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491 lines
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491 lines
23 KiB
<div align="center">
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<a href="https://swarms.world">
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<img src="https://github.com/kyegomez/swarms/blob/master/images/swarmslogobanner.png" style="margin: 15px; max-width: 500px" width="50%" alt="Logo">
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</a>
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</div>
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<p align="center">
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<em>The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework </em>
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</p>
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<p align="center">
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<a href="https://pypi.org/project/swarms/" target="_blank">
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<img alt="Python" src="https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54" />
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<img alt="Version" src="https://img.shields.io/pypi/v/swarms?style=for-the-badge&color=3670A0">
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</a>
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<a href="https://twitter.com/swarms_corp/">🐦 Twitter</a>
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<span> • </span>
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<a href="https://discord.gg/jM3Z6M9uMq">📢 Discord</a>
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<span> • </span>
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<a href="https://swarms.ai">Swarms Website</a>
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<span> • </span>
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<a href="https://docs.swarms.world">📙 Documentation</a>
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<span> • </span>
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<a href="https://swarms.world"> Swarms Marketplace</a>
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</p>
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<p align="center">
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<!-- Social Media -->
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<a href="https://discord.gg/jHnrkH5y">
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<a href="https://github.com/kyegomez/swarms/blob/main/LICENSE">
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<a href="https://pepy.tech/project/swarms">
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<img src="https://static.pepy.tech/badge/swarms/month" alt="Downloads">
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## ✨ Features
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| Category | Features | Benefits |
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|----------|----------|-----------|
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| 🏢 Enterprise Architecture | • Production-Ready Infrastructure<br>• High Reliability Systems<br>• Modular Design<br>• Comprehensive Logging | • Reduced downtime<br>• Easier maintenance<br>• Better debugging<br>• Enhanced monitoring |
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| 🤖 Agent Orchestration | • Hierarchical Swarms<br>• Parallel Processing<br>• Sequential Workflows<br>• Graph-based Workflows<br>• Dynamic Agent Rearrangement | • Complex task handling<br>• Improved performance<br>• Flexible workflows<br>• Optimized execution |
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| 🔄 Integration Capabilities | • Multi-Model Support<br>• Custom Agent Creation<br>• Extensive Tool Library<br>• Multiple Memory Systems | • Provider flexibility<br>• Custom solutions<br>• Extended functionality<br>• Enhanced memory management |
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| 📈 Scalability | • Concurrent Processing<br>• Resource Management<br>• Load Balancing<br>• Horizontal Scaling | • Higher throughput<br>• Efficient resource use<br>• Better performance<br>• Easy scaling |
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| 🛠️ Developer Tools | • Simple API<br>• Extensive Documentation<br>• Active Community<br>• CLI Tools | • Faster development<br>• Easy learning curve<br>• Community support<br>• Quick deployment |
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| 🔐 Security Features | • Error Handling<br>• Rate Limiting<br>• Monitoring Integration<br>• Audit Logging | • Improved reliability<br>• API protection<br>• Better monitoring<br>• Enhanced tracking |
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| 📊 Advanced Features | • SpreadsheetSwarm<br>• Group Chat<br>• Agent Registry<br>• Mixture of Agents | • Mass agent management<br>• Collaborative AI<br>• Centralized control<br>• Complex solutions |
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| 🔌 Provider Support | • OpenAI<br>• Anthropic<br>• ChromaDB<br>• Custom Providers | • Provider flexibility<br>• Storage options<br>• Custom integration<br>• Vendor independence |
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| 💪 Production Features | • Automatic Retries<br>• Async Support<br>• Environment Management<br>• Type Safety | • Better reliability<br>• Improved performance<br>• Easy configuration<br>• Safer code |
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| 🎯 Use Case Support | • Task-Specific Agents<br>• Custom Workflows<br>• Industry Solutions<br>• Extensible Framework | • Quick deployment<br>• Flexible solutions<br>• Industry readiness<br>• Easy customization |
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## Guides and Walkthroughs
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Refer to our documentation for production grade implementation details.
