Unlocking the Potential of L-Graph with LangGraph Engineer

In the rapidly evolving world of software development, efficiency and effectiveness in building applications are paramount. This is where tools like LangGraph Engineer come into the picture, blending innovation with practicality to streamline the application development process. Today, we’re delving deep into LangGraph Engineer, a project that not only exemplifies the capabilities of L-Graph but also offers tangible assistance in developing L-Graph applications.

Introduction to LangGraph Engineer

LangGraph Engineer is designed as a robust scaffolding tool to facilitate easier and faster development of L-Graph applications. By automating the initial stages of application setup, it allows developers to focus more on core business logic rather than the underlying framework. This tool is particularly beneficial for those who may not be deeply familiar with LangGraph constructs but need to deploy effective L-Graph solutions quickly.

How LangGraph Engineer Works

LangGraph Engineer utilizes a sophisticated agent within the L-Graph framework to interact with developers, gather requirements, and automatically generate the scaffolding of an application. This section breaks down its functionality and how it integrates into the development workflow.

Gathering Requirements

At the heart of LangGraph Engineer is the "Gather Requirements" node. This component interacts directly with developers to extract precise details about the application’s requirements. By engaging in a dynamic dialogue, it ensures that all necessary information is collected before proceeding to the drafting phase.

Drafting the Scaffolding

Once adequate information is secured, the process moves to the "Draft Answer" node. Here, another agent takes over to script the actual L-Graph application. This stage is crucial as it generates a complete graph structure which will form the backbone of the developed application.

Verification and Critique

Post-drafting, the "Check Node" performs basic validations, such as ensuring the inclusion of Python snippets and correct import statements. If the draft passes these checks, it progresses to the "Critique Node," where an LLM (Language Model) critiques the output, focusing on the quality and functionality of the code.

Practical Implementation and Testing

LangGraph Engineer isn’t just about theory. It embeds practicality by allowing developers to test the generated scaffolding directly within the L-Graph Studio. The platform supports public sharing links, enabling broader collaboration and testing.

Real-World Testing

Using the LangGraph Studio’s newest features, developers can swiftly transfer generated code into a development environment to conduct further tests and refinements. This testing phase is vital for ensuring that the scaffold meets the specific needs of the project before deeper development continues.

Iterative Refinement

Given the dynamic nature of software development, LangGraph Engineer supports iterative testing and refinement. Feedback loops incorporated into the system allow for continuous improvements, ensuring the scaffolding evolves in alignment with project requirements.

Open Source and Community Contributions

An exciting aspect of LangGraph Engineer is its open-source nature. The entire codebase is publicly available, allowing developers worldwide to contribute to its enhancement. This collaborative approach not only accelerates the tool’s development but also enriches it with diverse insights from the global developer community.

Exploring the Repository

The LangGraph Engineer repository contains individual Python files for each node, providing a clear view of the application’s architecture. Developers interested in contributing can start by reviewing these files, understanding the functionality, and identifying areas for enhancement.

Future Developments and Enhancements

Looking ahead, the LangGraph Engineer project is set for further enhancements. These include more sophisticated checks, enhanced prompts for gathering requirements, and deeper integration into LangGraph Studio for local runs and testing.

Conclusion: A Tool for Today and Tomorrow

LangGraph Engineer exemplifies how targeted tools can significantly enhance the workflow of developers, reducing complexity and accelerating deployment. Whether you are a seasoned developer or new to L-Graph, LangGraph Engineer offers a valuable resource for transforming ideas into functional applications efficiently.

As the platform continues to evolve, it promises to be an indispensable tool in the arsenal of developers looking to leverage the power of L-Graph in their projects. Try it out, contribute to its development, and harness the full potential of this innovative tool to streamline your application development process.

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


Try out the deployed version: https://smith.langchain.com/studio/thread?baseUrl=https://langgraph-engineer-23dacb3822e3589d80ff57de9ee94e1c.default.us.langgraph.app

This is an alpha version of an agent that can help bootstrap LangGraph applications. It will focus on creating the correct nodes and edges, but will not attempt to write the logic to fill in the nodes and edges – rather will leave that for you.

