Kye Gomez
3 weeks ago
parent
6e5bc850b7
commit
6c0b1fd5b0
@ -0,0 +1,13 @@
|
|||||||
|
from swarms.structs.deep_research_swarm import DeepResearchSwarm
|
||||||
|
|
||||||
|
|
||||||
|
swarm = DeepResearchSwarm(
|
||||||
|
name="Deep Research Swarm",
|
||||||
|
description="A swarm of agents that can perform deep research on a given topic",
|
||||||
|
output_type="all", # Change to string output type for better readability
|
||||||
|
)
|
||||||
|
|
||||||
|
out = swarm.run(
|
||||||
|
"What are the latest developments and news in the AI and cryptocurrency space?"
|
||||||
|
)
|
||||||
|
print(out)
|
||||||
@ -0,0 +1,4 @@
|
|||||||
|
from swarms.structs.deep_research_swarm import exa_search
|
||||||
|
|
||||||
|
|
||||||
|
print(exa_search("What are the best multi-agent frameworks "))
|
||||||
@ -0,0 +1,23 @@
|
|||||||
|
from swarms.structs.swarm_matcher import (
|
||||||
|
SwarmMatcher,
|
||||||
|
SwarmMatcherConfig,
|
||||||
|
)
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
|
||||||
|
load_dotenv()
|
||||||
|
|
||||||
|
# Example usage
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# Create configuration
|
||||||
|
config = SwarmMatcherConfig(
|
||||||
|
backend="openai", # Using local embeddings for this example
|
||||||
|
similarity_threshold=0.6, # Increase threshold for more strict matching
|
||||||
|
cache_embeddings=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Initialize matcher
|
||||||
|
matcher = SwarmMatcher(config)
|
||||||
|
|
||||||
|
task = "I need to build a hierarchical swarm of agents to solve a problem"
|
||||||
|
|
||||||
|
print(matcher.auto_select_swarm(task))
|
||||||
File diff suppressed because it is too large
Load Diff
@ -1,21 +0,0 @@
|
|||||||
from swarms.utils.formatter import formatter
|
|
||||||
|
|
||||||
|
|
||||||
def display_markdown_message(message: str, color: str = "cyan"):
|
|
||||||
"""
|
|
||||||
Display markdown message. Works with multiline strings with lots of indentation.
|
|
||||||
Will automatically make single line > tags beautiful.
|
|
||||||
"""
|
|
||||||
|
|
||||||
for line in message.split("\n"):
|
|
||||||
line = line.strip()
|
|
||||||
if line == "":
|
|
||||||
print()
|
|
||||||
elif line == "---":
|
|
||||||
formatter.print_panel("-" * 50)
|
|
||||||
else:
|
|
||||||
formatter.print_panel(line)
|
|
||||||
|
|
||||||
if "\n" not in message and message.startswith(">"):
|
|
||||||
# Aesthetic choice. For these tags, they need a space below them
|
|
||||||
print()
|
|
||||||
@ -1,510 +0,0 @@
|
|||||||
import asyncio
|
|
||||||
from dataclasses import dataclass
|
|
||||||
from datetime import datetime
|
|
||||||
from typing import Any, Callable, Dict, List, Optional
|
|
||||||
|
|
||||||
import psutil
|
|
||||||
from rich.console import Console
|
|
||||||
from rich.layout import Layout
|
|
||||||
from rich.live import Live
|
|
||||||
from rich.panel import Panel
|
|
||||||
from rich.progress import (
|
|
||||||
Progress,
|
|
||||||
SpinnerColumn,
|
|
||||||
TextColumn,
|
|
||||||
TimeElapsedColumn,
|
|
||||||
)
|
|
||||||
from rich.table import Table
|
|
||||||
from rich.text import Text
|
|
||||||
from rich.tree import Tree
|
|
||||||
|
|
||||||
from swarms.structs.agent import Agent
|
|
||||||
|
|
||||||
try:
|
|
||||||
import pynvml
|
|
||||||
|
|
||||||
pynvml.nvmlInit()
|
|
||||||
GPU_ENABLED = True
|
|
||||||
except ImportError:
|
|
||||||
GPU_ENABLED = False
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class SwarmMetadata:
|
|
||||||
name: Optional[str] = None
|
|
||||||
description: Optional[str] = None
|
|
||||||
version: Optional[str] = None
|
|
||||||
type: Optional[str] = None # hierarchical, parallel, sequential
|
|
||||||
created_at: Optional[datetime] = None
|
|
||||||
author: Optional[str] = None
|
|
||||||
tags: Optional[List[str]] = None
|
|
||||||
primary_objective: Optional[str] = None
|
|
||||||
secondary_objectives: Optional[List[str]] = None
|
|
||||||
|
|
||||||
def __post_init__(self):
|
|
||||||
self.tags = self.tags or []
|
|
||||||
self.secondary_objectives = self.secondary_objectives or []
|
|
||||||
self.created_at = self.created_at or datetime.now()
|
|
||||||
|
|
||||||
|
|
||||||
class SwarmVisualizationRich:
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
swarm_metadata: SwarmMetadata,
|
|
||||||
agents: List[Agent],
|
|
||||||
update_resources: bool = True,
|
|
||||||
refresh_rate: float = 0.1,
|
|
||||||
):
|
|
||||||
"""
|
|
||||||
Initializes the visualizer with a list of agents.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
swarm_metadata (SwarmMetadata): Metadata for the swarm.
