Ollama + OpenAI’s Swarm - EASILY Run AI Agents Locally

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AI Summary

Summary of Swarm Integration with Olama

• OpenAI’s Swarm tool allows the creation of AI agent swarms.
• The default setup uses OpenAI models like GPT-4.
• The video demonstrates how to run agent swarms locally using Olama.
• Olama supports function calling in the same format as OpenAI models.
• Running locally with Olama is free, unlike using GPT models with Swarm.
• The video provides a step-by-step guide on integrating Swarm with Olama.

Detailed Instructions and Tips

1. Changes for Olama Integration:
   • Import the OpenAI library.
   • Set up chat completion as you would for GPT.
   • Change the base URL to point to localhost:11434 (default Olama hosting port).
   • Adjust the API key if needed.
2. Swarm Repository Modifications:
   • Modify the instantiation of the OpenAI client to include the new base URL and API key for Olama.
   • No need to fork the Swarm code; just pass the Olama client when instantiating the swarm.
3. Environment Variable File:
   • Define the default model for your agents using an environment variable file.
   • Example model: quen 2.5 coder, a 7 billion parameter model.
4. Agent Configuration:
   • Define the model for each agent.
   • Router agents can use a lighter model, like a 3 billion parameter model.
   • Different agents can use various Olama models.
5. Running the Swarm with Local Models:
   • Use the run.py script to kick off the swarm instance with the Olama base URL.
   • The script runs a loop for querying the router agent, which directs to specific agents.
6. Agent Swarm Functionality:
   • The swarm consists of specialized agents for querying a database.
   • Router agent directs the user’s request to the appropriate specialized agent.
   • Specialized agents create SQL queries in real-time to answer user questions.
7. Installation and Setup:
   • Download Olama from ama.com with a download button for different operating systems.
   • Clone the GitHub repository with the code for the SQL agents.
   • Create and activate a Python virtual environment.
   • Install required packages using pip install -r requirements.txt.
   • Rename the .env.example file to .env and set the Olama model ID.
   • Pull the desired Olama model using the ama pull command with the model ID.
   • Run the run.py script to test the agents with local models.

Conclusion

• Local LLMs like Olama offer a free alternative to using OpenAI’s models with Swarm.
• The video provides a comprehensive guide to setting up and running a swarm of AI agents locally.
• The performance of local models is impressive, even with smaller parameter counts.
• The gap between open-source and closed-source models is closing rapidly.