Is LangGraph the Future of AgentExecutor? A Comprehensive Comparison

In the dynamic and swiftly evolving domain of generative AI and machine learning, efficiency and adaptability in tool implementation are critical. A prime illustration of this evolution is the advent of methods like LangGraph, which offers innovative solutions to tool management in AI-driven tasks. Today, we’ll delve into whether LangGraph could potentially reshape the future of implementing agent executors, drawing a precise comparison with traditional methods like the Lanchain core components. Join us as we dissect two distinct approaches to integrate agent executor technology, detailing the features, usability, and overall impact of each method.

Understanding Agent Executors

Before we compare the different implementations, it’s essential to establish what an agent executor is. In machine learning contexts, an agent executor is tasked with the coordination and execution of sequential actions or tools based on dynamic inputs. This technology is fundamental in applications requiring complex decision-making capabilities such as in natural language processing and automated reasoning systems.

Traditional Approach: Using Lanchain Core Components

In the traditional paradigm as seen with Lanchain core components, the implementation of an agent executor involves directly defining each component and manually integrating these parts into a cohesive system. This method is very structured and highly dependent on static tools and predefined prompts, which can be illustrated through a basic "react prompt" approach based on generative AI models.

Here, each tool and its functions need to be specified explicitly, and their integration into the agent executor happens through manual adjustments and coding. For instance, a basic function like getTextLength is created and integrated using the tool decorator from Lanchain. The process involves continuous loop checks to determine whether to continue the operation or conclude it.

Pros:

• Highly customizable.
• Direct control over every element of the implementation.

Cons:

• Complexity in setup and management.
• Less flexibility in modifying or scaling operations.

Innovative Approach: Implementing with LangGraph

LangGraph represents a leap towards more dynamic and scalable agent executor implementations. It advances a methodology where instead of writing complex interdependent code patterns, users can implement functionalities more visually and intuitively through graphs. This approach not only simplifies the creation and management of tools but also enhances clarity and debugging capability.

The LangGraph system utilizes nodes and states to manage the flow and execution of tasks. It leverages existing tools and pre-built prompts from sources like the Linkchain Hub, reducing the need to manually code every single component. For instance, setting up a tool like triple to multiply inputs becomes straightforward, and integrating a complex search tool becomes a matter of defining its role in the graph rather than embedding it deeply into the codebase.

Pros:

• Simplifies the implementation and management of agent executors.
• Improves visualization of processes, aiding in debugging and tracking.
• Eases the integration of pre-built and custom tools.

Cons:

• Might offer less granularity in control compared to the direct coding method.
• Dependency on the robustness of the LangGraph system and its components.

Comparison Revealed: Which Is Superior?

The choice between LangGraph and traditional methods like using Lanchain core components largely depends on the specific needs of the project. LangGraph offers a significant advantage in terms of usability and management for dynamic and complex systems. It allows for quick adaptations, easier management, and better visualization of the processes, making it particularly suitable for projects requiring frequent updates or modifications.

Conversely, traditional methods provide meticulous control over every aspect of the implementation, which may be necessary for highly specialized or sensitive applications where every detail matters.

Conclusion: The Future Belongs to Flexibility and Efficiency

As generative AI continues to advance, the methods that offer more flexibility and efficiency in adapting to new changes and integrating varied components will likely lead the way. LangGraph demonstrates these qualities, positioning it as a potentially pivotal tool in the future development of agent executors.

Whether LangGraph will become the definitive future of agent executors will depend on continuous improvements in its architecture and wider adoption by the community, but as it stands, it represents a significant step forward in making agent executor implementations more accessible and manageable.

[h3]Watch this video for the full details:[/h3]


🚀 Dive into AgentExecutor implementation in today’s video where I showcase a comparison between:
LangGraph 🦜🕸️ and LangChain Core 🦜🔗components!

🔧 What’s Inside:

Step-by-Step Implementation: Follow along as I implement the agent executor first with LangChain Core and then with LangGraph.
Detailed Comparison: See side-by-side how LangGraph stands

