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书生·浦语大模型实战营之Lagent & AgentLego 智能体应用搭建

作者：很楠不爱3 | 2024-05-06 00:56:07

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Lagent 简介
Lagent 是一个轻量级开源智能体框架，旨在让用户可以高效地构建基于大语言模型的智能体。同时它也提供了一些典型工具以增强大语言模型的能力。

Lagent 目前已经支持了包括 AutoGPT、ReAct 等在内的多个经典智能体范式，也支持了如下工具：

Arxiv 搜索
Bing 地图
Google 学术搜索
Google 搜索
交互式 IPython 解释器
IPython 解释器
PPT
Python 解释器
AgentLego 简介
AgentLego 是一个提供了多种开源工具 API 的多模态工具包，旨在像是乐高积木一样，让用户可以快速简便地拓展自定义工具，从而组装出自己的智能体。通过 AgentLego 算法库，不仅可以直接使用多种工具，也可以利用这些工具，在相关智能体框架（如 Lagent，Transformers Agent 等）的帮助下，快速构建可以增强大语言模型能力的智能体。

AgentLego 目前提供了如下工具：

Lagent 是一个智能体框架，而 AgentLego 与大模型智能体并不直接相关，而是作为工具包，在相关智能体的功能支持模块发挥作用。

两者之间的关系可以用下图来表示：

环境配置

在创建开发机界面选择镜像为 Cuda12.2-conda，并选择 GPU 为30% A100

创建一个用于存放 Agent 相关文件的目录

mkdir -p /root/agent

配置 conda 环境，可以输入如下指令

studio-conda -t agent -o pytorch-2.1.2

安装 Lagent 和 AgentLego
Lagent 和 AgentLego 都提供了两种安装方法，一种是通过 pip 直接进行安装，另一种则是从源码进行安装。为了方便使用 Lagent 的 Web Demo 以及 AgentLego 的 WebUI，我们选择直接从源码进行安装。此处附上源码安装的相关帮助文档：

Lagent：https://lagent.readthedocs.io/zh-cn/latest/get_started/install.html
AgentLego：https://agentlego.readthedocs.io/zh-cn/latest/get_started.html

可以执行如下命令进行安装：


cd /root/agent
conda activate agent
git clone https://gitee.com/internlm/lagent.git
cd lagent && git checkout 581d9fb && pip install -e . && cd ..
git clone https://gitee.com/internlm/agentlego.git
cd agentlego && git checkout 7769e0d && pip install -e . && cd ..

安装其他依赖

安装其他将要用到的依赖库，如 LMDeploy，可以执行如下命令：


conda activate agent
pip install lmdeploy==0.3.0

git clone 的方法下载 tutorial


cd /root/agent
git clone -b camp2 https://gitee.com/internlm/Tutorial.git

Lagent：轻量级智能体框架
在这一部分中，我们将带大家体验 Lagent 的 Web Demo，使用 Lagent 自定义工具，并体验自定义工具的效果。

详细文档可以访问：Lagent：轻量级智能体框架。

Lagent Web Demo
使用 LMDeploy 部署
由于 Lagent 的 Web Demo 需要用到 LMDeploy 所启动的 api_server，因此我们首先按照下图指示在 vscode terminal 中执行如下代码使用 LMDeploy 启动一个 api_server


conda activate agent
lmdeploy serve api_server /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-7b \
                            --server-name 127.0.0.1 \
                            --model-name internlm2-chat-7b \
                            --cache-max-entry-count 0.1

等待 LMDeploy 的 api_server 与 Lagent Web Demo 完全启动后，在本地进行端口映射，将 LMDeploy api_server 的23333端口以及 Lagent Web Demo 的7860端口映射到本地。可以执行：

ssh -CNg -L 7860:127.0.0.1:7860 -L 23333:127.0.0.1:23333 root@ssh.intern-ai.org.cn -p   43844

在本地的浏览器页面中打开 http://localhost:7860 以使用 Lagent Web Demo。首先输入模型 IP 为 127.0.0.1:23333，在输入完成后按下回车键以确认。并选择插件为 ArxivSearch，以让模型获得在 arxiv 上搜索论文的能力。


