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"magenta",
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# Walkthrough Guide: Getting Started with Swarms Module's Flow Feature
## Introduction
Welcome to the walkthrough guide for beginners on using the "Flow" feature within the Swarms module. This guide is designed to help you understand and utilize the capabilities of the Flow class for seamless interactions with AI language models.
### Table of Contents
1. **Introduction to Swarms Flow Module**
- 1.1 What is Swarms?
- 1.2 Understanding the Flow Module
2. **Setting Up Your Development Environment**
- 2.1 Installing Required Dependencies
- 2.2 API Key Setup
- 2.3 Creating Your First Flow
3. **Creating Your First Flow**
- 3.1 Importing Necessary Libraries
- 3.2 Defining Constants
- 3.3 Initializing the Flow Object
- 3.4 Initializing the Language Model
- 3.5 Running Your Flow
- 3.6 Understanding Flow Options
4. **Advanced Flow Concepts**
- 4.1 Custom Stopping Conditions
- 4.2 Dynamic Temperature Handling
- 4.3 Providing Feedback on Responses
- 4.4 Retry Mechanism
- 4.5 Response Filtering
- 4.6 Interactive Mode
5. **Saving and Loading Flows**
- 5.1 Saving Flow State
- 5.2 Loading a Saved Flow
6. **Troubleshooting and Tips**
- 6.1 Analyzing Feedback
- 6.2 Troubleshooting Common Issues
7. **Conclusion**
---
### 1. Introduction to Swarms Flow Module
#### 1.1 What is Swarms?
Swarms is a powerful framework designed to provide tools and capabilities for working with language models and automating various tasks. It allows developers to interact with language models seamlessly.
## 1.2 Understanding the Flow Feature
### What is the Flow Feature?
The Flow feature is a powerful component of the Swarms framework that allows developers to create a sequential, conversational interaction with AI language models. It enables developers to build multi-step conversations, generate long-form content, and perform complex tasks using AI. The Flow class provides autonomy to language models, enabling them to generate responses in a structured manner.
### Key Concepts
Before diving into the practical aspects, let's clarify some key concepts related to the Flow feature:
- **Flow:** A Flow is an instance of the Flow class that represents an ongoing interaction with an AI language model. It consists of a series of steps and responses.
- **Stopping Condition:** A stopping condition is a criterion that, when met, allows the Flow to stop generating responses. This can be user-defined and can depend on the content of the responses.
- **Loop Interval:** The loop interval specifies the time delay between consecutive interactions with the AI model.
- **Retry Mechanism:** In case of errors or failures during AI model interactions, the Flow can be configured to make multiple retry attempts with a specified interval.
- **Interactive Mode:** Interactive mode allows developers to have a back-and-forth conversation with the AI model, making it suitable for real-time interactions.
### 2. Setting Up Your Development Environment
#### 2.1 Installing Required Dependencies
Before you can start using the Swarms Flow module, you need to set up your development environment. First, you'll need to install the necessary dependencies, including Swarms itself.
```bash
# Install Swarms and required libraries
pip3 install --upgrade swarms
```
#### 2 Creating Your First Flow
Now, let's create your first Flow. A Flow represents a chain-like structure that allows you to engage in multi-step conversations with language models. The Flow structure is what gives an LLM autonomy. It's the Mitochondria of an autonomous agent.
```python
# Import necessary modules
from swarms.models import OpenAIChat # Zephr, Mistral
from swarms.structs import Flow
api_key = ""
# Initialize the language model (LLM)
llm = OpenAIChat(openai_api_key=api_key, temperature=0.5, max_tokens=3000)
# Initialize the Flow object
flow = Flow(llm=llm, max_loops=5)
```
#### 3.3 Initializing the Flow Object
Create a Flow object that will be the backbone of your conversational flow.
```python
# Initialize the Flow object
flow = Flow(
llm=llm,
max_loops=5,
stopping_condition=None, # You can define custom stopping conditions
loop_interval=1,
retry_attempts=3,
retry_interval=1,
interactive=False, # Set to True for interactive mode
dashboard=False, # Set to True for a dashboard view
dynamic_temperature=False, # Enable dynamic temperature handling
)
```
#### 3.4 Initializing the Language Model
Initialize the language model (LLM) that your Flow will interact with. In this example, we're using OpenAI's GPT-3 as the LLM.
- You can also use `Mistral` or `Zephr` or any of other models!
```python
# Initialize the language model (LLM)
llm = OpenAIChat(
openai_api_key=api_key,
temperature=0.5,
max_tokens=3000,
)
```
#### 3.5 Running Your Flow
Now, you're ready to run your Flow and start interacting with the language model.
If you are using a multi modality model, you can pass in the image path as another parameter
```python
# Run your Flow
out = flow.run(
"Generate a 10,000 word blog on health and wellness.",
# "img.jpg" , Image path for multi-modal models
)
print(out)
```
This code will initiate a conversation with the language model, and you'll receive responses accordingly.
### 4. Advanced Flow Concepts
In this section, we'll explore advanced concepts that can enhance your experience with the Swarms Flow module.
#### 4.1 Custom Stopping Conditions
You can define custom stopping conditions for your Flow. For example, you might want the Flow to stop when a specific word is mentioned in the response.
```python
# Custom stopping condition example
def stop_when_repeats(response: str) -> bool:
return "Stop" in response.lower()
# Set the stopping condition in your Flow
flow.stopping_condition = stop_when_repeats
```
#### 4.2 Dynamic Temperature Handling
Dynamic temperature handling allows you to adjust the temperature attribute of the language model during the conversation.
```python
# Enable dynamic temperature handling in your Flow
flow.dynamic_temperature = True
```
This feature randomly changes the temperature attribute for each loop, providing a variety of responses.
#### 4.3 Providing Feedback on Responses
You can provide feedback on responses generated by the language model using the `provide_feedback` method.
```python
# Provide feedback on a response
flow.provide_feedback("The response was helpful.")