|
|
||||||
agents (List[Agent]): List of root agents.
|
|
||||||
update_resources (bool): Whether to update system resource stats.
|
|
||||||
refresh_rate (float): Refresh rate for the live visualization.
|
|
||||||
"""
|
|
||||||
self.swarm_metadata = swarm_metadata
|
|
||||||
self.agents = agents
|
|
||||||
self.update_resources = update_resources
|
|
||||||
self.refresh_rate = refresh_rate
|
|
||||||
self.console = Console()
|
|
||||||
self.live = None
|
|
||||||
# A dictionary mapping agent names to list of output messages
|
|
||||||
self.output_history: Dict[str, List[Dict[str, Any]]] = {}
|
|
||||||
|
|
||||||
# System monitoring
|
|
||||||
self.cores_available = 0
|
|
||||||
self.memory_usage = "N/A"
|
|
||||||
self.gpu_power = "N/A"
|
|
||||||
self.start_time = datetime.now()
|
|
||||||
|
|
||||||
if self.update_resources:
|
|
||||||
self._update_resource_stats()
|
|
||||||
|
|
||||||
def _format_uptime(self) -> str:
|
|
||||||
"""Formats the swarm's uptime."""
|
|
||||||
delta = datetime.now() - self.start_time
|
|
||||||
hours, remainder = divmod(delta.seconds, 3600)
|
|
||||||
minutes, seconds = divmod(remainder, 60)
|
|
||||||
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"
|
|
||||||
|
|
||||||
def _build_agent_tree(self, agents: List[Agent]) -> Tree:
|
|
||||||
"""
|
|
||||||
Builds a detailed tree visualization for a list of agents.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
agents (List[Agent]): The list of root agents.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Tree: A rich Tree object displaying agent metadata.
|
|
||||||
"""
|
|
||||||
tree = Tree("[bold underline]Agents[/bold underline]")
|
|
||||||
for agent in agents:
|
|
||||||
self._add_agent_to_tree(agent, tree)
|
|
||||||
return tree
|
|
||||||
|
|
||||||
def _add_agent_to_tree(self, agent: Agent, tree: Tree) -> None:
|
|
||||||
"""
|
|
||||||
Recursively adds an agent and its children to the given tree.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
agent (Agent): The agent to add.
|
|
||||||
tree (Tree): The tree to update.