系统提示词:当开启工具以及代码时，根据需求选择合适的工具进行调用
 
数据分析提示词:你现在已经能够在一个有状态的 Jupyter 笔记本环境中运行 Python 代码。当你向 python 发送含有 Python 代码的消息时，它将在该环境中执行。这个工具适用于多种场景，如数据分析或处理（包括数据操作、统计分析、图表绘制），复杂的计算问题（解决数学和物理难题），编程示例（理解编程概念或特性），文本处理和分析（比如文本解析和自然语言处理），机器学习和数据科学（用于展示模型训练和数据可视化），以及文件操作和数据导入（处理CSV、JSON等格式的文件）。
 
插件提示词：
你可以使用如下工具：
{prompt}
如果你已经获得足够信息，请直接给出答案. 避免不必要的工具调用! 同时注意你可以使用的工具，不要随意捏造！

输入“请帮我搜索 InternLM2 Technical Report” 以让模型搜索书生·浦语2的技术报告。效果如下图所示，可以看到模型正确输出了 InternLM2 技术报告的相关信息。尽管还输出了其他论文，但这是由 arxiv 搜索 API 的相关行为导致的

lagent/agents/internlm2_agent.py


import json
import logging
from copy import deepcopy
from typing import Dict, List, Optional, Union
 
from lagent.actions import ActionExecutor
from lagent.agents.base_agent import BaseAgent
from lagent.llms import BaseAPIModel, BaseModel
from lagent.schema import ActionReturn, ActionStatusCode, AgentReturn, AgentStatusCode, ModelStatusCode  # noqa: E501
 
API_PREFIX = (
    "This is the subfunction for tool '{tool_name}', you can use this tool. "
    'The description of this function is: \n{description}')
 
META_CN = ('当开启工具以及代码时，根据需求选择合适的工具进行调用')
 
INTERPRETER_CN = ('你现在已经能够在一个有状态的 Jupyter 笔记本环境中运行 Python 代码。'
                  '当你向 python 发送含有 Python 代码的消息时，它将在该环境中执行。'
                  '这个工具适用于多种场景，如数据分析或处理（包括数据操作、统计分析、图表绘制），'
                  '复杂的计算问题（解决数学和物理难题），编程示例（理解编程概念或特性），'
                  '文本处理和分析（比如文本解析和自然语言处理），'
                  '机器学习和数据科学（用于展示模型训练和数据可视化），'
                  '以及文件操作和数据导入（处理CSV、JSON等格式的文件）。')
 
PLUGIN_CN = ('你可以使用如下工具：'
             '\n{prompt}\n'
             '如果你已经获得足够信息，请直接给出答案. 避免不必要的工具调用! '
             '同时注意你可以使用的工具，不要随意捏造！')
 
 
class Internlm2Protocol:
 
    def __init__(
        self,
        meta_prompt: str = META_CN,
        interpreter_prompt: str = INTERPRETER_CN,
        plugin_prompt: str = PLUGIN_CN,
        few_shot: Optional[List] = None,
        language: Dict = dict(
            begin='',
            end='',
            belong='assistant',
        ),
        tool: Dict = dict(
            begin='{start_token}{name}\n',
            start_token='<|action_start|>',
            name_map=dict(plugin='<|plugin|>', interpreter='<|interpreter|>'),
            belong='assistant',
            end='<|action_end|>\n',
        ),
        execute: Dict = dict(
            role='execute', begin='', end='', fallback_role='environment'),
    ) -> None:
        self.meta_prompt = meta_prompt
        self.interpreter_prompt = interpreter_prompt
        self.plugin_prompt = plugin_prompt
        self.roles_cfg = dict(tool=tool, language=language)
        self.language = language
        self.execute = execute
        self.tool = tool
        self.few_shot = few_shot
 
    def format_sub_role(self, messages: List[Dict]) -> List[Dict]:
 
        def format_interpreter(message):
            if isinstance(message['content'], dict):
                # assert message['content']['name'] == 'IPythonInterpreter'
                return dict(
                    role=message['role'],
                    name=message['name'],
                    content=message['content']['parameters']['command'])
            else:
                return message
 
        def format_plugin(message):
            if isinstance(message['content'], dict):
                return dict(
                    role=message['role'],
                    name=message['name'],
                    content=json.dumps(message['content']))
            else:
                return message
 