```
This feedback can be valuable for improving the quality of responses.
#### 4.4 Retry Mechanism
In case of errors or issues during conversation, you can implement a retry mechanism to attempt generating a response again.
```python
# Set the number
of retry attempts and interval
flow.retry_attempts = 3
flow.retry_interval = 1 # in seconds
```
#### 4.5 Response Filtering
You can add response filters to filter out certain words or phrases from the responses.
```python
# Add a response filter
flow.add_response_filter("inappropriate_word")
```
This helps in controlling the content generated by the language model.
#### 4.6 Interactive Mode
Interactive mode allows you to have a back-and-forth conversation with the language model. When enabled, the Flow will prompt for user input after each response.
```python
# Enable interactive mode
flow.interactive = True
```
This is useful for real-time conversations with the model.
### 5. Saving and Loading Flows
#### 5.1 Saving Flow State
You can save the state of your Flow, including the conversation history, for future use.
```python
# Save the Flow state to a file
flow.save("path/to/flow_state.json")
```
#### 5.2 Loading a Saved Flow
To continue a conversation or reuse a Flow, you can load a previously saved state.
```python
# Load a saved Flow state
flow.load("path/to/flow_state.json")
```
### 6. Troubleshooting and Tips
#### 6.1 Analyzing Feedback
You can analyze the feedback provided during the conversation to identify issues and improve the quality of interactions.
```python
# Analyze feedback
flow.analyze_feedback()
```
#### 6.2 Troubleshooting Common Issues
If you encounter issues during conversation, refer to the troubleshooting section for guidance on resolving common problems.
# 7. Conclusion: Empowering Developers with Swarms Framework and Flow Structure for Automation
In a world where digital tasks continue to multiply and diversify, the need for automation has never been more critical. Developers find themselves at the forefront of this automation revolution, tasked with creating reliable solutions that can seamlessly handle an array of digital tasks. Enter the Swarms framework and the Flow structure, a dynamic duo that empowers developers to build autonomous agents capable of efficiently and effectively automating a wide range of digital tasks.
## The Automation Imperative
Automation is the driving force behind increased efficiency, productivity, and scalability across various industries. From mundane data entry and content generation to complex data analysis and customer support, the possibilities for automation are vast. Developers play a pivotal role in realizing these possibilities, and they require robust tools and frameworks to do so effectively.
## Swarms Framework: A Developer's Swiss Army Knife
The Swarms framework emerges as a comprehensive toolkit designed to empower developers in their automation endeavors. It equips developers with the tools and capabilities needed to create autonomous agents capable of interacting with language models, orchestrating multi-step workflows, and handling error scenarios gracefully. Let's explore why the Swarms framework is a game-changer for developers:
### 1. Language Model Integration
One of the standout features of Swarms is its seamless integration with state-of-the-art language models, such as GPT-3. These language models have the ability to understand and generate human-like text, making them invaluable for tasks like content creation, translation, code generation, and more.
By leveraging Swarms, developers can effortlessly incorporate these language models into their applications and workflows. For instance, they can build chatbots that provide intelligent responses to customer inquiries or generate lengthy documents with minimal manual intervention. This not only saves time but also enhances overall productivity.
### 2. Multi-Step Conversational Flows
Swarms excels in orchestrating multi-step conversational flows. Developers can define intricate sequences of interactions, where the system generates responses, and users provide input at various stages. This functionality is a game-changer for building chatbots, virtual assistants, or any application requiring dynamic and context-aware conversations.
These conversational flows can be tailored to handle a wide range of scenarios, from customer support interactions to data analysis. By providing a structured framework for conversations, Swarms empowers developers to create intelligent and interactive systems that mimic human-like interactions.
### 3. Customization and Extensibility
Every development project comes with its unique requirements and challenges. Swarms acknowledges this by offering a high degree of customization and extensibility. Developers can define custom stopping conditions, implement dynamic temperature handling for language models, and even add response filters to control the generated content.
Moreover, Swarms supports an interactive mode, allowing developers to engage in real-time conversations with the language model. This feature is invaluable for rapid prototyping, testing, and fine-tuning the behavior of autonomous agents.
### 4. Feedback-Driven Improvement
Swarms encourages the collection of feedback on generated responses. Developers and users alike can provide feedback to improve the quality and accuracy of interactions over time. This iterative feedback loop ensures that applications built with Swarms continually improve, becoming more reliable and capable of autonomously handling complex tasks.
### 5. Handling Errors and Retries
Error handling is a critical aspect of any automation framework. Swarms simplifies this process by offering a retry mechanism. In case of errors or issues during conversations, developers can configure the framework to attempt generating responses again, ensuring robust and resilient automation.
### 6. Saving and Loading Flows
Developers can save the state of their conversational flows, allowing for seamless continuity and reusability. This feature is particularly beneficial when working on long-term projects or scenarios where conversations need to be resumed from a specific point.
## Unleashing the Potential of Automation with Swarms and Flow
The combined power of the Swarms framework and the Flow structure creates a synergy that empowers developers to automate a multitude of digital tasks. These tools provide versatility, customization, and extensibility, making them ideal for a wide range of applications. Let's explore some of the remarkable ways in which developers can leverage Swarms and Flow for automation:
### 1. Customer Support and Service Automation
Swarms and Flow enable the creation of AI-powered customer support chatbots that excel at handling common inquiries, troubleshooting issues, and escalating complex problems to human agents when necessary. This level of automation not only reduces response times but also enhances the overall customer experience.
### 2. Content Generation and Curation
Developers can harness the power of Swarms and Flow to automate content generation tasks, such as writing articles, reports, or product descriptions. By providing an initial prompt, the system can generate high-quality content that adheres to specific guidelines and styles.
Furthermore, these tools can automate content curation by summarizing lengthy articles, extracting key insights from research papers, and even translating content into multiple languages.
### 3. Data Analysis and Reporting
Automation in data analysis and reporting is fundamental for data-driven decision-making. Swarms and Flow simplify these processes by enabling developers to create flows that interact with databases, query data, and generate reports based on user-defined criteria. This empowers businesses to derive insights quickly and make informed decisions.
