LangGraph: Creating A Multi-Agent LLM Coding Framework!
Summary
TLDR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
🤖 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。
🧩 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。
🔗 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。
Mindmap
Keywords
💡Lang Graph
💡多代理框架
💡专门代理
💡Lang Chain
💡Streamlit
💡斐波那契序列
💡执行器代理
💡调试器代理
💡测试代理
💡Patreon
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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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
my Consulting Services where I can help
you grow your business or basically give
you a lot of different types of
solutions with AI definitely take a look
at the calendar Link in the description
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
you check out the patreon page if you
haven't already this is a great way for
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opportunities collaboration and so much
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to date with the latest AI news and
lastly make sure you guys subscribe turn
on notification Bell like this video and
check out our previous videos so you can
stay up to date with the latest AI news
I'm always posting different videos
every single day so this is something
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
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