Update README.md

README.md

@@ -1173,82 +1173,78 @@ autoswarm.run("Analyze these financial data and give me a summary")
 Inspired by Einops and einsum, this orchestration techniques enables you to map out the relationships between various agents. For example you specify linear and sequential relationships like `a -> a1 -> a2 -> a3` or concurrent relationships where the first agent will send a message to 3 agents all at once: `a -> a1, a2, a3`. You can customize your workflow to mix sequential and concurrent relationships
 
 ```python
-from swarms import Agent, Anthropic, AgentRearrange, 
+from swarms import Agent, AgentRearrange, rearrange, Anthropic
 
-## Initialize the workflow
-agent = Agent(
-    agent_name="t",
-    agent_description=(
-        "Generate a transcript for a youtube video on what swarms"
-        " are!"
-    ),
-    system_prompt=(
-        "Generate a transcript for a youtube video on what swarms"
-        " are!"
-    ),
+
+# Initialize the director agent
+
+director = Agent(
+    agent_name="Director",
+    system_prompt="Directs the tasks for the workers",
     llm=Anthropic(),
     max_loops=1,
-    autosave=True,
     dashboard=False,
     streaming_on=True,
     verbose=True,
     stopping_token="<DONE>",
+    state_save_file_type="json",
+    saved_state_path="director.json",
 )
 
-agent2 = Agent(
-    agent_name="t1",
-    agent_description=(
-        "Generate a transcript for a youtube video on what swarms"
-        " are!"
-    ),
+
+# Initialize worker 1
+
+worker1 = Agent(
+    agent_name="Worker1",
+    system_prompt="Generates a transcript for a youtube video on what swarms are",
     llm=Anthropic(),
     max_loops=1,
-    system_prompt="Summarize the transcript",
-    autosave=True,
     dashboard=False,
     streaming_on=True,
     verbose=True,
     stopping_token="<DONE>",
+    state_save_file_type="json",
+    saved_state_path="worker1.json",
 )
 
-agent3 = Agent(
-    agent_name="t2",
-    agent_description=(
-        "Generate a transcript for a youtube video on what swarms"
-        " are!"
-    ),
+
+# Initialize worker 2
+worker2 = Agent(
+    agent_name="Worker2",
+    system_prompt="Summarizes the transcript generated by Worker1",
     llm=Anthropic(),
     max_loops=1,
-    system_prompt="Finalize the transcript",
-    autosave=True,
     dashboard=False,
     streaming_on=True,
     verbose=True,
     stopping_token="<DONE>",
+    state_save_file_type="json",
+    saved_state_path="worker2.json",
 )
 
 
-# Rearrange the agents
-rearrange = AgentRearrange(
-    agents=[agent, agent2, agent3],
-    verbose=True,
-    # custom_prompt="Summarize the transcript",
+# Create a list of agents
+agents = [director, worker1, worker2]
+
+# Define the flow pattern
+flow = "Director -> Worker1 -> Worker2"
+
+# Using AgentRearrange class
+agent_system = AgentRearrange(agents=agents, flow=flow)
+output = agent_system.run(
+    "Create a format to express and communicate swarms of llms in a structured manner for youtube"
 )
+print(output)
 
-# Run the workflow on a task
-results = rearrange(
-    # pattern="t -> t1, t2 -> t2",
-    pattern="t -> t1 -> t2",
-    default_task=(
-        "Generate a transcript for a YouTube video on what swarms"
-        " are!"
-    ),
-    t="Generate a transcript for a YouTube video on what swarms are!",
-    # t2="Summarize the transcript",
-    # t3="Finalize the transcript",
+
+# Using rearrange function
+output = rearrange(
+    agents,
+    flow,
+    "Create a format to express and communicate swarms of llms in a structured manner for youtube",
 )
-# print(results)
 
+print(output)
 
 ```