swarms

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## Overview

>
> Swarms, The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework 

Swarms is the most reliable, scalable, and adaptive multi-agent orchestration framework available today. We provide a comprehensive suite of production-ready, prebuilt multi-agent architectures, including sequential, concurrent, and hierarchical systems. Additionally, Swarms offers backward compatibility with leading agent frameworks and interoperability with protocols such as MCP, x402, skills, and much more.


## Install

### Using pip

```bash
$ pip3 install -U swarms
```

### Using uv (Recommended)

[uv](https://github.com/astral-sh/uv) is a fast Python package installer and resolver, written in Rust.

```bash
$ uv pip install swarms
```

### Using poetry

```bash
$ poetry add swarms
```

### From source

```bash
# Clone the repository
$ git clone https://github.com/kyegomez/swarms.git
$ cd swarms
$ pip install -r requirements.txt
```

<!-- ### Using Docker

The easiest way to get started with Swarms is using our pre-built Docker image:

```bash
# Pull and run the latest image
$ docker pull kyegomez/swarms:latest
$ docker run --rm kyegomez/swarms:latest python -c "import swarms; print('Swarms is ready!')"

# Run interactively for development
$ docker run -it --rm -v $(pwd):/app kyegomez/swarms:latest bash

# Using docker-compose (recommended for development)
$ docker-compose up -d
```

For more Docker options and advanced usage, see our [Docker documentation](/scripts/docker/DOCKER.md). -->

---

## Environment Configuration

[Learn more about the environment configuration here](https://docs.swarms.world/environment-setup)

```
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](https://docs.swarms.world/api/agent)

```python
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?")
```

### Autonomous Agent with `max_loops="auto"`

Setting `max_loops="auto"` lets the agent decide for itself when the task is complete — it keeps reasoning and acting until it reaches a stopping condition, rather than halting after a fixed number of iterations. This is the recommended mode for open-ended, multi-step tasks where the number of steps isn't known in advance.

```python
from swarms import Agent

agent = Agent(
    agent_name="Autonomous-Research-Agent",
    agent_description="An autonomous agent that conducts multi-step research independently.",
    system_prompt=(
        "You are an autonomous research agent. Break down complex tasks into steps, "
        "execute each step thoroughly, and signal completion only when the full task is done."
    ),
    model_name="gpt-5.4",
    max_loops="auto",       # Agent decides when it's done — no fixed iteration cap
    autosave=True,
    verbose=True,
)

# The agent will keep looping — planning, executing, and reflecting — until it
# determines the task is fully complete.
result = agent.run(
    "Research the current state of quantum computing, identify the top three "
    "hardware approaches, and summarize the key challenges each faces."
)
print(result)
```

**When to use `max_loops="auto"`:**
- Open-ended research or analysis tasks
- Tasks that require iterative refinement (e.g., write → review → revise)
- Any workflow where the number of steps depends on intermediate results

**When to use a fixed `max_loops` value:**
- Latency-sensitive or cost-sensitive production pipelines
- Tasks with a well-defined, bounded number of steps

### 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](https://docs.swarms.world/api/sequential-workflow)

```python
from swarms import Agent, SequentialWorkflow

# Agent 1: The Researcher
researcher = Agent(
    agent_name="Researcher",
    system_prompt="Your job is to research the provided topic and provide a detailed summary.",
    model_name="gpt-5.4",
)

# Agent 2: The Writer
writer = Agent(
    agent_name="Writer",
    system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.",
    model_name="gpt-5.4",
)

# Create a sequential workflow where the researcher's output feeds into the writer's input
workflow = SequentialWorkflow(agents=[researcher, writer])

# Run the workflow on a task
final_post = workflow.run("The history and future of artificial intelligence")
print(final_post)

```

-----

## Available Multi-Agent Architectures

`swarms` provides a variety of powerful, pre-built multi-agent architectures enabling you to orchestrate agents in various ways. Choose the right structure for your specific problem to build efficient and reliable production systems.

| **Architecture** | **Description** | **Best For** |
|---|---|---|
| **[SequentialWorkflow](https://docs.swarms.world/api/sequential-workflow)** | Agents execute tasks in a linear chain; the output of one agent becomes the input for the next. | Step-by-step processes such as data transformation pipelines and report generation. |
| **[ConcurrentWorkflow](https://docs.swarms.world/api/concurrent-workflow)** | Agents run tasks simultaneously for maximum efficiency. | High-throughput tasks such as batch processing and parallel data analysis. |
| **[AgentRearrange](https://docs.swarms.world/api/agent-rearrange)** | Dynamically maps complex relationships (e.g., `a -> b, c`) between agents. | Flexible and adaptive workflows, task distribution, and dynamic routing. |
| **[GraphWorkflow](https://docs.swarms.world/api/graph-workflow)** | Orchestrates agents as nodes in a Directed Acyclic Graph (DAG). | Complex projects with intricate dependencies, such as software builds. |
| **[MixtureOfAgents (MoA)](https://docs.swarms.world/api/mixture-of-agents)** | Utilizes multiple expert agents in parallel and synthesizes their outputs. | Complex problem-solving and achieving state-of-the-art performance through collaboration. |
| **[GroupChat](https://docs.swarms.world/api/group-chat)** | Agents collaborate and make decisions through a conversational interface. | Real-time collaborative decision-making, negotiations, and brainstorming. |
| **[ForestSwarm](https://docs.swarms.world/api/forest-swarm)** | Dynamically selects the most suitable agent or tree of agents for a given task. | Task routing, optimizing for expertise, and complex decision-making trees. |
| **[HierarchicalSwarm](https://docs.swarms.world/api/hierarchical-swarm)** | Orchestrates agents with a director who creates plans and distributes tasks to specialized worker agents. | Complex project management, team coordination, and hierarchical decision-making with feedback loops. |
| **[HeavySwarm](https://docs.swarms.world/api/heavy-swarm)** | Implements a five-phase workflow with specialized agents (Research, Analysis, Alternatives, Verification) for comprehensive task analysis. | Complex research and analysis tasks, financial analysis, strategic planning, and comprehensive reporting. |
| **[SwarmRouter](https://docs.swarms.world/api/swarm-router)** | A universal orchestrator that provides a single interface to run any type of swarm with dynamic selection. | Simplifying complex workflows, switching between swarm strategies, and unified multi-agent management. |

-----

### SequentialWorkflow

A `SequentialWorkflow` executes tasks in a strict order, forming a pipeline where each agent builds upon the work of the previous one. `SequentialWorkflow` is Ideal for processes that have clear, ordered steps. This ensures that