LangGraph: Creating A Multi-Agent LLM Coding Framework!

Summary

这个视频介绍了一个利用Lang Graph框架构建的多智能体大型语言模型编码系统的原型。该系统由不同的智能体代理组成,每个智能体专注于特定的编码任务,如编程员代理编写代码、测试员代理生成测试用例、执行器代理执行代码等。通过Lang Graph的状态图、节点和边缘的设计,这些智能体可以协同工作,完成从代码生成到调试的全流程。视频作者认为这个原型展示了Lang Graph在创建复杂AI代理系统方面的潜力,值得关注。

Takeaways

• 📌 Lang图是建立在Lang链之上的一个模块，用于更好地创建图形和AI代理。
• 🚀 一个名为unaj的用户使用Lang图创建了一个多代理大型语言模型编码框架的原型。
• 👩‍💻 这个框架定义了不同代理的架构流程和角色，包括程序员代理、测试者代理、执行者代理和调试器代理等。
• 🛠️ 每个代理都专注于特定的任务，例如编写代码、生成输入测试用例、执行代码等。
• 🌐 这个多代理框架被整合到了Streamlit作为前端，允许用户查询与编程相关的问题。
• 🔍 使用该框架可以动态调用专门的代理来生成和优化代码，最终生成可以在其他工作流中执行的代码。
• 🎉 通过Patreon提供了价值超过700美元的9个付费订阅服务，以及与大型AI公司的合作，免费提供AI工具和框架。
• 🌟 Lang图作为一个新兴工具，虽然不为人熟知，但通过这个项目展示了其在创建AI代理方面的潜力。
• 📊 该多代理框架使用Lang图的节点、状态图和边缘来定义不同组件之间的信息流和操作。
• 🔗 在GitHub上提供了该多代理框架的环境和图形创建的仓库，方便用户尝试和实现。
• 📢 作者鼓励观众通过提供的Patreon链接和其他资源链接深入了解并加入他们的AI社区。

Q & A

这个多代理大型语言模型编码框架的主要目的是什么?

该框架的主要目的是突出大型语言模型在自动化软件开发任务(如编码、测试和调试)中的日益增长的用例,并探索使用LangGraph框架创建这些代理的能力。

Lang Graph是什么,它的三个主要组件是什么?

Lang Graph是一个增强Lang Chain生态系统的模块,用于促进创建各种高级代理运行时。它的三个主要组件是:1)状态图 2)节点 3)边缘。

在这个多代理框架中,程序员代理的作用是什么?

程序员代理负责根据给定的需求编写代码。它利用Lang Graph的节点生成、优化无错误的Python代码。

测试员代理和执行员代理分别扮演什么角色?

测试员代理负责生成输入测试用例和预期输出,基于代码的要求。执行员代理则执行由前一步骤提供的Python代码,并在Python环境中使用生成的测试用例进行评估。

调试器代理的作用是什么?

调试器代理利用大型语言模型的知识来调试代码。它能够返回执行员以修复任何错误。

在这个框架中,条件边缘的作用是什么?

条件边缘由大型语言模型支持的函数实现,用于决定先执行哪个节点。它基于上游节点、函数节点和映射来创建。在这种情况下,它决定是结束执行还是将代码发送给调试器进行错误解决。

这个框架是如何与Streamlit集成的?

作者在Streamlit前端集成了这个多代理编码框架,允许用户提出与编码相关的查询,然后框架调用专门的代理(如程序员、调试器、执行员和测试员)来生成和优化代码。

为什么作者认为这个框架值得关注?

作者认为,尽管这只是一个原型,但它展示了使用Lang Graph可以实现的强大功能。它不太为人所知,但对于希望创建各种代理和执行代理的人来说,Lang Graph是一个非常有用的框架。

除了代码相关任务,Lang Graph还可以用于哪些其他领域?

视频中没有明确提及Lang Graph在其他领域的用途,但由于它是用于创建各种高级代理运行时的框架,因此它可能适用于需要代理协调工作的任何领域。

作者对自己的Patreon页面有何推广?