|
|
||||||
"""
|
|
||||||
agent_info = [
|
|
||||||
f"[bold cyan]{agent.name}[/bold cyan]",
|
|
||||||
f"[yellow]Role:[/yellow] {agent.role}",
|
|
||||||
]
|
|
||||||
|
|
||||||
# # Add any custom metadata from the agent (if available)
|
|
||||||
# for key, value in getattr(agent, "metadata", {}).items():
|
|
||||||
# agent_info.append(f"[white]{key}:[/white] {value}")
|
|
||||||
|
|
||||||
# # Parameters summary if available
|
|
||||||
# parameters = getattr(agent, "parameters", {})
|
|
||||||
# if parameters:
|
|
||||||
# param_summary = ", ".join(f"{k}: {v}" for k, v in parameters.items())
|
|
||||||
# agent_info.append(f"[white]Parameters:[/white] {param_summary}")
|
|
||||||
|
|
||||||
node_text = "\n".join(agent_info)
|
|
||||||
branch = tree.add(node_text)
|
|
||||||
for child in getattr(agent, "children", []):
|
|
||||||
self._add_agent_to_tree(child, branch)
|
|
||||||
|
|
||||||
def _count_agents(self, agents: List[Agent]) -> int:
|
|
||||||
"""
|
|
||||||
Recursively counts total number of agents from a list of root agents.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
agents (List[Agent]): List of agents.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
int: Total count of agents including children.
|
|
||||||
"""
|
|
||||||
return len(agents)
|
|
||||||
|
|
||||||
def _create_unified_info_panel(self) -> Panel:
|
|
||||||
"""
|
|
||||||
Creates a unified panel showing swarm metadata and agents' metadata.
|
|
||||||
"""
|
|
||||||
info_layout = Layout()
|
|
||||||
info_layout.split_column(
|
|
||||||
Layout(name="metadata", size=15),
|
|
||||||
Layout(name="architecture"),
|
|
||||||
)
|
|
||||||
|
|
||||||
total_agents = self._count_agents(self.agents)
|
|
||||||
|
|
||||||
# Metadata section
|
|
||||||
metadata_table = Table.grid(padding=1, expand=True)
|
|
||||||
metadata_table.add_column("Label", style="bold cyan")
|
|
||||||
metadata_table.add_column("Value", style="white")
|
|
||||||
|
|
||||||
# Update system resources if needed
|
|
||||||
if self.update_resources:
|
|
||||||
self._update_resource_stats()
|
|
||||||
|
|
||||||
# Wrap the description text properly
|
|
||||||
description_text = Text(
|
|
||||||
self.swarm_metadata.description or "", style="italic"
|
|
||||||
)
|
|
||||||
description_text.wrap(self.console, width=60, overflow="fold")
|
|
||||||
|
|
||||||
metadata_table.add_row(
|
|
||||||
"Swarm Name", self.swarm_metadata.name or "N/A"
|
|
||||||
)
|
|
||||||
metadata_table.add_row("Description", description_text)
|
|
||||||
metadata_table.add_row(
|
|
||||||
"Version", self.swarm_metadata.version or "N/A"
|
|
||||||
)
|
|
||||||
metadata_table.add_row("Total Agents", str(total_agents))
|
|
||||||
metadata_table.add_row(
|
|
||||||
"Author", self.swarm_metadata.author or "N/A"
|
|
||||||
)
|
|
||||||
metadata_table.add_row(
|
|
||||||
"System",
|
|
||||||
f"CPU: {self.cores_available} cores | Memory: {self.memory_usage}",
|
|
||||||
)
|
|
||||||
metadata_table.add_row(
|
|
||||||
"Primary Objective",
|
|
||||||
self.swarm_metadata.primary_objective or "N/A",
|
|
||||||
)
|
|
||||||
|
|
||||||
info_layout["metadata"].update(metadata_table)
|
|
||||||
|
|
||||||
# Architecture section with the agent tree
|
|
||||||
architecture_tree = self._build_agent_tree(self.agents)
|
|
||||||
info_layout["architecture"].update(architecture_tree)
|
|
||||||
|
|
||||||
return Panel(
|
|
||||||
info_layout,
|
|
||||||
title="[bold]Swarm Information & Architecture[/bold]",
|
|
||||||
)
|
|
||||||
|
|
||||||
def _create_outputs_panel(self) -> Panel:
|
|
||||||
"""
|
|
||||||
Creates a panel that displays stacked message history for all agents.
|
|
||||||
"""
|
|
||||||
all_messages = []
|
|
||||||
|
|
||||||
def collect_agent_messages(agent: Agent):
|
|
||||||
"""Recursively collect messages from an agent and its children."""
