swarms
5.3.7
Kerangka kerja orkestrasi multi-agen yang siap diproduksi oleh perusahaan
? Twitter • ? Perselisihan • Platform Swarms • ? Dokumentasi
| Kategori | Fitur | Manfaat |
|---|---|---|
| ? Arsitektur Perusahaan | • Infrastruktur siap-produksi • Sistem keandalan yang tinggi • Desain modular • Pencatatan komprehensif | • Mengurangi waktu henti • Perawatan yang lebih mudah • Debugging yang lebih baik • Peningkatan pemantauan |
| ? Orkestrasi Agen | • Kawanan hierarki • Pemrosesan paralel • Alur kerja berurutan • Alur kerja berbasis grafik • Penataan ulang agen dinamis | • Penanganan tugas yang kompleks • Peningkatan kinerja • Alur kerja yang fleksibel • Eksekusi yang dioptimalkan |
| Kemampuan integrasi | • Dukungan multi-model • Pembuatan Agen Kustom • Perpustakaan Alat yang Luas • Beberapa sistem memori | • Fleksibilitas penyedia • Solusi khusus • Fungsionalitas yang diperluas • Manajemen memori yang ditingkatkan |
| ? Skalabilitas | • Pemrosesan bersamaan • Manajemen Sumber Daya • Load Balancing • Penskalaan horizontal | • Throughput yang lebih tinggi • Penggunaan sumber daya yang efisien • Kinerja yang lebih baik • Penskalaan yang mudah |
| Alat pengembang | • API sederhana • Dokumentasi yang luas • Komunitas aktif • Alat CLI | • Pengembangan yang lebih cepat • Kurva belajar yang mudah • Dukungan masyarakat • Penempatan cepat |
| ? Fitur Keamanan | • Penanganan kesalahan • Batas tingkat • Integrasi pemantauan • Audit logging | • Peningkatan keandalan • Perlindungan API • Pemantauan yang lebih baik • Pelacakan yang ditingkatkan |
| Fitur canggih | • Spreadsheetswarm • Obrolan grup • Registri Agen • Campuran agen | • Manajemen Agen Massal • AI kolaboratif • Kontrol terpusat • Solusi kompleks |
| ? Dukungan Penyedia | • Openai • Antropik • Chromadb • Penyedia khusus | • Fleksibilitas penyedia • Opsi penyimpanan • Integrasi khusus • Kemandirian vendor |
| ? Fitur Produksi | • Retries Otomatis • Dukungan async • Manajemen Lingkungan • Ketik keamanan | • Keandalan yang lebih baik • Peningkatan kinerja • Konfigurasi yang mudah • Kode yang lebih aman |
| Gunakan dukungan kasus | • Agen khusus tugas • Alur kerja khusus • Solusi Industri • Kerangka kerja yang dapat diperluas | • Penempatan cepat • Solusi fleksibel • kesiapan industri • Kustomisasi mudah |
python3.10 atau lebih tinggi!$ pip install -U swarms dan, jangan lupa untuk memasang kawanan!.env dengan kunci API dari penyedia Anda seperti OPENAI_API_KEY , ANTHROPIC_API_KEY.env dengan direkripsi workspace yang Anda inginkan: WORKSPACE_DIR="agent_workspace" atau lakukan di terminal Anda dengan export WORKSPACE_DIR="agent_workspace"swarms onboarding untuk membantu Anda memulai. Lihat dokumentasi kami untuk detail implementasi tingkat produksi.
