[FEAT][SpreadSheetSwarm] [FEAT][TaskQueueSwarm]

.env.example

@@ -15,7 +15,7 @@ EVAL_PORT=8000
 MODEL_NAME="gpt-4"
 
 USE_GPU=True
-PLAYGROUND_DIR="playground"
+PLAYGROUND_DIR="examples"
 
 LOG_LEVEL="INFO"
 BOT_NAME="Orca"

.gitignore

@@ -13,6 +13,7 @@ static/generated
 runs
 Financial-Analysis-Agent_state.json
 artifacts_five
+encryption
 errors
 chroma
 agent_workspace

README.md

@@ -63,7 +63,7 @@ $ pip3 install -U swarms
 
 # Usage Examples 🤖
 
-Run example in Collab: <a target="_blank" href="https://colab.research.google.com/github/kyegomez/swarms/blob/master/playground/collab/swarms_example.ipynb">
+Run example in Collab: <a target="_blank" href="https://colab.research.google.com/github/kyegomez/swarms/blob/master/examples/collab/swarms_example.ipynb">
 <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 

agent_settings.py

@@ -21,20 +21,10 @@ agent = Agent(
     autosave=False,
     dashboard=False,
     verbose=True,
-    # interactive=True, # Set to False to disable interactive mode
     dynamic_temperature_enabled=True,
     saved_state_path="finance_agent.json",
-    # tools=[#Add your functions here# ],
-    # stopping_token="Stop!",
-    # docs_folder="docs", # Enter your folder name
-    # pdf_path="docs/finance_agent.pdf",
-    # sop="Calculate the profit for a company.",
-    # sop_list=["Calculate the profit for a company."],
     user_name="swarms_corp",
-    # # docs="",
     retry_attempts=3,
-    # context_length=1000,
-    # tool_schema = dict
     context_length=200000,
     tool_system_prompt=None,
 )

agent_with_rag.py

@@ -0,0 +1,43 @@
+import os
+
+from swarms_memory import ChromaDB
+
+from swarms import Agent, Anthropic
+from swarms.prompts.finance_agent_sys_prompt import (
+    FINANCIAL_AGENT_SYS_PROMPT,
+)
+
+# Initilaize the chromadb client
+chromadb = ChromaDB(
+    metric="cosine",
+    output_dir="fiance_agent_rag",
+    # docs_folder="artifacts", # Folder of your documents
+)
+
+# Model
+model = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
+
+
+# Initialize the agent
+agent = Agent(
+    agent_name="Financial-Analysis-Agent",
+    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+    agent_description="Agent creates ",
+    llm=model,
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    streaming_on=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="finance_agent.json",
+    user_name="swarms_corp",
+    retry_attempts=3,
+    context_length=200000,
+    long_term_memory=chromadb,
+)
+
+
+agent.run(
+    "What are the components of a startups stock incentive equity plan"
+)