|
|
||||||
messages = self.output_history.get(agent.name, [])
|
|
||||||
for msg in messages:
|
|
||||||
all_messages.append(
|
|
||||||
{
|
|
||||||
"agent": agent.name,
|
|
||||||
"time": msg["time"],
|
|
||||||
"content": msg["content"],
|
|
||||||
"style": msg["style"],
|
|
||||||
}
|
|
||||||
)
|
|
||||||
for child in getattr(agent, "children", []):
|
|
||||||
collect_agent_messages(child)
|
|
||||||
|
|
||||||
# Collect messages from every root agent
|
|
||||||
for agent in self.agents:
|
|
||||||
collect_agent_messages(agent)
|
|
||||||
|
|
||||||
# Sort messages by timestamp
|
|
||||||
all_messages.sort(key=lambda x: x["time"])
|
|
||||||
|
|
||||||
messages_container = []
|
|
||||||
for msg in all_messages:
|
|
||||||
message_text = Text()
|
|
||||||
message_text.append(f"[{msg['time']}] ", style="dim")
|
|
||||||
message_text.append(
|
|
||||||
f"{msg['agent']}: ", style="bold cyan"
|
|
||||||
)
|
|
||||||
message_text.append(msg["content"], style=msg["style"])
|
|
||||||
messages_container.append(message_text)
|
|
||||||
|
|
||||||
if messages_container:
|
|
||||||
final_text = Text("\n").join(messages_container)
|
|
||||||
else:
|
|
||||||
final_text = Text("No messages yet...", style="dim")
|
|
||||||
|
|
||||||
return Panel(
|
|
||||||
final_text,
|
|
||||||
title="[bold]Agent Communication Log[/bold]",
|
|
||||||
border_style="green",
|
|
||||||
padding=(1, 2),
|
|
||||||
)
|
|
||||||
|
|
||||||
def _update_resource_stats(self):
|
|
||||||
"""Updates system resource statistics."""
|
|
||||||
self.cores_available = psutil.cpu_count(logical=True)
|
|
||||||
mem_info = psutil.virtual_memory()
|
|
||||||
total_gb = mem_info.total / (1024**3)
|
|
||||||
used_gb = mem_info.used / (1024**3)
|
|
||||||
self.memory_usage = f"{used_gb:.1f}GB / {total_gb:.1f}GB ({mem_info.percent}%)"
|
|
||||||
|
|
||||||
if GPU_ENABLED:
|
|
||||||
try:
|
|
||||||
device_count = pynvml.nvmlDeviceGetCount()
|
|
||||||
gpu_info = []
|
|
||||||
for i in range(device_count):
|
|
||||||
handle = pynvml.nvmlDeviceGetHandleByIndex(i)
|
|
||||||
name = pynvml.nvmlDeviceGetName(handle).decode()
|
|
||||||
mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
|
|
||||||
usage = (mem.used / mem.total) * 100
|
|
||||||
gpu_info.append(f"{name}: {usage:.1f}%")
|
|
||||||
self.gpu_power = " | ".join(gpu_info)
|
|
||||||
except Exception as e:
|
|
||||||
self.gpu_power = f"GPU Error: {str(e)}"
|
|
||||||
else:
|
|
||||||
self.gpu_power = "No GPU detected"
|
|
||||||
|
|
||||||
async def stream_output(
|
|
||||||
self,
|
|
||||||
agent: Agent,
|
|
||||||
text: str,
|
|
||||||
title: Optional[str] = None,
|
|
||||||
style: str = "bold cyan",
|
|
||||||
delay: float = 0.05,
|
|
||||||
by_word: bool = False,
|
|
||||||
):
|
|
||||||
"""
|
|
||||||
Streams output for a specific agent with token-by-token animation.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
agent (Agent): The agent whose output is being streamed.
|
|
||||||
text (str): The text to stream.
|
|
||||||
title (Optional[str]): Custom title for the output panel.
|
|
||||||
style (str): Style for the output text.
|
|
||||||
delay (float): Delay between tokens.
|
|
||||||
by_word (bool): If True, stream word by word instead of character by character.