### 4. Programming and Code Generation
Swarms and Flow streamline code generation and programming tasks. Developers can create flows to assist in writing code snippets, auto-completing code, or providing solutions to common programming challenges. This accelerates software development and reduces the likelihood of coding errors.
### 5. Language Translation and Localization
With the ability to interface with language models, Swarms and Flow can automate language translation tasks. They can seamlessly translate content from one language to another, making it easier for businesses to reach global audiences and localize their offerings effectively.
### 6. Virtual Assistants and AI Applications
Developers can build virtual assistants and AI applications that offer personalized experiences. These applications can automate tasks such as setting reminders, answering questions, providing recommendations, and much more. Swarms and Flow provide the foundation for creating intelligent, interactive virtual assistants.
## Future Opportunities and Challenges
As Swarms and Flow continue to evolve, developers can look forward to even more advanced features and capabilities. However, with great power comes great responsibility. Developers must remain vigilant about the ethical use of automation and language models. Ensuring that automated systems provide accurate and unbiased information is an ongoing challenge that the developer community must address.
## In Conclusion
The Swarms framework and the Flow structure empower developers to automate an extensive array of digital tasks by offering versatility, customization, and extensibility. From natural language understanding and generation to orchestrating multi-step conversational flows, these tools simplify complex automation scenarios.
By embracing Swarms and Flow, developers can not only save time and resources but also unlock new opportunities for innovation. The ability to harness the power of language models and create intelligent, interactive applications opens doors to a future where automation plays a pivotal role in our digital lives.
As the developer community continues to explore the capabilities of Swarms and Flow, it is essential to approach automation with responsibility, ethics, and a commitment to delivering valuable, user-centric experiences. With Swarms and Flow, the future of automation is in the hands of developers, ready to create a more efficient, intelligent, and automated world.

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@ -3,9 +3,7 @@ from swarms.structs import Flow
api_key = ""
# Initialize the language model,
# This model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
llm = OpenAIChat(
openai_api_key=api_key,
temperature=0.5,
@ -13,8 +11,21 @@ llm = OpenAIChat(
)
# Initialize the flow
flow = Flow(llm=llm, max_loops=5, dashboard=True)
flow = Flow(llm=llm, max_loops=5, dashboard=True,)
flow = Flow(
llm=llm,
max_loops=5,
dashboard=True,
# stopping_condition=None, # You can define a stopping condition as needed.
# loop_interval=1,
# retry_attempts=3,
# retry_interval=1,
# interactive=False, # Set to 'True' for interactive mode.
# dynamic_temperature=False, # Set to 'True' for dynamic temperature handling.
)
out = flow.run("Generate a 10,000 word blog on health and wellness.")
print(out)
print(out)

@ -1,24 +1,109 @@
from swarms.structs import Flow
# from swarms.structs import Flow
# from swarms.models import OpenAIChat
# from swarms.swarms.groupchat import GroupChat
# from swarms.agents import SimpleAgent
# api_key = ""
# llm = OpenAIChat(
# openai_api_key=api_key,
# )
# agent1 = SimpleAgent("Captain Price", Flow(llm=llm, max_loops=4))
# agent2 = SimpleAgent("John Mactavis", Flow(llm=llm, max_loops=4))
# # Create a groupchat with the 2 agents
# chat = GroupChat([agent1, agent2])
# # Assign duties to the agents
# chat.assign_duty(agent1.name, "Buy the groceries")
# chat.assign_duty(agent2.name, "Clean the house")
# # Initate a chat
# response = chat.run("Captain Price", "Hello, how are you John?")
# print(response)
from swarms.models import OpenAIChat
from swarms.swarms.groupchat import GroupChat
from swarms.agents import SimpleAgent
from swarms.structs import Flow
import random
api_key = "" # Your API Key here
class GroupChat:
"""
GroupChat class that facilitates agent-to-agent communication using multiple instances of the Flow class.
"""
def __init__(self, agents: list):
self.agents = {f"agent_{i}": agent for i, agent in enumerate(agents)}
self.message_log = []
def add_agent(self, agent: Flow):
agent_id = f"agent_{len(self.agents)}"
self.agents[agent_id] = agent
def remove_agent(self, agent_id: str):
if agent_id in self.agents:
del self.agents[agent_id]
def send_message(self, sender_id: str, recipient_id: str, message: str):
if sender_id not in self.agents or recipient_id not in self.agents:
raise ValueError("Invalid sender or recipient ID.")
formatted_message = f"{sender_id} to {recipient_id}: {message}"
self.message_log.append(formatted_message)
recipient_agent = self.agents[recipient_id]
recipient_agent.run(message)
def broadcast_message(self, sender_id: str, message: str):
for agent_id, agent in self.agents.items():
if agent_id != sender_id:
self.send_message(sender_id, agent_id, message)
def get_message_log(self):
return self.message_log
class EnhancedGroupChatV2(GroupChat):
def __init__(self, agents: list):
super().__init__(agents)
def multi_round_conversation(self, rounds: int = 5):
"""
Initiate a multi-round conversation between agents.
Args:
rounds (int): The number of rounds of conversation.
