[CLEANUP]

.gitignore

@@ -8,7 +8,8 @@ audio/
 video/
 artifacts_three
 dataframe/
-
+.ruff_cache
+.pytest_cache
 static/generated
 runs
 Financial-Analysis-Agent_state.json

docs/swarms/install/install.md

@@ -127,7 +127,7 @@ Before you begin, ensure you have the following installed:
                 poetry install --extras "desktop"
                 ```
 
-=== "Using Docker"
+=== "Using Docker COMING SOON [DOES NOT WORK YET]"
 
     Docker is an excellent option for creating isolated and reproducible environments, suitable for both development and production.
 

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "5.4.8"
+version = "5.5.0"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]

swarms/structs/agent.py

@@ -14,8 +14,6 @@ from loguru import logger
 from pydantic import BaseModel
 from termcolor import colored
 
-import agentops
-
 from swarms.memory.base_vectordb import BaseVectorDatabase
 from swarms.models.tiktoken_wrapper import TikTokenizer
 from swarms.prompts.agent_system_prompts import AGENT_SYSTEM_PROMPT_3
@@ -93,7 +91,6 @@ ToolUsageType = Union[BaseModel, Dict[str, Any]]
 
 
 # [FEAT][AGENT]
-@agentops.track_agent()
 class Agent(BaseStructure):
     """
     Agent is the backbone to connect LLMs with tools and long term memory. Agent also provides the ability to

swarms/structs/multi_agent_collab.py

@@ -128,7 +128,13 @@ class MultiAgentCollaboration(BaseSwarm):
         *args,
         **kwargs,
     ):
-        super().__init__(*args, **kwargs)
+        super().__init__(
+            name=name,
+            description=description,
+            agents=agents,
+            *args,
+            **kwargs,
+        )
         self.name = name
         self.description = description
         self.director = director