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Swarms is an enterprise grade and production ready multi-agent collaboration framework that enables you to orchestrate many agents to work collaboratively at scale to automate real-world activities.
-`.env` file with API keys from your providers like `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`
- Set an `.env` Variable with your desired workspace dir: `WORKSPACE_DIR="agent_workspace"` or do it in your terminal with `export WORKSPACE_DIR="agent_workspace"`
Here are some simple examples but we have more comprehensive documentation at our [docs here](https://docs.swarms.world/en/latest/)
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The `Agent` class is a fundamental component of the Swarms framework, designed to execute tasks autonomously. It fuses llms, tools and long-term memory capabilities to create a full stack agent. The `Agent` class is highly customizable, allowing for fine-grained control over its behavior and interactions.
The `run` method is the primary entry point for executing tasks with an `Agent` instance. It accepts a task string as the main input task and processes it according to the agent's configuration. And, it can also accept an `img` parameter such as `img="image_filepath.png` to process images if you have a VLM
### Settings and Customization
The `Agent` class offers a range of settings to tailor its behavior to specific needs. Some key settings include:
| Setting | Description | Default Value |
| --- | --- | --- |
| `agent_name` | The name of the agent. | "DefaultAgent" |
| `system_prompt` | The system prompt to use for the agent. | "Default system prompt." |
| `llm` | The language model to use for processing tasks. | `OpenAIChat` instance |
| `max_loops` | The maximum number of loops to execute for a task. | 1 |
| `autosave` | Enables or disables autosaving of the agent's state. | False |
| `dashboard` | Enables or disables the dashboard for the agent. | False |
| `verbose` | Controls the verbosity of the agent's output. | False |
| `dynamic_temperature_enabled` | Enables or disables dynamic temperature adjustment for the language model. | False |
| `saved_state_path` | The path to save the agent's state. | "agent_state.json" |
| `user_name` | The username associated with the agent. | "default_user" |
| `retry_attempts` | The number of retry attempts for failed tasks. | 1 |
| `context_length` | The maximum length of the context to consider for tasks. | 200000 |
| `return_step_meta` | Controls whether to return step metadata in the output. | False |
| `output_type` | The type of output to return (e.g., "json", "string"). | "string" |
### Integrating RAG with Swarms for Enhanced Long-Term Memory
`Agent` equipped with quasi-infinite long term memory using RAG (Relational Agent Graph) for advanced document understanding, analysis, and retrieval capabilities.
"Analyze this image of an assembly line and identify any issues such as"
" misaligned parts, defects, or deviations from the standard assembly"
" process. IF there is anything unsafe in the image, explain why it is"
" unsafe and how it could be improved."
)
img = "assembly_line.jpg"
## Initialize the workflow
agent = Agent(
agent_name = "Multi-ModalAgent",
llm=llm,
max_loops="auto",
autosave=True,
dashboard=True,
multi_modal=True
)
# Run the workflow on a task
agent.run(task, img)
```
----
### `ToolAgent`
ToolAgent is an agent that can use tools through JSON function calling. It intakes any open source model from huggingface and is extremely modular and plug in and play. We need help adding general support to all models soon.
```python
from pydantic import BaseModel, Field
from transformers import AutoModelForCausalLM, AutoTokenizer
from swarms import ToolAgent
from swarms.utils.json_utils import base_model_to_json
Integrating external agents from other agent frameworks is easy with swarms.
Steps:
1. Create a new class that inherits `Agent`
2. Create a `.run(task: str) -> str` method that runs the agent and returns the response.
3. The new Agent must return a string of the response. But you may add additional methods to save the output to JSON.
### Griptape Example
For example, here's an example on how to create an agent from griptape.
Here’s how you can create a custom **Griptape** agent that integrates with the **Swarms** framework by inheriting from the `Agent` class in **Swarms** and overriding the `run(task: str) -> str` method.
