agent . to_toml ())Agent以下是攝入Pydantic basemodel並同時輸出的代理的示例：
from pydantic import BaseModel , Field
from swarms import Agent
from swarm_models import Anthropic
# Initialize the schema for the person's information
class Schema ( BaseModel ):
name : str = Field (..., title = "Name of the person" )
agent : int = Field (..., title = "Age of the person" )
is_student : bool = Field (..., title = "Whether the person is a student" )
courses : list [ str ] = Field (
..., title = "List of courses the person is taking"
)
# Convert the schema to a JSON string
tool_schema = Schema (
name = "Tool Name" ,
agent = 1 ,
is_student = True ,
courses = [ "Course1" , "Course2" ],
)
# Define the task to generate a person's information
task = "Generate a person's information based on the following schema:"
# Initialize the agent
agent = Agent (
agent_name = "Person Information Generator" ,
system_prompt = (
"Generate a person's information based on the following schema:"
),
# Set the tool schema to the JSON string -- this is the key difference
tool_schema = tool_schema ,
llm = Anthropic (),
max_loops = 3 ,
autosave = True ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
interactive = True ,
# Set the output type to the tool schema which is a BaseModel
output_type = tool_schema , # or dict, or str
metadata_output_type = "json" ,
# List of schemas that the agent can handle
list_base_models = [ tool_schema ],
function_calling_format_type = "OpenAI" ,
function_calling_type = "json" , # or soon yaml
)
# Run the agent to generate the person's information
generated_data = agent . run ( task )
# Print the generated data
print ( f"Generated data: { generated_data } " )
運行具有多種模式的代理商，可用於製造，物流和健康方面的各種現實世界任務。
import os
from dotenv import load_dotenv
from swarms import Agent
from swarm_models import GPT4VisionAPI
# Load the environment variables
load_dotenv ()
# Initialize the language model
llm = GPT4VisionAPI (
openai_api_key = os . environ . get ( "OPENAI_API_KEY" ),
max_tokens = 500 ,
)
# Initialize the task
task = (
"Analyze this image of an assembly line and identify any issues such as"
" misaligned parts, defects, or deviations from the standard assembly"
" process. IF there is anything unsafe in the image, explain why it is"
" unsafe and how it could be improved."
)
img = "assembly_line.jpg"
## Initialize the workflow
agent = Agent (
agent_name = "Multi-ModalAgent" ,
llm = llm ,
max_loops = "auto" ,
autosave = True ,
dashboard = True ,
multi_modal = True
)
# Run the workflow on a task
agent . run ( task , img )ToolAgentToolagent是可以通過JSON函數調用使用工具的代理。它從HuggingFace中攝入任何開源模型，並且非常模塊化，並插入並播放。我們需要幫助盡快為所有模型增加一般支持。
from pydantic import BaseModel , Field
from transformers import AutoModelForCausalLM , AutoTokenizer
from swarms import ToolAgent
from swarms . utils . json_utils import base_model_to_json
# Load the pre-trained model and tokenizer
model = AutoModelForCausalLM . from_pretrained (
"databricks/dolly-v2-12b" ,
load_in_4bit = True ,
device_map = "auto" ,
)
tokenizer = AutoTokenizer . from_pretrained ( "databricks/dolly-v2-12b" )
# Initialize the schema for the person's information
class Schema ( BaseModel ):
name : str = Field (..., title = "Name of the person" )
agent : int = Field (..., title = "Age of the person" )
is_student : bool = Field (
..., title = "Whether the person is a student"
)
courses : list [ str ] = Field (
..., title = "List of courses the person is taking"
)
# Convert the schema to a JSON string
tool_schema = base_model_to_json ( Schema )
# Define the task to generate a person's information
task = (
"Generate a person's information based on the following schema:"
)
# Create an instance of the ToolAgent class
agent = ToolAgent (
name = "dolly-function-agent" ,
description = "Ana gent to create a child data" ,
model = model ,
tokenizer = tokenizer ,
json_schema = tool_schema ,
)
# Run the agent to generate the person's information
generated_data = agent . run ( task )
# Print the generated data
print ( f"Generated data: { generated_data } " )與其他代理框架集成外部代理很容易與群相連。
步驟：
Agent的新類.run(task: str) -> str方法，該方法運行代理並返迴響應。例如，這是如何從griptape創建代理的示例。
您可以通過從群中的Agent類繼承並覆蓋run(task: str) -> str方法來創建與Swarms框架集成的自定義griptape代理。
from swarms import (
Agent as SwarmsAgent ,
) # Import the base Agent class from Swarms
from griptape . structures import Agent as GriptapeAgent
from griptape . tools import (
WebScraperTool ,
FileManagerTool ,
PromptSummaryTool ,
)
# Create a custom agent class that inherits from SwarmsAgent
class GriptapeSwarmsAgent ( SwarmsAgent ):
def __init__ ( self , * args , ** kwargs ):
# Initialize the Griptape agent with its tools
self . agent = GriptapeAgent (
input = "Load {{ args[0] }}, summarize it, and store it in a file called {{ args[1] }}." ,
tools = [
WebScraperTool ( off_prompt = True ),
PromptSummaryTool ( off_prompt = True ),
FileManagerTool (),
],
* args ,
** kwargs ,
# Add additional settings
)
# Override the run method to take a task and execute it using the Griptape agent
def run ( self , task : str ) -> str :
# Extract URL and filename from task (you can modify this parsing based on task structure)
url , filename = task . split (
","
) # Example of splitting task string
# Execute the Griptape agent with the task inputs
result = self . agent . run ( url . strip (), filename . strip ())
# Return the final result as a string
return str ( result )
# Example usage:
griptape_swarms_agent = GriptapeSwarmsAgent ()
output = griptape_swarms_agent . run (
"https://griptape.ai, griptape.txt"
)
print ( output )SwarmsAgent類別繼承並集成了griptape代理的自定義類。WebScraperTool ， PromptSummaryTool ， FileManagerTool ）允許網絡刮擦，摘要和文件管理。現在，您可以輕鬆地將此自定義的Griptape代理插入Swarms框架中，並使用它來運行任務！
一群人是指一個以上的代理人共同努力以實現共同目標。這些代理可以是軟件實體，例如相互交互以執行複雜任務的LLM。群的概念靈感來自天然系統，例如螞蟻菌落或鳥類羊群，簡單的個人行為導致了複雜的群體動態和解決問題的能力。
群架構旨在建立和管理群體內代理之間的通信。這些體系結構定義了代理如何交互，共享信息和協調其行動以實現所需結果。以下是群體體系結構的一些關鍵方面：
層次交流：在等級群中，交流從高級代理到低級代理。高級代理人充當協調員，分發任務和匯總結果。這種結構對於需要自上而下的控制和決策的任務有效。
並行通信：在平行群中，代理人獨立運行並根據需要相互通信。該體系結構適用於可以同時處理而無需依賴的任務，從而可以更快地執行和可擴展性。