| Bagian | Tautan |
|---|---|
| Instalasi | Instalasi |
| QuickStart | Mulai |
| Mekanisme internal agen | Arsitektur Agen |
| API Agen | API Agen |
| Mengintegrasikan agen eksternal griptape, autogen, dll | Mengintegrasikan API eksternal |
| Membuat agen dari YAML | Membuat agen dari YAML |
| Mengapa Anda Membutuhkan Kawanan | Mengapa Kolaborasi Multiagent Diperlukan |
| Analisis Arsitektur Swarm | Arsitektur gerombolan |
| Memilih segerombolan yang tepat untuk masalah bisnis Anda | KLIK DISINI |
| AgenRearrange docs | KLIK DISINI |
$ pip3 install -U swarms Sekarang Anda telah mengunduh kawanan dengan pip3 install -U swarms , kami mendapatkan akses ke CLI . Ikut serta dengan CLI sekarang dengan:
swarms onboardingAnda juga dapat menjalankan perintah ini untuk mendapatkan bantuan:
swarms helpUntuk dokumentasi lebih lanjut tentang CLI klik di sini
Berikut adalah beberapa contoh skrip untuk Anda mulai. Untuk dokumentasi yang lebih komprehensif, kunjungi dokumen kami.
| Nama contoh | Keterangan | Jenis contoh | Link |
|---|---|---|---|
| Contoh kawanan | Kumpulan contoh sederhana untuk menunjukkan kemampuan kawanan. | Penggunaan dasar | https://github.com/the-swarm-corporation/swarms-examples?tab=readme-ov-file |
| Buku masak | Panduan komprehensif dengan resep untuk berbagai kasus penggunaan dan skenario. | Penggunaan lanjutan | https://github.com/the-swarm-corporation/cookbook |
Agent Kelas Agent adalah komponen mendasar dari kerangka kerja Swarms, yang dirancang untuk melaksanakan tugas secara mandiri. Ini memadukan LLMS, alat, dan kemampuan memori jangka panjang untuk membuat agen tumpukan penuh. Kelas Agent sangat dapat disesuaikan, memungkinkan untuk kontrol berbutir halus atas perilaku dan interaksinya.
run Metode run adalah titik masuk utama untuk mengeksekusi tugas dengan instance Agent . Ia menerima string tugas sebagai tugas input utama dan memprosesnya sesuai dengan konfigurasi agen. Dan, itu juga dapat menerima parameter img seperti img="image_filepath.png untuk memproses gambar jika Anda memiliki VLM
Kelas Agent menawarkan berbagai pengaturan untuk menyesuaikan perilakunya dengan kebutuhan spesifik. Beberapa pengaturan kunci meliputi:
| Pengaturan | Keterangan | Nilai default |
|---|---|---|
agent_name | Nama agen. | "Defaultagent" |
system_prompt | Prompt sistem untuk digunakan untuk agen. | "Prompt sistem default." |
llm | Model bahasa yang akan digunakan untuk memproses tugas. | Instance OpenAIChat |
max_loops | Jumlah maksimum loop untuk dieksekusi untuk suatu tugas. | 1 |
autosave | Mengaktifkan atau menonaktifkan autosaving dari keadaan agen. | PALSU |
dashboard | Mengaktifkan atau menonaktifkan dasbor untuk agen. | PALSU |
verbose | Mengontrol verbositas output agen. | PALSU |
dynamic_temperature_enabled | Mengaktifkan atau menonaktifkan penyesuaian suhu dinamis untuk model bahasa. | PALSU |
saved_state_path | Jalan untuk menyelamatkan keadaan agen. | "agen_state.json" |
user_name | Nama pengguna yang terkait dengan agen. | "Default_user" |
retry_attempts | Jumlah upaya coba lagi untuk tugas yang gagal. | 1 |
context_length | Panjang maksimum konteks untuk dipertimbangkan untuk tugas. | 200000 |
return_step_meta | Mengontrol apakah akan mengembalikan metadata langkah dalam output. | PALSU |
output_type | Jenis output untuk mengembalikan (misalnya, "json", "string"). | "rangkaian" |
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"
) Agent yang dilengkapi dengan memori jangka panjang semu-tak terbatas menggunakan RAG (grafik agen relasional) untuk pemahaman dokumen lanjutan, analisis, dan kemampuan pengambilan.