        new_message = list()
        for message in messages:
            if message['role'] in [
                    'assistant', 'user', 'system', 'environment'
            ]:
                new_message.append(message)
                continue
            role_cfg = self.roles_cfg[message['role']]
            begin = role_cfg['begin']
            if message['role'] == 'tool':
                if message['name'] == 'interpreter':
                    message = format_interpreter(message)
                elif message['name'] == 'plugin':
                    message = format_plugin(message)
                else:
                    raise NotImplementedError
                begin = role_cfg['begin'].format(
                    start_token=role_cfg.get('start_token', ''),
                    name=role_cfg.get('name_map', {}).get(message['name'], ''))
            new_content = begin + message['content'] + role_cfg['end']
            if role_cfg.get('fallback_role'):
                new_message.append(
                    dict(role=role_cfg['fallback_role'], content=new_content))
            elif role_cfg.get('belong'):
                if new_message[-1]['role'] != role_cfg.get('belong'):
                    new_message.append(
                        dict(role=role_cfg.get('belong'), content=new_content))
                else:
                    new_message[-1]['content'] += new_content
            else:
                new_message.append(
                    dict(role=message['role'], content=new_content))
 
        return new_message
 
    def format(self,
               inner_step: List[Dict],
               plugin_executor: ActionExecutor = None,
               interpreter_executor: ActionExecutor = None,
               **kwargs) -> list:
        formatted = []
        if self.meta_prompt:
            formatted.append(dict(role='system', content=self.meta_prompt))
        if interpreter_executor and self.interpreter_prompt:
            interpreter_info = interpreter_executor.get_actions_info()[0]
            interpreter_prompt = self.interpreter_prompt.format(
                code_prompt=interpreter_info['description'])
            formatted.append(
                dict(
                    role='system',
                    content=interpreter_prompt,
                    name='interpreter'))
        if plugin_executor and plugin_executor.actions and self.plugin_prompt:
            plugin_descriptions = []
            for api_info in plugin_executor.get_actions_info():
                plugin = deepcopy(api_info)
                if isinstance(api_info, dict):
                    tool_name = api_info['name'].split('.')[0]
                    plugin['description'] = API_PREFIX.format(
                        tool_name=tool_name, description=plugin['description'])
                    # only keep required parameters
                    required_parameters = [
                        param for param in plugin['parameters']
                        if param['name'] in plugin['required']
                    ]
                    plugin['parameters'] = required_parameters
                plugin_descriptions.append(plugin)
            plugin_prompt = self.plugin_prompt.format(
                prompt=json.dumps(
                    plugin_descriptions, ensure_ascii=False, indent=4))
            formatted.append(
                dict(role='system', content=plugin_prompt, name='plugin'))
        if self.few_shot:
            for few_shot in self.few_shot:
                formatted += self.format_sub_role(few_shot)
        formatted += self.format_sub_role(inner_step)
        return formatted
 
    def parse(self, message, plugin_executor: ActionExecutor,
              interpreter_executor: ActionExecutor):
        if self.language['begin']:
            message = message.split(self.language['begin'])[-1]
        if self.tool['name_map']['plugin'] in message:
            message, action = message.split(
                f"{self.tool['start_token']}{self.tool['name_map']['plugin']}")
            action = action.split(self.tool['end'].strip())[0]
            return 'plugin', message, action
        if self.tool['name_map']['interpreter'] in message:
            message, code = message.split(
                f"{self.tool['start_token']}"
                f"{self.tool['name_map']['interpreter']}")
            code = code.split(self.tool['end'].strip())[0].strip()
            return 'interpreter', message, dict(
                name=interpreter_executor.action_names()[0],
                parameters=dict(
                    command=code)) if interpreter_executor else None
        return None, message.split(self.tool['start_token'])[0], None
 
    def format_response(self, action_return, name) -> dict:
        if action_return.state == ActionStatusCode.SUCCESS:
            response = action_return.format_result()
        else:
            response = str(action_return.errmsg)
        content = self.execute['begin'] + response + self.execute['end']
        if self.execute.get('fallback_role'):
            return dict(
                role=self.execute['fallback_role'], content=content, name=name)
        elif self.execute.get('belong'):
            return dict(
                role=self.execute['belong'], content=content, name=name)
        return dict(role=self.execute['role'], content=response, name=name)
 
 
class Internlm2Agent(BaseAgent):
 
