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+--- 
+title: "Using OpenAI Swarm with Hal9: Unlocking New Potential for Enterprise AI"
+authors: "Karan Saxena" 
+tags:
+  - "AI"
+  - "Swarm Intelligence"
+description: "Explore the integration of OpenAI Swarm with Hal9 to
+unlock innovative AI capabilities" 
+---
+
+## Using OpenAI Swarm with Hal9: Unlocking New Potential for EnterpriseAI
+
+As enterprises seek innovative ways to harness the power of AI,
+combining the best tools for flexibility, speed, and collaborative
+insights becomes essential. OpenAI Swarm is a promising approach that
+leverages multiple instances of AI models working together in a
+coordinated manner, effectively mimicking a \"swarm intelligence\"
+system. When paired with Hal9---a platform optimized for customizing and
+deploying generative AI---OpenAI Swarm opens new horizons for
+enterprise-level solutions.
+
+In this post, we'll dive into what OpenAI Swarm is, why it's a valuable
+asset for enterprise use, and how integrating it with Hal9 can transform
+your AI-driven applications. We\'ll also walk through some example code
+and provide visuals generated with this integration.
+
+### What is OpenAI Swarm?
+
+OpenAI Swarm is a system designed to run multiple AI models in parallel,
+each contributing to a collective response based on the input provided.
+By orchestrating a "swarm" of AI models that analyze the same prompt
+from various perspectives, OpenAI Swarm generates richer, more nuanced
+responses. This can be incredibly useful for applications requiring deep
+analytical insights, creative brainstorming, or decision-making in
+complex scenarios.
+
+### Why Use OpenAI Swarm with Hal9?
+
+Hal9 makes it simple for businesses to customize and deploy AI models at
+scale. By integrating OpenAI Swarm with Hal9, enterprises can harness
+the combined power of multiple AI models tailored specifically for their
+unique needs. This setup is especially advantageous in scenarios where a
+single model may not capture the full spectrum of insights required.
+
+### Example Code
+
+```python 
+import json import hal9 as h9 from dotenv import
+load_dotenv from swarm import Swarm, Agent, repl from recommendations
+import book_recommendation, comic_recommendation, movie_recommendation
+
+load_dotenv()
+
+Define transfer functions def transfer_to_receptionist(): return receptionist
+
+def transfer_to_book_expert(): return book_expert
+
+def transfer_to_comic_expert(): return comic_expert
+
+def transfer_to_movie_expert(): return movie_expert
+
+#Define expert agents book_expert = Agent( name="Book Expert", instructions="You are a classic books expert. Provide bookrecommendations.
+#If the conversation drifts away, return to the receptionist.", functions=[book_recommendation, transfer_to_receptionist], )
+
+comic_expert = Agent( name=\"Comic Expert\", instructions=\"You are an
+expert in comics. Provide comic recommendations. If the conversation
+drifts away, return to the receptionist.\",
+functions=\[comic_recommendation, transfer_to_receptionist\], )
+
+movie_expert = Agent( name=\"Movie Expert\", instructions=\"You are an
+expert in movies. Provide movie recommendations. If the conversation
+drifts away, return to the receptionist.\",
+functions=\[movie_recommendation, transfer_to_receptionist\], )
+
+receptionist = Agent( name=\"Receptionist\", instructions=\"You are a
+receptionist. Direct users to a book, comic, or movie expert based on
+their input.\", functions=\[transfer_to_book_expert,
+transfer_to_comic_expert, transfer_to_movie_expert\], )
+
+client = Swarm() messages = h9.load(\'messages\', \[\]) agents =
+{\'Receptionist\': receptionist, \'Comic Expert\': comic_expert, \'Movie
+Expert\': movie_expert} agent = agents\[h9.load(\'last_agent\',
+\'Receptionist\')\]
+
+\# Handle user input user_input = input() messages.append({\"role\":
+\"user\", \"content\": user_input})
+
+response = client.run(agent=agent, messages=messages)
+
+for message in response.messages: if message\[\"role\"\] ==
+\"assistant\": print(f\"{message\[\'sender\'\]}:
+{message\[\'content\'\]}\")
+
+messages.extend(response.messages) agent = response.agent
+
+h9.save(\'messages\', messages, hidden=True) h9.save(\'last_agent\',
+agent.name, hidden=True)
+```
+
+## Code Explanation
+
+This code is a recommendation system using a "swarm" of AI agents on the Hal9 platform, each specializing in a category: books, comics, or movies.
+
+#### •	Agents: There are four agents: receptionist, book_expert, comic_expert, and movie_expert. The receptionist determines the user’s interest and directs them to the appropriate expert.
+#### •	Functionality: Each expert agent provides recommendations in its domain, and if the conversation shifts away, it returns control to the receptionist.
+#### •	Session Management: The code maintains conversation history and the last active agent, enabling smooth, continuous interactions.
+#### •	Execution: The system takes user input, selects the relevant agent, generates a response, and logs any tool (function) calls made for recommendations.
+This setup allows for interactive, specialized recommendations that adapt to user preferences in real-time.
+
+
+## Sample Conversation
+![movie recommendations](https://github.com/user-attachments/assets/bdb64b5d-b3df-4fc6-815e-9eae4277c29d)

website/blog/authors.yml

@@ -9,3 +9,8 @@ diego:
   title: Data Scientist
   url: https://github.com/diegoarceof
   image_url: https://github.com/diegoarceof.png
+
+karan:
+  name: Karan Saxena
+  title: Data Analyst
+  url: https://github.com/Karan0310