What is the project about?

Swarm is an experimental, educational framework for exploring lightweight multi-agent orchestration. It focuses on making agent coordination and execution controllable and testable.

What problem does it solve?

It provides a way to build and manage systems with multiple interacting agents, where each agent has specific capabilities and instructions. It addresses the complexity of coordinating multiple agents, particularly in scenarios where a single, large prompt is insufficient. It simplifies the orchestration of agents by using handoffs and routines.

What are the features of the project?

• Agents: Encapsulate instructions and tools (functions).
• Handoffs: Agents can transfer control to other agents.
• Functions: Agents can directly call Python functions.
• Context Variables: Shared state between agents and functions.
• Streaming: Supports streaming responses, similar to OpenAI's Chat Completions API.
• Automatic Function Schema Generation: Converts Python functions to JSON Schema for the Chat Completions API.
• REPL: Includes a run_demo_loop for interactive testing.
• Stateless: Like the Chat Completions API, Swarm is stateless between calls.

What are the technologies used in the project?

• Python 3.10+: The core programming language.
• OpenAI Chat Completions API: Powers the agents' language understanding and generation. Not the Assistants API.
• JSON Schema: Used for defining function interfaces.

What are the benefits of the project?

• Lightweight: Simple abstractions make it easy to understand and use.
• Scalable: Designed for systems with many independent capabilities.
• Customizable: Provides fine-grained control over agent behavior.
• Testable: Clear structure facilitates testing.
• Educational: Serves as a learning resource for multi-agent system design.
• Client-side: Runs primarily on the client, offering more control.

What are the use cases of the project?

• Customer service bots: Handling different types of inquiries (e.g., airline example).
• Triage systems: Routing tasks to the appropriate agent (e.g., triage agent example).
• Personal shoppers: Assisting with sales and refunds (e.g., personal shopper example).
• Complex workflows: Breaking down tasks into smaller, manageable steps handled by different agents.
• Educational purposes: Learning about and experimenting with multi-agent systems.
• Any scenario where multiple specialized agents need to collaborate to achieve a goal.