openagi

Openagi

使所有人都可以使用自主的人类般的代理

Openagi的目标是使每个人都可以使用类似人类的代理商，从而为开放代理商铺平了道路，并最终为所有人铺平了AGI。我们坚信AI的变革力量，并有信心这项倡议将有助于解决许多现实生活中的问题。目前，OpenAgi旨在为开发人员提供一个创建自主类人类代理的框架。

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安装

1. 设置虚拟环境。

 # For Mac and Linux users
python3 -m venv venv
source venv/bin/activate

# For Windows users
python -m venv venv
venv/scripts/activate

2. 安装OpenAgi

pip install openagi

或者

 git clone https://github.com/aiplanethub/openagi.git
pip install -e .

示例（手动代理执行）

工人用于创建多代理体系结构。

请按照此示例创建一个旅行计划代理，该示例可以帮助您计划SF的行程。

 from openagi . agent import Admin
from openagi . planner . task_decomposer import TaskPlanner
from openagi . actions . tools . ddg_search import DuckDuckGoSearch
from openagi . llms . openai import OpenAIModel
from openagi . worker import Worker

plan = TaskPlanner ( human_intervene = False )
action = DuckDuckGoSearch

import os
os . environ [ 'OPENAI_API_KEY' ] = "sk-xxxx"
config = OpenAIModel . load_from_env_config ()
llm = OpenAIModel ( config = config )

trip_plan = Worker (
        role = "Trip Planner" ,
        instructions = """
        User loves calm places, suggest the best itinerary accordingly.
        """ ,
        actions = [ action ],
        max_iterations = 10 )

admin = Admin (
    llm = llm ,
    actions = [ action ],
    planner = plan ,
)
admin . assign_workers ([ trip_plan ])

res = admin . run (
    query = "Give me total 3 Days Trip to San francisco Bay area" ,
    description = "You are a knowledgeable local guide with extensive information about the city, it's attractions and customs" ,
)
print ( res )

示例（自主多代理执行）

现在，让我们建立一个体育代理，可以在没有任何工人的情况下自主运行。

 from openagi . planner . task_decomposer import TaskPlanner
from openagi . actions . tools . tavilyqasearch import TavilyWebSearchQA
from openagi . agent import Admin
from openagi . llms . gemini import GeminiModel

import os
os . environ [ 'TAVILY_API_KEY' ] = "<replace with Tavily key>"
os . environ [ 'GOOGLE_API_KEY' ] = "<replace with Gemini key>"
os . environ [ 'Gemini_MODEL' ] = "gemini-1.5-flash"
os . environ [ 'Gemini_TEMP' ] = "0.1"

gemini_config = GeminiModel . load_from_env_config ()
llm = GeminiModel ( config = gemini_config )

# define the planner
plan = TaskPlanner ( autonomous = True , human_intervene = True )

admin = Admin (
    actions = [ TavilyWebSearchQA ],
    planner = plan ,
    llm = llm ,
)
res = admin . run (
    query = "I need cricket updates from India vs Sri lanka 2024 ODI match in Sri Lanka" ,
    description = f"give me the results of India vs Sri Lanka ODI and respective Man of the Match" ,
)
print ( res )

长期记忆以前从未

使用LTM，Openagi代理现在可以：

• 回想过去的互动以提供对话中的连续性。
• 随着时间的推移，根据用户输入来学习和适应。
• 通过引用先前的对话来提供上下文相关的响应。
• 通过每次连续的相互作用提高其准确性和效率。

 import os
from openagi . agent import Admin
from openagi . llms . openai import OpenAIModel
from openagi . memory import Memory
from openagi . planner . task_decomposer import TaskPlanner
from openagi . worker import Worker
from openagi . actions . tools . ddg_search import DuckDuckGoSearch

memory = Memory ( long_term = True )

os . environ [ 'OPENAI_API_KEY' ] = "-"
config = OpenAIModel . load_from_env_config ()
llm = OpenAIModel ( config = config )

web_searcher = Worker (
    role = "Web Researcher" ,
    instructions = """
    You are tasked with conducting web searches using DuckDuckGo.
    Find the most relevant and accurate information based on the user's query.
    """ ,
    actions = [ DuckDuckGoSearch ], 
)

admin = Admin (
    actions = [ DuckDuckGoSearch ],
    planner = TaskPlanner ( human_intervene = False ),
    memory = memory ,
    llm = llm ,
)
admin . assign_workers ([ web_searcher ])

query = input ( "Enter your search query: " )
description = f"Find accurate and relevant information for the query: { query } "

res = admin . run ( query = query , description = description )
print ( res )

文档

有关更多查询

用例：

• 教育：在教育方面，代理商可以提供个性化的学习经验。他们根据学生的进步，表现和兴趣来适应和量身定制学习内容。它可以扩展到自动执行其他各种行政任务，并协助教师提高其生产率。
• 财务和银行业务：金融服务可以使用代理人进行欺诈检测，风险评估，个性化银行建议，自动交易和客户服务。他们有助于分析大量交易，以确定可疑活动并提供量身定制的投资建议。
• 医疗保健：可以部署代理商来监视患者，提供个性化的健康建议，管理患者数据并自动化行政任务。他们还可以根据症状和病史协助诊断疾病。

取得联系

有关任何查询/建议/支持

贡献指南

Openagi在开源项目的快速发展的景观中蓬勃发展。我们全心全意地欢迎各种能力，无论是通过创新功能，增强的基础设施还是精致文档。

有关贡献过程的综合指南，请单击此处。