```python
from swarms import (
Agent as SwarmsAgent,
) # Import the base Agent class from Swarms
from griptape.structures import Agent as GriptapeAgent
from griptape.tools import (
WebScraperTool,
FileManagerTool,
PromptSummaryTool,
)
# Create a custom agent class that inherits from SwarmsAgent
class GriptapeSwarmsAgent(SwarmsAgent):
def __init__(self, *args, **kwargs):
# Initialize the Griptape agent with its tools
self.agent = GriptapeAgent(
input="Load {{ args[0] }}, summarize it, and store it in a file called {{ args[1] }}.",
tools=[
WebScraperTool(off_prompt=True),
PromptSummaryTool(off_prompt=True),
FileManagerTool(),
],
*args,
**kwargs,
# Add additional settings
)
# Override the run method to take a task and execute it using the Griptape agent
def run(self, task: str) -> str:
# Extract URL and filename from task (you can modify this parsing based on task structure)
url, filename = task.split(
","
) # Example of splitting task string
# Execute the Griptape agent with the task inputs
result = self.agent.run(url.strip(), filename.strip())
# Return the final result as a string
return str(result)
# Example usage:
griptape_swarms_agent = GriptapeSwarmsAgent()
output = griptape_swarms_agent.run(
"https://griptape.ai, griptape.txt"
)
print(output)
```
### Key Components:
1.**GriptapeSwarmsAgent**: A custom class that inherits from the `SwarmsAgent` class and integrates the Griptape agent.
2.**run(task: str) -> str**: A method that takes a task string, processes it (e.g., splitting into a URL and filename), and runs the Griptape agent with the provided inputs.
3.**Griptape Tools**: The tools integrated into the Griptape agent (e.g., `WebScraperTool`, `PromptSummaryTool`, `FileManagerTool`) allow for web scraping, summarization, and file management.
You can now easily plug this custom Griptape agent into the **Swarms Framework** and use it to run tasks!
Swarms was designed to facilitate the communication between many different and specialized agents from a vast array of other frameworks such as langchain, autogen, crew, and more.
In traditional swarm theory, there are many types of swarms usually for very specialized use-cases and problem sets. Such as Hiearchical and sequential are great for accounting and sales, because there is usually a boss coordinator agent that distributes a workload to other specialized agents.
| Hierarchical Swarms | A system where agents are organized in a hierarchy, with higher-level agents coordinating lower-level agents to achieve complex tasks. | [Code Link](https://docs.swarms.world/en/latest/swarms/concept/swarm_architectures/#hierarchical-swarm) | Manufacturing process optimization, multi-level sales management, healthcare resource coordination |
| Agent Rearrange | A setup where agents rearrange themselves dynamically based on the task requirements and environmental conditions. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/) | Adaptive manufacturing lines, dynamic sales territory realignment, flexible healthcare staffing |
| Concurrent Workflows | Agents perform different tasks simultaneously, coordinating to complete a larger goal. | [Code Link](https://docs.swarms.world/en/latest/swarms/concept/swarm_architectures/#concurrent-workflows) | Concurrent production lines, parallel sales operations, simultaneous patient care processes |
| Sequential Coordination | Agents perform tasks in a specific sequence, where the completion of one task triggers the start of the next. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/sequential_workflow/) | Step-by-step assembly lines, sequential sales processes, stepwise patient treatment workflows |
| Parallel Processing | Agents work on different parts of a task simultaneously to speed up the overall process. | [Code Link](https://docs.swarms.world/en/latest/swarms/concept/swarm_architectures/#parallel-processing) | Parallel data processing in manufacturing, simultaneous sales analytics, concurrent medical tests |
| Mixture of Agents | A heterogeneous swarm where agents with different capabilities are combined to solve complex problems. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/moa/) | Financial forecasting, complex problem-solving requiring diverse skills |
| Graph Workflow | Agents collaborate in a directed acyclic graph (DAG) format to manage dependencies and parallel tasks. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/graph_workflow/) | AI-driven software development pipelines, complex project management |
| Group Chat | Agents engage in a chat-like interaction to reach decisions collaboratively. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/group_chat/) | Real-time collaborative decision-making, contract negotiations |
| Agent Registry | A centralized registry where agents are stored, retrieved, and invoked dynamically. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/agent_registry/) | Dynamic agent management, evolving recommendation engines |
| Spreadsheet Swarm | Manages tasks at scale, tracking agent outputs in a structured format like CSV files. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/spreadsheet_swarm/) | Large-scale marketing analytics, financial audits |
| Forest Swarm | A swarm structure that organizes agents in a tree-like hierarchy for complex decision-making processes. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/forest_swarm/) | Multi-stage workflows, hierarchical reinforcement learning |
Sequential Workflow enables you to sequentially execute tasks with `Agent` and then pass the output into the next agent and onwards until you have specified your max loops.
In this example, each `Agent` represents a task that is executed sequentially. The output of each agent is passed to the next agent in the sequence until the maximum number of loops is reached. This workflow is particularly useful for tasks that require a series of steps to be executed in a specific order, such as data processing pipelines or complex calculations that rely on the output of previous steps.