|
|
||||||
"""
|
|
||||||
display_text = Text(style=style)
|
|
||||||
current_output = ""
|
|
||||||
|
|
||||||
tokens = text.split() if by_word else text
|
|
||||||
title = title or f"{agent.name} Output"
|
|
||||||
|
|
||||||
for token in tokens:
|
|
||||||
token_with_space = token + (" " if by_word else "")
|
|
||||||
current_output += token_with_space
|
|
||||||
display_text.append(token_with_space)
|
|
||||||
|
|
||||||
if agent.name not in self.output_history:
|
|
||||||
self.output_history[agent.name] = []
|
|
||||||
|
|
||||||
if token == tokens[-1]:
|
|
||||||
timestamp = datetime.now().strftime("%H:%M:%S")
|
|
||||||
self.output_history[agent.name].append(
|
|
||||||
{
|
|
||||||
"time": timestamp,
|
|
||||||
"content": current_output,
|
|
||||||
"style": style,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
if self.live:
|
|
||||||
self.live.update(self._create_layout())
|
|
||||||
await asyncio.sleep(delay)
|
|
||||||
|
|
||||||
def log_agent_output(self, agent: Agent, text: str):
|
|
||||||
asyncio.create_task(
|
|
||||||
self.stream_output(
|
|
||||||
agent=agent,
|
|
||||||
text=text,
|
|
||||||
title=f"{agent.name} Output {agent.max_loops}",
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
async def print_progress(
|
|
||||||
self,
|
|
||||||
description: str,
|
|
||||||
task_fn: Callable,
|
|
||||||
*args: Any,
|
|
||||||
**kwargs: Any,
|
|
||||||
) -> Any:
|
|
||||||
"""
|
|
||||||
Displays a progress spinner while executing a task.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
description (str): Task description.
|
|
||||||
task_fn (Callable): Function to execute.
|
|
||||||
*args (Any): Arguments for task_fn.
|
|
||||||
**kwargs (Any): Keyword arguments for task_fn.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Any: The result of task_fn.
|
|
||||||
"""
|
|
||||||
progress = Progress(
|
|
||||||
SpinnerColumn(),
|
|
||||||
TextColumn("[progress.description]{task.description}"),
|
|
||||||
TimeElapsedColumn(),
|
|
||||||
)
|
|
||||||
|
|
||||||
try:
|
|
||||||
with progress:
|
|
||||||
task = progress.add_task(description, total=None)
|
|
||||||
result = await task_fn(*args, **kwargs)
|
|
||||||
progress.update(task, completed=True)
|
|
||||||
return result
|
|
||||||
except Exception as e:
|
|
||||||
progress.stop()
|
|
||||||
raise e
|
|
||||||
|
|
||||||
def _create_layout(self) -> Layout:
|
|
||||||
"""Creates the main visualization layout."""
|
|
||||||
layout = Layout()
|
|
||||||
layout.split_row(
|
|
||||||
Layout(name="info", ratio=2),
|
|
||||||
Layout(name="outputs", ratio=3),
|
|
||||||
)
|
|
||||||
|
|
||||||
layout["info"].update(self._create_unified_info_panel())
|
|
||||||
layout["outputs"].update(self._create_outputs_panel())
|
|
||||||
|
|
||||||
return layout
|
|
||||||
|
|
||||||
async def start(self):
|
|
||||||
"""Starts the visualization with live updates."""
