Your Name
4 months ago
parent
63a5ad4fd5
commit
c254ec1742
@ -0,0 +1,45 @@
|
|||||||
|
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)
|
||||||
@ -0,0 +1,135 @@
|
|||||||
|
from typing import Any, Dict, List, Optional
|
||||||
|
|
||||||
|
from langchain import hub
|
||||||
|
from langchain.agents import AgentExecutor, create_openai_tools_agent
|
||||||
|
from langchain_community.tools.tavily_search import (
|
||||||
|
TavilySearchResults,
|
||||||
|
)
|
||||||
|
from langchain_openai import ChatOpenAI
|
||||||
|
from loguru import logger
|
||||||
|
|
||||||
|
|
||||||
|
class LangchainAgent:
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
tavily_api_key: str,
|
||||||
|
llm_model: str = "gpt-3.5-turbo",
|
||||||
|
temperature: float = 0.7,
|
||||||
|
tavily_max_results: int = 1,
|
||||||
|
prompt_hub_url: str = "hwchase17/openai-tools-agent",
|
||||||
|
verbose: bool = True,
|
||||||
|
log_file: Optional[str] = None,
|
||||||
|
openai_api_key: Optional[str] = None,
|
||||||
|
) -> None:
|
||||||
|
"""
|
||||||
|
Initializes the LangchainAgent with given tools and parameters.
|
||||||
|
|
||||||
|
:param tavily_api_key: The API key for the Tavily search tool.
|
||||||
|
:param llm_model: The OpenAI language model to be used (default: "gpt-3.5-turbo").
|
||||||
|
:param temperature: Temperature for the language model (default: 0.7).
|
||||||
|
:param tavily_max_results: Maximum results for the Tavily search (default: 1).
|
||||||
|
:param prompt_hub_url: URL of the prompt hub to fetch the agent prompt (default: "hwchase17/openai-tools-agent").
|
||||||
|
:param verbose: If True, the agent will print detailed logs (default: True).
|
||||||
|
:param log_file: Optional log file to store logs using Loguru.
|
||||||
|
:param openai_api_key: Optional OpenAI API key for connecting to OpenAI services.
|
||||||
|
"""
|
||||||
|
# Setup Loguru for logging
|
||||||
|
if log_file:
|
||||||
|
logger.add(log_file, rotation="500 MB")
|
||||||
|
|
||||||
|
# Log initialization
|
||||||
|
logger.info(
|
||||||
|
"Initializing LangchainAgent with model: {}, temperature: {}",
|
||||||
|
llm_model,
|
||||||
|
temperature,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Set up Tavily Search tool
|
||||||
|
logger.info(
|
||||||
|
"Setting up Tavily Search with max_results: {}",
|
||||||
|
tavily_max_results,
|
||||||
|
)
|
||||||
|
self.tavily_search = TavilySearchResults(
|
||||||
|
api_key=tavily_api_key, max_results=tavily_max_results
|
||||||
|
)
|
||||||
|
|
||||||
|
# Tools list (can be expanded)
|
||||||
|
self.tools = [self.tavily_search]
|
||||||
|
|
||||||
|
# Initialize the LLM (OpenAI Chat model)
|
||||||
|
logger.info("Initializing OpenAI model: {}", llm_model)
|
||||||
|
self.llm = ChatOpenAI(
|
||||||
|
model=llm_model,
|
||||||
|
temperature=temperature,
|
||||||
|
openai_api_key=openai_api_key,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Fetch the prompt template from LangChain hub
|
||||||
|
logger.info(
|
||||||
|
"Fetching prompt template from {}", prompt_hub_url
|
||||||
|
)
|
||||||
|
self.prompt = hub.pull(prompt_hub_url)
|
||||||
|
|
||||||
|
# Create the OpenAI Tools agent
|
||||||
|
logger.info(
|
||||||
|
"Creating OpenAI Tools agent with fetched prompt and LLM."