順序通信：順序群體以線性順序處理任務，其中每個代理的輸出成為下一個代理的輸入。這樣可以確保以正確的順序處理具有依賴項的任務，從而保持工作流的完整性。
網格通信：在網格群中，代理人已完全連接，允許任何代理商與任何其他代理進行通信。該設置可提供高靈活性和冗餘，使其非常適合需要動態交互的複雜系統。
聯邦溝通：聯合群體涉及多個獨立群，通過共享信息和結果來協作。每個群都自主運行，但可以促進更大的任務，從而使分佈式問題在不同的節點上解決。
Swarm體系結構利用這些通信模式來確保代理有效地一起工作，並適應當前任務的特定要求。通過定義清晰的通信協議和交互模型，Swarm Architectures可以使多個代理的無縫編排，從而增強了性能和解決問題的能力。
| 姓名 | 描述 | 代碼鏈接 | 用例 |
|---|---|---|---|
| 分層群 | 在層次結構中組織代理的系統，高級代理協調低級代理以實現複雜的任務。 | 代碼鏈接 | 製造過程優化，多層次銷售管理，醫療保健資源協調 |
| 代理重新排列 | 一個設置，代理根據任務要求和環境條件動態重新安排自己。 | 代碼鏈接 | 自適應製造線，動態銷售領土重新調整，靈活的醫療保健人員配備 |
| 並發工作流程 | 代理人同時執行不同的任務，協調以完成更大的目標。 | 代碼鏈接 | 並發生產線，平行銷售操作，同時進行患者護理過程 |
| 順序協調 | 代理以特定順序執行任務，其中一個任務的完成會觸發下一個任務的開始。 | 代碼鏈接 | 分步組件，順序銷售過程，逐步患者治療工作流程 |
| 並行處理 | 代理商同時在任務的不同部分工作，以加快整個過程。 | 代碼鏈接 | 製造，同時銷售分析，並發醫學測試中的並行數據處理 |
| 劑的混合物 | 一個異質的群，將具有不同功能的藥物結合在一起以解決複雜問題。 | 代碼鏈接 | 財務預測，複雜的解決問題需要多樣化的技能 |
| 圖形工作流程 | 代理商以定向的無環圖（DAG）格式進行協作，以管理依賴關係和並行任務。 | 代碼鏈接 | AI驅動的軟件開發管道，複雜的項目管理 |
| 小組聊天 | 代理商進行類似聊天的互動以協作做出決策。 | 代碼鏈接 | 實時協作決策，合同談判 |
| 代理註冊表 | 一個集中式註冊表，該註冊表被動態存儲，檢索和調用。 | 代碼鏈接 | 動態代理管理，不斷發展的建議引擎 |
| 電子表格群 | 管理任務，以結構化格式跟踪代理這樣的輸出，例如CSV文件。 | 代碼鏈接 | 大規模營銷分析，財務審核 |
| 森林群 | 一種群結構，該結構在樹狀的層次結構中組織代理，以進行複雜的決策過程。 | 代碼鏈接 | 多階段工作流程，分層增強學習 |
| 群路由器 | 根據任務要求和可用代理來路由和選擇群體系結構。 | 代碼鏈接 | 動態任務路由，自適應群體系結構，優化的代理分配 |
SequentialWorkflow順序工作流使您能夠使用Agent執行任務，然後將輸出傳遞到下一個代理，然後繼續執行，直到您指定了最大循環為止。
圖LR
A [代理1] - > B [代理2]
B-> C [代理3]
C-> D [代理4]
D-> E [Max Loop]
e-> f [end]
| 方法 | 描述 | 參數 | 返回值 |
|---|---|---|---|
__init__ | 初始化sequentionWorkFlow | agents ：代理對象列表max_loops ：最大迭代次數verbose ：布爾值 | 沒有任何 |
run | 執行工作流程 | input_data ：第一個代理的初始輸入 | 所有代理商處理後的最終輸出 |
| 輸入 | 類型 | 描述 |
|---|---|---|
agents | 列表[代理] | 要順序執行的代理對象列表 |
max_loops | int | 將重複整個序列的最多次數 |
verbose | 布爾 | 如果是真的，請在執行過程中打印詳細信息 |
在所有代理都依次處理輸入後， run方法返回最終輸出。
在此示例中，每個Agent代表一個依次執行的任務。每個代理的輸出將傳遞給序列的下一個代理，直到達到最大循環數為止。此工作流程對於需要以特定順序執行一系列步驟的任務特別有用，例如數據處理管道或依賴上述步驟輸出的複雜計算。
import os
from swarms import Agent , SequentialWorkflow
from swarm_models import OpenAIChat
# model = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
company = "Nvidia"
# Get the OpenAI API key from the environment variable
api_key = os . getenv ( "GROQ_API_KEY" )
# Model
model = OpenAIChat (
openai_api_base = "https://api.groq.com/openai/v1" ,
openai_api_key = api_key ,
model_name = "llama-3.1-70b-versatile" ,
temperature = 0.1 ,
)
# Initialize the Managing Director agent
managing_director = Agent (
agent_name = "Managing-Director" ,
system_prompt = f"""
As the Managing Director at Blackstone, your role is to oversee the entire investment analysis process for potential acquisitions.
Your responsibilities include:
1. Setting the overall strategy and direction for the analysis
2. Coordinating the efforts of the various team members and ensuring a comprehensive evaluation
3. Reviewing the findings and recommendations from each team member
4. Making the final decision on whether to proceed with the acquisition
For the current potential acquisition of { company } , direct the tasks for the team to thoroughly analyze all aspects of the company, including its financials, industry position, technology, market potential, and regulatory compliance. Provide guidance and feedback as needed to ensure a rigorous and unbiased assessment.
""" ,
llm = model ,
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "managing-director.json" ,
)
# Initialize the Vice President of Finance
vp_finance = Agent (
agent_name = "VP-Finance" ,
system_prompt = f"""
As the Vice President of Finance at Blackstone, your role is to lead the financial analysis of potential acquisitions.
For the current potential acquisition of { company } , your tasks include:
1. Conducting a thorough review of { company } ' financial statements, including income statements, balance sheets, and cash flow statements
2. Analyzing key financial metrics such as revenue growth, profitability margins, liquidity ratios, and debt levels
3. Assessing the company's historical financial performance and projecting future performance based on assumptions and market conditions
4. Identifying any financial risks or red flags that could impact the acquisition decision
5. Providing a detailed report on your findings and recommendations to the Managing Director
Be sure to consider factors such as the sustainability of { company } ' business model, the strength of its customer base, and its ability to generate consistent cash flows. Your analysis should be data-driven, objective, and aligned with Blackstone's investment criteria.
""" ,
llm = model ,
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "vp-finance.json" ,
)
# Initialize the Industry Analyst
industry_analyst = Agent (
agent_name = "Industry-Analyst" ,
system_prompt = f"""
As the Industry Analyst at Blackstone, your role is to provide in-depth research and analysis on the industries and markets relevant to potential acquisitions.
For the current potential acquisition of { company } , your tasks include:
1. Conducting a comprehensive analysis of the industrial robotics and automation solutions industry, including market size, growth rates, key trends, and future prospects
2. Identifying the major players in the industry and assessing their market share, competitive strengths and weaknesses, and strategic positioning
3. Evaluating { company } ' competitive position within the industry, including its market share, differentiation, and competitive advantages
4. Analyzing the key drivers and restraints for the industry, such as technological advancements, labor costs, regulatory changes, and economic conditions
5. Identifying potential risks and opportunities for { company } based on the industry analysis, such as disruptive technologies, emerging markets, or shifts in customer preferences
Your analysis should provide a clear and objective assessment of the attractiveness and future potential of the industrial robotics industry, as well as { company } ' positioning within it. Consider both short-term and long-term factors, and provide evidence-based insights to inform the investment decision.
