Langchain Reflection Agent Tutorial: Advanced AI Workflows w/ LangGraph LangSmith OpenAI & Anthropic

🚀 *Langchain Modern Agents P6: Reflection Agent Orchestration w/ LangGraph LangSmith OpenAI & Anthropic* 🌟
Welcome to Part 6 of my *Modern Agents Series**, where I take a deep dive into building and orchestrating a **Reflection Agent* using Langchain’s latest tools like *LangGraph**, **LangSmith**, **OpenAI**, and **Anthropic**! In this video, I explain how to create a sophisticated AI-driven orchestration that combines **generative agents**, **critique agents**, and **reflection workflows* to produce high-quality Twitter posts.
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🔥 *What’s Covered in This Video:*
1️⃣ *Project Overview with Diagram (see above):*
Walkthrough of the *Reflection Agent Orchestration* flow.
Explanation of each agent and decision-making process:
*Generate Agent* (GPT-4o): Creates the initial Twitter post.
*Reflection Agent* (Claude Sonet 3.1): Critiques and refines the generated post.
**Conditional Edges**: Logic for deciding whether to loop or finalize the post.
How the user request flows through the entire system and returns the final response.
2️⃣ *Building the Orchestration:*
Step-by-step code walkthrough for creating the **LangGraph flow**.
Use of *Langchain v0.3+* to construct agents with *custom tools* and **decision nodes**.
Explanation of **normal edges**, **conditional edges**, and iteration logic.
3️⃣ *Reflection Workflow:*
How to set up a *multi-agent loop* where the Reflection Agent critiques and improves the post iteratively.
Explanation of when and why the loop exits, ensuring the final response meets the desired quality.
4️⃣ *Custom Verbose Functionality:*
Since *LangGraph* doesn’t have `verbose=True`, I show how to build a *custom verbose reporting function* in Python.
Detailed logging and visualization of the agent’s thought process and actions.
5️⃣ *Integration with LangSmith:*
Using *LangSmith* to trace and debug agent flows for better visibility and optimization.
6️⃣ *Hands-on Google Colab Demo:*
Running the full Reflection Agent Orchestration in **Google Colab**.
Testing the system with various user requests to showcase its versatility and effectiveness.
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💡 *Key Features of This Orchestration:*
*Dynamic Decision Making:* Conditional edges to control agent flow based on iteration limits and critique outcomes.
*High-Quality Outputs:* Iterative refinement ensures the generated Twitter posts are polished and impactful.
*Modular Design:* Easy to extend and adapt for other use cases like blog writing, ad generation, or content critique.
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🌟 *Why Watch This Video?*
If you’re looking to:
Learn how to create *modern Langchain agents* with LangGraph and LangSmith.
Master *reflection-based workflows* for AI-generated content.
Build production-ready orchestration systems combining OpenAI and Anthropic models.
Understand the future of *AI-driven content creation* workflows.
This video is packed with insights, live demos, and practical tips to level up your Langchain expertise.
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🎥 *Next Video in the Series:*
In the upcoming **Part 7**, I’ll enhance this Reflection Agent Orchestration by:
Adding *memory storage* for better contextual understanding.
Integrating additional tools to expand agent capabilities.
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#Langchain #ReflectionAgent #LangGraph #LangSmith #OpenAI #Anthropic #ClaudeSonet #GPT4 #AIOrchestration #Python

[@volpir4672]

this is great I'm going to dive into lang* as I've been doing it all from scratch, I have the models from huggingface talking to each other and revising, iterating but the output is one long message with a summary at the bottom, was stuck at executing the final commands to pull it out of the memory pool, thanks v much for the vid

[@htmlfivedev]

Glad it was helpful!

[@lganeshv1080]

Wonderful content, I am following closely this series. It would be great if u can share the code repo as well!

[@htmlfivedev]

github.com/ahmedmusawir/modern-agents-youtube-series

[@santoshbthvr]

This is another great tutorial. I am using gpt or llama for reflection chain as I don't have anthropic credits. I am facing issue while running graph = builder.compile(). It is giving the error "TypeError: issubclass() arg 1 must be a class". I am unable to fix it. Please help me. Thank you

[@htmlfivedev]

Sorry cannot help without looking at the code ... from the error it seems the issue is coming from the data structure ... I mean when you're initializing the graph w/ StateGraph ... make sure you're declaring your State correctly ...