agent_with_rag_and_tools.py

@@ -0,0 +1,116 @@
+from swarms import Agent, OpenAIChat
+from swarms_memory import ChromaDB
+import subprocess
+import os
+
+# Making an instance of the ChromaDB class
+memory = ChromaDB(
+    metric="cosine",
+    n_results=3,
+    output_dir="results",
+    docs_folder="docs",
+)
+
+# Model
+model = OpenAIChat(
+    api_key=os.getenv("OPENAI_API_KEY"),
+    model_name="gpt-4o-mini",
+    temperature=0.1,
+)
+
+
+# Tools in swarms are simple python functions and docstrings
+def terminal(
+    code: str,
+):
+    """
+    Run code in the terminal.
+
+    Args:
+        code (str): The code to run in the terminal.
+
+    Returns:
+        str: The output of the code.
+    """
+    out = subprocess.run(
+        code, shell=True, capture_output=True, text=True
+    ).stdout
+    return str(out)
+
+
+def browser(query: str):
+    """
+    Search the query in the browser with the `browser` tool.
+
+    Args:
+        query (str): The query to search in the browser.
+
+    Returns:
+        str: The search results.
+    """
+    import webbrowser
+
+    url = f"https://www.google.com/search?q={query}"
+    webbrowser.open(url)
+    return f"Searching for {query} in the browser."
+
+
+def create_file(file_path: str, content: str):
+    """
+    Create a file using the file editor tool.
+
+    Args:
+        file_path (str): The path to the file.
+        content (str): The content to write to the file.
+
+    Returns:
+        str: The result of the file creation operation.
+    """
+    with open(file_path, "w") as file:
+        file.write(content)
+    return f"File {file_path} created successfully."
+
+
+def file_editor(file_path: str, mode: str, content: str):
+    """
+    Edit a file using the file editor tool.
+
+    Args:
+        file_path (str): The path to the file.
+        mode (str): The mode to open the file in.
+        content (str): The content to write to the file.
+
+    Returns:
+        str: The result of the file editing operation.
+    """
+    with open(file_path, mode) as file:
+        file.write(content)
+    return f"File {file_path} edited successfully."
+
+
+# Agent
+agent = Agent(
+    agent_name="Devin",
+    system_prompt=(
+        "Autonomous agent that can interact with humans and other"
+        " agents. Be Helpful and Kind. Use the tools provided to"
+        " assist the user. Return all code in markdown format."
+    ),
+    llm=model,
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    stopping_token="<DONE>",
+    interactive=True,
+    tools=[terminal, browser, file_editor, create_file],
+    streaming=True,
+    long_term_memory=memory,
+)
+
+# Run the agent
+out = agent(
+    "Create a CSV file with the latest tax rates for C corporations in the following ten states and the District of Columbia: Alabama, California, Florida, Georgia, Illinois, New York, North Carolina, Ohio, Texas, and Washington."
+)
+print(out)

docs/applications/business-analyst-agent.md

@@ -1,6 +1,6 @@
 ## Building Analyst Agents with Swarms to write Business Reports
 
-> Jupyter Notebook accompanying this post is accessible at: [Business Analyst Agent Notebook](https://github.com/kyegomez/swarms/blob/master/playground/demos/business_analysis_swarm/business-analyst-agent.ipynb)
+> Jupyter Notebook accompanying this post is accessible at: [Business Analyst Agent Notebook](https://github.com/kyegomez/swarms/blob/master/examples/demos/business_analysis_swarm/business-analyst-agent.ipynb)
 
 Solving a business problem often involves preparing a Business Case Report. This report comprehensively analyzes the problem, evaluates potential solutions, and provides evidence-based recommendations and an implementation plan to effectively address the issue and drive business value. While the process of preparing one requires an experienced business analyst, the workflow can be augmented using AI agents. Two candidates stick out as areas to work on:
 

docs/swarms/agents/third_party.md

@@ -174,7 +174,7 @@ from typing import List, Optional
 
 from langchain.agents import AgentExecutor, LLMSingleActionAgent, Tool
 from langchain.chains import LLMChain
-from langchain.llms import OpenAI
+from langchain_community.llms import OpenAI
 from langchain.prompts import StringPromptTemplate
 from langchain.tools import DuckDuckGoSearchRun
 