|
||||||
|
)
|
||||||
|
self.agent = create_openai_tools_agent(
|
||||||
|
self.llm, self.tools, self.prompt
|
||||||
|
)
|
||||||
|
|
||||||
|
# Create AgentExecutor with the agent and tools
|
||||||
|
logger.info(
|
||||||
|
"Setting up AgentExecutor with verbose: {}", verbose
|
||||||
|
)
|
||||||
|
self.agent_executor = AgentExecutor(
|
||||||
|
agent=self.agent, tools=self.tools, verbose=verbose
|
||||||
|
)
|
||||||
|
|
||||||
|
def run(
|
||||||
|
self,
|
||||||
|
task: str,
|
||||||
|
chat_history: Optional[List[Dict[str, str]]] = None,
|
||||||
|
) -> str:
|
||||||
|
"""
|
||||||
|
Run the LangchainAgent with a specific task.
|
||||||
|
|
||||||
|
:param task: The task (query) for the agent to handle.
|
||||||
|
:param chat_history: Optional previous chat history for context (default: None).
|
||||||
|
:return: The result of the task.
|
||||||
|
"""
|
||||||
|
logger.info("Running agent with task: {}", task)
|
||||||
|
|
||||||
|
# Create input for agent execution
|
||||||
|
input_data: Dict[str, Any] = {"input": task}
|
||||||
|
if chat_history:
|
||||||
|
logger.info("Passing chat history for context.")
|
||||||
|
input_data["chat_history"] = chat_history
|
||||||
|
|
||||||
|
# Invoke the agent
|
||||||
|
logger.info("Invoking the agent executor.")
|
||||||
|
result = self.agent_executor.invoke(input_data)
|
||||||
|
|
||||||
|
# Log the result
|
||||||
|
logger.info(
|
||||||
|
"Task executed successfully. Result: {}", result["output"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Return the output from the agent
|
||||||
|
# return result["output"]
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
# # Example usage:
|
||||||
|
# agent = LangchainAgent(
|
||||||
|
# tavily_api_key="your_tavily_api_key",
|
||||||
|
# llm_model="gpt-3.5-turbo",
|
||||||
|
# temperature=0.5,
|
||||||
|
# tavily_max_results=3,
|
||||||
|
# prompt_hub_url="your-prompt-url",
|
||||||
|
# verbose=True,
|
||||||
|
# log_file="agent.log",
|
||||||
|
# openai_api_key="your_openai_api_key"
|
||||||
|
# )
|
||||||
|
# result = agent.run("What is LangChain?")
|
||||||
|
# print(result)
|
||||||
@ -1,84 +0,0 @@
|
|||||||
import os
|
|
||||||
from swarms.structs.agent import Agent
|
|
||||||
from swarm_models.popular_llms import OpenAIChat
|
|
||||||
from swarms.structs.agent_registry import AgentRegistry
|
|
||||||
|
|
||||||
# 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(
|
|
||||||
api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
# Registry of agents
|
|
||||||
agent_registry = AgentRegistry(
|
|
||||||
name="Swarms CLI",
|
|
||||||
description="A registry of agents for the Swarms CLI",
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def create_agent(name: str, system_prompt: str, max_loops: int = 1):
|
|
||||||
"""
|
|
||||||
Create and initialize an agent with the given parameters.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
name (str): The name of the agent.
|
|
||||||
system_prompt (str): The system prompt for the agent.
|
|
||||||
max_loops (int, optional): The maximum number of loops the agent can perform. Defaults to 1.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Agent: The initialized agent.