""" ,
llm = model ,
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "industry-analyst.json" ,
)
# Initialize the Technology Expert
tech_expert = Agent (
agent_name = "Tech-Expert" ,
system_prompt = f"""
As the Technology Expert at Blackstone, your role is to assess the technological capabilities, competitive advantages, and potential risks of companies being considered for acquisition.
For the current potential acquisition of { company } , your tasks include:
1. Conducting a deep dive into { company } ' proprietary technologies, including its robotics platforms, automation software, and AI capabilities
2. Assessing the uniqueness, scalability, and defensibility of { company } ' technology stack and intellectual property
3. Comparing { company } ' technologies to those of its competitors and identifying any key differentiators or technology gaps
4. Evaluating { company } ' research and development capabilities, including its innovation pipeline, engineering talent, and R&D investments
5. Identifying any potential technology risks or disruptive threats that could impact { company } ' long-term competitiveness, such as emerging technologies or expiring patents
Your analysis should provide a comprehensive assessment of { company } ' technological strengths and weaknesses, as well as the sustainability of its competitive advantages. Consider both the current state of its technology and its future potential in light of industry trends and advancements.
""" ,
llm = model ,
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "tech-expert.json" ,
)
# Initialize the Market Researcher
market_researcher = Agent (
agent_name = "Market-Researcher" ,
system_prompt = f"""
As the Market Researcher at Blackstone, your role is to analyze the target company's customer base, market share, and growth potential to assess the commercial viability and attractiveness of the potential acquisition.
For the current potential acquisition of { company } , your tasks include:
1. Analyzing { company } ' current customer base, including customer segmentation, concentration risk, and retention rates
2. Assessing { company } ' market share within its target markets and identifying key factors driving its market position
3. Conducting a detailed market sizing and segmentation analysis for the industrial robotics and automation markets, including identifying high-growth segments and emerging opportunities
4. Evaluating the demand drivers and sales cycles for { company } ' products and services, and identifying any potential risks or limitations to adoption
5. Developing financial projections and estimates for { company } ' revenue growth potential based on the market analysis and assumptions around market share and penetration
Your analysis should provide a data-driven assessment of the market opportunity for { company } and the feasibility of achieving our investment return targets. Consider both bottom-up and top-down market perspectives, and identify any key sensitivities or assumptions in your projections.
""" ,
llm = model ,
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "market-researcher.json" ,
)
# Initialize the Regulatory Specialist
regulatory_specialist = Agent (
agent_name = "Regulatory-Specialist" ,
system_prompt = f"""
As the Regulatory Specialist at Blackstone, your role is to identify and assess any regulatory risks, compliance requirements, and potential legal liabilities associated with potential acquisitions.
For the current potential acquisition of { company } , your tasks include:
1. Identifying all relevant regulatory bodies and laws that govern the operations of { company } , including industry-specific regulations, labor laws, and environmental regulations
2. Reviewing { company } ' current compliance policies, procedures, and track record to identify any potential gaps or areas of non-compliance
3. Assessing the potential impact of any pending or proposed changes to relevant regulations that could affect { company } ' business or create additional compliance burdens
4. Evaluating the potential legal liabilities and risks associated with { company } ' products, services, and operations, including product liability, intellectual property, and customer contracts
5. Providing recommendations on any regulatory or legal due diligence steps that should be taken as part of the acquisition process, as well as any post-acquisition integration considerations
Your analysis should provide a comprehensive assessment of the regulatory and legal landscape surrounding { company } , and identify any material risks or potential deal-breakers. Consider both the current state and future outlook, and provide practical recommendations to mitigate identified risks.
""" ,
llm = model ,
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "regulatory-specialist.json" ,
)
# Create a list of agents
agents = [
managing_director ,
vp_finance ,
industry_analyst ,
tech_expert ,
market_researcher ,
regulatory_specialist ,
]
swarm = SequentialWorkflow (
name = "blackstone-private-equity-advisors" ,
agents = agents ,
)
print (
swarm . run (
"Analyze nvidia if it's a good deal to invest in now 10B"
)
)AgentRearrange由Einops和Einsum啟發的AgentRearrange編排技術使您可以定義和繪製各種代理之間的關係。它提供了一個有力的工具來協調複雜的工作流程，使您能夠指定線性和順序關係，例如a -> a1 -> a2 -> a3或併發關係，其中第一個代理同時將消息發送給3個代理： a -> a1, a2, a3 。這種自定義級別允許創建高效和動態的工作流程，其中代理可以根據需要並行或順序工作。 AgentRearrange技術是群庫中的寶貴補充，為代理的編排提供了新的靈活性和控制水平。有關更多詳細信息和示例，請參閱官方文檔。
| 方法 | 描述 | 參數 | 返回值 |
|---|---|---|---|
__init__ | 初始化AgentRearrange | agents ：代理對象列表flow ：描述代理流的字符串 | 沒有任何 |
run | 執行工作流程 | input_data ：第一個代理的初始輸入 | 所有代理商處理後的最終輸出 |
| 輸入 | 類型 | 描述 |
|---|---|---|
agents | 列表[代理] | 要精心策劃的代理對象列表 |
flow | str | 描述代理流動的字符串（例如，“ A-> B，C”） |
在所有代理商根據指定流量處理輸入後， run方法返回最終輸出。
from swarms import Agent , AgentRearrange
from swarm_models import Anthropic
# Initialize the director agent
director = Agent (
agent_name = "Director" ,
system_prompt = "Directs the tasks for the workers" ,
llm = Anthropic (),
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "director.json" ,
)
# Initialize worker 1
worker1 = Agent (
agent_name = "Worker1" ,
system_prompt = "Generates a transcript for a youtube video on what swarms are" ,
llm = Anthropic (),
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "worker1.json" ,
)
# Initialize worker 2
worker2 = Agent (
agent_name = "Worker2" ,
system_prompt = "Summarizes the transcript generated by Worker1" ,
llm = Anthropic (),
max_loops = 1 ,
dashboard = False ,
streaming_on = True ,
verbose = True ,
stopping_token = "<DONE>" ,
state_save_file_type = "json" ,
saved_state_path = "worker2.json" ,
)
# Create a list of agents
agents = [ director , worker1 , worker2 ]
# Define the flow pattern
flow = "Director -> Worker1 -> Worker2"
# Using AgentRearrange class
agent_system = AgentRearrange ( agents = agents , flow = flow )
output = agent_system . run (
"Create a format to express and communicate swarms of llms in a structured manner for youtube"
)
print ( output )HierarhicalSwarm即將推出...