作者推广了他的Patreon页面,称它为加入的一个绝佳方式,可免费获取AI工具和框架的订阅、网络机会、合作机会等。

Outlines

00:00

🤖 多智能体大型语言模型编码框架概述

这一段主要介绍了一种使用 LangGraph 框架创建的基于多智能体的大型语言模型编码系统原型。该系统由不同的智能体代理组成,每个代理专注于特定任务,如程序员代理负责编写代码、测试代理生成测试用例、执行代理执行代码、调试代理调试代码等。这种多智能体架构通过代理之间的协作,能够自动完成从编码到测试、调试的整个软件开发过程。该系统整合了 LangGraph 的状态图、节点和边缘等概念,展示了利用大型语言模型和图形化流程实现软件开发自动化的潜力。

05:01

🧩 多智能体编码框架的工作流程

这一段详细阐述了上述多智能体编码框架的具体工作流程。首先定义了不同智能体代理的角色,如程序员代理、测试代理、执行代理和调试代理。其次利用 LangGraph 的节点和边创建了代理之间的信息流动路径。条件边则根据大型语言模型的判断,确定执行路径是结束还是送往调试代理进行错误解决。最终,经过代理们的分工协作,输出能满足初始需求的Python代码。该框架将 LangGraph 的状态图、节点、边等概念应用到实践中,构建了一个复杂的、自主运行的多智能体系统,用于编码任务的自动化。

10:02

🔗 GitHub 开源多智能体编码框架

这一段最后介绍了可在GitHub上获取并尝试使用这个多智能体编码框架。作者已在GitHub库中定义了环境和图形,可与流行的Streamlit集成,用于调试和执行Python代码。作者对该框架功能的展示受到了很高的赞扬,因为它是首个真正利用了LangGraph框架创建智能体应用系统的范例。虽然LangGraph尚不为人熟知,但它展现了通过图形化流程和大型语言模型实现软件开发各环节自动化的巨大潜力。该视频呼吁大家关注并使用LangGraph等新兴AI框架,共同推进人工智能在软件工程领域的创新应用。

Keywords

💡Lang Graph

Lang Graph是建立在Lang Chain之上的模块，用于更好地创建图形和AI代理。在视频中，Lang Graph允许用户通过定义不同的代理和它们的角色来构建一个多代理编程框架。例如，可以有编程代理、测试代理、执行器和调试器等专门负责特定任务的代理。这显示了Lang Graph如何促进复杂的软件开发任务自动化，通过其提供的结构化方法来实现。

💡多代理框架

多代理框架指的是一个系统，它包含多个AI代理，每个代理负责不同的任务，例如编程、测试、执行和调试代码。在视频中，通过Lang Graph构建的这个框架展示了如何利用多代理系统来处理编码相关的查询，并生成、优化代码。这个例子说明了多代理框架在软件开发中自动化和优化过程的潜力。

💡专门代理

视频中提到的专门代理是指在多代理框架内，每个代理都专注于完成一个具体任务的AI代理。这些任务可以是编写代码、生成测试用例、执行代码或调试。通过这种方式，每个代理可以充分利用其专业知识来提高整体框架的效率和效果。例如，编程代理专门负责根据给定要求编写代码，而调试器代理则帮助解决代码中的错误。

💡Lang Chain

Lang Chain是Lang Graph建立之上的底层技术，它提供了创建AI代理和图形所需的基础设施。视频中提到，Lang Graph作为一个模块，通过增强Lang Chain的功能，使得创建高级AI代理和运行时环境成为可能。这体现了Lang Chain在支持复杂AI应用开发中的核心角色。

💡Streamlit

Streamlit是一个开源的Python库，用于快速创建和分享数据应用。视频中展示了如何将多代理编程框架集成到Streamlit前端中，这使得用户能够通过一个友好的界面向框架提出编码相关的查询。这个例子展示了Streamlit在简化AI应用部署和交互中的有效性。

💡斐波那契序列

斐波那契序列是一个在数学和编程中常见的例子，用于测试算法和程序的正确性。视频中提到，使用Lang Graph构建的多代理框架被用来生成斐波那契序列的代码，展示了该框架在理解和实现算法方面的能力。这个应用案例强调了框架处理具体编程任务的实用性。

💡执行器代理

执行器代理是多代理框架中的一个专门代理，负责执行由其他代理生成的代码。在视频案例中，执行器代理执行Python代码并使用生成的测试用例进行评估，展示了其在自动化测试和验证代码正确性中的关键作用。

💡调试器代理

调试器代理是专门负责识别和解决代码中错误的AI代理。视频中，调试器代理使用大型语言模型的知识帮助修正代码中的错误，体现了AI在提高编程效率和代码质量中的潜力。

💡测试代理

测试代理是指在多代理框架中负责生成输入测试用例的AI代理。这些测试用例用于验证代码的正确执行。视频案例中提到的测试代理展示了自动化测试过程的实现，以及在保证软件质量方面的重要性。

💡Patreon

Patreon是一个会员订阅平台，让创作者可以通过提供额外的内容或服务来从他们的粉丝或支持者那里获得资金支持。视频提到通过Patreon提供的订阅，展示了作者如何利用这个平台与其社区互动，提供咨询服务和AI相关的资源，这强调了Patreon在建立与粉丝关系和资源共享方面的作用。

Highlights

Lang graph is a module built on top of Lang chain to better enable the creation of graphs and AI agents.

