Swarms SDK is a Python framework enabling orchestration of multiple LLM-based agents. It lets you define agents with specific roles, manage memory and context, and coordinate interactions. Integrations with OpenAI, Anthropic, and custom LLMs streamline collaborative workflows, while built-in logging and evaluation tools simplify monitoring and result aggregation.
Swarms SDK is a Python framework enabling orchestration of multiple LLM-based agents. It lets you define agents with specific roles, manage memory and context, and coordinate interactions. Integrations with OpenAI, Anthropic, and custom LLMs streamline collaborative workflows, while built-in logging and evaluation tools simplify monitoring and result aggregation.
Swarms SDK simplifies creation, configuration, and execution of collaborative multi-agent systems using large language models. Developers define agents with distinct roles—researcher, synthesizer, critic—and group them into swarms that exchange messages via a shared bus. The SDK handles scheduling, context persistence, and memory storage, enabling iterative problem solving. With native support for OpenAI, Anthropic, and other LLM providers, it offers flexible integrations. Utilities for logging, result aggregation, and performance evaluation help teams prototype and deploy AI-driven workflows for brainstorming, content generation, summarization, and decision support.
Who will use Swarms SDK?
AI developers
Data scientists
Machine learning engineers
Research teams
Software engineers
How to use the Swarms SDK?
Step1: Install via pip (pip install swarms-sdk)
Step2: Import Swarm and Agent classes in your Python script
Step3: Configure your LLM provider API keys
Step4: Define agents with roles and prompts
Step5: Create a Swarm instance and add defined agents
Step6: Call swarm.run(input) to execute the multi-agent workflow
Step7: Collect and process the aggregated output from agents
Platform
mac
windows
linux
Swarms SDK's Core Features & Benefits
The Core Features
Multi-agent orchestration framework
Role-based agent configuration
Memory and context management
Integration with OpenAI, Anthropic, and custom LLMs
Built-in logging and performance monitoring
Result aggregation and evaluation tools
The Benefits
Accelerates development of multi-agent workflows
Enhances task accuracy through collaborative reasoning