GraphSwarm GraphSwarm是一個工作流管理系統，旨在通過利用圖理論的力量來協調複雜的任務。它可以創建有向的無環圖（DAG）來建模任務和代理之間的依賴項。這允許有效的任務分配，執行和監視。
這是GraphSwarm工作原理的細分：
GraphSwarm工作流程由節點組成，可以是代理或任務。代理負責執行任務，任務代表需要執行的特定操作。在示例中，創建了兩個代理（ agent1和agent2 ）和一個任務（ task1 ）。agent1和agent2連接到task1的邊緣，表明兩個代理都能執行task1 。GraphSwarm工作流程需要定義入口點（工作流程開始的地方）和終點（工作流程結束）。在此示例中， agent1和agent2設置為入口點，將task1設置為終點。GraphSwarm提供了一個可視化功能，可以圖形地表示工作流程。這可以輕鬆理解和調試工作流程結構。GraphSwarm工作流是通過從入口點到終點的遍歷圖形來執行的。在這種情況下， agent1和agent2都同時執行task1 ，並收集結果。task1的結果是“任務完成”。 GraphSwarm提供了多種好處，包括：
通過利用GraphSwarm ，可以有效地管理複雜的工作流程，並且可以以協調和可擴展的方式執行任務。
| 方法 | 描述 | 參數 | 返回值 |
|---|---|---|---|
add_node | 將節點添加到圖表 | node ：節點對象 | 沒有任何 |
add_edge | 在圖表中添加邊緣 | edge ：邊緣對象 | 沒有任何 |
set_entry_points | 設置圖表的入口點 | entry_points ：節點ID列表 | 沒有任何 |
set_end_points | 設置圖的終點 | end_points ：節點ID列表 | 沒有任何 |
visualize | 生成圖表的視覺表示 | 沒有任何 | 圖形的字符串表示形式 |
run | 執行工作流程 | 沒有任何 | 執行結果詞典 |
| 輸入 | 類型 | 描述 |
|---|---|---|
Node | 目的 | 表示圖中的節點（代理或任務） |
Edge | 目的 | 表示連接兩個節點的邊緣 |
entry_points | 列表[Str] | 工作流啟動的節點ID列表 |
end_points | 列表[Str] | 工作流程結束的節點ID列表 |
該run方法返回包含圖中所有節點的執行結果的字典。
import os
from dotenv import load_dotenv
from swarms import Agent , Edge , GraphWorkflow , Node , NodeType
from swarm_models import OpenAIChat
load_dotenv ()
api_key = os . environ . get ( "OPENAI_API_KEY" )
llm = OpenAIChat (
temperature = 0.5 , openai_api_key = api_key , max_tokens = 4000
)
agent1 = Agent ( llm = llm , max_loops = 1 , autosave = True , dashboard = True )
agent2 = Agent ( llm = llm , max_loops = 1 , autosave = True , dashboard = True )
def sample_task ():
print ( "Running sample task" )
return "Task completed"
wf_graph = GraphWorkflow ()
wf_graph . add_node ( Node ( id = "agent1" , type = NodeType . AGENT , agent = agent1 ))
wf_graph . add_node ( Node ( id = "agent2" , type = NodeType . AGENT , agent = agent2 ))
wf_graph . add_node (
Node ( id = "task1" , type = NodeType . TASK , callable = sample_task )
)
wf_graph . add_edge ( Edge ( source = "agent1" , target = "task1" ))
wf_graph . add_edge ( Edge ( source = "agent2" , target = "task1" ))
wf_graph . set_entry_points ([ "agent1" , "agent2" ])
wf_graph . set_end_points ([ "task1" ])
print ( wf_graph . visualize ())
# Run the workflow
results = wf_graph . run ()
print ( "Execution results:" , results )MixtureOfAgents這是基於論文的實施：“代理的混合物增強了大型語言模型功能”，可通過https://arxiv.org/abs/2406.04692獲得。它在Alpacaeval 2.0，MT板凳和燒瓶上實現了最新的（SOTA）結果，超過了GPT-4 Omni。該體系結構特別適合需要並行化的任務，然後在另一個循環中進行順序處理。
| 方法 | 描述 | 參數 | 返回值 |
|---|---|---|---|
__init__ | 初始化混合物 | name ：群的名稱agents ：代理對象列表layers ：處理層的數量final_agent ：最終處理的代理 | 沒有任何 |
run | 執行群 | task ：群的輸入任務 | 所有代理商處理後的最終輸出 |
| 輸入 | 類型 | 描述 |
|---|---|---|
name | str | 群的名字 |
agents | 列表[代理] | 群中要使用的代理對象列表 |
layers | int | 群中的加工層數量 |
final_agent | 代理人 | 負責最終處理的代理商 |
在所有代理商都根據指定層和最終代理處理輸入後， run方法返回最終輸出。
import os
from swarm_models import OpenAIChat
from swarms import Agent , MixtureOfAgents
api_key = os . getenv ( "OPENAI_API_KEY" )
# Create individual agents with the OpenAIChat model
model = OpenAIChat (
openai_api_key = api_key , model_name = "gpt-4" , temperature = 0.1
)
# Agent 1: Financial Statement Analyzer
agent1 = Agent (
agent_name = "FinancialStatementAnalyzer" ,
llm = model ,
system_prompt = """You are a Financial Statement Analyzer specializing in 10-K SEC reports. Your primary focus is on analyzing the financial statements, including the balance sheet, income statement, and cash flow statement.
Key responsibilities:
1. Identify and explain significant changes in financial metrics year-over-year.
2. Calculate and interpret key financial ratios (e.g., liquidity ratios, profitability ratios, leverage ratios).
3. Analyze trends in revenue, expenses, and profitability.
4. Highlight any red flags or areas of concern in the financial statements.
5. Provide insights on the company's financial health and performance based on the data.
When analyzing, consider industry standards and compare the company's performance to its peers when possible. Your analysis should be thorough, data-driven, and provide actionable insights for investors and stakeholders.""" ,
max_loops = 1 ,
autosave = True ,
dashboard = False ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "financial_statement_analyzer_state.json" ,
user_name = "swarms_corp" ,
retry_attempts = 1 ,
context_length = 200000 ,
return_step_meta = False ,
)
# Agent 2: Risk Assessment Specialist
agent2 = Agent (
agent_name = "RiskAssessmentSpecialist" ,
llm = model ,
system_prompt = """You are a Risk Assessment Specialist focusing on 10-K SEC reports. Your primary role is to identify, analyze, and evaluate potential risks disclosed in the report.
Key responsibilities:
1. Thoroughly review the "Risk Factors" section of the 10-K report.
2. Identify and categorize different types of risks (e.g., operational, financial, legal, market, technological).
3. Assess the potential impact and likelihood of each identified risk.
4. Analyze the company's risk mitigation strategies and their effectiveness.
5. Identify any emerging risks not explicitly mentioned but implied by the company's operations or market conditions.
6. Compare the company's risk profile with industry peers when possible.
Your analysis should provide a comprehensive overview of the company's risk landscape, helping stakeholders understand the potential challenges and uncertainties facing the business. Be sure to highlight any critical risks that could significantly impact the company's future performance or viability.""" ,
max_loops = 1 ,
autosave = True ,
dashboard = False ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "risk_assessment_specialist_state.json" ,
user_name = "swarms_corp" ,
retry_attempts = 1 ,
context_length = 200000 ,
return_step_meta = False ,
)
# Agent 3: Business Strategy Evaluator
agent3 = Agent (
agent_name = "BusinessStrategyEvaluator" ,
llm = model ,
system_prompt = """You are a Business Strategy Evaluator specializing in analyzing 10-K SEC reports. Your focus is on assessing the company's overall strategy, market position, and future outlook.