Diagram putri duyung untuk integrasi kain
grafik td
A [Inisialisasi Agen dengan RAG] -> B [Tugas Terima]
B-> C [Kueri memori jangka panjang]
C -> D [Tugas proses dengan konteks]
D -> E [menghasilkan respons]
E-> f [Perbarui memori jangka panjang]
F -> g [output kembali]
Langkah 1: Inisialisasi klien Chromadb
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
)Langkah 2: Tentukan model
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" ))Langkah 3: Inisialisasi agen dengan kain
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"
)Kami menyediakan beragam fitur untuk menghemat negara agen menggunakan JSON, YAML, TOML, mengunggah PDF, pekerjaan batch, dan banyak lagi!
Tabel Metode
| Metode | Keterangan |
|---|---|
to_dict() | Mengubah objek agen menjadi kamus. |
to_toml() | Mengubah objek agen menjadi string toml. |
model_dump_json() | Membuang model ke file JSON. |
model_dump_yaml() | Membuang model ke file YAML. |
ingest_docs() | Memasukkan dokumen ke dalam basis pengetahuan agen. |
receive_message() | Menerima pesan dari pengguna dan memprosesnya. |
send_agent_message() | Mengirim pesan dari agen ke pengguna. |
filtered_run() | Menjalankan agen dengan prompt sistem yang difilter. |
bulk_run() | Menjalankan agen dengan beberapa petunjuk sistem. |
add_memory() | Menambahkan memori ke agen. |
check_available_tokens() | Periksa jumlah token yang tersedia untuk agen. |
tokens_checks() | Melakukan cek token untuk agen. |
print_dashboard() | Mencetak dasbor agen. |
get_docs_from_doc_folders() | Ambil semua dokumen dari folder DOC. |
activate_agentops() | Mengaktifkan operasi agen. |
check_end_session_agentops() | Periksa akhir sesi untuk operasi agen. |
# # 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 dengan basemodel pydantic sebagai tipe outputBerikut ini adalah contoh agen yang mengupas basemodel pydantic dan mengeluarkannya pada saat yang sama:
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 } " )
Jalankan agen dengan beberapa modalitas yang berguna untuk berbagai tugas dunia nyata dalam manufaktur, logistik, dan kesehatan.
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 adalah agen yang dapat menggunakan alat melalui panggilan fungsi JSON. Ini mengupas setiap model open source dari huggingface dan sangat modular dan plug in dan mainkan. Kami membutuhkan bantuan menambahkan dukungan umum untuk semua model segera.
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 } " )Mengintegrasikan agen eksternal dari kerangka kerja agen lainnya mudah dengan kawanan.
Tangga:
Agent.run(task: str) -> str yang menjalankan agen dan mengembalikan respons.Misalnya, berikut adalah contoh tentang cara membuat agen dari griptape.
Inilah cara Anda dapat membuat agen griptape khusus yang terintegrasi dengan kerangka kerja Swarms dengan mewarisi dari kelas Agent di Swarms dan mengesampingkan run(task: str) -> str .
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 dan mengintegrasikan agen Griptape.WebScraperTool , PromptSummaryTool , FileManagerTool ) memungkinkan pengikis web, ringkasan, dan manajemen file.Anda sekarang dapat dengan mudah mencolokkan agen griptape khusus ini ke dalam kerangka kerja Swarms dan menggunakannya untuk menjalankan tugas!
Segerombolan mengacu pada sekelompok lebih dari dua agen yang bekerja secara kolaboratif untuk mencapai tujuan bersama. Agen -agen ini dapat berupa entitas perangkat lunak, seperti LLM yang berinteraksi satu sama lain untuk melakukan tugas -tugas kompleks. Konsep kawanan terinspirasi oleh sistem alami seperti koloni semut atau kawanan burung, di mana perilaku individu yang sederhana mengarah pada dinamika kelompok yang kompleks dan kemampuan pemecahan masalah.
Arsitektur Swarm dirancang untuk membangun dan mengelola komunikasi antar agen dalam sekejap. Arsitektur ini menentukan bagaimana agen berinteraksi, berbagi informasi, dan mengoordinasikan tindakan mereka untuk mencapai hasil yang diinginkan. Berikut adalah beberapa aspek kunci arsitektur Swarm:
Komunikasi hierarkis : Dalam kawanan hierarkis, komunikasi mengalir dari agen tingkat tinggi ke agen tingkat rendah. Agen tingkat yang lebih tinggi bertindak sebagai koordinator, mendistribusikan tugas dan hasil agregat. Struktur ini efisien untuk tugas yang membutuhkan kontrol top-down dan pengambilan keputusan.