|
|
||||||
with Live(
|
|
||||||
self._create_layout(),
|
|
||||||
refresh_per_second=int(1 / self.refresh_rate),
|
|
||||||
) as self.live:
|
|
||||||
while True:
|
|
||||||
|
|
||||||
def process_agent_streams(agent: Agent):
|
|
||||||
while not agent.output_stream.empty():
|
|
||||||
new_output = agent.output_stream.get()
|
|
||||||
asyncio.create_task(
|
|
||||||
self.stream_output(agent, new_output)
|
|
||||||
)
|
|
||||||
for child in getattr(agent, "children", []):
|
|
||||||
process_agent_streams(child)
|
|
||||||
|
|
||||||
# Process streams for each root agent
|
|
||||||
for agent in self.agents:
|
|
||||||
process_agent_streams(agent)
|
|
||||||
await asyncio.sleep(self.refresh_rate)
|
|
||||||
|
|
||||||
|
|
||||||
# # Example usage
|
|
||||||
# if __name__ == "__main__":
|
|
||||||
# # Create swarm metadata
|
|
||||||
# swarm_metadata = SwarmMetadata(
|
|
||||||
# name="Financial Advisory Swarm",
|
|
||||||
# description="Intelligent swarm for financial analysis and advisory",
|
|
||||||
# version="1.0.0",
|
|
||||||
# type="hierarchical",
|
|
||||||
# created_at=datetime.now(),
|
|
||||||
# author="AI Research Team",
|
|
||||||
# # tags=["finance", "analysis", "advisory"],
|
|
||||||
# primary_objective="Provide comprehensive financial analysis and recommendations",
|
|
||||||
# secondary_objectives=[
|
|
||||||
# "Monitor market trends",
|
|
||||||
# "Analyze competitor behavior",
|
|
||||||
# "Generate investment strategies",
|
|
||||||
# ],
|
|
||||||
# )
|
|
||||||
|
|
||||||
# # Create agent hierarchy with detailed parameters
|
|
||||||
# analyst = Agent(
|
|
||||||
# name="Financial Analyst",
|
|
||||||
# role="Analysis",
|
|
||||||
# description="Analyzes financial data and market trends",
|
|
||||||
# agent_type="LLM",
|
|
||||||
# capabilities=[
|
|
||||||
# "data analysis",
|
|
||||||
# "trend detection",
|
|
||||||
# "risk assessment",
|
|
||||||
# ],
|
|
||||||
# parameters={"model": "gpt-4", "temperature": 0.7},
|
|
||||||
# metadata={
|
|
||||||
# "specialty": "Market Analysis",
|
|
||||||
# "confidence_threshold": "0.85",
|
|
||||||
# },
|
|
||||||
# )
|
|
||||||
|
|
||||||
# researcher = Agent(
|
|
||||||
# name="Market Researcher",
|
|
||||||
# role="Research",
|
|
||||||
# description="Conducts market research and competitor analysis",
|
|
||||||
# agent_type="Neural",
|
|
||||||
# capabilities=[
|
|
||||||
# "competitor analysis",
|
|
||||||
# "market sentiment",
|
|
||||||
# "trend forecasting",
|
|
||||||
# ],
|
|
||||||
# parameters={"batch_size": 32, "learning_rate": 0.001},
|
|
||||||
# metadata={
|
|
||||||
# "data_sources": "Bloomberg, Reuters",
|
|
||||||
# "update_frequency": "1h",
|
|
||||||
# },
|
|
||||||
# )
|
|
||||||
|
|
||||||
# advisor = Agent(
|
|
||||||
# name="Investment Advisor",
|
|
||||||
# role="Advisory",
|
|
||||||
# description="Provides investment recommendations",
|
|
||||||
# agent_type="Hybrid",
|
|
||||||
# capabilities=[
|
|
||||||
# "portfolio optimization",
|
|
||||||
# "risk management",
|
|
||||||
# "strategy generation",
|
|
||||||
# ],
|
|
||||||
# parameters={
|
|
||||||
# "risk_tolerance": "moderate",
|
|
||||||
# "time_horizon": "long",
|
|
||||||
# },
|
|
||||||
# metadata={
|
|
||||||
# "certification": "CFA Level 3",
|
|
||||||
# "specialization": "Equity",
|
|
||||||
# },
|
|
||||||
# children=[analyst, researcher],
|
|
||||||
# )
|
|
||||||
|
|
||||||
# # Create visualization
|
|
||||||
# viz = SwarmVisualizationRich(
|
|
||||||
# swarm_metadata=swarm_metadata,
|
|
||||||
# root_agent=advisor,
|
|
||||||
# refresh_rate=0.1,
|
|
||||||
# )
|
|
||||||
|
|
||||||
# # Example of streaming output simulation
|
|
||||||
# async def simulate_outputs():
|
|
||||||
# await viz.stream_output(
|
|
||||||
# advisor,
|
|
||||||
# "Analyzing market conditions...\nGenerating investment advice...",
|
|
||||||