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| Section | Links |
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|----------------------|--------------------------------------------------------------------------------------------|
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| Installation | [Installation](https://docs.swarms.world/en/latest/swarms/install/install/) |
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| Quickstart | [Get Started](https://docs.swarms.world/en/latest/swarms/install/quickstart/) |
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| Agent Internal Mechanisms | [Agent Architecture](https://docs.swarms.world/en/latest/swarms/framework/agents_explained/) |
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| Agent API | [Agent API](https://docs.swarms.world/en/latest/swarms/structs/agent/) |
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| Integrating External Agents Griptape, Autogen, etc | [Integrating External APIs](https://docs.swarms.world/en/latest/swarms/agents/external_party_agents/) |
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| Creating Agents from YAML | [Creating Agents from YAML](https://docs.swarms.world/en/latest/swarms/agents/create_agents_yaml/) |
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| Why You Need Swarms | [Why MultiAgent Collaboration is Necessary](https://docs.swarms.world/en/latest/swarms/concept/why/) |
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| Swarm Architectures Analysis | [Swarm Architectures](https://docs.swarms.world/en/latest/swarms/concept/swarm_architectures/) |
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| Choosing the Right Swarm for Your Business Problem¶ | [CLICK HERE](https://docs.swarms.world/en/latest/swarms/concept/swarm_architectures/) |
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| AgentRearrange Docs| [CLICK HERE](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/) |
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## Install 💻
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### Using pip
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```bash
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$ pip3 install -U swarms
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```
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### Using uv (Recommended)
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[uv](https://github.com/astral-sh/uv) is a fast Python package installer and resolver, written in Rust.
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```bash
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# Install uv
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$ curl -LsSf https://astral.sh/uv/install.sh | sh
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# Install swarms using uv
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$ uv pip install swarms
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```
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### Using poetry
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```bash
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# Install poetry if you haven't already
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$ curl -sSL https://install.python-poetry.org | python3 -
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# Add swarms to your project
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$ poetry add swarms
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```
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### From source
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```bash
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# Clone the repository
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$ git clone https://github.com/kyegomez/swarms.git
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$ cd swarms
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# Install with pip
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$ pip install -e .
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```
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---
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## Environment Configuration
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[Learn more about the environment configuration here](https://docs.swarms.world/en/latest/swarms/install/env/)
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```
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OPENAI_API_KEY=""
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WORKSPACE_DIR="agent_workspace"
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ANTHROPIC_API_KEY=""
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GROQ_API_KEY=""
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```
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### 🤖 Your First Agent
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An **Agent** is the fundamental building block of a swarm—an autonomous entity powered by a large language model (LLM).
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```python
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from swarms import Agent
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# Initialize a new agent
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agent = Agent(
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model_name="gpt-4o-mini", # Specify the LLM
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max_loops=1, # Set the number of interactions
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interactive=True, # Enable interactive mode for real-time feedback
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)
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# Run the agent with a task
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agent.run("What are the key benefits of using a multi-agent system?")
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```
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### 🤝 Your First Swarm: Multi-Agent Collaboration
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A **Swarm** consists of multiple agents working together. This simple example creates a two-agent workflow for researching and writing a blog post.
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```python
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from swarms import Agent, SequentialWorkflow
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# Agent 1: The Researcher
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researcher = Agent(
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agent_name="Researcher",
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system_prompt="Your job is to research the provided topic and provide a detailed summary.",
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model_name="gpt-4o-mini",
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)
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# Agent 2: The Writer
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writer = Agent(
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agent_name="Writer",
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system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.",
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model_name="gpt-4o-mini",
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)
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# Create a sequential workflow where the researcher's output feeds into the writer's input
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workflow = SequentialWorkflow(agents=[researcher, writer])
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# Run the workflow on a task
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final_post = workflow.run("The history and future of artificial intelligence")
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print(final_post)
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```
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-----
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## 🏗️ Swarm Architectures for Production Workflows
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`swarms` provides a variety of powerful, pre-built architectures to orchestrate agents in different ways. Choose the right structure for your specific problem to build efficient and reliable production systems.
