Finally! A presentation on LangGraph that makes sense. Sign me up for any of your courses. I value your work.

Fantastic intro to langgraph. It's awesome how well you explain this complex topics. Keep up the good work! Cannot wait more real life examples with rag.

great, solid base concepts! converting from colab to code is the perfect exercise to digest what you explained. Thank you !

Please make a video where rag is used! Most companies use their own data to answer questions rather than web search.

Superb intro. Thanks for making such amazing content

I have finally understood LangGraph!

Excellent video as always ❤

Thank you! Your style of explanation is very clear.

Langgraph should add this video at their site. Its a great explanation much simpler and to the point. Thanks so much.

Great work

Thanks for your Birds Eye View of tying it all together. It has definitely relaxed my mind to have a structured approach that makes since. With this structure, I wonder if it can be built from a control sheet.............. which would introduce a DRY/RAD approach for AI.

Thank you so much. I'm so happy that I found your content! ❤❤❤❤

I've learned so much from you, thank you so much!!!

Thank you about the knowledge very good explanation

Thank you! It seems like the graph is built based of pre-defined chains and not an Agent who makes decision to independently call tools (kind of like crewai/autogen). Can you make a video where the agent makes decisions on a series of steps of what tools to use, through tool calling please?

Awesome vid Sam! Question on the Schema Parsing and retrying that you do. It looks like moving on from a node requires the LLM to output a particular schema (like JSON with particular keys). Are there easy integration points with Pydantic/Instructor so we can be sure of our output schemas with retry logic while getting the benefits of LangGraph’s simple flow abstractions?

Really great examples for routing. It’s kinda hard to get that down from the LangGraph examples

In this contrived example, it doesn’t look like LangGraph adds a lot of value, but requires quite a bit of setup. I mean, a simple script with no opaque marshaling and a few conditionals could achieve the same thing.

Hi Sam. I love your content! I don't know if this is the proper way to report this, but in the colab notebook, the function def for 'route_to_rewrite' has the line 'research_info = state["research_info"]' which throws a runtime error, and the variable is not referenced in this module. Removing that line fixes the problem. Keep up the fantastic work Sam!

Dude keep it up. This is gold i only ask you build this stuff in a codebase like you might see in production. I find it really difficult to transfer code from ipynb to a vscode project, call it a mental block, and maybe I'm alone in feeling like this.

Would you say Sam it would be better to use LangGraph from the get go. It seems straight forward enough and it wpuld appear you can get up and running quickly. I just dont see the point going through CrewAI first then transition to LangGraph? Another great video too love your work!

It's great ! BTW, if there's a complex graph, it's hard to build the relations without a map

CrewAI is essentially a LangChain wrapper (and IMO not a good one). This is actually a great show case of what customization you can do. If you need flexibility for your Agents, then LangGraph is the way to go.

Can you make a video that uses Lang Graph to create a cycle that repeatedly calls an LLM in a loop to determine the next action? I think what Lang Graph is designed for. In this example, we can definitely use the Lang Chain to achieve the same result because it linear. Anyway, your video is more easier to understand than the Lang Graph document. Thank you.

This is fantastic. Can the steps in LangGraph be captured by Langsmith? is there a way you could show this. Debugging through Langsmith these steps would be awesome

This is great. BTW, what do you use for a tool to design these flows to explain them?

This is just the descripting of an agent based Turing Machine. Definitely cool. "

Thanks for sharing. I have intrest in LangGraph with llama 3 on local such use ollama

Great video! I really like how clean and professional your diagrams look. What tool are you using to create them? I've tried Graphviz before but the results just aren't as polished and engaging. Would love to know your process for making such appealing visuals. Keep up the awesome work!

Curious whether this could also work in a Chatbot experience. So in this example you had an email trigger the event but could this work if a user wanted more functionality but within a Chat experience. Maybe you might have some ideas on how that might actually work.

Thank you for the video. I think the graph allows to go write_draft_email->categorize_email->rewrite_email and rewrite_email assumes that state contains research_info (which I would not based on this path) or did i misread that? This seems to be a way to sidestep the lack of langchain tool support in groq (which I did not find - although there is tool support ) Thank you.

Thanks for video, I was getting key error when test for the other email when it needs to use the def route_to_rewrite(state). look like the research_info key is not required for def route_to_rewrite(state).

Hi, video is great, exactly what I've been searching for! But I have offtop question, what app did you use to draw diagram? I like this style very much, and want to use it in my projects😄

Sam, question if you don't mind. My wife wants to have her sales information content incorporated behind a chat/RAG to answer questions from her content. Which system out there do you think would work best, that is low code for a non-dev. Ideally next part will be to access this via a membership website.

Can you show how to integrate with Gmail and to run locally with our own data ? Also, how to train on our own data. THANKS

⁠I slept over this and I now see a trend where people are obsessed with having any API interaction assisted or mediated by a LLM. It reminds me of the era of XML, when everyone wanted to use XML markup for everything, including network protocols. There’s a need to enable LLMs to interface with tools to extend their capabilities, but forcing natural language into every interaction seems a little weird. And, defining graphs to gate-keep reasoning flows seems brittle and limiting.