# )
|
|
||||||
# await viz.stream_output(
|
|
||||||
# analyst,
|
|
||||||
# "Processing financial data...\nIdentifying trends...",
|
|
||||||
# )
|
|
||||||
# await viz.stream_output(
|
|
||||||
# researcher,
|
|
||||||
# "Researching competitor movements...\nAnalyzing market share...",
|
|
||||||
# )
|
|
||||||
|
|
||||||
# # Run the visualization
|
|
||||||
# async def main():
|
|
||||||
# viz_task = asyncio.create_task(viz.start())
|
|
||||||
# await simulate_outputs()
|
|
||||||
# await viz_task
|
|
||||||
|
|
||||||
# asyncio.run(main())
|
|
||||||
@ -1,79 +0,0 @@
|
|||||||
# 'v0-1.0-md'
|
|
||||||
# https://api.v0.dev/v1/chat/completions
|
|
||||||
|
|
||||||
import time
|
|
||||||
from swarms import Agent
|
|
||||||
import os
|
|
||||||
from dotenv import load_dotenv
|
|
||||||
|
|
||||||
load_dotenv()
|
|
||||||
|
|
||||||
FRONT_END_DEVELOPMENT_PROMPT = """
|
|
||||||
You are an expert full-stack development agent with comprehensive expertise in:
|
|
||||||
|
|
||||||
Frontend Development:
|
|
||||||
- Modern React.js/Next.js architecture and best practices
|
|
||||||
- Advanced TypeScript implementation and type safety
|
|
||||||
- State-of-the-art UI/UX design patterns
|
|
||||||
- Responsive and accessible design principles
|
|
||||||
- Component-driven development with Storybook
|
|
||||||
- Modern CSS frameworks (Tailwind, Styled-Components)
|
|
||||||
- Performance optimization and lazy loading
|
|
||||||
|
|
||||||
Backend Development:
|
|
||||||
- Scalable microservices architecture
|
|
||||||
- RESTful and GraphQL API design
|
|
||||||
- Database optimization and schema design
|
|
||||||
- Authentication and authorization systems
|
|
||||||
- Serverless architecture and cloud services
|
|
||||||
- CI/CD pipeline implementation
|
|
||||||
- Security best practices and OWASP guidelines
|
|
||||||
|
|
||||||
Development Practices:
|
|
||||||
- Test-Driven Development (TDD)
|
|
||||||
- Clean Code principles
|
|
||||||
- Documentation (TSDoc/JSDoc)
|
|
||||||
- Git workflow and version control
|
|
||||||
- Performance monitoring and optimization
|
|
||||||
- Error handling and logging
|
|
||||||
- Code review best practices
|
|
||||||
|
|
||||||
Your core responsibilities include:
|
|
||||||
1. Developing production-grade TypeScript applications
|
|
||||||
2. Implementing modern, accessible UI components
|
|
||||||
3. Designing scalable backend architectures
|
|
||||||
4. Writing comprehensive documentation
|
|
||||||
5. Ensuring type safety across the stack
|
|
||||||
6. Optimizing application performance
|
|
||||||
7. Implementing security best practices
|
|
||||||
|
|
||||||
You maintain strict adherence to:
|
|
||||||
- TypeScript strict mode and proper typing
|
|
||||||
- SOLID principles and clean architecture
|
|
||||||
- Accessibility standards (WCAG 2.1)
|
|
||||||
- Performance budgets and metrics
|
|
||||||
- Security best practices
|
|
||||||
- Comprehensive test coverage
|
|
||||||
- Modern design system principles
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Initialize the agent
|
|
||||||
agent = Agent(
|
|
||||||
agent_name="Quantitative-Trading-Agent",
|
|
||||||
agent_description="Advanced quantitative trading and algorithmic analysis agent",
|
|
||||||
system_prompt=FRONT_END_DEVELOPMENT_PROMPT,
|
|
||||||
max_loops=1,
|
|
||||||
model_name="v0-1.0-md",
|
|
||||||
dynamic_temperature_enabled=True,
|
|
||||||
output_type="all",
|
|
||||||
# safety_prompt_on=True,
|
|
||||||
llm_api_key=os.getenv("V0_API_KEY"),
|
|
||||||
llm_base_url="https://api.v0.dev/v1/chat/completions",
|
|
||||||
)
|
|
||||||
|
|
||||||
out = agent.run(
|
|
||||||
"Build a simple web app that allows users to upload a file and then download it."
|
|
||||||
)
|
|
||||||
|
|
||||||
time.sleep(10)
|
|
||||||
print(out)
|
|
||||||
Loading…
Reference in new issue