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| **Architecture** | **Description** | **Best For** |
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|---|---|---|
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| **SequentialWorkflow** | Agents execute tasks in a linear chain; one agent's output is the next one's input. | Step-by-step processes like data transformation pipelines, report generation. |
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| **ConcurrentWorkflow** | Agents run tasks simultaneously for maximum efficiency. | High-throughput tasks like batch processing, parallel data analysis. |
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| **AgentRearrange** | Dynamically maps complex relationships (e.g., `a -> b, c`) between agents. | Flexible and adaptive workflows, task distribution, dynamic routing. |
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| **GraphWorkflow** | Orchestrates agents as nodes in a Directed Acyclic Graph (DAG). | Complex projects with intricate dependencies, like software builds. |
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| **MixtureOfAgents (MoA)** | Utilizes multiple expert agents in parallel and synthesizes their outputs. | Complex problem-solving, achieving state-of-the-art performance through collaboration. |
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| **GroupChat** | Agents collaborate and make decisions through a conversational interface. | Real-time collaborative decision-making, negotiations, brainstorming. |
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| **ForestSwarm** | Dynamically selects the most suitable agent or tree of agents for a given task. | Task routing, optimizing for expertise, complex decision-making trees. |
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| **SpreadSheetSwarm**| Manages thousands of agents concurrently, tracking tasks and outputs in a structured format. | Massive-scale parallel operations, large-scale data generation and analysis. |
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-----
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### SequentialWorkflow
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A `SequentialWorkflow` executes tasks in a strict order, forming a pipeline where each agent builds upon the work of the previous one.
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**Description:** Ideal for processes that have clear, ordered steps. This ensures that tasks with dependencies are handled correctly.
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```python
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from swarms import Agent, SequentialWorkflow
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# Initialize agents for a 3-step process
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# 1. Generate an idea
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idea_generator = Agent(agent_name="IdeaGenerator", system_prompt="Generate a unique startup idea.", model_name="gpt-4o-mini")
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# 2. Validate the idea
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validator = Agent(agent_name="Validator", system_prompt="Take this startup idea and analyze its market viability.", model_name="gpt-4o-mini")
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# 3. Create a pitch
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pitch_creator = Agent(agent_name="PitchCreator", system_prompt="Write a 3-sentence elevator pitch for this validated startup idea.", model_name="gpt-4o-mini")
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# Create the sequential workflow
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workflow = SequentialWorkflow(agents=[idea_generator, validator, pitch_creator])
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# Run the workflow
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elevator_pitch = workflow.run()
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print(elevator_pitch)
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```
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### ConcurrentWorkflow (with `SpreadSheetSwarm`)
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A concurrent workflow runs multiple agents simultaneously. `SpreadSheetSwarm` is a powerful implementation that can manage thousands of concurrent agents and log their outputs to a CSV file.
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**Description:** Use this for high-throughput tasks that can be performed in parallel, drastically reducing execution time.
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```python
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from swarms import Agent, SpreadSheetSwarm
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# Define a list of tasks (e.g., social media posts to generate)
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platforms = ["Twitter", "LinkedIn", "Instagram"]
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# Create an agent for each task
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agents = [
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Agent(
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agent_name=f"{platform}-Marketer",
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system_prompt=f"Generate a real estate marketing post for {platform}.",
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model_name="gpt-4o-mini",
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)
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for platform in platforms
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]
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# Initialize the swarm to run these agents concurrently
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swarm = SpreadSheetSwarm(
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agents=agents,
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autosave_on=True,
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save_file_path="marketing_posts.csv",
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)
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# Run the swarm with a single, shared task description
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property_description = "A beautiful 3-bedroom house in sunny California."
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swarm.run(task=f"Generate a post about: {property_description}")
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# Check marketing_posts.csv for the results!
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```
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### AgentRearrange
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Inspired by `einsum`, `AgentRearrange` lets you define complex, non-linear relationships between agents using a simple string-based syntax.
