시본AI
다양한 AI 에이전트 프레임워크와 AI 채용 도구를 통합하기 위한 Python 패키지입니다.
특징
- AI 에이전트를 위한 맞춤형 채용 도구에 액세스하세요
- 인기 있는 AI 에이전트 프레임워크와 MCP 도구 통합:
auth_token 생성
https://shivonai.com을 방문하여 auth_token을 생성하세요.
설치
지엑스피1
시작하기
LangChain 통합
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from shivonai.lyra import langchain_toolkit
# Replace with your actual MCP server details
auth_token = "shivonai_auth_token"
# Get LangChain tools
tools = langchain_toolkit(auth_token)
# Print available tools
print(f"Available tools: {[tool.name for tool in tools]}")
# Initialize LangChain agent with tools
llm = ChatOpenAI(
temperature=0,
model_name="gpt-4-turbo",
openai_api_key="openai-api-key"
)
agent = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
# Try running the agent with a simple task
try:
result = agent.run("what listing I have?")
print(f"Result: {result}")
except Exception as e:
print(f"Error: {e}")
LlamaIndex 통합
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
from shivonai.lyra import llamaindex_toolkit
# Set up OpenAI API key - you'll need this to use OpenAI models with LlamaIndex
os.environ["OPENAI_API_KEY"] = "openai_api_key"
# Your MCP server authentication details
MCP_AUTH_TOKEN = "shivonai_auth_token"
def main():
"""Test LlamaIndex integration with ShivonAI."""
print("Testing LlamaIndex integration with ShivonAI...")
# Get LlamaIndex tools from your MCP server
tools = llamaindex_toolkit(MCP_AUTH_TOKEN)
print(f"Found {len(tools)} MCP tools for LlamaIndex:")
for name, tool in tools.items():
print(f" - {name}: {tool.metadata.description[:60]}...")
# Create a LlamaIndex agent with these tools
llm = OpenAI(model="gpt-4")
# Convert tools dictionary to a list
tool_list = list(tools.values())
# Create the ReAct agent
agent = ReActAgent.from_tools(
tools=tool_list,
llm=llm,
verbose=True
)
# Test the agent with a simple query that should use one of your tools
# Replace this with a query that's relevant to your tools
query = "what listings I have?"
print("\nTesting agent with query:", query)
response = agent.chat(query)
print("\nAgent response:")
print(response)
if __name__ == "__main__":
main()
CrewAI 통합
from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI # or any other LLM you prefer
from shivonai.lyra import crew_toolkit
import os
os.environ["OPENAI_API_KEY"] = "oepnai_api_key"
llm = ChatOpenAI(temperature=0.7, model="gpt-4")
# Get CrewAI tools
tools = crew_toolkit("shivonai_auth_token")
# Print available tools
print(f"Available tools: {[tool.name for tool in tools]}")
# Create an agent with these tools
agent = Agent(
role="Data Analyst",
goal="Analyze data using custom tools",
backstory="You're an expert data analyst with access to custom tools",
tools=tools,
llm=llm # Provide the LLM here
)
# Create a task - note the expected_output field
task = Task(
description="what listings I have?",
expected_output="A detailed report with key insights and recommendations",
agent=agent
)
crew = Crew(
agents=[agent],
tasks=[task])
result = crew.kickoff()
print(result)
Agno 통합
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from shivonai.lyra import agno_toolkit
import os
from agno.models.aws import Claude
# Replace with your actual MCP server details
auth_token = "Shivonai_auth_token"
os.environ["OPENAI_API_KEY"] = "oepnai_api_key"
# Get Agno tools
tools = agno_toolkit(auth_token)
# Print available tools
print(f"Available MCP tools: {list(tools.keys())}")
# Create an Agno agent with tools
agent = Agent(
model=OpenAIChat(id="gpt-3.5-turbo"),
tools=list(tools.values()),
markdown=True,
show_tool_calls=True
)
# Try the agent with a simple task
try:
agent.print_response("what listing are there?", stream=True)
except Exception as e:
print(f"Error: {e}")
특허
이 프로젝트는 독점 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.