    def __init__(self,
                 llm: Union[BaseModel, BaseAPIModel],
                 plugin_executor: ActionExecutor = None,
                 interpreter_executor: ActionExecutor = None,
                 protocol=Internlm2Protocol(),
                 max_turn: int = 3) -> None:
        self.max_turn = max_turn
        self._interpreter_executor = interpreter_executor
        super().__init__(
            llm=llm, action_executor=plugin_executor, protocol=protocol)
 
    def chat(self, message: Union[str, Dict], **kwargs) -> AgentReturn:
        if isinstance(message, str):
            message = dict(role='user', content=message)
        if isinstance(message, dict):
            message = [message]
        inner_history = message[:]
        offset = len(inner_history)
        agent_return = AgentReturn()
        for _ in range(self.max_turn):
            # list of dict
            prompt = self._protocol.format(
                inner_step=inner_history,
                plugin_executor=self._action_executor,
                interpreter_executor=self._interpreter_executor,
            )
            response = self._llm.chat(prompt, **kwargs)
            name, language, action = self._protocol.parse(
                message=response,
                plugin_executor=self._action_executor,
                interpreter_executor=self._interpreter_executor,
            )
            if name:
                if name == 'plugin':
                    if self._action_executor:
                        executor = self._action_executor
                    else:
                        logging.info(msg='No plugin is instantiated!')
                        continue
                    try:
                        action = json.loads(action)
                    except Exception as e:
                        logging.info(msg=f'Invaild action {e}')
                        continue
                elif name == 'interpreter':
                    if self._interpreter_executor:
                        executor = self._interpreter_executor
                    else:
                        logging.info(msg='No interpreter is instantiated!')
                        continue
                else:
                    logging.info(
                        msg=(f"Invalid name '{name}'. Currently only 'plugin' "
                             "and 'interpreter' are supported."))
                    continue
                action_return: ActionReturn = executor(action['name'],
                                                       action['parameters'])
                action_return.thought = language
                agent_return.actions.append(action_return)
            inner_history.append(dict(role='language', content=language))
            if not name or action_return.type == executor.finish_action.name:
                agent_return.response = language
                agent_return.state = AgentStatusCode.END
                break
            else:
                inner_history.append(
                    dict(role='tool', content=action, name=name))
                inner_history.append(
                    self._protocol.format_response(action_return, name=name))
        agent_return.inner_steps = inner_history[offset:]
        return agent_return
 
    def stream_chat(self, message: List[dict], **kwargs) -> AgentReturn:
        if isinstance(message, str):
            message = dict(role='user', content=message)
        if isinstance(message, dict):
            message = [message]
        inner_history = message[:]
        offset = len(inner_history)
        agent_return = AgentReturn()
        last_agent_state = AgentStatusCode.SESSION_READY
        for _ in range(self.max_turn):
            # list of dict
            prompt = self._protocol.format(
                inner_step=inner_history,
                plugin_executor=self._action_executor,
                interpreter_executor=self._interpreter_executor,
            )
            response = ''
            for model_state, res, _ in self._llm.stream_chat(prompt, **kwargs):
                model_state: ModelStatusCode
                response = res
                if model_state.value < 0:
                    agent_return.state = getattr(AgentStatusCode,
                                                 model_state.name)
                    yield deepcopy(agent_return)
                    return
                else:
                    name, language, action = self._protocol.parse(
                        message=response,
                        plugin_executor=self._action_executor,
                        interpreter_executor=self._interpreter_executor,
                    )
                    if name:
                        if model_state == ModelStatusCode.END:
                            agent_state = last_agent_state + 1
                            if name == 'plugin':
                                if self._action_executor:
                                    executor = self._action_executor
                                else:
                                    logging.info(
                                        msg='No plugin is instantiated!')
                                    continue
                                try:
                                    action = json.loads(action)
                                except Exception as e:
                                    logging.info(msg=f'Invaild action {e}')
                                    continue
                            elif name == 'interpreter':
                                if self._interpreter_executor:
                                    executor = self._interpreter_executor
                                else:
                                    logging.info(
                                        msg='No interpreter is instantiated!')
                                    continue
                            agent_return.state = agent_state
                            agent_return.response = action
                        else:
                            agent_state = (
                                AgentStatusCode.PLUGIN_START if name
                                == 'plugin' else AgentStatusCode.CODING)
                            if agent_state != last_agent_state:
                                # agent_return.state = agent_state
                                agent_return.response = language
                                yield deepcopy(agent_return)
                            agent_return.state = agent_state
                            agent_return.response = action
                    else:
                        agent_state = AgentStatusCode.STREAM_ING
                        agent_return.state = agent_state
                        agent_return.response = language
                    last_agent_state = agent_state
                    yield deepcopy(agent_return)
            if name:
                action_return: ActionReturn = executor(action['name'],
                                                       action['parameters'])
                action_return.thought = language
                agent_return.actions.append(action_return)
            inner_history.append(dict(role='language', content=language))
            if not name:
                agent_return.response = language
                break
            elif action_return.type == executor.finish_action.name:
                try:
                    response = action_return.args['text']['response']
                except Exception:
                    logging.info(msg='Unable to parse FinishAction.')
                    response = ''
                agent_return.response = response
                break
            else:
                inner_history.append(
                    dict(role='tool', content=action, name=name))
                inner_history.append(
                    self._protocol.format_response(action_return, name=name))
                agent_state += 1
                agent_return.state = agent_state
                yield agent_return
        agent_return.inner_steps = deepcopy(inner_history[offset:])
        agent_return.state = AgentStatusCode.END
        yield agent_return