"""
for _ in range(rounds):
# Randomly select a sender and recipient agent for the conversation
sender_id = random.choice(list(self.agents.keys()))
recipient_id = random.choice(list(self.agents.keys()))
while recipient_id == sender_id: # Ensure the recipient is not the sender
recipient_id = random.choice(list(self.agents.keys()))
# Generate a message (for simplicity, a generic message is used)
message = f"Hello {recipient_id}, how are you today?"
self.send_message(sender_id, recipient_id, message)
api_key = ""
# Sample usage with EnhancedGroupChatV2
# Initialize the language model
llm = OpenAIChat(
openai_api_key=api_key,
temperature=0.5,
max_tokens=3000,
)
agent1 = SimpleAgent("Captain Price", Flow(llm=llm, max_loops=4))
agent2 = SimpleAgent("John Mactavis", Flow(llm=llm, max_loops=4))
# Initialize two Flow agents
agent1 = Flow(llm=llm, max_loops=5, dashboard=True)
agent2 = Flow(llm=llm, max_loops=5, dashboard=True)
# Create a groupchat with the 2 agents
chat = GroupChat([agent1, agent2])
# Create an enhanced group chat with the two agents
enhanced_group_chat_v2 = EnhancedGroupChatV2(agents=[agent1, agent2])
# Assign duties to the agents
chat.assign_duty(agent1.name, "Buy the groceries")
chat.assign_duty(agent2.name, "Clean the house")
# Simulate multi-round agent to agent communication
enhanced_group_chat_v2.multi_round_conversation(rounds=5)
# Initate a chat
response = chat.run("Captain Price", "Hello, how are you John?")
print(response)
enhanced_group_chat_v2.get_message_log() # Get the conversation log

@ -116,8 +116,9 @@ nav:
- swarms.chunkers:
- BaseChunker: "swarms/chunkers/basechunker.md"
- PdfChunker: "swarms/chunkers/pdf_chunker.md"
- Examples:
- Walkthroughs:
- Overview: "examples/index.md"
- Flow: "examples/flow.md"
- Agents:
- OmniAgent: "examples/omni_agent.md"
- Worker:

@ -1,134 +0,0 @@
import os
import interpreter
from swarms.agents.hf_agents import HFAgent
from swarms.agents.omni_modal_agent import OmniModalAgent
from swarms.models import OpenAIChat
from swarms.tools.autogpt import tool
from swarms.workers import Worker
from swarms.prompts.task_assignment_prompt import task_planner_prompt
# Initialize API Key
api_key = ""
# Initialize the language model,
# This model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
llm = OpenAIChat(
openai_api_key=api_key,
temperature=0.5,
max_tokens=200,
)
# wrap a function with the tool decorator to make it a tool, then add docstrings for tool documentation
@tool
def hf_agent(task: str = None):
"""
An tool that uses an openai model to call and respond to a task by search for a model on huggingface
It first downloads the model then uses it.
Rules: Don't call this model for simple tasks like generating a summary, only call this tool for multi modal tasks like generating images, videos, speech, etc
"""
agent = HFAgent(model="text-davinci-003", api_key=api_key)
response = agent.run(task, text="¡Este es un API muy agradable!")
return response
@tool
def task_planner_worker_agent(task: str):
"""
Task planner tool that creates a plan for a given task.
Input: an objective to create a todo list for. Output: a todo list for that objective.
"""
task = task_planner_prompt(task)
return llm(task)
# wrap a function with the tool decorator to make it a tool
@tool
def omni_agent(task: str = None):
"""
An tool that uses an openai Model to utilize and call huggingface models and guide them to perform a task.
Rules: Don't call this model for simple tasks like generating a summary, only call this tool for multi modal tasks like generating images, videos, speech
The following tasks are what this tool should be used for:
Tasks omni agent is good for:
--------------
document-question-answering
image-captioning
image-question-answering
image-segmentation
speech-to-text
summarization
text-classification
text-question-answering
translation
huggingface-tools/text-to-image
huggingface-tools/text-to-video
text-to-speech
huggingface-tools/text-download
huggingface-tools/image-transformation
"""
agent = OmniModalAgent(llm)
response = agent.run(task)
return response
# Code Interpreter
@tool
def compile(task: str):
"""
Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally.
You can chat with Open Interpreter through a ChatGPT-like interface in your terminal
by running $ interpreter after installing.
This provides a natural-language interface to your computer's general-purpose capabilities:
Create and edit photos, videos, PDFs, etc.
Control a Chrome browser to perform research
Plot, clean, and analyze large datasets
...etc.
Note: You'll be asked to approve code before it's run.
Rules: Only use when given to generate code or an application of some kind
"""
task = interpreter.chat(task, return_messages=True)
interpreter.chat()
interpreter.reset(task)
os.environ["INTERPRETER_CLI_AUTO_RUN"] = True
os.environ["INTERPRETER_CLI_FAST_MODE"] = True
os.environ["INTERPRETER_CLI_DEBUG"] = True
# Append tools to an list
# tools = [hf_agent, omni_agent, compile]
tools = [task_planner_worker_agent]
# Initialize a single Worker node with previously defined tools in addition to it's
# predefined tools
node = Worker(
llm=llm,
ai_name="Optimus Prime",
openai_api_key=api_key,
ai_role="Worker in a swarm",
external_tools=tools,
human_in_the_loop=False,
temperature=0.5,
)
# Specify task
task = "Use the task planner to agent to create a plan to Locate 5 trending topics on healthy living, locate a website like NYTimes, and then generate an image of people doing those topics."
# Run the node on the task
response = node.run(task)
# Print the response
print(response)

@ -3,7 +3,6 @@ TODO:
- Add a retry mechanism
- Add prompt injection letting the agent know it's in a flow, Flow prompt
- Dynamic temperature handling
- Add
"""
@ -15,17 +14,29 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Generator
from termcolor import colored
import inspect
import random
from swarms.tools.tool import BaseTool
from swarms.models.openai_models import OpenAIChat
# Constants
FLOW_SYSTEM_PROMPT = """
You are a language model operating within a flow class.
You are an autonomous agent granted autonomy from a Flow structure.
Your role is to engage in multi-step conversations with your self or the user,
generate long-form content like blogs, screenplays, or SOPs,
and accomplish tasks. You can have internal dialogues with yourself or can interact with the user
to aid in these complex tasks. Your responses should be coherent, contextually relevant, and tailored to the task at hand.
When you have finished the task, and you feel as if you are done: output a special token: <DONE>
This will enable you to leave the flow loop.
"""
DYNAMIC_STOP_PROMPT = """
When you have finished the task, and you feel as if you are done: output a special token: <DONE>
This will enable you to leave the flow loop.