A multi-agent large language model coding framework using Lang graph was created, featuring specialized agents for programming, testing, executing, and debugging code.

The framework integrates agents like a programmer agent to write code, a tester agent to generate test cases, an executor to run the code, and a debugger to fix errors using large language models.

The framework utilizes a multi-agent approach where agents collaborate to fulfill coding tasks based on user queries.

An example implementation integrates the multi-agent coding framework into Streamlit as a front-end, allowing users to input coding prompts.

The framework leverages Lang graph's state graph, nodes, and edges to define information flow and operations between components.

Lang graph facilitates creating sophisticated multi-agent systems for coding tasks by enabling the creation of specialized agents and their coordination.

A GitHub repo is available for trying out this multi-agent coding framework built with Lang graph.

The video emphasizes the transformative impact of AI on software engineering and the increasing use cases of large language models for automating development tasks.

The framework explores the capabilities of Lang graph for creating agents and enabling autonomous operations of programming, testing, and debugging agents.

Lang graph enhances the Lang chain ecosystem by facilitating the creation of advanced agent runtimes and utilizing large language models for reasoning and decision-making.

The state graph in Lang graph is a stateful graph defined by a state object passed to each node, allowing nodes to return operations to update the state.

Nodes in Lang graph represent agent components responsible for different tasks, interacting with the state object and returning operations to update it.

Edges in Lang graph connect nodes within the graph, defining the flow of information and operations between components, enabling communication and coordination.

The video encourages viewers to check out the previous video on Lang graph for more insights and to join the Patreon page for free AI tool subscriptions, networking opportunities, and collaborations.

Transcripts

[Music]

remember Lang graph well it's a module

that's built on top of Lang chain to

better enable the creation of graphs and

AI agents I have made a video on it so

if you're interested take a look at the

video link in the description below now

something cool I stumbled across was

where someone by the user named unaj i'

created a multi-agent large language

model coding framework using Lang graph

now this is a prototype of using

multi-agent based flows where he first

defined the architecture flow as well as

different agents that he specialized

where he the agent itself is to focus on

a specific task as well as assigning him

a certain role you have a programmer

agent that will write the code for you

you have a tester agent that will

generate the input test cases an

Executor who's going to execute the code

in different languages you have a

debugger agent that's going to help you

debug the code using large language

models knowledge as well as many other

agents that work together in this multi-

Asian framework to fulfill user queries

on coding based tasks now just take a

look at this example where you're able

to implement this multi-agent framework

built using Glen graph so here we can

see that he has integrated this multi-

Asian framework that was built using L

graphs framework which is something that

lets you build AI agents and in this

case he has integrated the multi-agent

coding framework into streamlet as a

front end now what he has done is that

you're allowed to ask it different

coding related queries and and in this

case he has asked the framework to

generate code for a Fibonacci series now

once the code was actually or the prompt

was actually sent in the framework

starts to call the specialized agents

for each task whether that's a

programmer a debugger an Executor agent

as well as a tester now in this case the

agent Works alongside with each other to

generate as well as the optimize the

code and at the end we can see that the

debugger is also working alongside with

the these agents to make sure that it's

resolving errors in the code and we saw

at the end that it's able to generate

the code that you can execute and

Implement into any other workflow what

an amazing year for the private Discord

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look at the patreon link in the

description below and join today this is

something that you should definitely not

miss out so definitely take a look at

this now this is no real big project or

anything but it's a prototype and I

really wanted to put emphasize on it cuz

it showcases what you can actually do

with Lang graph Lang graph is something

that is actually fairly new and not a

lot of people know about it but as you

see it's a module built on top of link

chain and it's to help you create

various sorts of AI agents and in this

case as a specific use case and helping

you create different agents so this is

something that we're going to take a

look at by taking a look at the

multi-agent framework that was developed

using L graphs so with that thought guys

stay tuned and let's get straight into

the video if you would like to book a

one-on-one with me where you can access

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solutions with AI definitely take a look