lagent/actions/arxiv_search.py 代码


from typing import Optional, Type
 
from lagent.actions.base_action import BaseAction, tool_api
from lagent.actions.parser import BaseParser, JsonParser
from lagent.schema import ActionReturn, ActionStatusCode
 
 
class ArxivSearch(BaseAction):
    """Search information from Arxiv.org. \
Useful for when you need to answer questions about Physics, Mathematics, \
Computer Science, Quantitative Biology, Quantitative Finance, Statistics, \
Electrical Engineering, and Economics from scientific articles on arxiv.org.
    """
 
    def __init__(self,
                 top_k_results: int = 3,
                 max_query_len: int = 300,
                 doc_content_chars_max: int = 1500,
                 description: Optional[dict] = None,
                 parser: Type[BaseParser] = JsonParser,
                 enable: bool = True):
        super().__init__(description, parser, enable)
        self.top_k_results = top_k_results
        self.max_query_len = max_query_len
        self.doc_content_chars_max = doc_content_chars_max
 
    @tool_api(explode_return=True)
    def get_arxiv_article_information(self, query: str) -> dict:
        """Run Arxiv search and get the article meta information.
        Args:
            query (:class:`str`): the content of search query
        Returns:
            :class:`dict`: article information
                * content (str): a list of 3 arxiv search papers
        """
        import arxiv
 
        try:
            results = arxiv.Search(  # type: ignore
                query[:self.max_query_len],
                max_results=self.top_k_results).results()
        except Exception as exc:
            return ActionReturn(
                errmsg=f'Arxiv exception: {exc}',
                state=ActionStatusCode.HTTP_ERROR)
        docs = [
            f'Published: {result.updated.date()}\nTitle: {result.title}\n'
            f'Authors: {", ".join(a.name for a in result.authors)}\n'
            f'Summary: {result.summary[:self.doc_content_chars_max]}'
            for result in results
        ]
        if docs:
            return {'content': '\n\n'.join(docs)}
        return {'content': 'No good Arxiv Result was found'}

用 Lagent 自定义工具
在本节中，我们将基于 Lagent 自定义一个工具。Lagent 中关于工具部分的介绍文档位于 https://lagent.readthedocs.io/zh-cn/latest/tutorials/action.html 。使用 Lagent 自定义工具主要分为以下几步：

继承 BaseAction 类
实现简单工具的 run 方法；或者实现工具包内每个子工具的功能
简单工具的 run 方法可选被 tool_api 装饰；工具包内每个子工具的功能都需要被 tool_api 装饰
下面我们将实现一个调用和风天气 API 的工具以完成实时天气查询的功能。

创建工具文件
首先通过 touch /root/agent/lagent/lagent/actions/weather.py 新建工具文件，该文件内容如下

vim /root/agent/lagent/lagent/actions/weather.py


import json
import os
import requests
from typing import Optional, Type
 
from lagent.actions.base_action import BaseAction, tool_api
from lagent.actions.parser import BaseParser, JsonParser
from lagent.schema import ActionReturn, ActionStatusCode
 
class WeatherQuery(BaseAction):
    """Weather plugin for querying weather information."""
    
