# Enterprise-Grade and Production Ready Agents
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.
| **Feature** | **Description** | **Performance Impact** | **Documentation Link** |
|------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------|-------------------------------|
| 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](https://github.com/kyegomez/swarms/blob/master/examples/structs/agent/agent_with_longterm_memory.py) |
| 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.
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## Install 💻
`$ pip3 install -U swarms`
---
# Usage Examples 🤖
### Google Collab Example
Run example in Collab:
---
## `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:
✅ Any LLM / Any framework
✅ Extremely customize-able with max loops, autosaving, import docs (PDFS, TXT, CSVs, etc), tool usage, etc etc
✅ Long term memory database with RAG (ChromaDB, Pinecone, Qdrant)
```python
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import Agent
from swarm_models import OpenAIChat
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
# Initialize the language model
llm = OpenAIChat(
temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000
)
## Initialize the workflow
agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
# Run the workflow on a task
agent.run("Generate a 10,000 word blog on health and wellness.")
```
### `Agent` + Long Term Memory
`Agent` equipped with quasi-infinite long term memory. Great for long document understanding, analysis, and retrieval.
```python
from swarms import Agent
from swarm_models import OpenAIChat
from swarms_memory import ChromaDB # Copy and paste the code and put it in your own local directory.
# Making an instance of the ChromaDB class
memory = ChromaDB(
metric="cosine",
n_results=3,
output_dir="results",
docs_folder="docs",
)
# Initializing the agent with the Gemini instance and other parameters
agent = Agent(
agent_name="Covid-19-Chat",
agent_description=(
"This agent provides information about COVID-19 symptoms."
),
llm=OpenAIChat(),
max_loops="auto",
autosave=True,
verbose=True,
long_term_memory=memory,
stopping_condition="finish",
)
# Defining the task and image path
task = ("What are the symptoms of COVID-19?",)
# Running the agent with the specified task and image
out = agent.run(task)
print(out)
```
### `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
from swarms import Agent, ChromaDB, OpenAIChat
# Making an instance of the ChromaDB class
memory = ChromaDB(
metric="cosine",
n_results=3,
output_dir="results",
docs_folder="docs",
)
# Initialize a tool
def search_api(query: str):
# Add your logic here
return query
# Initializing the agent with the Gemini instance and other parameters
agent = Agent(
agent_name="Covid-19-Chat",
agent_description=(
"This agent provides information about COVID-19 symptoms."
),
llm=OpenAIChat(),
max_loops="auto",
autosave=True,
verbose=True,
long_term_memory=memory,
stopping_condition="finish",
tools=[search_api],
)
# Defining the task and image path
task = ("What are the symptoms of COVID-19?",)
# Running the agent with the specified task and image
out = agent.run(task)
print(out)
```
### Devin
Implementation of Devin in less than 90 lines of code with several tools:
terminal, browser, and edit files.
```python
from swarms import Agent
from swarm_models import Anthropic
import subprocess
# Model
llm = Anthropic(
temperature=0.1,
)
# Tools
def terminal(
code: str,
):
"""
Run code in the terminal.
Args:
code (str): The code to run in the terminal.
Returns:
str: The output of the code.
"""
out = subprocess.run(
code, shell=True, capture_output=True, text=True
).stdout
return str(out)
def browser(query: str):
"""
Search the query in the browser with the `browser` tool.
Args:
query (str): The query to search in the browser.
Returns:
str: The search results.
"""
import webbrowser
url = f"https://www.google.com/search?q={query}"
webbrowser.open(url)
return f"Searching for {query} in the browser."
def create_file(file_path: str, content: str):
"""
Create a file using the file editor tool.
Args:
file_path (str): The path to the file.
content (str): The content to write to the file.
Returns:
str: The result of the file creation operation.
"""
with open(file_path, "w") as file:
file.write(content)
return f"File {file_path} created successfully."
def file_editor(file_path: str, mode: str, content: str):
"""
Edit a file using the file editor tool.
Args:
file_path (str): The path to the file.
mode (str): The mode to open the file in.
content (str): The content to write to the file.
Returns:
str: The result of the file editing operation.
"""
with open(file_path, mode) as file:
file.write(content)
return f"File {file_path} edited successfully."
# Agent
agent = Agent(
agent_name="Devin",
system_prompt=(
"Autonomous agent that can interact with humans and other"
" agents. Be Helpful and Kind. Use the tools provided to"
" assist the user. Return all code in markdown format."
),
llm=llm,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
code_interpreter=True,
# streaming=True,
)
# Run the agent
out = agent("Create a new file for a plan to take over the world.")
print(out)
```
### `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:
```python
from pydantic import BaseModel, Field
from swarms import Agent
from swarm_models import Anthropic
# Initialize the schema for the person's information
class Schema(BaseModel):
name: str = Field(..., title="Name of the person")
agent: int = Field(..., title="Age of the person")
is_student: bool = Field(..., title="Whether the person is a student")
courses: list[str] = Field(
..., title="List of courses the person is taking"
)
# Convert the schema to a JSON string
tool_schema = Schema(
name="Tool Name",
agent=1,
is_student=True,
courses=["Course1", "Course2"],
)
# Define the task to generate a person's information
task = "Generate a person's information based on the following schema:"
# Initialize the agent
agent = Agent(
agent_name="Person Information Generator",
system_prompt=(
"Generate a person's information based on the following schema:"
),
# Set the tool schema to the JSON string -- this is the key difference
tool_schema=tool_schema,
llm=Anthropic(),
max_loops=3,
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
interactive=True,
# Set the output type to the tool schema which is a BaseModel
output_type=tool_schema, # or dict, or str
metadata_output_type="json",
# List of schemas that the agent can handle
list_base_models=[tool_schema],
function_calling_format_type="OpenAI",
function_calling_type="json", # or soon yaml
)
# Run the agent to generate the person's information
generated_data = agent.run(task)
# Print the generated data
print(f"Generated data: {generated_data}")
```
### Multi Modal Autonomous Agent
Run the agent with multiple modalities useful for various real-world tasks in manufacturing, logistics, and health.
```python
# Description: This is an example of how to use the Agent class to run a multi-modal workflow
import os
from dotenv import load_dotenv
from swarm_models.gpt4_vision_api import GPT4VisionAPI
from swarms.structs import Agent
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
# Initialize the language model
llm = GPT4VisionAPI(
openai_api_key=api_key,
max_tokens=500,
)
# Initialize the task
task = (
"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(
llm=llm, max_loops="auto", autosave=True, dashboard=True, multi_modal=True
)
# Run the workflow on a task
agent.run(task=task, img=img)
```
----