Key responsibilities:
1. Analyze the company's business description, market opportunities, and competitive landscape.
2. Evaluate the company's products or services, including their market share and growth potential.
3. Assess the effectiveness of the company's current business strategy and its alignment with market trends.
4. Identify key performance indicators (KPIs) and evaluate the company's performance against these metrics.
5. Analyze management's discussion and analysis (MD&A) section to understand their perspective on the business.
6. Identify potential growth opportunities or areas for improvement in the company's strategy.
7. Compare the company's strategic position with key competitors in the industry.
Your analysis should provide insights into the company's strategic direction, its ability to create value, and its potential for future growth. Consider both short-term and long-term perspectives in your evaluation.""" ,
max_loops = 1 ,
autosave = True ,
dashboard = False ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "business_strategy_evaluator_state.json" ,
user_name = "swarms_corp" ,
retry_attempts = 1 ,
context_length = 200000 ,
return_step_meta = False ,
)
# Aggregator Agent
aggregator_agent = Agent (
agent_name = "10KReportAggregator" ,
llm = model ,
system_prompt = """You are the 10-K Report Aggregator, responsible for synthesizing and summarizing the analyses provided by the Financial Statement Analyzer, Risk Assessment Specialist, and Business Strategy Evaluator. Your goal is to create a comprehensive, coherent, and insightful summary of the 10-K SEC report.
Key responsibilities:
1. Integrate the financial analysis, risk assessment, and business strategy evaluation into a unified report.
2. Identify and highlight the most critical information and insights from each specialist's analysis.
3. Reconcile any conflicting information or interpretations among the specialists' reports.
4. Provide a balanced view of the company's overall performance, risks, and strategic position.
5. Summarize key findings and their potential implications for investors and stakeholders.
6. Identify any areas where further investigation or clarification may be needed.
Your final report should be well-structured, easy to understand, and provide a holistic view of the company based on the 10-K SEC report. It should offer valuable insights for decision-making while acknowledging any limitations or uncertainties in the analysis.""" ,
max_loops = 1 ,
autosave = True ,
dashboard = False ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "10k_report_aggregator_state.json" ,
user_name = "swarms_corp" ,
retry_attempts = 1 ,
context_length = 200000 ,
return_step_meta = False ,
)
# Create the Mixture of Agents class
moa = MixtureOfAgents (
agents = [ agent1 , agent2 , agent3 ],
aggregator_agent = aggregator_agent ,
aggregator_system_prompt = """As the 10-K Report Aggregator, your task is to synthesize the analyses provided by the Financial Statement Analyzer, Risk Assessment Specialist, and Business Strategy Evaluator into a comprehensive and coherent report.
Follow these steps:
1. Review and summarize the key points from each specialist's analysis.
2. Identify common themes and insights across the analyses.
3. Highlight any discrepancies or conflicting interpretations, if present.
4. Provide a balanced and integrated view of the company's financial health, risks, and strategic position.
5. Summarize the most critical findings and their potential impact on investors and stakeholders.
6. Suggest areas for further investigation or monitoring, if applicable.
Your final output should be a well-structured, insightful report that offers a holistic view of the company based on the 10-K SEC report analysis.""" ,
layers = 3 ,
)
# Example usage
company_name = "NVIDIA"
out = moa . run (
f"Analyze the latest 10-K SEC report for { company_name } . Provide a comprehensive summary of the company's financial performance, risk profile, and business strategy."
)
print ( out ) SpreadSheetSwarm設計用於同時管理和監督成千上萬的代理商，促進了一種一對多的方法，用於有效的任務處理和輸出分析。
| 方法 | 描述 | 參數 | 返回值 |
|---|---|---|---|
__init__ | 初始化電子表格工程 | name ：群的名稱description ：群的描述agents ：代理對象列表autosave_on ：布爾值啟用AutoSavesave_file_path ：保存電子表格的路徑run_all_agents ：布爾值是否運行所有代理max_loops ：最大循環數量 | 沒有任何 |
run | 執行群 | task ：群的輸入任務 | 代理輸出字典 |
| 輸入 | 類型 | 描述 |
|---|---|---|
name | str | 群的名字 |
description | str | 蜂群目的的描述 |
agents | 列表[代理] | 群中要使用的代理對象列表 |
autosave_on | 布爾 | 啟用自動儲備結果 |
save_file_path | str | 保存電子表格結果的途徑 |
run_all_agents | 布爾 | 是根據相關性運行所有代理還是根據相關性進行選擇 |
max_loops | int | 最大處理循環數 |
run方法返回包含每個處理任務的代理的輸出的字典。
在此處了解更多文檔：
import os
from swarms import Agent
from swarm_models import OpenAIChat
from swarms . structs . spreadsheet_swarm import SpreadSheetSwarm
# Define custom system prompts for each social media platform
TWITTER_AGENT_SYS_PROMPT = """
You are a Twitter marketing expert specializing in real estate. Your task is to create engaging, concise tweets to promote properties, analyze trends to maximize engagement, and use appropriate hashtags and timing to reach potential buyers.
"""
INSTAGRAM_AGENT_SYS_PROMPT = """
You are an Instagram marketing expert focusing on real estate. Your task is to create visually appealing posts with engaging captions and hashtags to showcase properties, targeting specific demographics interested in real estate.
"""
FACEBOOK_AGENT_SYS_PROMPT = """
You are a Facebook marketing expert for real estate. Your task is to craft posts optimized for engagement and reach on Facebook, including using images, links, and targeted messaging to attract potential property buyers.
"""
LINKEDIN_AGENT_SYS_PROMPT = """
You are a LinkedIn marketing expert for the real estate industry. Your task is to create professional and informative posts, highlighting property features, market trends, and investment opportunities, tailored to professionals and investors.
"""
EMAIL_AGENT_SYS_PROMPT = """
You are an Email marketing expert specializing in real estate. Your task is to write compelling email campaigns to promote properties, focusing on personalization, subject lines, and effective call-to-action strategies to drive conversions.
"""
# Example usage:
api_key = os . getenv ( "OPENAI_API_KEY" )
# Model
model = OpenAIChat (
openai_api_key = api_key , model_name = "gpt-4o-mini" , temperature = 0.1
)
# Initialize your agents for different social media platforms
agents = [
Agent (
agent_name = "Twitter-RealEstate-Agent" ,
system_prompt = TWITTER_AGENT_SYS_PROMPT ,
llm = model ,
max_loops = 1 ,
dynamic_temperature_enabled = True ,
saved_state_path = "twitter_realestate_agent.json" ,
user_name = "realestate_swarms" ,
retry_attempts = 1 ,
),
Agent (
agent_name = "Instagram-RealEstate-Agent" ,
system_prompt = INSTAGRAM_AGENT_SYS_PROMPT ,
llm = model ,
max_loops = 1 ,
dynamic_temperature_enabled = True ,
saved_state_path = "instagram_realestate_agent.json" ,
user_name = "realestate_swarms" ,
retry_attempts = 1 ,
),
Agent (
agent_name = "Facebook-RealEstate-Agent" ,
system_prompt = FACEBOOK_AGENT_SYS_PROMPT ,
llm = model ,
max_loops = 1 ,
dynamic_temperature_enabled = True ,
saved_state_path = "facebook_realestate_agent.json" ,
user_name = "realestate_swarms" ,
retry_attempts = 1 ,
),
Agent (
agent_name = "LinkedIn-RealEstate-Agent" ,
system_prompt = LINKEDIN_AGENT_SYS_PROMPT ,
llm = model ,
max_loops = 1 ,
dynamic_temperature_enabled = True ,
saved_state_path = "linkedin_realestate_agent.json" ,
user_name = "realestate_swarms" ,
retry_attempts = 1 ,
),
Agent (
agent_name = "Email-RealEstate-Agent" ,
system_prompt = EMAIL_AGENT_SYS_PROMPT ,
llm = model ,
max_loops = 1 ,
dynamic_temperature_enabled = True ,
saved_state_path = "email_realestate_agent.json" ,
user_name = "realestate_swarms" ,
retry_attempts = 1 ,
),
]
# Create a Swarm with the list of agents
swarm = SpreadSheetSwarm (
name = "Real-Estate-Marketing-Swarm" ,
description = "A swarm that processes real estate marketing tasks using multiple agents on different threads." ,
agents = agents ,
autosave_on = True ,
save_file_path = "real_estate_marketing_spreadsheet.csv" ,
run_all_agents = False ,
max_loops = 2 ,
)
# Run the swarm
swarm . run (
task = """
Create posts to promote luxury properties in North Texas, highlighting their features, location, and investment potential. Include relevant hashtags, images, and engaging captions.