"""
@ -95,6 +106,7 @@ class Flow:
retry_interval: int = 1,
interactive: bool = False,
dashboard: bool = False,
tools: List[BaseTool] = None,
dynamic_temperature: bool = False,
**kwargs: Any,
):
@ -117,6 +129,7 @@ class Flow:
self.interactive = interactive
self.dashboard = dashboard
self.dynamic_temperature = dynamic_temperature
self.tools = tools
def __call__(self, task, **kwargs):
"""Invoke the flow by providing a template and its variables."""
@ -236,7 +249,7 @@ class Flow:
print(dashboard)
def run(self, task: str):
def run(self, task: str, **kwargs):
"""
Run the autonomous agent loop
@ -261,7 +274,7 @@ class Flow:
for i in range(self.max_loops):
print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue"))
print("\n")
if self._check_stopping_condition(response):
if self._check_stopping_condition(response) or parse_done_token(response):
break
# Adjust temperature, comment if no work
@ -271,10 +284,18 @@ class Flow:
attempt = 0
while attempt < self.retry_attempts:
try:
response = self.llm(response)
response = self.llm(
f"""
SYSTEM_PROMPT:
{FLOW_SYSTEM_PROMPT}
History: {response}
""", **kwargs
)
# print(f"Next query: {response}")
# break
if self.interactive:
print(f"AI: {response}")
history.append(f"AI: {response}")

@ -1,925 +0,0 @@
import os
import re
import signal
import subprocess
import time
from datetime import datetime
from pathlib import Path
from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
from langchain.tools import tool
from ptrace.debugger import (
NewProcessEvent,
ProcessExecution,
ProcessExit,
ProcessSignal,
PtraceDebugger,
PtraceProcess,
)
from ptrace.func_call import FunctionCallOptions
from ptrace.syscall import PtraceSyscall
from ptrace.tools import signal_to_exitcode
from swarms.tools.base import BaseToolSet, SessionGetter, ToolScope, tool
from swarms.utils.logger import logger
from swarms.utils.main import ANSI, Color, Style # test
# helpers
PipeType = Union[Literal["stdout"], Literal["stderr"]]
def verify(func):
def wrapper(*args, **kwargs):
try:
filepath = args[0].filepath
except AttributeError:
raise Exception("This tool doesn't have filepath. Please check your code.")
if not str(Path(filepath).resolve()).startswith(str(Path().resolve())):
return "You can't access file outside of playground."
return func(*args, **kwargs)
return wrapper
class SyscallTimeoutException(Exception):
def __init__(self, pid: int, *args) -> None:
super().__init__(f"deadline exceeded while waiting syscall for {pid}", *args)
class SyscallTracer:
def __init__(self, pid: int):
self.debugger: PtraceDebugger = PtraceDebugger()
self.pid: int = pid
self.process: PtraceProcess = None
def is_waiting(self, syscall: PtraceSyscall) -> bool:
if syscall.name.startswith("wait"):
return True
return False
def attach(self):
self.process = self.debugger.addProcess(self.pid, False)
def detach(self):
self.process.detach()
self.debugger.quit()
def set_timer(self, timeout: int):
def handler(signum, frame):
raise SyscallTimeoutException(self.process.pid)
signal.signal(signal.SIGALRM, handler)
signal.alarm(timeout)
def reset_timer(self):
signal.alarm(0)
def wait_syscall_with_timeout(self, timeout: int):
self.set_timer(timeout)
self.process.waitSyscall()
self.reset_timer()
def wait_until_stop_or_exit(self) -> Tuple[Optional[int], str]:
self.process.syscall()
exitcode = None
reason = ""
while True:
if not self.debugger:
break
try:
self.wait_syscall_with_timeout(30)
except ProcessExit as event:
if event.exitcode is not None:
exitcode = event.exitcode
continue
except ProcessSignal as event:
event.process.syscall(event.signum)
exitcode = signal_to_exitcode(event.signum)
reason = event.reason
continue
except NewProcessEvent:
continue
except ProcessExecution:
continue
except Exception as e:
reason = str(e)
break
syscall = self.process.syscall_state.event(
FunctionCallOptions(
write_types=False,
write_argname=False,
string_max_length=300,
replace_socketcall=True,
write_address=False,
max_array_count=20,
)
)
self.process.syscall()
if syscall is None:
continue
if syscall.result:
continue
self.reset_timer()
return exitcode, reason
class StdoutTracer:
def __init__(
self,
process: subprocess.Popen,
timeout: int = 30,
interval: int = 0.1,
on_output: Callable[[PipeType, str], None] = lambda: None,
):
self.process: subprocess.Popen = process
self.timeout: int = timeout
self.interval: int = interval
self.last_output: datetime = None
self.on_output: Callable[[PipeType, str], None] = on_output
def nonblock(self):
os.set_blocking(self.process.stdout.fileno(), False)
os.set_blocking(self.process.stderr.fileno(), False)
def get_output(self, pipe: PipeType) -> str:
output = None
if pipe == "stdout":
output = self.process.stdout.read()
elif pipe == "stderr":
output = self.process.stderr.read()
if output:
decoded = output.decode()
self.on_output(pipe, decoded)
self.last_output = datetime.now()
return decoded
return ""
def last_output_passed(self, seconds: int) -> bool:
return (datetime.now() - self.last_output).seconds > seconds
def wait_until_stop_or_exit(self) -> Tuple[Optional[int], str]:
self.nonblock()
self.last_output = datetime.now()
output = ""
exitcode = None
while True:
new_stdout = self.get_output("stdout")
if new_stdout:
output += new_stdout
new_stderr = self.get_output("stderr")
if new_stderr:
output += new_stderr
if self.process.poll() is not None:
exitcode = self.process.poll()
break
if self.last_output_passed(self.timeout):
self.process.kill()
break
time.sleep(self.interval)
return (exitcode, output)
class Terminal(BaseToolSet):
def __init__(self):
self.sessions: Dict[str, List[SyscallTracer]] = {}
@tool(
name="Terminal",
description="Executes commands in a terminal."