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below

hey what is up guys welcome back to

another YouTube video at the world of AI

in today's video we're going to be

taking a look at a multi-agent large

langage model framework that was built

using Lang graph now in this article he

basically discusses the transformative

impact of AI as on software engineering

as well as emphasizing the role of

software development in shaping the

future and the main reason why he

developed this project is to highlight

the increasing use cases of lm's as well

as the LM based agents to automate

various software development tasks from

coding to testing as well as debugging

in this case he has made this beautiful

framework that is able to do all of it

using Lang graph now the focus on this

article or this framework is to develop

this framework so that it's able to

enable autonomous operations of these

agents whether that's a programmer a

debugger a tester or an Executor it

explores these capabilities of langra

for for creating these agents and it's

something that you can get started with

by integrating a chain-based large

langage model solution that langra

actually supplies so I know I made a

video on Lang graph previously but to

just briefly go over it once again it's

something that enhances the Lang chain

ecosystem and this is by facilitating

the creation of different various ranges

of advanced agent runtimes and it

utilizes the capabilities of large

language models for reasoning as well as

decision-making tasks within a cyclical

graph structure it's often needed for

Asia run times and it's basically

focusing on three main key components in

this L graph framework firstly you have

state graph this is the primary type of

graph in L graph and this is the

stateful graph which is defined by a

state object which is passed in each

node that is being attributed to it so

these nodes within the graph actually

return operations to the updates that

are being sent within the state so this

is is by either having it so that

there's a specific attribute or adding

the existing attributes secondly you

have nodes this is where in line graph

it represents the agent components and

they're responsible for different types

of tasks within this application so each

node can interact with the state object

which is the one that we talked about

previously and it returns operations to

update it based off the functions that

the that are actually prevalent in this

architecture you then have edges now

this is where it's basically focusing

after adding the nodes you are

connecting nodes within the graph and it

defines the flows of the information as

well as the operations between different

components within your own application

they basically enable the communication

as well as the coordination between

nodes to achieve the desired

functionality so we talked about what

Lang graphs framework is able to do but

how did he utilize Lang graph to create

this multi-agent coding system well if

we are to go to the medium article that

is describing this multi-agent framework

it begins by focusing on defining the

architecture flow as well as the roles

of different agents these are basically

specified in different task where he

first developed an agent node and this

is using Lang graph's node and what he

has done is that he created a programmer

and in this case this agent is

responsible for writing the code based

on a given requirement it will utilize L

graph's node to generate optimize an

error-free python code now in this case

you can optimize it for other

programming languages but what he has

done is that he revolved this

multi-agent framework for just python

now what he has done after is that he

focused on an agent node which is the

tester now this tester agent is going to

generate the input test cases and it

it's basically expected to Output based

on the provided requirements of the code

you then have the executor agent which

is focusing on the agent node and the

executor agent will actually execute the

provided python code from the previous

steps and in this case it's going to be

in a python environment and it's going

to use it so that the generated test

cases are for evaluation now in this

case it splits the graph off into two

different segments you have the agent

node which is a debugger and this will

utilize the line graph's capabilities to

debug the code using any sort of large

language model

knowledge lastly you can see that the

debugger is able to go back to the exec

computer to execute any sort of error

like again to make sure that's it's able

to fix it now in this case you have this

decision to end node which is focusing

on a conditional Edge so in this case

you're able to have these conditional

like edges or in this case that you're

able to have a function often powered by

the large language model which is used

to determine which node is to go first

to create this Edge you need to pass

through three different things you have

the Upstream node the function node as

well as a mapping so what he has done in

this case is that he basically created

this conditional Edge where it's

deciding whether to end the execution or

send the code to the debugger and this

is through the previous step for any

sort of error resolution to make sure

that there is fixes being made to the uh

the code that was actually faulty So

based off this outcome it's able to then

provide and execute the code afterwards

and you're going to be able to get this

new code that will be able to fulfiller

the fulfill the user prompt at the

beginning now in this case this whole

framework is integrating these different

agents as well as edges that are being

built by Lane graphs framework it's

utilizing its state graph the nodes the

edges to define the flow of information

as well as operations between different

components of the application so you can

simply see that by leveraging Lang graph

the framework is able to enable the

creation of the sophisticated

multi-agent system for coding tasks and

this was something that he was actually

able to do and it's something that you

can actually try out right now with this

GitHub repo that he has created he has

defined all the environments the graphs

for creating this multi-agent framework

and you can have it so that it can be

implemented in the streamlet so you can

use it for debugging as well as

executing python code now huge props to

this guy who developed this framework

cuz I never actually saw a real use case

as to what anyone was doing with line

graphs so this is something that I first

saw on Twitter and I really wanted to

make a video on it afterwards and I

thought it'd be something that would be

very interesting for a lot of people and

it would probably prompt a lot of people

to use Lang graph it's not a paid promo

or anything it's just something that I

thought people should actually use

because not a lot of people know about

it and it's really good framework that

many people can adopt for creating these

different nodes and execution agents so

with that thought guys I hope you

enjoyed today's video and you got some

sort of value out of it I'm going to

leave all these links in the description

below make make sure you check out my

previous video on L graph cuz it'll give

you more insights as to what you can do

with it but with that thought guys thank

you guys so much for watching make sure

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lastly make sure you guys subscribe turn

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check out our previous videos so you can

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I'm always posting different videos

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that you should definitely take a look

at but with that thought guys thank you

guys so much for watching have an

amazing day spread positivity and I'll

see you guys fairly shortly peace out

fellas