Can we use this process somehow to search the web and do research? Can't find this anywhere

Excellent explanation Sam, thanks. I have run the script with other models on Groq and got some errors. Have you tried to run it with models like "Mixtral-8x7b-32768", and "Gemma-7b-It"? Your last implementation with CrewAI seemed more robust, for me it ran with all models on Groq.

I like how you pronounce langchain

Do you have an opinion on AutoGen?

Thank you about the knowledge 🫶

Thanks so much for the explanation. I would like to add the logic for each step for more compréhensive humain understanding of this great exemple : step : ## Research Router change proposition in the prompt : "...Return the a JSON with a two keys 'router_decision' and research_router_logic to explain the logic behind it. use both the initial email and the email category to make your decision..." expected result : {'router_decision': 'draft_email', 'research_router_logic': 'The initial email is a thank you note from a customer, which only requires a simple response. The email category is customer_feedback, which also suggests a straightforward acknowledgement is sufficient.'}

🎯 Key points for quick navigation: 00:30 *🤔 LangGraph is a middle ground between LangChain and fully autonomous agents, providing structure and state to create agents that can be monitored and controlled.* 01:26 *🔒 CrewAI is suitable for testing ideas and prototyping, but not for production due to lack of control over the next steps.* 02:25 *📊 LangGraph consists of chains, nodes, edges, and conditional edges that connect nodes together to form a graph.* 03:51 *💡 The state graph maintains variables that get passed around from node to conditional edge to node as it goes through the flow.* 05:21 *⚖️ Conditional edges decide which node to go to next based on conditions such as research or rewriting an email.* 06:23 *📝 The state graph saves variables such as email categories and research results for use in subsequent nodes.* 07:25 *🔍 Conditional edges can be used for more advanced tasks such as checking brand standards or competitor mentions.* 09:51 *📝 LangGraph provides more control over the AI agent's flow and decision-making process compared to CrewAI.* 10:21 *💻 The code imports necessary libraries, including langchain.groq for Llama 3, and sets up the goal of categorizing an email and deciding whether to do research or not.* 11:24 *🔁 The agent will have a recursive improvement step to check if a draft reply needs rewriting.* 11:56 *📊 The chat model is set up using Llama 3 on Groq, with chat prompt templates and output passes for JSON and string outputs.* 12:29 *👉 Chains in LangGraph are used to categorize emails, decide on research, generate keywords, write draft emails, and check for rewrites.* 13:01 *🔄 Each chain has its own specific function in the agent's workflow.* 14:28 *💡 Testing each chain individually allows for debugging and refinement of the agent's performance.* 18:57 *📝 The model can be run on-premises, and the rewrite prompt is used for rewriting emails.* 19:24 *📊 The LangGraph part of the agent consists of nodes, edges (normal and conditional), and state.* 20:26 *💡 The state is used to pass variables around in the agent, allowing for tracking of variables like initial email, email category, draft email, and research info.* 20:55 *👉 The nodes in the LangGraph include categorized email node, research info node, draft email writer node, and others.* 21:25 *🔁 Print statements are used to help debug the agent's behavior.* 22:28 *🔎 Search functionality is used to retrieve documents based on keywords from an initial search query.* 23:28 *📝 The state dictionary is updated with new information as each node is called.* [24:59](outu [28:01] 📝 The standard edge in a graph is a hardwired edge that controls the flow of nodes. [28:34] 🔀 Conditional edges make decisions based on input and can change the flow of nodes. [29:06] 💡 Conditional edges can be used to decide which node to go to next, and can be set up to make decisions based on input. [30:05] 📊 The state printer node is used to see the final variables and outputs of the graph. [31:01] 💻 Once the workflow is set up, it can be compiled as an app and used with inputs. [32:38] 📝 The graph can be customized and reused with different inputs and prompts. [34:07] 🔧 Loops can be added to the graph, but care must be taken to avoid getting stuck in an infinite loop. Made with HARPA AI

Hello, I'm tearing out the few hairs I have left trying to adapt your excellent tutorial to create a workflow for detecting and tracking malicious emails (phishing). The process might look something like this: 1. Connector with an Outlook mailbox 2. Detection of received phishing emails 3. Collect received emails in Outlook into a file by batch or sync 4. Analysis of emails with Llama3/groq 5. Assign a score from 1 to 10 6. Classify phishing emails into 3 categories to create a security incident in EasyVista for those classified as critical 7. Tagging classified emails 8. Creation of a SharePoint folder that includes the metadata of analyzed emails 9. Reporting with BI 10. Tracing with LangSmith Kindly help. Thanks a lot.

what's the flowchart tool you're using?

Can I implement the same logic using JS instead??

Dark mode, please. Thanks.

When do you think someone will write a GUI design tool for LangGraph?

That's great, how about using an email from an account other than typing and passing the email prompt as it happens in the real world.

Instead of email, u should try to demonstrate langgraph using stock research agents!

What is groq’s role here ?

Does anybody have unlimited groq api? Mine is not active.