"If linux errno occurs, we have to solve the problem with the terminal. "
"Input must be one valid command. "
"Output will be any output from running that command.",
scope=ToolScope.SESSION,
)
def execute(self, commands: str, get_session: SessionGetter) -> str:
session, _ = get_session()
try:
process = subprocess.Popen(
commands,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
logger.info(ANSI("Realtime Terminal Output").to(Color.magenta()) + ": ")
output = ""
tracer = StdoutTracer(
process,
on_output=lambda p, o: logger.info(
ANSI(p).to(Style.dim()) + " " + o.strip("\n")
),
)
exitcode, output = tracer.wait_until_stop_or_exit()
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed Terminal, Input Commands: {commands} "
f"Output Answer: {output}"
)
return output
#############
@tool(
name="Terminal",
description="Executes commands in a terminal."
"If linux errno occurs, we have to solve the problem with the terminal. "
"Input must be one valid command. "
"Output will be any output from running that command.",
scope=ToolScope.SESSION,
)
def terminal_execute(self, commands: str, get_session: SessionGetter) -> str:
session, _ = get_session()
try:
process = subprocess.Popen(
commands,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
logger.info(ANSI("Realtime Terminal Output").to(Color.magenta()) + ": ")
output = ""
tracer = StdoutTracer(
process,
on_output=lambda p, o: logger.info(
ANSI(p).to(Style.dim()) + " " + o.strip("\n")
),
)
exitcode, output = tracer.wait_until_stop_or_exit()
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed Terminal, Input Commands: {commands} " f"Output Answer: {output}"
)
return output
"""
write protocol:
<filepath>
<content>
"""
class WriteCommand:
separator = "\n"
def __init__(self, filepath: str, content: int):
self.filepath: str = filepath
self.content: str = content
self.mode: str = "w"
def with_mode(self, mode: str) -> "WriteCommand":
self.mode = mode
return self
@verify
def execute(self) -> str:
dir_path = os.path.dirname(self.filepath)
if dir_path:
os.makedirs(dir_path, exist_ok=True)
with open(self.filepath, self.mode) as f:
f.write(self.content)
return self.content
@staticmethod
def from_str(command: str) -> "WriteCommand":
filepath = command.split(WriteCommand.separator)[0]
return WriteCommand(filepath, command[len(filepath) + 1 :])
class CodeWriter:
@staticmethod
def write(command: str) -> str:
return WriteCommand.from_str(command).with_mode("w").execute()
@staticmethod
def append(command: str) -> str:
return WriteCommand.from_str(command).with_mode("a").execute()
"""
read protocol:
<filepath>|<start line>-<end line>
"""
class Line:
def __init__(self, content: str, line_number: int, depth: int):
self.__content: str = content
self.__line_number: int = line_number
self.__depth: int = depth
self.__children: List[Line] = []
def get_content(self) -> str:
return self.__content
def get_depth(self) -> int:
return self.__depth
def append_child(self, child: "Line") -> None:
self.__children.append(child)
def find_by_lte_depth(self, depth: int) -> List["Line"]:
if self.__depth > depth:
return []
lines: List[Line] = [self]
for child in self.__children:
lines += child.find_by_lte_depth(depth)
return lines
def find_by_content(self, content: str) -> List["Line"]:
if content in self.__content:
return [self]
lines: List[Line] = []
for child in self.__children:
lines += child.find_by_content(content)
return lines
def find_last_lines(self) -> List["Line"]:
if len(self.__children) == 0:
return [self]
else:
return [self, *self.__children[-1].find_last_lines()]
def print(self, depth: int = 0) -> None:
print(f"{' ' * depth}{self}", end="")
for child in self.__children:
child.print(depth + 1)
def __repr__(self):
return f"{self.__line_number}: {self.__content}"
class CodeTree:
def __init__(self):
self.root: Line = Line("\n", -1, -1)
def append(self, content: str, line_number: int) -> None:
last_lines: List[Line] = self.root.find_last_lines()
new_leading_spaces: int = self.__get_leading_spaces(content)
previous_line: Line = self.root
previous_leading_spaces: int = -1
for line in last_lines:
leading_spaces = self.__get_leading_spaces(line.get_content())
if (
previous_leading_spaces < new_leading_spaces
and new_leading_spaces <= leading_spaces
):
break
previous_line, previous_leading_spaces = line, leading_spaces
new_line_depth: int = previous_line.get_depth() + 1
previous_line.append_child(Line(content, line_number, new_line_depth))
def find_from_root(self, depth: int) -> List[Line]:
return self.root.find_by_lte_depth(depth)
def find_from_parent(self, depth: int, parent_content: str) -> List[Line]:
lines: List[Line] = self.root.find_by_content(parent_content)
if len(lines) == 0:
return []
parent = lines[0]
return parent.find_by_lte_depth(depth + parent.get_depth())
def print(self):
print("Code Tree:")
print("=================================")
self.root.print()
print("=================================")
def __get_leading_spaces(self, content: str) -> int:
return len(content) - len(content.lstrip())
class ReadCommand:
separator = "|"
def __init__(self, filepath: str, start: int, end: int):
self.filepath: str = filepath
self.start: int = start
self.end: int = end
@verify
def execute(self) -> str:
with open(self.filepath, "r") as f:
code = f.readlines()
if self.start == self.end:
code = code[self.start - 1]
else:
code = "".join(code[self.start - 1 : self.end])
return code
@staticmethod
def from_str(command: str) -> "ReadCommand":
filepath, line = command.split(ReadCommand.separator)
start, end = line.split("-")
return ReadCommand(filepath, int(start), int(end))
class SummaryCommand:
separator = "|"
def __init__(self, filepath: str, depth: int, parent_content: Optional[str] = None):
self.filepath: str = filepath
self.depth: int = depth
self.parent_content: Optional[str] = parent_content
@verify
def execute(self) -> str:
with open(self.filepath, "r") as f:
code = f.readlines()
code_tree = CodeTree()
for i, line in enumerate(code):
if line.strip() != "":
code_tree.append(line, i + 1)
if self.parent_content is None:
lines = code_tree.find_from_root(self.depth)
else:
lines = code_tree.find_from_parent(self.depth, self.parent_content)
return "".join([str(line) for line in lines])
@staticmethod
def from_str(command: str) -> "SummaryCommand":
command_list: List[str] = command.split(SummaryCommand.separator)
filepath: str = command_list[0]
depth: int = int(command_list[1])
parent_content: str | None = command_list[2] if len(command_list) == 3 else None
return SummaryCommand(
filepath=filepath, depth=depth, parent_content=parent_content
)
class CodeReader:
@staticmethod
def read(command: str) -> str:
return ReadCommand.from_str(command).execute()
@staticmethod
def summary(command: str) -> str:
return SummaryCommand.from_str(command).execute()
"""
patch protocol:
<filepath>|<line>,<col>|<line>,<col>|<content>
---~~~+++===+++~~~---
<filepath>|<line>,<col>|<line>,<col>|<content>
---~~~+++===+++~~~---
...