Komunikasi Paralel : Dalam kawanan paralel, agen beroperasi secara mandiri dan berkomunikasi satu sama lain sesuai kebutuhan. Arsitektur ini cocok untuk tugas yang dapat diproses secara bersamaan tanpa ketergantungan, memungkinkan untuk eksekusi dan skalabilitas yang lebih cepat.
Komunikasi berurutan : Tugas Proses Kerumunan Berurutan dalam Urutan Linear, di mana output masing -masing agen menjadi input untuk agen berikutnya. Ini memastikan bahwa tugas dengan ketergantungan ditangani dalam urutan yang benar, mempertahankan integritas alur kerja.
Komunikasi mesh : Dalam kawanan jala, agen terhubung penuh, memungkinkan agen mana pun untuk berkomunikasi dengan agen lainnya. Pengaturan ini memberikan fleksibilitas dan redundansi yang tinggi, membuatnya ideal untuk sistem kompleks yang membutuhkan interaksi dinamis.
Komunikasi Federasi : Gerombolan federasi melibatkan banyak kawanan independen yang berkolaborasi dengan berbagi informasi dan hasil. Setiap Swarm beroperasi secara mandiri tetapi dapat berkontribusi pada tugas yang lebih besar, memungkinkan pemecahan masalah terdistribusi di berbagai node.
Arsitektur Swarm memanfaatkan pola komunikasi ini untuk memastikan bahwa agen bekerja bersama secara efisien, beradaptasi dengan persyaratan spesifik tugas yang dihadapi. Dengan mendefinisikan protokol komunikasi yang jelas dan model interaksi, arsitektur gerombolan memungkinkan orkestrasi multi-agen yang mulus, yang mengarah pada peningkatan kinerja dan kemampuan pemecahan masalah.
| Nama | Keterangan | Tautan kode | Menggunakan kasus |
|---|---|---|---|
| Kawanan hierarkis | Suatu sistem di mana agen diatur dalam hierarki, dengan agen tingkat lebih tinggi mengoordinasikan agen tingkat bawah untuk mencapai tugas yang kompleks. | Tautan kode | Optimalisasi Proses Pabrikan, Manajemen Penjualan Multi-Level, Koordinasi Sumber Daya Kesehatan |
| RETRENE AGEN | Pengaturan di mana agen mengatur ulang diri mereka secara dinamis berdasarkan persyaratan tugas dan kondisi lingkungan. | Tautan kode | Jalur manufaktur adaptif, penataan kembali wilayah penjualan dinamis, staf perawatan kesehatan yang fleksibel |
| Alur kerja bersamaan | Agen melakukan tugas yang berbeda secara bersamaan, berkoordinasi untuk menyelesaikan tujuan yang lebih besar. | Tautan kode | Jalur produksi bersamaan, operasi penjualan paralel, proses perawatan pasien secara bersamaan |
| Koordinasi berurutan | Agen melakukan tugas dalam urutan tertentu, di mana penyelesaian satu tugas memicu awal yang berikutnya. | Tautan kode | Jalur perakitan langkah demi langkah, proses penjualan berurutan, alur kerja perawatan pasien bertahap |
| Pemrosesan paralel | Agen bekerja pada berbagai bagian tugas secara bersamaan untuk mempercepat proses keseluruhan. | Tautan kode | Pemrosesan Data Paralel dalam Manufaktur, Analisis Penjualan Simultan, Tes Medis Bersamaan |
| Campuran agen | Kawanan heterogen di mana agen dengan kemampuan berbeda digabungkan untuk menyelesaikan masalah yang kompleks. | Tautan kode | Peramalan keuangan, pemecahan masalah kompleks yang membutuhkan keterampilan yang beragam |
| Alur kerja grafik | Agen berkolaborasi dalam format grafik asiklik terarah (DAG) untuk mengelola dependensi dan tugas paralel. | Tautan kode | Pipa Pengembangan Perangkat Lunak AI, Manajemen Proyek yang Kompleks |