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**Description:** Perfect for orchestrating dynamic workflows where agents might work in parallel, sequence, or a combination of both.
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```python
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from swarms import Agent, AgentRearrange
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# Define agents
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researcher = Agent(agent_name="researcher", model_name="gpt-4o-mini")
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writer = Agent(agent_name="writer", model_name="gpt-4o-mini")
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editor = Agent(agent_name="editor", model_name="gpt-4o-mini")
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# Define a flow: researcher sends work to both writer and editor simultaneously
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# This is a one-to-many relationship
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flow = "researcher -> writer, editor"
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# Create the rearrangement system
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rearrange_system = AgentRearrange(
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agents=[researcher, writer, editor],
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flow=flow,
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)
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# Run the system
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# The researcher will generate content, and then both the writer and editor
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# will process that content in parallel.
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outputs = rearrange_system.run("Analyze the impact of AI on modern cinema.")
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print(outputs)
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```
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### GraphWorkflow
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`GraphWorkflow` orchestrates tasks using a Directed Acyclic Graph (DAG), allowing you to manage complex dependencies where some tasks must wait for others to complete.
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**Description:** Essential for building sophisticated pipelines, like in software development or complex project management, where task order and dependencies are critical.
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```python
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from swarms import Agent, GraphWorkflow, Node, Edge, NodeType
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# Define agents and a simple python function as nodes
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code_generator = Agent(agent_name="CodeGenerator", system_prompt="Write Python code for the given task.", model_name="gpt-4o-mini")
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code_tester = Agent(agent_name="CodeTester", system_prompt="Test the given Python code and find bugs.", model_name="gpt-4o-mini")
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# Create nodes for the graph
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node1 = Node(id="generator", agent=code_generator)
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node2 = Node(id="tester", agent=code_tester)
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# Create the graph and define the dependency
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graph = GraphWorkflow()
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graph.add_nodes([node1, node2])
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graph.add_edge(Edge(source="generator", target="tester")) # Tester runs after generator
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# Set entry and end points
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graph.set_entry_points(["generator"])
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graph.set_end_points(["tester"])
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# Run the graph workflow
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results = graph.run("Create a function that calculates the factorial of a number.")
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print(results)
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```
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### MixtureOfAgents (MoA)
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The `MixtureOfAgents` architecture processes tasks by feeding them to multiple "expert" agents in parallel. Their diverse outputs are then synthesized by an aggregator agent to produce a final, high-quality result.
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**Description:** Use this to achieve state-of-the-art performance on complex reasoning tasks by leveraging the collective intelligence of specialized agents.
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```python
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from swarms import Agent, MixtureOfAgents
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# Define expert agents
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financial_analyst = Agent(agent_name="FinancialAnalyst", system_prompt="Analyze financial data.", model_name="gpt-4o-mini")
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market_analyst = Agent(agent_name="MarketAnalyst", system_prompt="Analyze market trends.", model_name="gpt-4o-mini")
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risk_analyst = Agent(agent_name="RiskAnalyst", system_prompt="Analyze investment risks.", model_name="gpt-4o-mini")
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# Define the aggregator agent
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aggregator = Agent(
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agent_name="InvestmentAdvisor",
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system_prompt="Synthesize the financial, market, and risk analyses to provide a final investment recommendation.",
|
|
model_name="gpt-4o-mini"
|
|
)
|
|
|
|
# Create the MoA swarm
|
|
moa_swarm = MixtureOfAgents(
|
|
agents=[financial_analyst, market_analyst, risk_analyst],
|
|
aggregator_agent=aggregator,
|
|
)
|
|
|
|
# Run the swarm
|
|
recommendation = moa_swarm.run("Should we invest in NVIDIA stock right now?")
|
|
print(recommendation)
|
|
```
|
|
|
|
### GroupChat
|
|
|
|
`GroupChat` creates a conversational environment where multiple agents can interact, discuss, and collaboratively solve a problem. You can define the speaking order or let it be determined dynamically.