|
|
||||||
|
|
||||||
"""
|
|
||||||
# Initialize the agent
|
|
||||||
agent = Agent(
|
|
||||||
agent_name=name,
|
|
||||||
system_prompt=system_prompt,
|
|
||||||
llm=model,
|
|
||||||
max_loops=max_loops,
|
|
||||||
autosave=True,
|
|
||||||
dashboard=False,
|
|
||||||
verbose=True,
|
|
||||||
dynamic_temperature_enabled=True,
|
|
||||||
saved_state_path=f"{name}.json",
|
|
||||||
user_name="swarms_corp",
|
|
||||||
retry_attempts=1,
|
|
||||||
context_length=200000,
|
|
||||||
# return_step_meta=True,
|
|
||||||
# disable_print_every_step=True,
|
|
||||||
# output_type="json",
|
|
||||||
interactive=True,
|
|
||||||
)
|
|
||||||
|
|
||||||
agent_registry.add(agent)
|
|
||||||
|
|
||||||
return agent
|
|
||||||
|
|
||||||
|
|
||||||
# Run the agents in the registry
|
|
||||||
def run_agent_by_name(name: str, task: str, *args, **kwargs):
|
|
||||||
"""
|
|
||||||
Run an agent by its name and perform a specified task.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- name (str): The name of the agent.
|
|
||||||
- task (str): The task to be performed by the agent.
|
|
||||||
- *args: Variable length argument list.
|
|
||||||
- **kwargs: Arbitrary keyword arguments.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
- output: The output of the agent's task.
|
|
||||||
|
|
||||||
"""
|
|
||||||
agent = agent_registry.get_agent_by_name(name)
|
|
||||||
|
|
||||||
output = agent.run(task, *args, **kwargs)
|
|
||||||
|
|
||||||
return output
|
|
||||||
|
|
||||||
|
|
||||||
# Test
|
|
||||||
out = create_agent("Accountant1", "Prepares financial statements")
|
|
||||||
print(out)
|
|
||||||
@ -1,104 +0,0 @@
|
|||||||
from datetime import datetime
|
|
||||||
|
|
||||||
from pydantic import BaseModel
|
|
||||||
|
|
||||||
from swarms.structs.omni_agent_types import AgentListType
|
|
||||||
from swarms.utils.loguru_logger import logger
|
|
||||||
from typing import Callable
|
|
||||||
|
|
||||||
|
|
||||||
class AgentProcess(BaseModel):
|
|
||||||
agent_id: int
|
|
||||||
agent_name: str
|
|
||||||
prompt: str
|
|
||||||
response: str = None
|
|
||||||
time: Callable = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|
||||||
priority: int = 0
|
|
||||||
status: str = "Waiting"
|
|
||||||
pid: int = None
|
|
||||||
|
|
||||||
def set_pid(self, pid: int):
|
|
||||||
self.pid = pid
|
|
||||||
|
|
||||||
def get_pid(self):
|
|
||||||
return self.pid
|
|
||||||
|
|
||||||
def set_time(self, time: callable):
|
|
||||||
self.time = time
|
|
||||||
|
|
||||||
def get_time(self):
|
|
||||||
return self.time
|
|
||||||
|
|
||||||
|
|
||||||
class AgentProcessQueue:
|
|
||||||
"""
|
|
||||||
A class representing a queue of agent processes.
|
|
||||||
|
|
||||||
Attributes:
|
|
||||||
MAX_PID (int): The maximum process ID.
|
|
||||||
pid_pool (list): A list representing the availability of process IDs.
|
|
||||||
agent_process_queue (list): A list representing the queue of agent processes.
|
|
||||||
|
|
||||||
Methods:
|
|
||||||
add(agent_process): Adds an agent process to the queue.
|
|
||||||
print(): Prints the details of all agent processes in the queue.