| Obrolan grup | Agen terlibat dalam interaksi seperti obrolan untuk mencapai keputusan secara kolaboratif. | Tautan kode | Pengambilan keputusan kolaboratif waktu nyata, negosiasi kontrak |
| Registry Agen | Registri terpusat di mana agen disimpan, diambil, dan dipanggil secara dinamis. | Tautan kode | Manajemen Agen Dinamis, Berevolusi Mesin Rekomendasi |
| Sanggul spreadsheet | Mengelola tugas pada skala, pelacakan output agen dalam format terstruktur seperti file CSV. | Tautan kode | Analisis pemasaran skala besar, audit keuangan |
| Segerombolan hutan | Struktur segerombolan yang mengatur agen dalam hierarki seperti pohon untuk proses pengambilan keputusan yang kompleks. | Tautan kode | Alur kerja multi-tahap, pembelajaran penguatan hierarkis |
| Router berkerumun | Rute dan memilih arsitektur Swarm berdasarkan persyaratan tugas dan agen yang tersedia. | Tautan kode | Perutean tugas dinamis, pemilihan arsitektur gerombolan adaptif, alokasi agen yang dioptimalkan |
SequentialWorkflow Alur kerja berurutan memungkinkan Anda untuk secara berurutan menjalankan tugas dengan Agent dan kemudian meneruskan output ke agen berikutnya dan ke depan sampai Anda telah menentukan loop max Anda.
grafik lr
A [Agen 1] -> B [Agen 2]
B -> C [Agen 3]
C -> D [Agen 4]
D -> E [max loops]
E -> f [end]
| Metode | Keterangan | Parameter | Nilai pengembalian |
|---|---|---|---|
__init__ | Inisialisasi flowflow berurutan | agents : Daftar Objek Agenmax_loops : jumlah maksimum iterasiverbose : boolean untuk output verbose | Tidak ada |
run | Jalankan alur kerja | input_data : input awal untuk agen pertama | Output akhir setelah semua agen telah diproses |
| Masukan | Jenis | Keterangan |
|---|---|---|
agents | Daftar [Agen] | Daftar objek agen yang akan dieksekusi secara berurutan |
max_loops | int | Jumlah maksimum kali seluruh urutan akan diulangi |
verbose | bool | Jika benar, cetak informasi terperinci selama eksekusi |
Metode run mengembalikan output akhir setelah semua agen telah memproses input secara berurutan.
Dalam contoh ini, setiap Agent mewakili tugas yang dieksekusi secara berurutan. Output dari masing -masing agen diteruskan ke agen berikutnya dalam urutan sampai jumlah maksimum loop tercapai. Alur kerja ini sangat berguna untuk tugas -tugas yang membutuhkan serangkaian langkah untuk dieksekusi dalam urutan tertentu, seperti pipa pemrosesan data atau perhitungan kompleks yang mengandalkan output dari langkah -langkah sebelumnya.
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 Teknik orkestrasi AgentRearrange , yang diilhami oleh Einops dan Einsum, memungkinkan Anda untuk mendefinisikan dan memetakan hubungan antara berbagai agen. Ini menyediakan alat yang ampuh untuk mengatur alur kerja yang kompleks, memungkinkan Anda untuk menentukan hubungan linier dan berurutan seperti a -> a1 -> a2 -> a3 , atau hubungan bersamaan di mana agen pertama mengirimkan pesan ke 3 agen secara bersamaan: a -> a1, a2, a3 . Tingkat kustomisasi ini memungkinkan penciptaan alur kerja yang sangat efisien dan dinamis, di mana agen dapat bekerja secara paralel atau secara berurutan sesuai kebutuhan. Teknik AgentRearrange adalah tambahan yang berharga untuk perpustakaan Swarms, memberikan tingkat fleksibilitas dan kontrol yang baru atas orkestrasi agen. Untuk informasi dan contoh lebih rinci, silakan merujuk ke dokumentasi resmi.