LangGraph: https://github.com/langchain-ai/langgraph

The agent consists of a few steps:

1. Converse with the user to gather all requirements
2. Write a draft
3. Run programatic checks against the generated draft (right now just checking that the response has the right format). If it fails, then go back to step 2. If it passes, then continue to step 4.
4. Run an LLM critique against the generated draft. If it fails, go back to step 2. If it passes, the continue to the end.

[h3]Transcript[/h3]
hello today I want to talk a little bit about a project that we’ve been working on kind of as a side project but we’re excited to share it both one as an example of what you can build with L graph but then also two as something that’s hopefully useful when you’re developing with L graph so it’s called Lang graph engineer and the idea is that it will help you build the scaffolding for your L graph applications so the idea is that we have this graph this agent which you can talk to it will get requirements from you and then it will create the scaffolding of a lingraph app and what does that mean that means that it will try to get all the nodes and edges correct but it won’t try to necessarily fill in the contents of the nodes or edges we’ll leave that to you and the logic that that you write I think this is helpful because you shouldn’t have to think deeply about Lang graph constructs in order to build an agent but you should have to think about your business logic and so that’s kind of like the line that we’re trying to draw here we’ll try to write the lane graph boiler plate for you but you’ll have to write the business logic that lives inside the nodes and edges of your graph so this is the Lang graph application that we’ve created that represents Lang graph engineer and what you’re seeing right now is Lang graph Studio it’s deployed on Lang graph cloud and it’s deployed publicly so this is a new beta feature we’re testing where you can share public links to a langra deployment and so in the link down below where you can try this out you’ll you’ll see the public link to try it out so here is roughly what the langra engineer app is doing so there’s a few different nodes so there’s this gather requirements node which can go to either draft answer or to the end what this node does is it tries to talk to you and gather requirements for the application so it tries to get more information from you about what exactly you want to do once it has enough information it will call this draft answer node and this will go to a separate agent this agent is focused on writing the laying graph application for you so it will generate a full graph and then it will go to this check Noe this check node is a very basic check to make sure that the draft node generated some markdown that contains a python snippet in the future we’ll add other checks in here like making sure that all import statements are correct or even trying to run the code after we run these checks which are programmatic checks if it passes we’ll go onto a critique node if it fails we’ll feed that back into the draft answer node and it will try again what does the critique node do the critique node uses an llm to critique the outputs so it will pass the generated python code into an llm and say hey does this look reasonable so we’re running two different types of checks here one are programmatic checks the others using an llm if the critique node thinks it’s good then it will go go to the end and it will finish you can find all the code for this in Lang graph engineer so this is an open source repo and if we look inside this folder we can see that there’s a python file roughly for each of of the nodes so let’s look inside the draft node because this is doing a lot of the heavy lifting so we can see that it’s a pretty simple prompt it’s basically some brief description about what Lang graph is a path to a unit test file so I’ll talk more about this later and then it has some more instructions some of these instructions include how to generate things and then others include the specific format so to use this python markdown syntax we pass in a unit test file because we actually have a lot of pretty good unit test coverage in L graph and these unit tests are actually a really good way of describing the API of what Lang graph should do and how you should invoke it so actually we found that by passing this in it’s able to generate pretty reasonable looking results there’s other files here for other nodes and you can check those out agentp is another great file to look at this is where the agent is actually defined so let’s try it out let’s ask it to generate a rag app please so this is pretty vague it’s not entirely sure what uh you know it should do and so it asks some kind of like follow-up questions um do I have any specific steps um um retrieve documents first then generate answer um it’s pretty straightforward so now it’s calling the draft answer because it has enough information this is the draft node responding we can see that is writing some code it did the check pretty fast because the checks deterministic and now the critique node and so the critique node seems good so it’s finished if we want to look at the output we can click this button here and we can see the full output so this is a new thing we’ve added to Lang graph Studio to make it easy to see the desired types of output so let’s copy this code let’s put it in a notebook see if it actually runs so it actually runs so it it got kind of like the ability to define a uh workflow a graph it got all the Imports correct now if you notice here the the innards of the retrieve documents node and the generate answer node they’re still completely mocked out you got to put your logic here and that’s the idea you’ll put your business logic will help create the scaffolding that’s it for this video as I mentioned this is publicly available so you can try it out um we’ll be improving it uh over the next few weeks so we’ll be adding in some more checks we’ll be improving the prompts maybe adding in some other critique steps maybe integrating it into langra studio so it can run locally thank you all for watching and please reach out if any questions