Property:
$10,399,000
1609 Meandering Way Dr, Roanoke, TX 76262
Link to the property: https://www.zillow.com/homedetails/1609-Meandering-Way-Dr-Roanoke-TX-76262/308879785_zpid/
What's special
Unveiling a new custom estate in the prestigious gated Quail Hollow Estates! This impeccable residence, set on a sprawling acre surrounded by majestic trees, features a gourmet kitchen equipped with top-tier Subzero and Wolf appliances. European soft-close cabinets and drawers, paired with a double Cambria Quartzite island, perfect for family gatherings. The first-floor game room&media room add extra layers of entertainment. Step into the outdoor sanctuary, where a sparkling pool and spa, and sunken fire pit, beckon leisure. The lavish master suite features stunning marble accents, custom his&her closets, and a secure storm shelter.Throughout the home,indulge in the visual charm of designer lighting and wallpaper, elevating every space. The property is complete with a 6-car garage and a sports court, catering to the preferences of basketball or pickleball enthusiasts. This residence seamlessly combines luxury&recreational amenities, making it a must-see for the discerning buyer.
Facts & features
Interior
Bedrooms & bathrooms
Bedrooms: 6
Bathrooms: 8
Full bathrooms: 7
1/2 bathrooms: 1
Primary bedroom
Bedroom
Features: Built-in Features, En Suite Bathroom, Walk-In Closet(s)
Cooling
Central Air, Ceiling Fan(s), Electric
Appliances
Included: Built-In Gas Range, Built-In Refrigerator, Double Oven, Dishwasher, Gas Cooktop, Disposal, Ice Maker, Microwave, Range, Refrigerator, Some Commercial Grade, Vented Exhaust Fan, Warming Drawer, Wine Cooler
Features
Wet Bar, Built-in Features, Dry Bar, Decorative/Designer Lighting Fixtures, Eat-in Kitchen, Elevator, High Speed Internet, Kitchen Island, Pantry, Smart Home, Cable TV, Walk-In Closet(s), Wired for Sound
Flooring: Hardwood
Has basement: No
Number of fireplaces: 3
Fireplace features: Living Room, Primary Bedroom
Interior area
Total interior livable area: 10,466 sqft
Total spaces: 12
Parking features: Additional Parking
Attached garage spaces: 6
Carport spaces: 6
Features
Levels: Two
Stories: 2
Patio & porch: Covered
Exterior features: Built-in Barbecue, Barbecue, Gas Grill, Lighting, Outdoor Grill, Outdoor Living Area, Private Yard, Sport Court, Fire Pit
Pool features: Heated, In Ground, Pool, Pool/Spa Combo
Fencing: Wrought Iron
Lot
Size: 1.05 Acres
Details
Additional structures: Outdoor Kitchen
Parcel number: 42232692
Special conditions: Standard
Construction
Type & style
Home type: SingleFamily
Architectural style: Contemporary/Modern,Detached
Property subtype: Single Family Residence
"""
)ForestSwarm ForestSwarm體系結構旨在通過動態從樹木集合中動態選擇最合適的代理，供有效的任務分配。這是通過異步任務處理來實現的，其中代理是根據其與手頭任務的相關性選擇的。相關性是通過計算與每個代理相關的系統提示與任務本身中存在的關鍵字之間的相似性來確定的。有關對ForestSwarm工作原理的更深入的了解，請參閱官方文件。
| 方法 | 描述 | 參數 | 返回值 |
|---|---|---|---|
__init__ | 初始化Forestswarm | trees ：樹對象列表 | 沒有任何 |
run | 執行ForestSwarm | task ：群的輸入任務 | 最相關代理的輸出 |
| 輸入 | 類型 | 描述 |
|---|---|---|
trees | 列表[樹] | 樹對象列表，每個對像都包含treagent對象 |
task | str | Forestswarm要處理的任務 |
run方法從基於輸入任務選擇的最相關代理返回輸出。
from swarms . structs . tree_swarm import TreeAgent , Tree , ForestSwarm
# Create agents with varying system prompts and dynamically generated distances/keywords
agents_tree1 = [
TreeAgent (
system_prompt = """You are an expert Stock Analysis Agent with deep knowledge of financial markets, technical analysis, and fundamental analysis. Your primary function is to analyze stock performance, market trends, and provide actionable insights. When analyzing stocks:
1. Always start with a brief overview of the current market conditions.
2. Use a combination of technical indicators (e.g., moving averages, RSI, MACD) and fundamental metrics (e.g., P/E ratio, EPS growth, debt-to-equity).
3. Consider both short-term and long-term perspectives in your analysis.
4. Provide clear buy, hold, or sell recommendations with supporting rationale.
5. Highlight potential risks and opportunities specific to each stock or sector.
6. Use bullet points for clarity when listing key points or metrics.
7. If relevant, compare the stock to its peers or sector benchmarks.
Remember to maintain objectivity and base your analysis on factual data. If asked about future performance, always include a disclaimer about market unpredictability. Your goal is to provide comprehensive, accurate, and actionable stock analysis to inform investment decisions.""" ,
agent_name = "Stock Analysis Agent" ,
),
TreeAgent (
system_prompt = """You are a highly skilled Financial Planning Agent, specializing in personal and corporate financial strategies. Your role is to provide comprehensive financial advice tailored to each client's unique situation. When creating financial plans:
1. Begin by asking key questions about the client's financial goals, current situation, and risk tolerance.
2. Develop a holistic view of the client's finances, including income, expenses, assets, and liabilities.
3. Create detailed, step-by-step action plans to achieve financial goals.
4. Provide specific recommendations for budgeting, saving, and investing.
5. Consider tax implications and suggest tax-efficient strategies.
6. Incorporate risk management and insurance planning into your recommendations.
7. Use charts or tables to illustrate financial projections and scenarios.
8. Regularly suggest reviewing and adjusting the plan as circumstances change.
Always prioritize the client's best interests and adhere to fiduciary standards. Explain complex financial concepts in simple terms, and be prepared to justify your recommendations with data and reasoning.""" ,
agent_name = "Financial Planning Agent" ,
),
TreeAgent (
agent_name = "Retirement Strategy Agent" ,
system_prompt = """You are a specialized Retirement Strategy Agent, focused on helping individuals and couples plan for a secure and comfortable retirement. Your expertise covers various aspects of retirement planning, including savings strategies, investment allocation, and income generation during retirement. When developing retirement strategies:
