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[README][CLEANUP]
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Expand Up @@ -81,9 +81,10 @@ Refer to our documentation for production grade implementation details.


## Install 💻
Install the following packages with copy and paste

```bash
$ pip3 install -U swarms
$ pip3 install -U swarms swarm-models swarms-memory
```


Expand Down Expand Up @@ -168,28 +169,15 @@ The `Agent` class offers a range of settings to tailor its behavior to specific
```python
import os
from swarms import Agent
from swarm_models import OpenAIChat

from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
)
from dotenv import load_dotenv

load_dotenv()

# Get the OpenAI API key from the environment variable
api_key = os.getenv("OPENAI_API_KEY")

# Create an instance of the OpenAIChat class
model = OpenAIChat(
openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)

# Initialize the agent
agent = Agent(
agent_name="Financial-Analysis-Agent",
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
llm=model,
model_name="gpt-4o-mini",
max_loops=1,
autosave=True,
dashboard=False,
Expand All @@ -211,11 +199,10 @@ agent.run(

```
-----

### 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.



**Mermaid Diagram for RAG Integration**
```mermaid
graph TD
Expand All @@ -227,8 +214,11 @@ graph TD
F --> G[Return Output]
```

**Step 1: Initialize the ChromaDB Client**
```python
from swarms import Agent
from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
)
import os

from swarms_memory import ChromaDB
Expand All @@ -239,29 +229,13 @@ chromadb = ChromaDB(
output_dir="finance_agent_rag", # Directory for storing RAG data
# docs_folder="artifacts", # Uncomment and specify the folder containing your documents
)
```

**Step 2: Define the Model**
```python
from swarm_models import Anthropic
from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
)

# Define the Anthropic model for language processing
model = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
```

**Step 3: Initialize the Agent with RAG**
```python
from swarms import Agent

# Initialize the agent with RAG capabilities
agent = Agent(
agent_name="Financial-Analysis-Agent",
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
agent_description="Agent creates a comprehensive financial analysis",
llm=model,
model_name="gpt-4o-mini",
max_loops="auto", # Auto-adjusts loops based on task complexity
autosave=True, # Automatically saves agent state
dashboard=False, # Disables dashboard for this example
Expand Down Expand Up @@ -378,7 +352,6 @@ The following is an example of an agent that intakes a pydantic basemodel and ou
```python
from pydantic import BaseModel, Field
from swarms import Agent
from swarm_models import Anthropic


# Initialize the schema for the person's information
Expand Down Expand Up @@ -410,7 +383,7 @@ agent = Agent(
),
# Set the tool schema to the JSON string -- this is the key difference
tool_schema=tool_schema,
llm=Anthropic(),
model_name="gpt-4o",
max_loops=3,
autosave=True,
dashboard=False,
Expand Down Expand Up @@ -617,8 +590,6 @@ You can now easily plug this custom Griptape agent into the **Swarms Framework**

## Understanding Swarms

### What is a Swarm?

A swarm refers to a group of more than two agents working collaboratively to achieve a common goal. These agents can be software entities, such as llms that interact with each other to perform complex tasks. The concept of a swarm is inspired by natural systems like ant colonies or bird flocks, where simple individual behaviors lead to complex group dynamics and problem-solving capabilities.

### How Swarm Architectures Facilitate Communication
Expand All @@ -631,9 +602,6 @@ Swarm architectures are designed to establish and manage communication between a

3. **Sequential Communication**: Sequential swarms process tasks in a linear order, where each agent's output becomes the input for the next agent. This ensures that tasks with dependencies are handled in the correct sequence, maintaining the integrity of the workflow.

4. **Mesh Communication**: In mesh swarms, agents are fully connected, allowing any agent to communicate with any other agent. This setup provides high flexibility and redundancy, making it ideal for complex systems requiring dynamic interactions.

5. **Federated Communication**: Federated swarms involve multiple independent swarms that collaborate by sharing information and results. Each swarm operates autonomously but can contribute to a larger task, enabling distributed problem-solving across different nodes.

Swarm architectures leverage these communication patterns to ensure that agents work together efficiently, adapting to the specific requirements of the task at hand. By defining clear communication protocols and interaction models, swarm architectures enable the seamless orchestration of multiple agents, leading to enhanced performance and problem-solving capabilities.