|
|
||||||
|
|
||||||
Private Methods:
|
|
||||||
_get_available_pid(): Returns an available process ID from the pool.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, max_pid: int = 1024):
|
|
||||||
self.MAX_PID = max_pid
|
|
||||||
self.pid_pool = [False for i in range(self.MAX_PID)]
|
|
||||||
self.agent_process_queue = (
|
|
||||||
[]
|
|
||||||
) # Currently use list to simulate queue
|
|
||||||
|
|
||||||
def add(self, agents: AgentListType):
|
|
||||||
"""
|
|
||||||
Adds an agent process to the queue.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
agent_process (AgentProcess): The agent process to be added.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
None
|
|
||||||
"""
|
|
||||||
for agent in agents:
|
|
||||||
agent_process = AgentProcess(
|
|
||||||
agent_id=agent.id,
|
|
||||||
agent_name=agent.agent_name,
|
|
||||||
prompt=agent.short_memory.return_history_as_string(),
|
|
||||||
)
|
|
||||||
pid = self._get_available_pid()
|
|
||||||
if pid is None:
|
|
||||||
logger.warning("No available PID")
|
|
||||||
return
|
|
||||||
agent_process.set_pid(pid)
|
|
||||||
agent_process.set_status("Waiting")
|
|
||||||
self.agent_process_queue.append(agent_process)
|
|
||||||
|
|
||||||
def print(self):
|
|
||||||
"""
|
|
||||||
Prints the details of all agent processes in the queue.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
None
|
|
||||||
"""
|
|
||||||
for agent_process in self.agent_process_queue:
|
|
||||||
logger.info(
|
|
||||||
f"| Agent-process ID: {agent_process.get_pid()} |"
|
|
||||||
f" Status: {agent_process.get_status()} |"
|
|
||||||
)
|
|
||||||
|
|
||||||
def _get_available_pid(self):
|
|
||||||
"""
|
|
||||||
Returns an available process ID from the pool.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
int or None: The available process ID, or None if no ID is available.
|
|
||||||
"""
|
|
||||||
for i, used in enumerate(self.pid_pool):
|
|
||||||
if not used:
|
|
||||||
return i
|
|
||||||
return None
|
|
||||||
@ -1,101 +0,0 @@
|
|||||||
import os
|
|
||||||
import json
|
|
||||||
from typing import Dict, List
|
|
||||||
|
|
||||||
import requests
|
|
||||||
from loguru import logger
|
|
||||||
from swarms.structs.agent import Agent
|
|
||||||
|
|
||||||
|
|
||||||
def add_agent_to_marketplace(
|
|
||||||
name: str,
|
|
||||||
agent: str,
|
|
||||||
language: str,
|
|
||||||
description: str,
|
|
||||||
use_cases: List[Dict[str, str]],
|
|
||||||
requirements: List[Dict[str, str]],
|
|
||||||
tags: str,
|
|
||||||
) -> Dict[str, str]:
|
|
||||||
"""
|
|
||||||
Add an agent to the marketplace.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
name (str): The name of the agent.
|
|
||||||
agent (str): The agent code.
|
|
||||||
language (str): The programming language of the agent.
|
|
||||||
description (str): The description of the agent.
|
|
||||||
use_cases (List[Dict[str, str]]): The list of use cases for the agent.
|
|
||||||
requirements (List[Dict[str, str]]): The list of requirements for the agent.
|
|
||||||
tags (str): The tags for the agent.
|
|
||||||
api_key (str): The API key for authentication.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Dict[str, str]: The response from the API.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
requests.exceptions.RequestException: If there is an error making the API request.
|
|
||||||
"""
|
|
||||||
logger.info("Adding agent to marketplace...")