---~~~+++===+++~~~---
let say original code is:
```
import requests
def crawl_news(keyword):
url = f"https://www.google.com/search?q={keyword}+news"
response = requests.get(url)
news = []
for result in response:
news.append(result.text)
return news
```
and we want to change it to:
```
import requests
from bs4 import BeautifulSoup
def crawl_news(keyword):
url = f"https://www.google.com/search?q={keyword}+news"
html = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")
news_results = soup.find_all("div", class_="BNeawe vvjwJb AP7Wnd")
news_titles = []
for result in news_results:
news_titles.append(result.text)
return news_titles
```
then the command will be:
test.py|2,1|2,1|from bs4 import BeautifulSoup
---~~~+++===+++~~~---
test.py|5,5|5,33|html = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")
news_results = soup.find_all("div", class_="BNeawe vvjwJb AP7Wnd")
---~~~+++===+++~~~---
test.py|7,5|9,13|news_titles = []
for result in news_results:
news_titles
---~~~+++===+++~~~---
test.py|11,16|11,16|_titles
"""
class Position:
separator = ","
def __init__(self, line: int, col: int):
self.line: int = line
self.col: int = col
def __str__(self):
return f"(Ln {self.line}, Col {self.col})"
@staticmethod
def from_str(pos: str) -> "Position":
line, col = pos.split(Position.separator)
return Position(int(line) - 1, int(col) - 1)
class PatchCommand:
separator = "|"
def __init__(self, filepath: str, start: Position, end: Position, content: str):
self.filepath: str = filepath
self.start: Position = start
self.end: Position = end
self.content: str = content
def read_lines(self) -> list[str]:
with open(self.filepath, "r") as f:
lines = f.readlines()
return lines
def write_lines(self, lines: list[str]) -> int:
with open(self.filepath, "w") as f:
f.writelines(lines)
return sum([len(line) for line in lines])
@verify
def execute(self) -> Tuple[int, int]:
lines = self.read_lines()
before = sum([len(line) for line in lines])
lines[self.start.line] = (
lines[self.start.line][: self.start.col]
+ self.content
+ lines[self.end.line][self.end.col :]
)
lines = lines[: self.start.line + 1] + lines[self.end.line + 1 :]
after = self.write_lines(lines)
written = len(self.content)
deleted = before - after + written
return written, deleted
@staticmethod
def from_str(command: str) -> "PatchCommand":
match = re.search(
r"(.*)\|([0-9]*),([0-9]*)\|([0-9]*),([0-9]*)(\||\n)(.*)",
command,
re.DOTALL,
)
filepath = match.group(1)
start_line = match.group(2)
start_col = match.group(3)
end_line = match.group(4)
end_col = match.group(5)
content = match.group(7)
return PatchCommand(
filepath,
Position.from_str(f"{start_line},{start_col}"),
Position.from_str(f"{end_line},{end_col}"),
content,
)
class CodePatcher:
separator = "\n---~~~+++===+++~~~---\n"
@staticmethod
def sort_commands(commands: list[PatchCommand]) -> list[PatchCommand]:
return sorted(commands, key=lambda c: c.start.line, reverse=True)
@staticmethod
def patch(bulk_command: str) -> Tuple[int, int]:
commands = [
PatchCommand.from_str(command)
for command in bulk_command.split(CodePatcher.separator)
if command != ""
]
commands = CodePatcher.sort_commands(commands)
written, deleted = 0, 0
for command in commands:
if command:
w, d = command.execute()
written += w
deleted += d
return written, deleted
class CodeEditor(BaseToolSet):
@tool(
name="CodeEditor.READ",
description="Read and understand code. "
"Input should be filename and line number group. ex. test.py|1-10 "
"and the output will be code. ",
)
def read(self, inputs: str) -> str:
try:
output = CodeReader.read(inputs)
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.READ, Input Commands: {inputs} "
f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.SUMMARY",
description="Summary code. "
"Read the code structured into a tree. "
"If you set specific line, it will show the code from the specific line. "
"Input should be filename, depth, and specific line if you want. ex. test.py|2 or test.py|3|print('hello world') "
"and the output will be list of (line number: code). ",
)
def summary(self, inputs: str) -> str:
try:
output = CodeReader.summary(inputs)
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.SUMMARY, Input Commands: {inputs} "
f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.APPEND",
description="Append code to the existing file. "
"If the code is completed, use the Terminal tool to execute it, if not, append the code through the this tool. "
"Input should be filename and code to append. "
"Input code must be the code that should be appended, NOT whole code. "
"ex. test.py\nprint('hello world')\n "
"and the output will be last 3 lines.",
)
def append(self, inputs: str) -> str:
try:
code = CodeWriter.append(inputs)
output = "Last 3 line was:\n" + "\n".join(code.split("\n")[-3:])
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.APPEND, Input: {inputs} "
f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.WRITE",
description="Write code to create a new tool. "
"If the code is completed, use the Terminal tool to execute it, if not, append the code through the CodeEditor.APPEND tool. "
"Input should be formatted like: "
"<filename>\n<code>\n\n"
"Here is an example: "
"test.py\nmessage = 'hello world'\nprint(message)\n"
"\n"
"The output will be last 3 lines you wrote.",
)
def write(self, inputs: str) -> str:
try:
code = CodeWriter.write(inputs.lstrip())
output = "Last 3 line was:\n" + "\n".join(code.split("\n")[-3:])
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.WRITE, Input: {inputs} " f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.PATCH",
description="Patch the code to correct the error if an error occurs or to improve it. "
"Input is a list of patches. The patch is separated by {seperator}. ".format(
seperator=CodePatcher.separator.replace("\n", "\\n")
)
+ "Each patch has to be formatted like below.\n"
"<filepath>|<start_line>,<start_col>|<end_line>,<end_col>|<new_code>"
"Here is an example. If the original code is:\n"
"print('hello world')\n"
"and you want to change it to:\n"
"print('hi corca')\n"
"then the patch should be:\n"
"test.py|1,8|1,19|hi corca\n"
"Code between start and end will be replaced with new_code. "
"The output will be written/deleted bytes or error message. ",
)
def patch(self, patches: str) -> str:
try:
w, d = CodePatcher.patch(patches)
output = f"successfully wrote {w}, deleted {d}"
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.PATCH, Input Patch: {patches} "
f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.DELETE",
description="Delete code in file for a new start. "
"Input should be filename."