    def __init__(self,
                 key: Optional[str] = None,
                 description: Optional[dict] = None,
                 parser: Type[BaseParser] = JsonParser,
                 enable: bool = True) -> None:
        super().__init__(description, parser, enable)
        key = os.environ.get('WEATHER_API_KEY', key)
        if key is None:
            raise ValueError(
                'Please set Weather API key either in the environment '
                'as WEATHER_API_KEY or pass it as `key`')
        self.key = key
        self.location_query_url = 'https://geoapi.qweather.com/v2/city/lookup'
        self.weather_query_url = 'https://devapi.qweather.com/v7/weather/now'
 
    @tool_api
    def run(self, query: str) -> ActionReturn:
        """一个天气查询API。可以根据城市名查询天气信息。
        
        Args:
            query (:class:`str`): The city name to query.
        """
        tool_return = ActionReturn(type=self.name)
        status_code, response = self._search(query)
        if status_code == -1:
            tool_return.errmsg = response
            tool_return.state = ActionStatusCode.HTTP_ERROR
        elif status_code == 200:
            parsed_res = self._parse_results(response)
            tool_return.result = [dict(type='text', content=str(parsed_res))]
            tool_return.state = ActionStatusCode.SUCCESS
        else:
            tool_return.errmsg = str(status_code)
            tool_return.state = ActionStatusCode.API_ERROR
        return tool_return
    
    def _parse_results(self, results: dict) -> str:
        """Parse the weather results from QWeather API.
        
        Args:
            results (dict): The weather content from QWeather API
                in json format.
        
        Returns:
            str: The parsed weather results.
        """
        now = results['now']
        data = [
            f'数据观测时间: {now["obsTime"]}',
            f'温度: {now["temp"]}°C',
            f'体感温度: {now["feelsLike"]}°C',
            f'天气: {now["text"]}',
            f'风向: {now["windDir"]}，角度为 {now["wind360"]}°',
            f'风力等级: {now["windScale"]}，风速为 {now["windSpeed"]} km/h',
            f'相对湿度: {now["humidity"]}',
            f'当前小时累计降水量: {now["precip"]} mm',
            f'大气压强: {now["pressure"]} 百帕',
            f'能见度: {now["vis"]} km',
        ]
        return '\n'.join(data)
 
    def _search(self, query: str):
        # get city_code
        try:
            city_code_response = requests.get(
                self.location_query_url,
                params={'key': self.key, 'location': query}
            )
        except Exception as e:
            return -1, str(e)
        if city_code_response.status_code != 200:
            return city_code_response.status_code, city_code_response.json()
        city_code_response = city_code_response.json()
        if len(city_code_response['location']) == 0:
            return -1, '未查询到城市'
        city_code = city_code_response['location'][0]['id']
        # get weather
        try:
            weather_response = requests.get(
                self.weather_query_url,
                params={'key': self.key, 'location': city_code}
            )
        except Exception as e:
            return -1, str(e)
        return weather_response.status_code, weather_response.json()

为了获得稳定的天气查询服务，首先要获取 API KEY。打开开发文档 | 和风天气开发服务

体验自定义工具效果

在两个 terminal 中分别启动 LMDeploy 服务和 Tutorial 已经写好的用于这部分的 Web Demo


conda activate agent
lmdeploy serve api_server /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-7b \
                            --server-name 127.0.0.1 \
                            --model-name internlm2-chat-7b \
                            --cache-max-entry-count 0.1


export WEATHER_API_KEY=在2.2节获取的API KEY
# 比如 export WEATHER_API_KEY=1234567890abcdef
conda activate agent
cd /root/agent/Tutorial/agent
streamlit run internlm2_weather_web_demo.py --server.address 127.0.0.1 --server.port 7860

在本地执行如下操作以进行端口映射

在输入模型地址并选择好工具后，就可以开始体验了

读者可以关注大模型返回的工具名称及参数。query参数解析为上海


{
    "name": "WeatherQuery",
    "parameters": {
        "query": "上海"
    }
}

AgentLego：组装智能体“乐高”
使用 AgentLego 工具，体验 AgentLego 的 WebUI，以及基于 AgentLego 自定义工具并体验自定义工具的效果。

https://github.com/InternLM/Tutorial/tree/camp2/agent

Tutorial/agent/lagent.md at camp2 · InternLM/Tutorial · GitHub

https://github.com/InternLM/Tutorial/blob/camp2/agent/agentlego.md

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