docs/swarms/structs/index.md

@@ -7,7 +7,7 @@ Swarms is an enterprise grade and production ready multi-agent collaboration fra
 | Models                       | Pre-trained models that can be utilized for various tasks within the swarm framework.                                                                                  | ⭐⭐⭐                    | [Documentation](https://docs.swarms.world/en/latest/swarms/models/)            |
 | Models APIs                  | APIs to interact with and utilize the models effectively, providing interfaces for inference, training, and fine-tuning.                                               | ⭐⭐⭐                    | [Documentation](https://docs.swarms.world/en/latest/swarms/models/)            |
 | Agents with Tools            | Agents equipped with specialized tools to perform specific tasks more efficiently, such as data processing, analysis, or interaction with external systems.            | ⭐⭐⭐⭐                   | [Documentation](https://medium.com/@kyeg/the-swarms-tool-system-functions-pydantic-basemodels-as-tools-and-radical-customization-c2a2e227b8ca)            |
-| Agents with Memory                       | Mechanisms for agents to store and recall past interactions, improving learning and adaptability over time.                                                            | ⭐⭐⭐⭐                   | [Documentation](https://github.com/kyegomez/swarms/blob/master/playground/structs/agent/agent_with_longterm_memory.py)            |
+| Agents with Memory                       | Mechanisms for agents to store and recall past interactions, improving learning and adaptability over time.                                                            | ⭐⭐⭐⭐                   | [Documentation](https://github.com/kyegomez/swarms/blob/master/examples/structs/agent/agent_with_longterm_memory.py)            |
 | Multi-Agent Orchestration    | Coordination of multiple agents to work together seamlessly on complex tasks, leveraging their individual strengths to achieve higher overall performance.              | ⭐⭐⭐⭐⭐                  | [Documentation]()            |
 
 The performance impact is rated on a scale from one to five stars, with multi-agent orchestration being the highest due to its ability to combine the strengths of multiple agents and optimize task execution.
@@ -22,7 +22,7 @@ The performance impact is rated on a scale from one to five stars, with multi-ag
 # Usage Examples 🤖
 
 ### Google Collab Example
-Run example in Collab: <a target="_blank" href="https://colab.research.google.com/github/kyegomez/swarms/blob/master/playground/collab/swarms_example.ipynb">
+Run example in Collab: <a target="_blank" href="https://colab.research.google.com/github/kyegomez/swarms/blob/master/examples/collab/swarms_example.ipynb">
 <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 

examples/agents/3rd_party_agents/langchain.py

@@ -2,7 +2,7 @@ from typing import List, Optional
 
 from langchain.agents import AgentExecutor, LLMSingleActionAgent, Tool
 from langchain.chains import LLMChain
-from langchain.llms import OpenAI
+from langchain_community.llms import OpenAI
 from langchain.prompts import StringPromptTemplate
 from langchain.tools import DuckDuckGoSearchRun
 

examples/agents/monitoring/agent_ops.py

@@ -14,7 +14,7 @@ Requirements:
 """
 
 ################ Adding project root to PYTHONPATH ################################
-# If you are running playground examples in the project files directly, use this:
+# If you are running examples examples in the project files directly, use this:
 
 import sys
 import os

examples/agents/monitoring/agent_ops_tools.py

@@ -14,7 +14,7 @@ Requirements:
 """
 
 ################ Adding project root to PYTHONPATH ################################
-# If you are running playground examples in the project files directly, use this:
+# If you are running examples examples in the project files directly, use this:
 
 import sys
 import os

examples/agents/tools/function_calling/audience_generator_agent.py

@@ -122,7 +122,6 @@ model = OpenAIFunctionCaller(
 # The OpenAIFunctionCaller class is used to interact with the OpenAI API and make function calls.
 out = model.run(
     """
-
 Announcing, The Agent Marketplace 🤖🤖🤖
 Your one-stop hub to discover and share agents, prompts, and tools.
 

examples/demos/agent_in_5/youtube_demo_agent.py

@@ -6,7 +6,7 @@ Building an Autonomous Agent in 5 minutes with:
 """
 
 from swarms import Agent, OpenAIChat, tool
-from playground.demos.agent_in_5.chroma_db import ChromaDB
+from examples.demos.agent_in_5.chroma_db import ChromaDB
 
 # Initialize the memory
 chroma = ChromaDB(

examples/demos/agentic_space_traffic_control/game.py

@@ -3,7 +3,7 @@ from swarms import (
     llama3Hosted,
     AgentRearrange,
 )
-from playground.demos.agentic_space_traffic_control.prompts import (
+from examples.demos.agentic_space_traffic_control.prompts import (
     WEATHER_ANALYST_SYSTEM_PROMPT,
     SPACE_TRAFFIC_CONTROLLER_SYS_PROMPT,
 )