@ -10,25 +30,42 @@ Orchestrate swarms of agents for production-grade applications.
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</div>
Individual agents face 5 significant challenges that hinder their deployment in production: short memory, single-task threading, hallucinations, high cost, and lack of collaboration. Multi-agent collaboration offers a solution to all these issues. Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.
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.
| Models | Pre-trained models that can be utilized for various tasks within the swarm framework. | ⭐⭐⭐ | [Documentation](https://docs.swarms.world/en/latest/swarms/models/) |
| Models APIs | APIs to interact with and utilize the models effectively, providing interfaces for inference, training, and fine-tuning. | ⭐⭐⭐ | [Documentation](https://docs.swarms.world/en/latest/swarms/models/) |
| Agents with Tools | Agents equipped with specialized tools to perform specific tasks more efficiently, such as data processing, analysis, or interaction with external systems. | ⭐⭐⭐⭐ | [Documentation](https://medium.com/@kyeg/the-swarms-tool-system-functions-pydantic-basemodels-as-tools-and-radical-customization-c2a2e227b8ca) |
| Agents with Memory | Mechanisms for agents to store and recall past interactions, improving learning and adaptability over time. | ⭐⭐⭐⭐ | [Documentation]() |
| Multi-Agent Orchestration | Coordination of multiple agents to work together seamlessly on complex tasks, leveraging their individual strengths to achieve higher overall performance. | ⭐⭐⭐⭐⭐ | [Documentation]() |
The performance impact is rated on a scale from one to five stars, with multi-agent orchestration being the highest due to its ability to combine the strengths of multiple agents and optimize task execution.
----
## Install
## Install 💻
`$ pip3 install -U swarms`
---
## Usage
# Usage Examples 🤖
### Google Collab Example
Run example in Collab: <atarget="_blank"href="https://colab.research.google.com/github/kyegomez/swarms/blob/master/playground/swarms_example.ipynb">
<imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"/>
</a>
### `Agent`
---
## `Agents`
A fully plug-and-play autonomous agent powered by an LLM extended by a long-term memory database, and equipped with function calling for tool usage! By passing in an LLM, you can create a fully autonomous agent with extreme customization and reliability, ready for real-world task automation!
Features:
@ -67,7 +104,7 @@ agent.run("Generate a 10,000 word blog on health and wellness.")
```
#`Agent` with Long Term Memory
### `Agent` + Long Term Memory
`Agent` equipped with quasi-infinite long term memory. Great for long document understanding, analysis, and retrieval.
```python
@ -106,7 +143,7 @@ print(out)
```
#`Agent` with Long Term Memory ++ Tools!
### `Agent` ++ Long Term Memory ++ Tools!
An LLM equipped with long term memory and tools, a full stack agent capable of automating all and any digital tasks given a good prompt.
```python
@ -149,40 +186,10 @@ print(out)
```
### Simple Conversational Agent
A Plug in and play conversational agent with `GPT4`, `Mixytral`, or any of our models
- Reliable conversational structure to hold messages together with dynamic handling for long context conversations and interactions with auto chunking
- Reliable, this simple system will always provide responses you want.
```python
from swarms import Agent, Anthropic
## Initialize the workflow
agent = Agent(
agent_name="Transcript Generator",
agent_description=(
"Generate a transcript for a youtube video on what swarms"
" are!"
),
llm=Anthropic(),
max_loops=3,
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True, # Set to True
)
# Run the workflow on a task
agent("Generate a transcript for a youtube video on what swarms are!")
```
## Devin
### Devin
Implementation of Devin in less than 90 lines of code with several tools:
terminal, browser, and edit files!
terminal, browser, and edit files.
```python
from swarms import Agent, Anthropic
@ -286,7 +293,7 @@ print(out)
```
## `Agent`with Pydantic BaseModel as Output Type
###`Agent`with Pydantic BaseModel as Output Type
The following is an example of an agent that intakes a pydantic basemodel and outputs it at the same time:
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.
For deeper control of your agent stack, `Task` is a simple structure for task execution with the `Agent`. Imagine zapier like LLM-based workflow automation.
✅ Task is a structure for task execution with the Agent.
✅ Tasks can have descriptions, scheduling, triggers, actions, conditions, dependencies, priority, and a history.
✅ The Task structure allows for efficient workflow automation with LLM-based agents.
```python
import os
from dotenv import load_dotenv
from swarms.structs import Agent, OpenAIChat, Task
Swarms was designed to faciliate 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](#) | 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](#) | 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](#) | Parallel data processing in manufacturing, simultaneous sales analytics, concurrent medical tests |
### `SequentialWorkflow`
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. `SequentialWorkflow` is wonderful for real-world business tasks like sending emails, summarizing documents, and analyzing data.
@ -588,78 +721,7 @@ out = swarmnet.run_many_agents("Generate a 10,000 word blog on health and wellne
print(out)
```
### `Task`
`Task` is a simple structure for task execution with the `Agent`. Imagine zapier for LLM-based workflow automation
✅ Task is a structure for task execution with the Agent.
✅ Tasks can have descriptions, scheduling, triggers, actions, conditions, dependencies, priority, and a history.
✅ The Task structure allows for efficient workflow automation with LLM-based agents.
```python
import os
from dotenv import load_dotenv
from swarms.structs import Agent, OpenAIChat, Task
"Generate a report on the top 3 biggest expenses for small"
" businesses and how businesses can save 20%"
),
agent=agent,
)
# Set the action and condition
task.set_action(my_action)
task.set_condition(my_condition)
# Execute the task
print("Executing task...")
task.run()
# Check if the task is completed
if task.is_completed():
print("Task completed")
else:
print("Task not completed")
# Output the result of the task
print(f"Task result: {task.result}")
```
---
## Majority Voting
### Majority Voting
Multiple-agents will evaluate an idea based off of an parsing or evaluation function. From papers like "[More agents is all you need](https://arxiv.org/pdf/2402.05120.pdf)
```python
@ -707,7 +769,7 @@ mv = MajorityVoting(
mv.run("What is your stance on healthcare?")
```
## Real-World Deployment
### Real-World Deployment
### Multi-Agent Swarm for Logistics
Here's a production grade swarm ready for real-world deployment in a factory and logistics settings like warehouses. This swarm can automate 3 costly and inefficient workflows, safety checks, productivity checks, and warehouse security.
evelynmitchell
11 months ago
committed by
GitHub