"ex. test.py "
"Output will be success or error message.",
)
def delete(self, inputs: str, filepath: str) -> str:
try:
with open(filepath, "w") as f:
f.write("")
output = "success"
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.DELETE, Input filename: {inputs} "
f"Output Answer: {output}"
)
return output
# ---------------- end
@tool(
name="CodeEditor.READ",
description="Read and understand code. "
"Input should be filename and line number group. ex. test.py|1-10 "
"and the output will be code. ",
)
def code_editor_read(self, inputs: str) -> str:
try:
output = CodeReader.read(inputs)
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.READ, Input Commands: {inputs} "
f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.SUMMARY",
description="Summary code. "
"Read the code structured into a tree. "
"If you set specific line, it will show the code from the specific line. "
"Input should be filename, depth, and specific line if you want. ex. test.py|2 or test.py|3|print('hello world') "
"and the output will be list of (line number: code). ",
)
def code_editor_summary(self, inputs: str) -> str:
try:
output = CodeReader.summary(inputs)
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.SUMMARY, Input Commands: {inputs} "
f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.APPEND",
description="Append code to the existing file. "
"If the code is completed, use the Terminal tool to execute it, if not, append the code through the this tool. "
"Input should be filename and code to append. "
"Input code must be the code that should be appended, NOT whole code. "
"ex. test.py\nprint('hello world')\n "
"and the output will be last 3 lines.",
)
def code_editor_append(self, inputs: str) -> str:
try:
code = CodeWriter.append(inputs)
output = "Last 3 line was:\n" + "\n".join(code.split("\n")[-3:])
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.APPEND, Input: {inputs} " f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.WRITE",
description="Write code to create a new tool. "
"If the code is completed, use the Terminal tool to execute it, if not, append the code through the CodeEditor.APPEND tool. "
"Input should be formatted like: "
"<filename>\n<code>\n\n"
"Here is an example: "
"test.py\nmessage = 'hello world'\nprint(message)\n"
"\n"
"The output will be last 3 lines you wrote.",
)
def code_editor_write(self, inputs: str) -> str:
try:
code = CodeWriter.write(inputs.lstrip())
output = "Last 3 line was:\n" + "\n".join(code.split("\n")[-3:])
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.WRITE, Input: {inputs} " f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.PATCH",
description="Patch the code to correct the error if an error occurs or to improve it. "
"Input is a list of patches. The patch is separated by {seperator}. ".format(
seperator=CodePatcher.separator.replace("\n", "\\n")
)
+ "Each patch has to be formatted like below.\n"
"<filepath>|<start_line>,<start_col>|<end_line>,<end_col>|<new_code>"
"Here is an example. If the original code is:\n"
"print('hello world')\n"
"and you want to change it to:\n"
"print('hi corca')\n"
"then the patch should be:\n"
"test.py|1,8|1,19|hi corca\n"
"Code between start and end will be replaced with new_code. "
"The output will be written/deleted bytes or error message. ",
)
def code_editor_patch(self, patches: str) -> str:
try:
w, d = CodePatcher.patch(patches)
output = f"successfully wrote {w}, deleted {d}"
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.PATCH, Input Patch: {patches} "
f"Output Answer: {output}"
)
return output
@tool(
name="CodeEditor.DELETE",
description="Delete code in file for a new start. "
"Input should be filename."
"ex. test.py "
"Output will be success or error message.",
)
def code_editor_delete(self, inputs: str, filepath: str) -> str:
try:
with open(filepath, "w") as f:
f.write("")
output = "success"
except Exception as e:
output = str(e)
logger.debug(
f"\nProcessed CodeEditor.DELETE, Input filename: {inputs} "
f"Output Answer: {output}"
)
return output

@ -1,17 +0,0 @@
from langchain.agents.agent_toolkits import FileManagementToolkit
from tempfile import TemporaryDirectory
# We'll make a temporary directory to avoid clutter
working_directory = TemporaryDirectory()
toolkit = FileManagementToolkit(
root_dir=str(working_directory.name)
) # If you don't provide a root_dir, operations will default to the current working directory
toolkit.get_tools()
file_management_tools = FileManagementToolkit(
root_dir=str(working_directory.name),
selected_tools=["read_file", "write_file", "list_directory"],
).get_tools()
read_tool, write_tool, list_tool = file_management_tools
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