The `AgentRearrange` orchestration technique, inspired by Einops and einsum, allows you to define and map out the relationships between various agents. It provides a powerful tool for orchestrating complex workflows, enabling you to specify linear and sequential relationships such as `a -> a1 -> a2 -> a3`, or concurrent relationships where the first agent sends a message to 3 agents simultaneously: `a -> a1, a2, a3`. This level of customization allows for the creation of highly efficient and dynamic workflows, where agents can work in parallel or in sequence as needed. The `AgentRearrange` technique is a valuable addition to the swarms library, providing a new level of flexibility and control over the orchestration of agents. For more detailed information and examples, please refer to the [official documentation](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/).
The `GraphSwarm` is a workflow management system designed to orchestrate complex tasks by leveraging the power of graph theory. It enables the creation of a directed acyclic graph (DAG) to model dependencies between tasks and agents. This allows for efficient task assignment, execution, and monitoring.
Here's a breakdown of how the `GraphSwarm` works:
1.**Node Creation**: The `GraphSwarm` workflow is composed of nodes, which can be either agents or tasks. Agents are responsible for executing tasks, and tasks represent specific operations that need to be performed. In the example, two agents (`agent1` and `agent2`) and one task (`task1`) are created.
2.**Edge Definition**: Edges are used to define the relationships between nodes. In this case, edges are created to connect `agent1` and `agent2` to `task1`, indicating that both agents are capable of executing `task1`.
3.**Entry and End Points**: The `GraphSwarm` workflow requires the definition of entry points (where the workflow starts) and end points (where the workflow concludes). In this example, `agent1` and `agent2` are set as entry points, and `task1` is set as the end point.
4.**Visualization**: The `GraphSwarm` provides a visualization feature to graphically represent the workflow. This allows for easy understanding and debugging of the workflow structure.
5.**Execution**: The `GraphSwarm` workflow is executed by traversing the graph from the entry points to the end points. In this case, both `agent1` and `agent2` execute `task1` concurrently, and the results are collected.
6.**Results**: The final results of the workflow execution are aggregated and returned. In this example, the result of executing `task1` is "Task completed".
The `GraphSwarm` offers several benefits, including:
* **Concurrency**: Enables the execution of tasks concurrently, improving overall workflow efficiency.
* **Flexibility**: Allows for dynamic task assignment based on agent availability and task requirements.
* **Scalability**: Supports the addition of new agents and tasks as needed, making it suitable for large-scale workflows.
* **Visualization**: Provides a graphical representation of the workflow, facilitating understanding and debugging.
By leveraging the `GraphSwarm`, complex workflows can be efficiently managed, and tasks can be executed in a coordinated and scalable manner.
This is an implementation based on the paper: "Mixture-of-Agents Enhances Large Language Model Capabilities" by together.ai, available at [https://arxiv.org/abs/2406.04692](https://arxiv.org/abs/2406.04692). It achieves state-of-the-art (SOTA) results on AlpacaEval 2.0, MT-Bench, and FLASK, surpassing GPT-4 Omni. This architecture is particularly suitable for tasks that require parallelization followed by sequential processing in another loop.
A revolutionary swarm architecture designed for concurrent management and oversight of thousands of agents, facilitating a one-to-many approach for efficient task processing and output analysis.
### Key Features
* **Concurrency**: Enables the simultaneous execution of multiple agents, significantly reducing processing time and increasing overall system efficiency.
* **One-to-Many**: Allows a single task to be dynamically distributed among multiple agents, ensuring that each agent is utilized to its full potential.
* **Scalability**: Supports the integration of thousands of agents, making it an ideal solution for large-scale task processing and data analysis.
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm
# Define custom system prompts for each social media platform
TWITTER_AGENT_SYS_PROMPT = """
You are a Twitter marketing expert specializing in real estate. Your task is to create engaging, concise tweets to promote properties, analyze trends to maximize engagement, and use appropriate hashtags and timing to reach potential buyers.
"""
INSTAGRAM_AGENT_SYS_PROMPT = """
You are an Instagram marketing expert focusing on real estate. Your task is to create visually appealing posts with engaging captions and hashtags to showcase properties, targeting specific demographics interested in real estate.
"""
FACEBOOK_AGENT_SYS_PROMPT = """
You are a Facebook marketing expert for real estate. Your task is to craft posts optimized for engagement and reach on Facebook, including using images, links, and targeted messaging to attract potential property buyers.
"""
LINKEDIN_AGENT_SYS_PROMPT = """
You are a LinkedIn marketing expert for the real estate industry. Your task is to create professional and informative posts, highlighting property features, market trends, and investment opportunities, tailored to professionals and investors.
