Refactor example to embed model in agents

docs/examples/index.md

@@ -79,6 +79,7 @@ Additionally, we have more comprehensive examples available in [The Swarms Cookb
 | Scheduling | [Round Robin](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/multi_agent_collaboration/round_robin_example.py) | Round-robin task scheduling and execution |
 | Load Balancing | [Load Balancer](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/multi_agent_collaboration/load_balancer_example.py) | Dynamic task distribution system for optimal resource utilization |
 | Consensus | [Majority Voting](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/multi_agent_collaboration/majority_voting.py) | Consensus-building system using democratic voting among agents |
+| Workflow | [MultiAgentCollab Workflow](https://github.com/kyegomez/swarms/blob/master/examples/multi_agent/collaboration_examples/multiagent_collab_workflow.py) | Simple collaborative workflow using `MultiAgentCollaboration` |
 
 ### Industry Applications
 | Category | Example | Description |

examples/multi_agent/collaboration_examples/multiagent_collab_workflow.py

@@ -0,0 +1,32 @@
+from swarms import Agent
+from swarms.structs import MultiAgentCollaboration
+
+# Agents will directly initialize their language models
+
+# Define collaborating agents
+planner = Agent(
+    agent_name="Planner",
+    system_prompt="Break the objective into clear steps.",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+writer = Agent(
+    agent_name="Writer",
+    system_prompt="Use the plan to craft the final answer.",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+# Initialize the collaborative workflow
+swarm = MultiAgentCollaboration(
+    agents=[planner, writer],
+    max_loops=4,
+)
+
+# Kick off the collaboration
+swarm.inject("Manager", "Produce a short overview of reinforcement learning.")
+
+result = swarm.run("Begin")
+print(result)
+