Swarms
The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework. Website: https://swarms.ai
Install / Use
/learn @kyegomez/SwarmsREADME
Features
Swarms delivers a comprehensive, enterprise-grade multi-agent infrastructure platform designed for production-scale deployments and seamless integration with existing systems. Learn more about the swarms feature set here
| Category | Features | Benefits | |----------|----------|-----------| | Enterprise Architecture | • Production-Ready Infrastructure<br>• High Availability Systems<br>• Modular Microservices Design<br>• Comprehensive Observability<br>• Backwards Compatibility | • 99.9%+ Uptime Guarantee<br>• Reduced Operational Overhead<br>• Seamless Legacy Integration<br>• Enhanced System Monitoring<br>• Risk-Free Migration Path | | Multi-Agent Orchestration | • Hierarchical Agent Swarms<br>• Parallel Processing Pipelines<br>• Sequential Workflow Orchestration<br>• Graph-Based Agent Networks<br>• Dynamic Agent Composition<br>• Agent Registry Management | • Complex Business Process Automation<br>• Scalable Task Distribution<br>• Flexible Workflow Adaptation<br>• Optimized Resource Utilization<br>• Centralized Agent Governance<br>• Enterprise-Grade Agent Lifecycle Management | | Enterprise Integration | • Multi-Model Provider Support<br>• Custom Agent Development Framework<br>• Extensive Enterprise Tool Library<br>• Multiple Memory Systems<br>• Backwards Compatibility with LangChain, AutoGen, CrewAI<br>• Standardized API Interfaces | • Vendor-Agnostic Architecture<br>• Custom Solution Development<br>• Extended Functionality Integration<br>• Enhanced Knowledge Management<br>• Seamless Framework Migration<br>• Reduced Integration Complexity | | Enterprise Scalability | • Concurrent Multi-Agent Processing<br>• Intelligent Resource Management<br>• Load Balancing & Auto-Scaling<br>• Horizontal Scaling Capabilities<br>• Performance Optimization<br>• Capacity Planning Tools | • High-Throughput Processing<br>• Cost-Effective Resource Utilization<br>• Elastic Scaling Based On Demand<br>• Linear Performance Scaling<br>• Optimized Response Times<br>• Predictable Growth Planning | | Developer Experience | • Intuitive Enterprise API<br>• Comprehensive Documentation<br>• Active Enterprise Community<br>• CLI & SDK Tools<br>• IDE Integration Support<br>• Code Generation Templates | • Accelerated Development Cycles<br>• Reduced Learning Curve<br>• Expert Community Support<br>• Rapid Deployment Capabilities<br>• Enhanced Developer Productivity<br>• Standardized Development Patterns |
Supported Protocols & Integrations
Swarms seamlessly integrates with industry-standard protocols and open specifications, unlocking powerful capabilities for tool integration, payment processing, distributed agent orchestration, and model interoperability.
| Protocol | Description | Use Cases | Documentation | |----------|-------------|-----------|---------------| | MCP (Model Context Protocol) | Standardized protocol for AI agents to interact with external tools and services through MCP servers. Enables dynamic tool discovery and execution. | • Tool integration<br>• Multi-server connections<br>• External API access<br>• Database connectivity | MCP Integration Guide | | X402 | Cryptocurrency payment protocol for API endpoints. Enables monetization of agents with pay-per-use models. | • Agent monetization<br>• Payment gate protection<br>• Crypto payments<br>• Pay-per-use services | X402 Quickstart | | AOP (Agent Orchestration Protocol) | Framework for deploying and managing agents as distributed services. Enables agent discovery, management, and execution through standardized protocols. | • Distributed agent deployment<br>• Agent discovery<br>• Service orchestration<br>• Scalable multi-agent systems | AOP Reference | | Swarms Marketplace | Platform for discovering and sharing production-ready prompts, agents, and tools. Enables automatic prompt loading from the marketplace and publishing your own prompts directly from code. | • Prompt discovery and reuse<br>• One-line prompt loading<br>• Community prompt sharing<br>• Prompt monetization | Marketplace Tutorial | | Open Responses | Open-source specification and ecosystem for multi-provider, interoperable LLM interfaces based on the OpenAI Responses API. Provides a unified schema and tooling for calling language models, streaming results, and composing agentic workflows—independent of provider. | • Unified LLM interfaces<br>• Streaming outputs<br>• Multi-provider orchestration<br>• Interoperable agent workflows | Open Responses Website | | Agent Skills | Lightweight, markdown-based format for defining modular, reusable agent capabilities introduced by Anthropic. Enables specialization of agents without modifying code by loading skill definitions from simple SKILL.md files. | • Agent specialization<br>• Reusable skill libraries<br>• Code-free agent customization<br>• Claude Code compatibility | Agent Skills Documentation |
Install
Using pip
$ pip3 install -U swarms
Using uv (Recommended)
uv is a fast Python package installer and resolver, written in Rust.
$ uv pip install swarms
Using poetry
$ poetry add swarms
From source
# Clone the repository
$ git clone https://github.com/kyegomez/swarms.git
$ cd swarms
$ pip install -r requirements.txt
Environment Configuration
Learn more about the environment configuration here
OPENAI_API_KEY=""
WORKSPACE_DIR="agent_workspace"
ANTHROPIC_API_KEY=""
GROQ_API_KEY=""
Your First Agent
An Agent is the fundamental building block of a swarm—an autonomous entity powered by an LLM + Tools + Memory. Learn more Here
from swarms import Agent
# Initialize a new agent
agent = Agent(
model_name="gpt-5.4", # Specify the LLM
max_loops="auto", # Set the number of interactions
interactive=True, # Enable interactive mode for real-time feedback
)
# Run the agent with a task
agent.run("What are the key benefits of using a multi-agent system?")
Your First Swarm: Multi-Agent Collaboration
A Swarm consists of multiple agents working together. This simple example creates a two-agent workflow for researching and writing a blog post. Learn More About SequentialWorkflow
from swarms import Agent, SequentialWorkflow
# Agent 1: The Researcher
researcher = Agent(
agent_name="Researcher",