"""
EMAIL_AGENT_SYS_PROMPT = """
You are an Email marketing expert specializing in real estate. Your task is to write compelling email campaigns to promote properties, focusing on personalization, subject lines, and effective call-to-action strategies to drive conversions.
Create posts to promote luxury properties in North Texas, highlighting their features, location, and investment potential. Include relevant hashtags, images, and engaging captions.
Property:
$10,399,000
1609 Meandering Way Dr, Roanoke, TX 76262
Link to the property: https://www.zillow.com/homedetails/1609-Meandering-Way-Dr-Roanoke-TX-76262/308879785_zpid/
What's special
Unveiling a new custom estate in the prestigious gated Quail Hollow Estates! This impeccable residence, set on a sprawling acre surrounded by majestic trees, features a gourmet kitchen equipped with top-tier Subzero and Wolf appliances. European soft-close cabinets and drawers, paired with a double Cambria Quartzite island, perfect for family gatherings. The first-floor game room&media room add extra layers of entertainment. Step into the outdoor sanctuary, where a sparkling pool and spa, and sunken fire pit, beckon leisure. The lavish master suite features stunning marble accents, custom his&her closets, and a secure storm shelter.Throughout the home,indulge in the visual charm of designer lighting and wallpaper, elevating every space. The property is complete with a 6-car garage and a sports court, catering to the preferences of basketball or pickleball enthusiasts. This residence seamlessly combines luxury&recreational amenities, making it a must-see for the discerning buyer.
Facts & features
Interior
Bedrooms & bathrooms
Bedrooms: 6
Bathrooms: 8
Full bathrooms: 7
1/2 bathrooms: 1
Primary bedroom
Bedroom
Features: Built-in Features, En Suite Bathroom, Walk-In Closet(s)
Cooling
Central Air, Ceiling Fan(s), Electric
Appliances
Included: Built-In Gas Range, Built-In Refrigerator, Double Oven, Dishwasher, Gas Cooktop, Disposal, Ice Maker, Microwave, Range, Refrigerator, Some Commercial Grade, Vented Exhaust Fan, Warming Drawer, Wine Cooler
Features
Wet Bar, Built-in Features, Dry Bar, Decorative/Designer Lighting Fixtures, Eat-in Kitchen, Elevator, High Speed Internet, Kitchen Island, Pantry, Smart Home, Cable TV, Walk-In Closet(s), Wired for Sound
Flooring: Hardwood
Has basement: No
Number of fireplaces: 3
Fireplace features: Living Room, Primary Bedroom
Interior area
Total interior livable area: 10,466 sqft
Total spaces: 12
Parking features: Additional Parking
Attached garage spaces: 6
Carport spaces: 6
Features
Levels: Two
Stories: 2
Patio & porch: Covered
Exterior features: Built-in Barbecue, Barbecue, Gas Grill, Lighting, Outdoor Grill, Outdoor Living Area, Private Yard, Sport Court, Fire Pit
Pool features: Heated, In Ground, Pool, Pool/Spa Combo
The `ForestSwarm` architecture is designed for efficient task assignment by dynamically selecting the most suitable agent from a collection of trees. This is achieved through asynchronous task processing, where agents are chosen based on their relevance to the task at hand. The relevance is determined by calculating the similarity between the system prompts associated with each agent and the keywords present in the task itself. For a more in-depth understanding of how `ForestSwarm` works, please refer to the [official documentation](https://docs.swarms.world/en/latest/swarms/structs/forest_swarm/).
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)
-----
## Folder Structure
The swarms package has been meticlously crafted for extreme use-ability and understanding, the swarms package is split up into various modules such as `swarms.agents` that holds pre-built agents, `swarms.structs`that holds a vast array of structures like `Agent` and multi agent structures. The 3 most important are `structs`, `models`, and `agents`.
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!
----
## Accelerate Backlog
Accelerate Bugs, Features, and Demos to implement by supporting us here:
- Chat live with us on [Discord](https://discord.gg/kS3rwKs3ZC)
- Follow us on [Twitter](https://twitter.com/kyegomez)
- Connect with us on [LinkedIn](https://www.linkedin.com/company/the-swarm-corporation)
- Visit us on [YouTube](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ)
- [Join the Swarms community on Discord!](https://discord.gg/AJazBmhKnr)
- Join our Swarms Community Gathering every Thursday at 1pm NYC Time to unlock the potential of autonomous agents in automating your daily tasks [Sign up here](https://lu.ma/5p2jnc2v)
