swarms docs

README.md

@@ -93,10 +93,11 @@ out = flow.run("Generate a 10,000 word blog on health and wellness.")
 - Integrate Flow's with various LLMs and Multi-Modality Models
 
 ```python
-from swarms.models import OpenAIChat
+from swarms.models import OpenAIChat, BioGPT, Anthropic
 from swarms.structs import Flow
 from swarms.structs.sequential_workflow import SequentialWorkflow
 
+
 # Example usage
 api_key = (
     ""  # Your actual API key here
@@ -109,13 +110,21 @@ llm = OpenAIChat(
     max_tokens=3000,
 )
 
-# Initialize the Flow with the language flow
+biochat = BioGPT()
+
+# Use Anthropic
+anthropic = Anthropic()
+
+# Initialize the agent with the language flow
 agent1 = Flow(llm=llm, max_loops=1, dashboard=False)
 
-# Create another Flow for a different task
+# Create another agent for a different task
 agent2 = Flow(llm=llm, max_loops=1, dashboard=False)
 
-agent3 = Flow(llm=llm, max_loops=1, dashboard=False)
+# Create another agent for a different task
+agent3 = Flow(llm=biochat, max_loops=1, dashboard=False)
+
+# agent4 = Flow(llm=anthropic, max_loops="auto")
 
 # Create the workflow
 workflow = SequentialWorkflow(max_loops=1)

docs/swarms/index.md

@@ -1,22 +1,7 @@
 # Swarms
 
-<div align="center">
-
 Swarms is a modular framework that enables reliable and useful multi-agent collaboration at scale to automate real-world tasks.
 
-<!-- 
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 ## Vision
 At Swarms, we're transforming the landscape of AI from siloed AI agents to a unified 'swarm' of intelligence. Through relentless iteration and the power of collective insight from our 1500+ Agora researchers, we're developing a groundbreaking framework for AI collaboration. Our mission is to catalyze a paradigm shift, advancing Humanity with the power of unified autonomous AI agent swarms.

sequential_workflow_example.py

@@ -1,9 +1,12 @@
-from swarms.models import OpenAIChat
+from swarms.models import OpenAIChat, BioGPT, Anthropic
 from swarms.structs import Flow
 from swarms.structs.sequential_workflow import SequentialWorkflow
 
+
 # Example usage
-api_key = ""
+api_key = (
+    ""  # Your actual API key here
+)
 
 # Initialize the language flow
 llm = OpenAIChat(
@@ -12,24 +15,36 @@
     max_tokens=3000,
 )
 
-# Initialize the Flow with the language flow
-flow1 = Flow(llm=llm, max_loops=1, dashboard=False)
+biochat = BioGPT()
+
+# Use Anthropic
+anthropic = Anthropic()
+
+# Initialize the agent with the language flow
+agent1 = Flow(llm=llm, max_loops=1, dashboard=False)
 
-# Create another Flow for a different task
-flow2 = Flow(llm=llm, max_loops=1, dashboard=False)
+# Create another agent for a different task
+agent2 = Flow(llm=llm, max_loops=1, dashboard=False)
+
+# Create another agent for a different task
+agent3 = Flow(llm=biochat, max_loops=1, dashboard=False)
+
+# agent4 = Flow(llm=anthropic, max_loops="auto")
 
 # Create the workflow
 workflow = SequentialWorkflow(max_loops=1)
 
 # Add tasks to the workflow
-workflow.add("Generate a 10,000 word blog on health and wellness.", flow1)
+workflow.add("Generate a 10,000 word blog on health and wellness.", agent1)
 
 # Suppose the next task takes the output of the first task as input
-workflow.add("Summarize the generated blog", flow2)
+workflow.add("Summarize the generated blog", agent2)
+
+workflow.add("Create a references sheet of materials for the curriculm", agent3)
 
 # Run the workflow
 workflow.run()
 
 # Output the results
 for task in workflow.tasks:
-    print(f"Task: {task.description}, Result: {task.result}")
+    print(f"Task: {task.description}, Result: {task.result}")