1. Start by assessing the client's current age, desired retirement age, and expected lifespan.
2. Calculate retirement savings goals based on desired lifestyle and projected expenses.
3. Analyze current retirement accounts (e.g., 401(k), IRA) and suggest optimization strategies.
4. Provide guidance on asset allocation and rebalancing as retirement approaches.
5. Explain various retirement income sources (e.g., Social Security, pensions, annuities).
6. Discuss healthcare costs and long-term care planning.
7. Offer strategies for tax-efficient withdrawals during retirement.
8. Consider estate planning and legacy goals in your recommendations.
Use Monte Carlo simulations or other statistical tools to illustrate the probability of retirement success. Always emphasize the importance of starting early and the power of compound interest. Be prepared to adjust strategies based on changing market conditions or personal circumstances.""" ,
),
]
agents_tree2 = [
TreeAgent (
system_prompt = """You are a knowledgeable Tax Filing Agent, specializing in personal and business tax preparation and strategy. Your role is to ensure accurate tax filings while maximizing legitimate deductions and credits. When assisting with tax matters:
1. Start by gathering all necessary financial information and documents.
2. Stay up-to-date with the latest tax laws and regulations, including state-specific rules.
3. Identify all applicable deductions and credits based on the client's situation.
4. Provide step-by-step guidance for completing tax forms accurately.
5. Explain tax implications of various financial decisions.
6. Offer strategies for tax-efficient investing and income management.
7. Assist with estimated tax payments for self-employed individuals or businesses.
8. Advise on record-keeping practices for tax purposes.
Always prioritize compliance with tax laws while ethically minimizing tax liability. Be prepared to explain complex tax concepts in simple terms and provide rationale for your recommendations. If a situation is beyond your expertise, advise consulting a certified tax professional or IRS resources.""" ,
agent_name = "Tax Filing Agent" ,
),
TreeAgent (
system_prompt = """You are a sophisticated Investment Strategy Agent, adept at creating and managing investment portfolios to meet diverse financial goals. Your expertise covers various asset classes, market analysis, and risk management techniques. When developing investment strategies:
1. Begin by assessing the client's investment goals, time horizon, and risk tolerance.
2. Provide a comprehensive overview of different asset classes and their risk-return profiles.
3. Create diversified portfolio recommendations based on modern portfolio theory.
4. Explain the benefits and risks of various investment vehicles (e.g., stocks, bonds, ETFs, mutual funds).
5. Incorporate both passive and active investment strategies as appropriate.
6. Discuss the importance of regular portfolio rebalancing and provide a rebalancing strategy.
7. Consider tax implications of investment decisions and suggest tax-efficient strategies.
8. Provide ongoing market analysis and suggest portfolio adjustments as needed.
Use historical data and forward-looking projections to illustrate potential outcomes. Always emphasize the importance of long-term investing and the risks of market timing. Be prepared to explain complex investment concepts in clear, accessible language.""" ,
agent_name = "Investment Strategy Agent" ,
),
TreeAgent (
system_prompt = """You are a specialized ROTH IRA Agent, focusing on the intricacies of Roth Individual Retirement Accounts. Your role is to provide expert guidance on Roth IRA rules, benefits, and strategies to maximize their value for retirement planning. When advising on Roth IRAs:
1. Explain the fundamental differences between traditional and Roth IRAs.
2. Clarify Roth IRA contribution limits and income eligibility requirements.
3. Discuss the tax advantages of Roth IRAs, including tax-free growth and withdrawals.
4. Provide guidance on Roth IRA conversion strategies and their tax implications.
5. Explain the five-year rule and how it affects Roth IRA withdrawals.
6. Offer strategies for maximizing Roth IRA contributions, such as the backdoor Roth IRA method.
7. Discuss how Roth IRAs fit into overall retirement and estate planning strategies.
8. Provide insights on investment choices within a Roth IRA to maximize tax-free growth.
Always stay current with IRS regulations regarding Roth IRAs. Be prepared to provide numerical examples to illustrate the long-term benefits of Roth IRAs. Emphasize the importance of considering individual financial situations when making Roth IRA decisions.""" ,
agent_name = "ROTH IRA Agent" ,
),
]
# Create trees
tree1 = Tree ( tree_name = "Financial Tree" , agents = agents_tree1 )
tree2 = Tree ( tree_name = "Investment Tree" , agents = agents_tree2 )
# Create the ForestSwarm
multi_agent_structure = ForestSwarm ( trees = [ tree1 , tree2 ])
# Run a task
task = "What are the best platforms to do our taxes on"
output = multi_agent_structure . run ( task )
print ( output )SwarmRouter SwarmRouter類是一個靈活的路由系統，旨在管理不同類型的群以執行任務。它提供了一個統一的接口，可以與各種群類型進行交互，包括AgentRearrange ， MixtureOfAgents ， SpreadSheetSwarm ， SequentialWorkflow和ConcurrentWorkflow 。隨著新體系結構的發展，我們將在這裡不斷添加越來越多的群架構。
name （str）：swarmrouter實例的名稱。description （str）：Swarmrouter實例的描述。max_loops （INT）：最大循環數量。agents （列表[代理]）：群中要使用的代理對象列表。swarm_type （swarmType）：要使用的群體類型。swarm （union [agentRearrange，混合物代碼，電子表格swarm，sequentionworkflow，conturrentworkflow]）：實例化的群體。logs （列表[swarmlog]）：操作過程中捕獲的日誌條目列表。 __init__(self, name: str, description: str, max_loops: int, agents: List[Agent], swarm_type: SwarmType, *args, **kwargs) ：初始化swarmrouter。_create_swarm(self, *args, **kwargs) ：創建並返回指定的群體類型。_log(self, level: str, message: str, task: str, metadata: Dict[str, Any]) ：創建一個日誌條目並將其添加到日誌列表中。run(self, task: str, *args, **kwargs) ：在選定的群體上運行指定的任務。get_logs(self) ：檢索所有記錄的條目。 import os
from dotenv import load_dotenv
from swarms import Agent
from swarm_models import OpenAIChat
from swarms . structs . swarm_router import SwarmRouter , SwarmType
load_dotenv ()
# Get the OpenAI API key from the environment variable
api_key = os . getenv ( "GROQ_API_KEY" )
# Model
model = OpenAIChat (
openai_api_base = "https://api.groq.com/openai/v1" ,
openai_api_key = api_key ,
model_name = "llama-3.1-70b-versatile" ,
temperature = 0.1 ,
)
# Define specialized system prompts for each agent
DATA_EXTRACTOR_PROMPT = """You are a highly specialized private equity agent focused on data extraction from various documents. Your expertise includes:
1. Extracting key financial metrics (revenue, EBITDA, growth rates, etc.) from financial statements and reports
2. Identifying and extracting important contract terms from legal documents
3. Pulling out relevant market data from industry reports and analyses
4. Extracting operational KPIs from management presentations and internal reports
5. Identifying and extracting key personnel information from organizational charts and bios
Provide accurate, structured data extracted from various document types to support investment analysis."""
SUMMARIZER_PROMPT = """You are an expert private equity agent specializing in summarizing complex documents. Your core competencies include:
1. Distilling lengthy financial reports into concise executive summaries
2. Summarizing legal documents, highlighting key terms and potential risks
3. Condensing industry reports to capture essential market trends and competitive dynamics
4. Summarizing management presentations to highlight key strategic initiatives and projections
5. Creating brief overviews of technical documents, emphasizing critical points for non-technical stakeholders
Deliver clear, concise summaries that capture the essence of various documents while highlighting information crucial for investment decisions."""