| Metode | Keterangan | Parameter | Nilai pengembalian |
|---|---|---|---|
__init__ | Inisialisasi AgenRearRange | agents : Daftar Objek Agenflow : String yang menggambarkan aliran agen | Tidak ada |
run | Jalankan alur kerja | input_data : input awal untuk agen pertama | Output akhir setelah semua agen telah diproses |
| Masukan | Jenis | Keterangan |
|---|---|---|
agents | Daftar [Agen] | Daftar objek agen yang akan diatur |
flow | str | String yang menggambarkan aliran agen (misalnya, "a -> b, c") |
Metode run mengembalikan output akhir setelah semua agen telah memproses input sesuai dengan aliran yang ditentukan.
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 )HierarhicalSwarmSegera hadir...
GraphSwarm GraphSwarm adalah sistem manajemen alur kerja yang dirancang untuk mengatur tugas -tugas kompleks dengan memanfaatkan kekuatan teori grafik. Ini memungkinkan pembuatan grafik asiklik terarah (DAG) untuk memodelkan ketergantungan antara tugas dan agen. Ini memungkinkan penugasan tugas, eksekusi, dan pemantauan yang efisien.
Berikut adalah rincian cara kerja GraphSwarm :
GraphSwarm terdiri dari node, yang dapat berupa agen atau tugas. Agen bertanggung jawab untuk melaksanakan tugas, dan tugas mewakili operasi spesifik yang perlu dilakukan. Dalam contoh, dua agen ( agent1 dan agent2 ) dan satu tugas ( task1 ) dibuat.agent1 dan agent2 ke task1 , menunjukkan bahwa kedua agen mampu melaksanakan task1 .GraphSwarm memerlukan definisi titik masuk (di mana alur kerja dimulai) dan titik akhir (di mana alur kerja diakhiri). Dalam contoh ini, agent1 dan agent2 ditetapkan sebagai titik masuk, dan task1 ditetapkan sebagai titik akhir.GraphSwarm menyediakan fitur visualisasi untuk mewakili alur kerja secara grafis. Ini memungkinkan pemahaman dan debugging struktur alur kerja yang mudah.GraphSwarm dieksekusi dengan melintasi grafik dari titik masuk ke titik akhir. Dalam hal ini, baik agent1 dan agent2 mengeksekusi task1 secara bersamaan, dan hasilnya dikumpulkan.task1 adalah "tugas selesai". GraphSwarm menawarkan beberapa manfaat, termasuk:
Dengan memanfaatkan GraphSwarm , alur kerja yang kompleks dapat dikelola secara efisien, dan tugas dapat dieksekusi dengan cara yang terkoordinasi dan dapat diukur.
| Metode | Keterangan | Parameter | Nilai pengembalian |
|---|---|---|---|
add_node | Tambahkan node ke grafik | node : Objek Node | Tidak ada |
add_edge | Tambahkan tepi ke grafik | edge : Objek tepi | Tidak ada |
set_entry_points | Atur titik masuk grafik | entry_points : Daftar ID Node | Tidak ada |
set_end_points | Atur titik akhir grafik | end_points : Daftar ID Node | Tidak ada |
visualize | Menghasilkan representasi visual dari grafik | Tidak ada | Representasi string grafik |
run | Jalankan alur kerja | Tidak ada | Kamus Hasil Eksekusi |
| Masukan | Jenis | Keterangan |
|---|---|---|
Node | Obyek | Mewakili node dalam grafik (agen atau tugas) |
Edge | Obyek | Mewakili tepi yang menghubungkan dua node |
entry_points | Daftar [str] | Daftar ID Node di mana alur kerja dimulai |
end_points | Daftar [str] | Daftar id node di mana alur kerja berakhir |
Metode run mengembalikan kamus yang berisi hasil eksekusi dari semua node dalam grafik.
