Merge branch 'kyegomez:master' into Longterm-Memory-Access-Issue

agent_with_auto_generate_prompt.py

@@ -0,0 +1,48 @@
+import os
+from swarms import Agent
+from swarm_models import OpenAIChat
+
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Retrieve the OpenAI API key from the environment variable
+api_key = os.getenv("GROQ_API_KEY")
+
+# Initialize the model for OpenAI Chat
+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 agent with automated prompt engineering enabled
+agent = Agent(
+    agent_name="Financial-Analysis-Agent",
+    system_prompt=None,  # System prompt is dynamically generated
+    agent_description=None,
+    llm=model,
+    max_loops=1,
+    autosave=True,
+    dashboard=False,
+    verbose=False,
+    dynamic_temperature_enabled=True,
+    saved_state_path="finance_agent.json",
+    user_name="Human:",
+    return_step_meta=False,
+    output_type="string",
+    streaming_on=False,
+    auto_generate_prompt=True,  # Enable automated prompt engineering
+)
+
+# Run the agent with a task description and specify the device
+agent.run(
+    "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria",
+    ## Will design a system prompt based on the task if description and system prompt are None
+    device="cpu",
+)
+
+# Print the dynamically generated system prompt
+print(agent.system_prompt)

agents_multi_agent.yaml

@@ -51,3 +51,7 @@ swarm_architecture:
   max_loops: 5
   swarm_type: "ConcurrentWorkflow"
   task: "How can we trademark concepts as a delaware C CORP for free"
+  autosave: true
+  return_json: false
+  # flow: "agent_name -> agent_name 2 -> agent_name 3" # not neccessary if not using agent rearrange
+

concurrent_swarm_example.py

@@ -0,0 +1,100 @@
+import os
+from swarms import Agent, ConcurrentWorkflow
+from swarm_models import OpenAIChat
+from loguru import logger
+
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Retrieve the OpenAI API key from the environment variable
+api_key = os.getenv("GROQ_API_KEY")
+
+# Initialize the model for OpenAI Chat
+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,
+)
+
+
+logger.add("swarms_example.log", rotation="10 MB")
+
+agents = [
+    Agent(
+        agent_name=f"Term-Sheet-Analysis-Agent-{i}",
+        system_prompt="Analyze the term sheet for investment opportunities.",
+        llm=model,
+        max_loops=1,
+        autosave=True,
+        dashboard=False,
+        verbose=True,
+        dynamic_temperature_enabled=True,
+        saved_state_path=f"term_sheet_analysis_agent_{i}.json",
+        user_name="swarms_corp",
+        retry_attempts=1,
+        context_length=200000,
+        return_step_meta=False,
+    )
+    for i in range(3)  # Adjust number of agents as needed
+]
+
+# Initialize the workflow with the list of agents
+workflow = ConcurrentWorkflow(
+    agents=agents,
+    metadata_output_path="term_sheet_analysis_metadata.json",
+    return_str_on=True,
+    auto_generate_prompts=True,
+    auto_save=True,
+)
+
+# Define the task for all agents
+task = "Analyze the term sheet for investment opportunities and identify key terms and conditions."
+
+# Run the workflow and save metadata
+metadata = workflow.run(task)
+logger.info(metadata)
+
+
+# # Example usage of the run_batched method
+# tasks = [
+#     "What are the benefits of a ROTH IRA?",
+#     "How do I open a ROTH IRA account?",
+# ]
+# results = workflow.run_batched(tasks)
+# print("\nRun Batched Method Output:")
+# print(results)
+
+# # Example usage of the run_async method
+# async def run_async_example():
+#     future = workflow.run_async(task)
+#     result = await future
+#     print("\nRun Async Method Output:")
+#     print(result)
+
+# # Example usage of the run_batched_async method
+# async def run_batched_async_example():
+#     futures = workflow.run_batched_async(tasks)
+#     results = await asyncio.gather(*futures)
+#     print("\nRun Batched Async Method Output:")
+#     print(results)
+
+# # Example usage of the run_parallel method
+# parallel_results = workflow.run_parallel(tasks)
+# print("\nRun Parallel Method Output:")
+# print(parallel_results)
+
+# # Example usage of the run_parallel_async method
+# async def run_parallel_async_example():
+#     parallel_futures = workflow.run_parallel_async(tasks)
+#     parallel_results = await asyncio.gather(*parallel_futures)
+#     print("\nRun Parallel Async Method Output:")
+#     print(parallel_results)
+
+# # To run the async examples, you would typically use an event loop
+# if __name__ == "__main__":
+#     asyncio.run(run_async_example())
+#     asyncio.run(run_batched_async_example())
+#     asyncio.run(run_parallel_async_example())