Github Repo:
https://github.com/emarco177/react-langgraph

[h3]Transcript[/h3]
hey there even here and I want to show you something very cool in my opinion so in this video we’re going to implement the agent executor with the lanching core component and then we’re going to be implementing the exact same agent executor but using langra and my goal of this video is to show you how cool is lra and to show you how easy it is to implement Advanced agent with it so this is the version using linkchain core and I’ll be using solely length chain components I won’t be using Lang graph here so you can see that I defined a function which is called get text length and I converted it into a tool with the tool decorator of length chain and over here I’ve implemented a help of function find tool by name which receives the list of tools and a tool name and return us the tool with the matching name and let’s check out my implementation of the agent executor and here we have the famous react prompt based on the react paper now this prompt was written by the linkchain team and I have to say that personally I think this is one of the most beautiful prompts in generative Ai and I’m sure that there has been a lot of work and prompt engineering to get to this prompt so if anybody from the linkchain team is hearing me then nicely done and because currently our tools are static then we can simply plug in into the placeholders of tools and Tool names the tool descriptions and the tool names so so that’s what I’m doing here with the partial method and here I’m simply initializing an llm and giving it the observation and back slash and observation stop sequences and this is to prevent hallucinations and finally in the agent variable I’m holding here the agent chain or the react chain whatever you want to call it which simply takes this prompt it runs it in the llm which is going to serve now as a reasoning engine and it’s going to pars out the outputs into agent finish or agent action objects and now the beautiful react Loop is starting so we’re going to be running this while loop as long as we don’t have an agent finish object in our head so this means that the llm didn’t determine that we need to finish our run so we’re going to invoke the prompt to the llm the react prompt we’re going to get a result back so it’s either going to be an agent action and this is in case we need to actually run it tool and when we get the agent action we have already the information of all the tools we need to run and what’s their input so we can simply use the helper function find tool by name that we’ve implemented and after we do that we get the tool result which is called the observation we print it we append it to the results and we now starting to iterate it again so it can continue and continue and run tools if we get agent action so this means the llm decided Ed that we need to use a tool however if we get an agent finish so this means that the llm our reasoning engine decided that we want to finish our run then we won’t be going into this if statement in line 94 so this Loop is going to be broken and finally we print the result all righty let’s go and check out the L graph implementation and here we’ll go and first start with the react prompt so instead of writing the promp manually I’m going to download it from The linkchain Hub so if I’ll go to linkchain HUB I can show you this front over here very famous and I’m simply going to download it dynamically and I’m going to define a tool which is called triple which takes a number and triples it and we’re going to use the Tav Search tool which is an amazing search engine for generative applications because the result we get back is going to be very oriented for Gen applications so we’re going to get the exact results that we want and this is because the search engine was designed to be Downstream into an llm and in line 28 I create the react agent chain so it’s the runnable exactly like we did over here the lunching core component and we can see that this function also returns something similar so to take the prompt to send it to the llm with the um stop signals and to Simply parse out the output anyways let’s go go and check out the um agent State and the state is going to be passed around our graph and our nodes are going to update the state so we have the user input which is not going to change a lot and we have here the agent outcome so every time we reason so the reasoning would be to send this react prompt we saw before to dlm then we’re going to save it and update the agent outcome and in the intermediate steps we’re simply going to save all of their tool execution results so we’re going to save the agent action object which has all the information about the tool that was invoked and we’re going to be saving the second element of the tel is going to be the result of that tool converted into a string and the operator. add is to tell Ang graph to append every tool execution result into this variable and not to overwrite it Al righty let’s go to nodes py and here we have our nodes logic implementation so we have only two nodes one is going to be called run agent reasoning agent and it’s going to take our state and it’s going to run the react prompt with it now notice in our state we have a input attribute so it’s going to invoke the react prompt with the input attribute at the first time and that would compile and run fine and now we get the agent outcome which hopefully is going to be a tool to run or to say that we finished if it’s an agent finish object and we we want to update now our state in the graph so the second node is going to be execute tools and it’s simply going to take the agent outcome which is going to be an agent action type so this is the Assumption over here this node is only run when there is an agent action and then we’re simply going to execute the tools and append the results into the intermediate steps in our state all right let’s go now to our main file and here we’re going to Define our graph so we’re going to define a state graph which takes in our agent states that we wrote We want to build the nodes and set the agent reason to be our entry point to the graph execution and we also want to define a conditional Edge so after we reason from our agent we want to decide whether we want to finish the graph execution or to go and invoke some tools so for that we’re going to be using the should continue function which outputs which node should we go next in our graph execution and we’re going to use that when we defined a conditional Edge from the agent reason node so that’s going to create two conditional edges one to the ACT node and the other to the end node and finally we compile a graph and we simply invoke it and let’s go and invoke it with the input what is the weather in San Francisco list it and triple it and let’s see the result now and our graph ran successfully and the current weather in San Francisco is 10.6 Celsius pretty cold and tripled it’s 31.8 so this looks legit all righty let’s go to langmi and I want to show you the traces so here we have the tools execution we have the Tav Search tool execution and the triple function execution now we can see that the input was 10.6 and the result was 31.8 and those decimal points make me question my implementation so I’m simply going to test it and I just remember that those are because I casted the integer into a float and that’s why we got this result anyways this is the Tav search results on on the weather in San Francisco and here we can see our land graph execution and here we can see that we have all of our nodes executions so the first one was the agent reason and then we decided to act and get the weather and then we reason it again and the agent told us that we need to go and use the multiplication tool the triple tool and then it reasoned again and it decided to finish so I hope you enjoyed this video and my goal here was to show you the two flavors of implementing an agent executor one with L chain core components and the other using langra I personally like more the L graph implementation simply because it’s much easier to describe and to illustrate using the graph and also the debugging is much more easier when we have nodes and edges and the flow is well defined so I’m a big fan of lra