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 )MixtureOfAgentsIni adalah implementasi yang didasarkan pada kertas: "Campuran agen meningkatkan kemampuan model bahasa besar" oleh Together.ai, tersedia di https://arxiv.org/abs/2406.04692. Ini mencapai hasil canggih (SOTA) pada alpacaeval 2.0, MT-Bench, dan Flask, melampaui GPT-4 Omni. Arsitektur ini sangat cocok untuk tugas yang membutuhkan paralelisasi diikuti oleh pemrosesan berurutan di loop lain.
| Metode | Keterangan | Parameter | Nilai pengembalian |
|---|---|---|---|
__init__ | Inisialisasi campuran | name : Nama Swarmagents : Daftar Objek Agenlayers : Jumlah lapisan pemrosesanfinal_agent : agen untuk pemrosesan akhir | Tidak ada |
run | Jalankan gerombolan | task : Masukkan tugas untuk gerombolan | Output akhir setelah semua agen telah diproses |
| Masukan | Jenis | Keterangan |
|---|---|---|
name | str | Nama gerombolan |
agents | Daftar [Agen] | Daftar objek agen yang akan digunakan di gerombolan |
layers | int | Jumlah lapisan pemrosesan di gerombolan |
final_agent | Agen | Agen yang bertanggung jawab untuk pemrosesan akhir |
Metode run mengembalikan output akhir setelah semua agen telah memproses input sesuai dengan lapisan yang ditentukan dan agen akhir.
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 dirancang untuk manajemen bersamaan dan pengawasan ribuan agen, memfasilitasi pendekatan satu-ke-banyak untuk pemrosesan tugas dan analisis output yang efisien.
| Metode | Keterangan | Parameter | Nilai pengembalian |
|---|---|---|---|
__init__ | Inisialisasi spreadsheetswarm | name : Nama Swarmdescription : Deskripsi Swarmagents : Daftar Objek Agenautosave_on : boolean untuk mengaktifkan autosavesave_file_path : jalur untuk menyimpan spreadsheetrun_all_agents : boolean untuk menjalankan semua agen atau tidakmax_loops : Jumlah loop maksimum | Tidak ada |
run | Jalankan gerombolan | task : Masukkan tugas untuk gerombolan | Kamus output agen |
| Masukan | Jenis | Keterangan |
|---|---|---|
name | str | Nama gerombolan |
description | str | Deskripsi Tujuan Swarm |
agents | Daftar [Agen] | Daftar objek agen yang akan digunakan di gerombolan |
autosave_on | bool | Aktifkan autosaving hasil |
save_file_path | str | Jalur untuk menyimpan hasil spreadsheet |
run_all_agents | bool | Apakah akan menjalankan semua agen atau memilih berdasarkan relevansi |
max_loops | int | Jumlah maksimum pemrosesan loop |
Metode run mengembalikan kamus yang berisi output dari masing -masing agen yang memproses tugas.
Pelajari lebih lanjut di dokumen di sini:
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 Arsitektur ForestSwarm dirancang untuk penugasan tugas yang efisien dengan secara dinamis memilih agen yang paling cocok dari kumpulan pohon. Ini dicapai melalui pemrosesan tugas asinkron, di mana agen dipilih berdasarkan relevansinya dengan tugas yang dihadapi. Relevansi ditentukan dengan menghitung kesamaan antara petunjuk sistem yang terkait dengan masing -masing agen dan kata kunci yang ada dalam tugas itu sendiri. Untuk pemahaman yang lebih mendalam tentang bagaimana ForestSwarm bekerja, silakan merujuk ke dokumentasi resmi.
| Metode | Keterangan | Parameter | Nilai pengembalian |
|---|---|---|---|
__init__ | Inisialisasi Forestswarm | trees : Daftar Objek Pohon | Tidak ada |
run | Jalankan Forestswarm | task : Masukkan tugas untuk gerombolan | Output dari agen yang paling relevan |
| Masukan | Jenis | Keterangan |
|---|---|---|
trees | Daftar [pohon] | Daftar objek pohon, masing -masing berisi objek Treeagent |
task | str | Tugas yang akan diproses oleh Forestswarm |
Metode run mengembalikan output dari agen paling relevan yang dipilih berdasarkan tugas input.
