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[BabyAGI](https://github.com/yoheinakajima/babyagi) was one of the first multi-agent systems that used LLMs for performing useful tasks. It makes use of several different types of agents, such as a task creation agent and an execution agent, as shown in the diagram below.
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However, here's Yohei (creator of BabyAGI) admitting that [BabyAGI isn't a true multi-agent system](https://x.com/yoheinakajima/status/1781183534998380576) since the agents use the same LLM and code base, and run on the same server.
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Using the Naptha SDK and node infrastructure, we implemented BabyAGI as a true multi-agent system for the first time. You can check out a demo video of this multi-node workflow [here](https://www.youtube.com/watch?v=nzV04zOA0f0).
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Using the Naptha SDK and node infrastructure, we implemented BabyAGI as a true multi-agent system for the first time. You can check out a demo video of this multi-node workflow [here](https://www.youtube.com/watch?v=nzV04zOA0f0).
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naptha run babyagi -p "objective='Research the history of football'" --worker_nodes "http://node.naptha.ai:7001,http://node1.naptha.ai:7001"
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```
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This runs the flow across three nodes in total - one orchestrator node (whichever you have set as the ```NODE_URL``` in the .env file of the Naptha SDK), and two worker nodes (that you have set using the ```--worker_nodes``` flag).
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### Configuration Breakdown:
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-`objective`: Your desired research or task objective
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-`worker_nodes`: Comma-separated list of worker node URLs
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This runs the flow across three nodes in total - one orchestrator node (whichever you have set as the ```NODE_URL``` in the .env file of the Naptha SDK),
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and two worker nodes (that you have set using the ```--worker_nodes``` flag).
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:::tip
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Start with simple objectives to understand the workflow, then gradually increase complexity as you become familiar with the system.
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