|
|
|
|
**Description:** Ideal for tasks that benefit from debate and multi-perspective reasoning, such as contract negotiation, brainstorming, or complex decision-making.
|
|
|
|
```python
|
|
from swarms import Agent, GroupChat
|
|
|
|
# Define agents for a debate
|
|
tech_optimist = Agent(agent_name="TechOptimist", system_prompt="Argue for the benefits of AI in society.", model_name="gpt-4o-mini")
|
|
tech_critic = Agent(agent_name="TechCritic", system_prompt="Argue against the unchecked advancement of AI.", model_name="gpt-4o-mini")
|
|
|
|
# Create the group chat
|
|
chat = GroupChat(
|
|
agents=[tech_optimist, tech_critic],
|
|
max_loops=4, # Limit the number of turns in the conversation
|
|
)
|
|
|
|
# Run the chat with an initial topic
|
|
conversation_history = chat.run(
|
|
"Let's discuss the societal impact of artificial intelligence."
|
|
)
|
|
|
|
# Print the full conversation
|
|
for message in conversation_history:
|
|
print(f"[{message['agent_name']}]: {message['content']}")
|
|
```
|
|
|
|
----------
|
|
|
|
## Onboarding Session
|
|
|
|
Get onboarded now with the creator and lead maintainer of Swarms, Kye Gomez, who will show you how to get started with the installation, usage examples, and starting to build your custom use case! [CLICK HERE](https://cal.com/swarms/swarms-onboarding-session)
|
|
|
|
|
|
---
|
|
|
|
## Documentation
|
|
|
|
Documentation is located here at: [docs.swarms.world](https://docs.swarms.world)
|
|
|
|
-----
|
|
|
|
## 🫶 Contributions:
|
|
|
|
The easiest way to contribute is to pick any issue with the `good first issue` tag 💪. Read the Contributing guidelines [here](/CONTRIBUTING.md). Bug Report? [File here](https://github.com/swarms/gateway/issues) | Feature Request? [File here](https://github.com/swarms/gateway/issues)
|
|
|
|
Swarms is an open-source project, and contributions are VERY welcome. If you want to contribute, you can create new features, fix bugs, or improve the infrastructure. Please refer to the [CONTRIBUTING.md](https://github.com/kyegomez/swarms/blob/master/CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) to participate in Roadmap discussions!
|
|
|
|
----
|
|
|
|
|
|
### Connect With Us
|
|
|
|
| Platform | Link | Description |
|
|
|----------|------|-------------|
|
|
| 📚 Documentation | [docs.swarms.world](https://docs.swarms.world) | Official documentation and guides |
|
|
| 📝 Blog | [Medium](https://medium.com/@kyeg) | Latest updates and technical articles |
|
|
| 💬 Discord | [Join Discord](https://discord.gg/jM3Z6M9uMq) | Live chat and community support |
|
|
| 🐦 Twitter | [@kyegomez](https://twitter.com/kyegomez) | Latest news and announcements |
|
|
| 👥 LinkedIn | [The Swarm Corporation](https://www.linkedin.com/company/the-swarm-corporation) | Professional network and updates |
|
|
| 📺 YouTube | [Swarms Channel](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ) | Tutorials and demos |
|
|
| 🎫 Events | [Sign up here](https://lu.ma/5p2jnc2v) | Join our community events |
|
|
|
|
|
|
|
|
## Citation
|
|
|
|
If you use **swarms** in your research, please cite the project by referencing the metadata in [CITATION.cff](./CITATION.cff).
|
|
|
|
```bibtex
|
|
@misc{SWARMS_2022,
|
|
author = {Gomez, Kye and Pliny and More, Harshal and Swarms Community},
|
|
title = {{Swarms: Production-Grade Multi-Agent Infrastructure Platform}},
|
|
year = {2022},
|
|
howpublished = {\url{https://github.com/kyegomez/swarms}},
|
|
note = {Documentation available at \url{https://docs.swarms.world}},
|
|
version = {latest}
|
|
}
|
|
```
|
|
|
|
# License
|
|
|
|
APACHE
|