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 Kelas SwarmRouter adalah sistem perutean fleksibel yang dirancang untuk mengelola berbagai jenis kawanan untuk pelaksanaan tugas. Ini menyediakan antarmuka terpadu untuk berinteraksi dengan berbagai tipe Swarm, termasuk AgentRearrange , MixtureOfAgents , SpreadSheetSwarm , SequentialWorkflow , dan ConcurrentWorkflow . Kami akan terus menambahkan lebih banyak arsitektur kawanan di sini saat kami maju dengan arsitektur baru.
name (str): Nama instance SwarmRouter.description (str): Deskripsi instance SwarmRouter.max_loops (int): Jumlah loop maksimum yang harus dilakukan.agents (Daftar [Agen]): Daftar objek agen yang akan digunakan di Swarm.swarm_type (swarmType): Jenis gerombolan yang akan digunakan.swarm (Union [AgentRearRange, campuran, spreadsheetswarm, sekuensialworkflow, concurrentworkflow]): objek Swarm yang instantiated.logs (Daftar [swarmlog]): Daftar entri log yang ditangkap selama operasi. __init__(self, name: str, description: str, max_loops: int, agents: List[Agent], swarm_type: SwarmType, *args, **kwargs) : menginisialisasi swarmrouter._create_swarm(self, *args, **kwargs) : Buat dan kembalikan tipe Swarm yang ditentukan._log(self, level: str, message: str, task: str, metadata: Dict[str, Any]) : Buat entri log dan tambahkan ke daftar log.run(self, task: str, *args, **kwargs) : Jalankan tugas yang ditentukan pada gerombolan yang dipilih.get_logs(self) : Ambil semua entri yang dicatat. 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 } " )Anda dapat membuat beberapa instance swarmrouter dengan berbagai jenis kawanan:
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
)Use case: Mengoptimalkan pesanan agen untuk tugas multi-langkah yang kompleks.
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" )Penggunaan kasus: Menggabungkan beragam agen ahli untuk analisis komprehensif.
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." )Dapatkan ikut serta dengan pencipta dan pemelihara utama Swarms, Kye Gomez, yang akan menunjukkan kepada Anda cara memulai dengan instalasi, contoh penggunaan, dan mulai membangun kasing penggunaan khusus Anda! KLIK DISINI
Dokumentasi terletak di sini di: docs.swarms.world
Paket Swarms telah dibuat dengan sangat baik untuk kemampuan penggunaan dan pemahaman yang ekstrem, paket Swarms dibagi menjadi berbagai modul seperti swarms.agents yang memegang agen pra-built, swarms.structs yang memiliki berbagai struktur seperti Agent dan multi struktur agen. 3 yang paling penting adalah structs , models , dan agents .
├── __init__.py
├── agents
├── artifacts
├── memory
├── schemas
├── models - > swarm_models
├── prompts
├── structs
├── telemetry
├── tools
├── utils
└── workers Cara termudah untuk berkontribusi adalah dengan memilih masalah apa pun dengan tag good first issue ? Baca pedoman yang berkontribusi di sini. Laporan Bug? File di sini | Permintaan fitur? File di sini
Swarms adalah proyek open-source, dan kontribusi sangat disambut. Jika Anda ingin berkontribusi, Anda dapat membuat fitur baru, memperbaiki bug, atau meningkatkan infrastruktur. Silakan merujuk ke kontribusi.MD dan Dewan Kontribusi kami untuk berpartisipasi dalam diskusi peta jalan!
Akselerasi bug, fitur, dan demo untuk diimplementasikan dengan mendukung kami di sini:
Bergabunglah dengan komunitas kami yang berkembang di seluruh dunia, untuk dukungan, ide, dan diskusi waktu nyata tentang Swarms?
Lisensi Publik Umum GNU Affero
