diff --git a/pyproject.toml b/pyproject.toml
index 7b286f85..6afd6211 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "swarms"
-version = "7.2.6"
+version = "7.2.7"
description = "Swarms - TGSC"
license = "MIT"
authors = ["Kye Gomez <kye@apac.ai>"]
diff --git a/swarms/structs/auto_swarm_builder.py b/swarms/structs/auto_swarm_builder.py
index 9e757c70..f7c35aa3 100644
--- a/swarms/structs/auto_swarm_builder.py
+++ b/swarms/structs/auto_swarm_builder.py
@@ -257,7 +257,6 @@ class AutoSwarmBuilder:
return agent
- # @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def swarm_router(
self,
agents: List[Agent],
@@ -292,14 +291,14 @@ class AutoSwarmBuilder:
)
-example = AutoSwarmBuilder(
- name="ChipDesign-Swarm",
- description="A swarm of specialized AI agents collaborating on chip architecture, logic design, verification, and optimization to create novel semiconductor designs",
- max_loops=1,
-)
+# example = AutoSwarmBuilder(
+# name="ChipDesign-Swarm",
+# description="A swarm of specialized AI agents collaborating on chip architecture, logic design, verification, and optimization to create novel semiconductor designs",
+# max_loops=1,
+# )
-print(
- example.run(
- "Design a new AI accelerator chip optimized for transformer model inference. Consider the following aspects: 1) Overall chip architecture and block diagram 2) Memory hierarchy and interconnects 3) Processing elements and data flow 4) Power and thermal considerations 5) Physical layout recommendations -> "
- )
-)
+# print(
+# example.run(
+# "Design a new AI accelerator chip optimized for transformer model inference. Consider the following aspects: 1) Overall chip architecture and block diagram 2) Memory hierarchy and interconnects 3) Processing elements and data flow 4) Power and thermal considerations 5) Physical layout recommendations -> "
+# )
+# )
diff --git a/loguru_test.py b/swarms/utils/loguru_test.py
similarity index 79%
rename from loguru_test.py
rename to swarms/utils/loguru_test.py
index f72ca938..6d3d777f 100644
--- a/loguru_test.py
+++ b/swarms/utils/loguru_test.py
@@ -1,23 +1,23 @@
-from typing import Callable, Any
-import psutil
import asyncio
+from dataclasses import dataclass
+from datetime import datetime
+from queue import Queue
+from typing import Any, Callable, Dict, List, Optional
+
+import psutil
from rich.console import Console
-from rich.table import Table
-from rich.panel import Panel
from rich.layout import Layout
-from rich.tree import Tree
+from rich.live import Live
+from rich.panel import Panel
from rich.progress import (
Progress,
- TimeElapsedColumn,
- TextColumn,
SpinnerColumn,
+ TextColumn,
+ TimeElapsedColumn,
)
-from rich.live import Live
+from rich.table import Table
from rich.text import Text
-from typing import Dict, Optional, List
-from dataclasses import dataclass
-from queue import Queue
-from datetime import datetime
+from rich.tree import Tree
try:
import pynvml
@@ -400,107 +400,107 @@ class SwarmVisualizationRich:
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())
+# # 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())