Expand Down Expand Up @@ -911,14 +879,12 @@ The `run` method returns the final output after all agents have processed the in
from swarms import Agent, AgentRearrange


from swarm_models import Anthropic

# Initialize the director agent

director = Agent(
agent_name="Director",
system_prompt="Directs the tasks for the workers",
llm=Anthropic(),
model_name="claude-2",
max_loops=1,
dashboard=False,
streaming_on=True,
Expand All @@ -934,7 +900,7 @@ director = Agent(
worker1 = Agent(
agent_name="Worker1",
system_prompt="Generates a transcript for a youtube video on what swarms are",
llm=Anthropic(),
model_name="claude-2",
max_loops=1,
dashboard=False,
streaming_on=True,
Expand All @@ -949,7 +915,7 @@ worker1 = Agent(
worker2 = Agent(
agent_name="Worker2",
system_prompt="Summarizes the transcript generated by Worker1",
llm=Anthropic(),
model_name="claude-2",
max_loops=1,
dashboard=False,
streaming_on=True,
Expand Down Expand Up @@ -1103,20 +1069,12 @@ The `run` method returns the final output after all agents have processed the in
```python

import os
from swarm_models import OpenAIChat
from swarms import Agent, MixtureOfAgents

api_key = os.getenv("OPENAI_API_KEY")

# Create individual agents with the OpenAIChat model
model = OpenAIChat(
openai_api_key=api_key, model_name="gpt-4", temperature=0.1
)

# Agent 1: Financial Statement Analyzer
agent1 = Agent(
agent_name="FinancialStatementAnalyzer",
llm=model,
model_name="gpt-4o",
system_prompt="""You are a Financial Statement Analyzer specializing in 10-K SEC reports. Your primary focus is on analyzing the financial statements, including the balance sheet, income statement, and cash flow statement.
Key responsibilities:
Expand All @@ -1142,7 +1100,7 @@ When analyzing, consider industry standards and compare the company's performanc
# Agent 2: Risk Assessment Specialist
agent2 = Agent(
agent_name="RiskAssessmentSpecialist",
llm=model,
model_name="gpt-4o",
system_prompt="""You are a Risk Assessment Specialist focusing on 10-K SEC reports. Your primary role is to identify, analyze, and evaluate potential risks disclosed in the report.
Key responsibilities:
Expand All @@ -1169,7 +1127,7 @@ Your analysis should provide a comprehensive overview of the company's risk land
# Agent 3: Business Strategy Evaluator
agent3 = Agent(
agent_name="BusinessStrategyEvaluator",
llm=model,
model_name="gpt-4o",
system_prompt="""You are a Business Strategy Evaluator specializing in analyzing 10-K SEC reports. Your focus is on assessing the company's overall strategy, market position, and future outlook.
Key responsibilities:
Expand Down Expand Up @@ -1197,7 +1155,7 @@ Your analysis should provide insights into the company's strategic direction, it
# Aggregator Agent
aggregator_agent = Agent(
agent_name="10KReportAggregator",
llm=model,
model_name="gpt-4o",
system_prompt="""You are the 10-K Report Aggregator, responsible for synthesizing and summarizing the analyses provided by the Financial Statement Analyzer, Risk Assessment Specialist, and Business Strategy Evaluator. Your goal is to create a comprehensive, coherent, and insightful summary of the 10-K SEC report.
Key responsibilities:
Expand Down Expand Up @@ -1287,9 +1245,8 @@ The `run` method returns a dictionary containing the outputs of each agent that

```python
import os
from swarms import Agent
from swarms import Agent, SpreadSheetSwarm
from swarm_models import OpenAIChat
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm

# Define custom system prompts for each social media platform
TWITTER_AGENT_SYS_PROMPT = """
Expand All @@ -1312,20 +1269,12 @@ 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.
"""

# Example usage:
api_key = os.getenv("OPENAI_API_KEY")

# Model
model = OpenAIChat(
openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)

# Initialize your agents for different social media platforms
agents = [
Agent(
agent_name="Twitter-RealEstate-Agent",
system_prompt=TWITTER_AGENT_SYS_PROMPT,
llm=model,
model_name="gpt-4o",
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="twitter_realestate_agent.json",
Expand All @@ -1335,7 +1284,7 @@ agents = [
Agent(
agent_name="Instagram-RealEstate-Agent",
system_prompt=INSTAGRAM_AGENT_SYS_PROMPT,
llm=model,
model_name="gpt-4o",
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="instagram_realestate_agent.json",
Expand All @@ -1345,7 +1294,7 @@ agents = [
Agent(
agent_name="Facebook-RealEstate-Agent",
system_prompt=FACEBOOK_AGENT_SYS_PROMPT,
llm=model,
model_name="gpt-4o",
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="facebook_realestate_agent.json",
Expand All @@ -1355,7 +1304,7 @@ agents = [
Agent(
agent_name="LinkedIn-RealEstate-Agent",
system_prompt=LINKEDIN_AGENT_SYS_PROMPT,
llm=model,
model_name="gpt-4o",
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="linkedin_realestate_agent.json",
Expand All @@ -1365,7 +1314,7 @@ agents = [
Agent(
agent_name="Email-RealEstate-Agent",
system_prompt=EMAIL_AGENT_SYS_PROMPT,
llm=model,
model_name="gpt-4o",
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="email_realestate_agent.json",
Expand Down Expand Up @@ -1474,7 +1423,7 @@ The `run` method returns the output from the most relevant agent selected based


```python
from swarms.structs.tree_swarm import TreeAgent, Tree, ForestSwarm
from swarms import TreeAgent, Tree, ForestSwarm

# Create agents with varying system prompts and dynamically generated distances/keywords
agents_tree1 = [
Expand Down
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