|
|
||||||
|
|
||||||
url = "https://swarms.world/api/add-agent"
|
|
||||||
headers = {
|
|
||||||
"Content-Type": "application/json",
|
|
||||||
"Authorization": f"Bearer {os.getenv("SWARMS_API_KEY")}",
|
|
||||||
}
|
|
||||||
data = {
|
|
||||||
"name": name,
|
|
||||||
"agent": agent,
|
|
||||||
"description": description,
|
|
||||||
"language": language,
|
|
||||||
"useCases": use_cases,
|
|
||||||
"requirements": requirements,
|
|
||||||
"tags": tags,
|
|
||||||
}
|
|
||||||
|
|
||||||
try:
|
|
||||||
response = requests.post(
|
|
||||||
url, headers=headers, data=json.dumps(data)
|
|
||||||
)
|
|
||||||
response.raise_for_status()
|
|
||||||
return response.json()
|
|
||||||
except requests.exceptions.RequestException as e:
|
|
||||||
logger.error(f"Error making API request: {e}")
|
|
||||||
|
|
||||||
|
|
||||||
def add_agent_to_marketplace_sync(
|
|
||||||
agent: Agent,
|
|
||||||
use_cases: List[Dict[str, str]],
|
|
||||||
requirements: List[Dict[str, str]],
|
|
||||||
tags: str,
|
|
||||||
):
|
|
||||||
return add_agent_to_marketplace(
|
|
||||||
name=agent.agent_name,
|
|
||||||
description=agent.description,
|
|
||||||
language="python",
|
|
||||||
use_cases=use_cases,
|
|
||||||
requirements=requirements,
|
|
||||||
tags=tags,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
# # Example usage
|
|
||||||
# async def main():
|
|
||||||
# name = "Example Agent"
|
|
||||||
# agent = "This is an example agent from an API route."
|
|
||||||
# description = "Description of the agent."
|
|
||||||
# language = "python"
|
|
||||||
# use_cases = [
|
|
||||||
# {"title": "Use case 1", "description": "Description of use case 1"},
|
|
||||||
# {"title": "Use case 2", "description": "Description of use case 2"}
|
|
||||||
# ]
|
|
||||||
# requirements = [
|
|
||||||
# {"package": "pip", "installation": "pip install"},
|
|
||||||
# {"package": "pip3", "installation": "pip3 install"}
|
|
||||||
# ]
|
|
||||||
# tags = "example, agent"
|
|
||||||
# api_key = "YOUR_API_KEY"
|
|
||||||
|
|
||||||
# result = await add_agent_to_marketplace(name, agent, language, description, use_cases, requirements, tags, api_key)
|
|
||||||
# print(result)
|
|
||||||
|
|
||||||
# asyncio.run(main())
|
|
||||||
@ -1,47 +0,0 @@
|
|||||||
import logging
|
|
||||||
from abc import ABC
|
|
||||||
from dataclasses import dataclass
|
|
||||||
|
|
||||||
from tenacity import Retrying, stop_after_attempt, wait_exponential
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class ExponentialBackoffMixin(ABC):
|
|
||||||
"""
|
|
||||||
A mixin class that provides exponential backoff functionality.
|
|
||||||
"""
|
|
||||||
|
|
||||||
min_retry_delay: float = 2
|
|
||||||
"""
|
|
||||||
The minimum delay between retries in seconds.
|
|
||||||
"""
|
|
||||||
|
|
||||||
max_retry_delay: float = 10
|
|
||||||
"""
|
|
||||||
The maximum delay between retries in seconds.
|
|
||||||
"""
|
|
||||||
|
|
||||||
max_attempts: int = 10
|
|
||||||
"""
|
|
||||||
The maximum number of retry attempts.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def after_hook(s: str) -> None:
|
|
||||||
return logging.warning(s)
|
|
||||||
|
|
||||||
"""
|
|
||||||
A callable that is executed after each retry attempt.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def retrying(self) -> Retrying:
|
|
||||||
"""
|
|
||||||
Returns a Retrying object configured with the exponential backoff settings.
|
|
||||||
"""
|
|
||||||
return Retrying(
|
|
||||||
wait=wait_exponential(
|
|
||||||
min=self.min_retry_delay, max=self.max_retry_delay
|
|
||||||
),
|
|
||||||
stop=stop_after_attempt(self.max_attempts),
|
|
||||||
reraise=True,
|
|
||||||
after=self.after_hook,
|
|
||||||
)
|
|
||||||
Loading…
Reference in new issue