FINANCIAL_ANALYST_PROMPT = """You are a specialized private equity agent focused on financial analysis. Your key responsibilities include:
1. Analyzing historical financial statements to identify trends and potential issues
2. Evaluating the quality of earnings and potential adjustments to EBITDA
3. Assessing working capital requirements and cash flow dynamics
4. Analyzing capital structure and debt capacity
5. Evaluating financial projections and underlying assumptions
Provide thorough, insightful financial analysis to inform investment decisions and valuation."""
MARKET_ANALYST_PROMPT = """You are a highly skilled private equity agent specializing in market analysis. Your expertise covers:
1. Analyzing industry trends, growth drivers, and potential disruptors
2. Evaluating competitive landscape and market positioning
3. Assessing market size, segmentation, and growth potential
4. Analyzing customer dynamics, including concentration and loyalty
5. Identifying potential regulatory or macroeconomic impacts on the market
Deliver comprehensive market analysis to assess the attractiveness and risks of potential investments."""
OPERATIONAL_ANALYST_PROMPT = """You are an expert private equity agent focused on operational analysis. Your core competencies include:
1. Evaluating operational efficiency and identifying improvement opportunities
2. Analyzing supply chain and procurement processes
3. Assessing sales and marketing effectiveness
4. Evaluating IT systems and digital capabilities
5. Identifying potential synergies in merger or add-on acquisition scenarios
Provide detailed operational analysis to uncover value creation opportunities and potential risks."""
# Initialize specialized agents
data_extractor_agent = Agent (
agent_name = "Data-Extractor" ,
system_prompt = DATA_EXTRACTOR_PROMPT ,
llm = model ,
max_loops = 1 ,
autosave = True ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "data_extractor_agent.json" ,
user_name = "pe_firm" ,
retry_attempts = 1 ,
context_length = 200000 ,
output_type = "string" ,
)
summarizer_agent = Agent (
agent_name = "Document-Summarizer" ,
system_prompt = SUMMARIZER_PROMPT ,
llm = model ,
max_loops = 1 ,
autosave = True ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "summarizer_agent.json" ,
user_name = "pe_firm" ,
retry_attempts = 1 ,
context_length = 200000 ,
output_type = "string" ,
)
financial_analyst_agent = Agent (
agent_name = "Financial-Analyst" ,
system_prompt = FINANCIAL_ANALYST_PROMPT ,
llm = model ,
max_loops = 1 ,
autosave = True ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "financial_analyst_agent.json" ,
user_name = "pe_firm" ,
retry_attempts = 1 ,
context_length = 200000 ,
output_type = "string" ,
)
market_analyst_agent = Agent (
agent_name = "Market-Analyst" ,
system_prompt = MARKET_ANALYST_PROMPT ,
llm = model ,
max_loops = 1 ,
autosave = True ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "market_analyst_agent.json" ,
user_name = "pe_firm" ,
retry_attempts = 1 ,
context_length = 200000 ,
output_type = "string" ,
)
operational_analyst_agent = Agent (
agent_name = "Operational-Analyst" ,
system_prompt = OPERATIONAL_ANALYST_PROMPT ,
llm = model ,
max_loops = 1 ,
autosave = True ,
verbose = True ,
dynamic_temperature_enabled = True ,
saved_state_path = "operational_analyst_agent.json" ,
user_name = "pe_firm" ,
retry_attempts = 1 ,
context_length = 200000 ,
output_type = "string" ,
)
# Initialize the SwarmRouter
router = SwarmRouter (
name = "pe-document-analysis-swarm" ,
description = "Analyze documents for private equity due diligence and investment decision-making" ,
max_loops = 1 ,
agents = [
data_extractor_agent ,
summarizer_agent ,
financial_analyst_agent ,
market_analyst_agent ,
operational_analyst_agent ,
],
swarm_type = "ConcurrentWorkflow" , # or "SequentialWorkflow" or "ConcurrentWorkflow" or
)
# Example usage
if __name__ == "__main__" :
# Run a comprehensive private equity document analysis task
result = router . run (
"Where is the best place to find template term sheets for series A startups. Provide links and references"
)
print ( result )
# Retrieve and print logs
for log in router . get_logs ():
print ( f" { log . timestamp } - { log . level } : { log . message } " )您可以創建具有不同類型類型的多個群體實例：
sequential_router = SwarmRouter (
name = "SequentialRouter" ,
agents = [
data_extractor_agent ,
summarizer_agent ,
financial_analyst_agent ,
market_analyst_agent ,
operational_analyst_agent ,
],
swarm_type = SwarmType . SequentialWorkflow
)
concurrent_router = SwarmRouter (
name = "ConcurrentRouter" ,
agents = [
data_extractor_agent ,
summarizer_agent ,
financial_analyst_agent ,
market_analyst_agent ,
operational_analyst_agent ,
],
swarm_type = SwarmType . ConcurrentWorkflow
)用例：為複雜的多步驟任務優化代理順序。
rearrange_router = SwarmRouter (
name = "TaskOptimizer" ,
description = "Optimize agent order for multi-step tasks" ,
max_loops = 3 ,
agents = [
data_extractor_agent ,
summarizer_agent ,
financial_analyst_agent ,
market_analyst_agent ,
operational_analyst_agent ,
],
swarm_type = SwarmType . AgentRearrange ,
flow = f" { data_extractor . name } -> { analyzer . name } -> { summarizer . name } "
)
result = rearrange_router . run ( "Analyze and summarize the quarterly financial report" )用例：將多樣化的專家代理結合起來進行全面分析。
mixture_router = SwarmRouter (
name = "ExpertPanel" ,
description = "Combine insights from various expert agents" ,
max_loops = 1 ,
agents = [
data_extractor_agent ,
summarizer_agent ,
financial_analyst_agent ,
market_analyst_agent ,
operational_analyst_agent ,
],
swarm_type = SwarmType . MixtureOfAgents
)
result = mixture_router . run ( "Evaluate the potential acquisition of TechStartup Inc." )立即與Swarms的創建者和鉛維護者Kye Gomez一起入門，Kye Gomez將向您展示如何開始安裝，用法示例並開始構建自定義用例！點擊這裡
文檔位於以下網址：docs.swarms.world
群套件已精心製作，用於極端的使用和理解，群swarms.structs分為各種模塊Agent例如swarms.agents 。代理結構。最重要的3個是structs ， models和agents 。
├── __init__.py
├── agents
├── artifacts
├── memory
├── schemas
├── models - > swarm_models
├── prompts
├── structs
├── telemetry
├── tools
├── utils
└── workers最簡單的貢獻方法是選擇good first issue標籤的任何問題？。在此處閱讀貢獻指南。錯誤報告？文件在這裡|功能請求？在這里文件
Swarms是一個開源項目，非常歡迎捐款。如果您想貢獻，則可以創建新功能，修復錯誤或改進基礎架構。請參考貢獻。MD和我們的貢獻委員會參加路線圖討論！
加速錯誤，功能和演示可以通過在此處支持我們來實現：
加入我們在世界各地不斷增長的社區，以實時支持，想法和群體討論？
GNU AFFERO通用公共許可證
