Uploaded October 2025 | Updated September 2026, 2 weeks ago
π The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
πΊ Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
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Welcome to Episode 7 of the LangChain v1 Agents Series! In this episode, you'll learn how to get structured, validated data from your LangChain agents using Pydantic V2 models instead of free-form text responses.
What You'll Learn:
- Converting free-form text responses to structured data objects
- Using Pydantic V2 models with LangChain agents
- Defining simple and complex data structures
- Extracting single objects vs lists of objects
- Validating structured output automatically
- Accessing structured response data programmatically
- Building agents without tools for pure data extraction
Tech Stack:
- LangChain v1 (latest prerelease)
- Pydantic V2 (data validation)
- ChatOllama
- Qwen 3 model
- Python type hints
- Field descriptions for better extraction
Code Repository:
github.com/laxmimerit/Langchain-v1-Agents
Full Playlist:
youtube.com/playlist?list=PLc2rvfiptPSR2fnZQLmq5TB36E0S0hdW4
Langchain v1 Projects Playlist:
youtube.com/watch?v=xOE1yDFGePA&list=PLc2rvfiptPSQzrdPe5apNAq1bbBROUXKh&pp=0gcJCaIEOCosWNin
π Learn More with My Udemy Courses
π₯ MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
udemy.com/course/mcp-mastery-build-ai-apps-with-claude-langchain-and-ollama/?referralCode=31C17C306A59601B8689
π Agentic RAG with LangChain & LangGraph
udemy.com/course/agentic-rag-with-langchain-and-langgraph/?referralCode=C0BCC208F53AF2C98AC5
π§ LangGraph with Ollama
udemy.com/course/langgraph-with-ollama/?referralCode=B646DCB44A189BEBC20C
β‘ Ollama and LangChain
udemy.com/course/ollama-and-langchain/?referralCode=7F4C0C7B8CF223BA9327
π§ Fine-Tuning LLM with Hugging Face Transformers
udemy.com/course/fine-tuning-llm-with-hugging-face-transformers/?referralCode=6DEB3BE17C2644422D8E
π NLP with BERT in Python
udemy.com/course/nlp-with-bert-in-python/?referralCode=063516494616C76907CD
π Connect with Me
Website & Blogs: kgptalkie.com
LinkedIn: linkedin.com/in/laxmimerit
GitHub: github.com/laxmimerit
Twitter (X): twitter.com/laxmimerit
π Support the Channel
π Like the video if it helps you
π¬ Comment your doubts & feedback
π Subscribe for free weekly AI & Data Science content
#DataScience #MachineLearning #LangChain #LangGraph #Ollama #Python #AI #DeepLearning #NLP #GenerativeAI #LLM #HuggingFace #BERT
π The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
πΊ Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
----------
Welcome to Episode 7 of the LangChain v1 Agents Series! In this episode, you'll learn how to get structured, validated data from your LangChain agents using Pydantic V2 models instead of free-form text responses.
What You'll Learn:
- Converting free-form text responses to structured data objects
- Using Pydantic V2 models with LangChain agents
- Defining simple and complex data structures
- Extracting single objects vs lists of objects
- Validating structured output automatically
- Accessing structured response data programmatically
- Building agents without tools for pure data extraction
Tech Stack:
- LangChain v1 (latest prerelease)
- Pydantic V2 (data validation)
- ChatOllama
- Qwen 3 model
- Python type hints
- Field descriptions for better extraction
Code Repository:
github.com/laxmimerit/Langchain-v1-Agents
Full Playlist:
youtube.com/playlist?list=PLc2rvfiptPSR2fnZQLmq5TB36E0S0hdW4
Langchain v1 Projects Playlist:
youtube.com/watch?v=xOE1yDFGePA&list=PLc2rvfiptPSQzrdPe5apNAq1bbBROUXKh&pp=0gcJCaIEOCosWNin
π Learn More with My Udemy Courses
π₯ MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
udemy.com/course/mcp-mastery-build-ai-apps-with-claude-langchain-and-ollama/?referralCode=31C17C306A59601B8689
π Agentic RAG with LangChain & LangGraph
udemy.com/course/agentic-rag-with-langchain-and-langgraph/?referralCode=C0BCC208F53AF2C98AC5
π§ LangGraph with Ollama
udemy.com/course/langgraph-with-ollama/?referralCode=B646DCB44A189BEBC20C
β‘ Ollama and LangChain
udemy.com/course/ollama-and-langchain/?referralCode=7F4C0C7B8CF223BA9327
π§ Fine-Tuning LLM with Hugging Face Transformers
udemy.com/course/fine-tuning-llm-with-hugging-face-transformers/?referralCode=6DEB3BE17C2644422D8E
π NLP with BERT in Python
udemy.com/course/nlp-with-bert-in-python/?referralCode=063516494616C76907CD
π Connect with Me
Website & Blogs: kgptalkie.com
LinkedIn: linkedin.com/in/laxmimerit
GitHub: github.com/laxmimerit
Twitter (X): twitter.com/laxmimerit
π Support the Channel
π Like the video if it helps you
π¬ Comment your doubts & feedback
π Subscribe for free weekly AI & Data Science content
#DataScience #MachineLearning #LangChain #LangGraph #Ollama #Python #AI #DeepLearning #NLP #GenerativeAI #LLM #HuggingFace #BERT

![OpenClaw Self Improving Agent Setup - Full Tutorial (2026)
π The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
πΊ Full playlist: https://www.youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
Learn how to set up a self-improving agent in OpenClaw β it remembers your corrections and applies your preferences automatically in every new session.
This tutorial starts with the exact problem: you correct your agents output format, it works once, but the next session it reverts. Then step by step: install the self-improving agent skill from ClawHub, configure the self-improvement hook in openclaw.json, restart the gateway, and verify its active. Once set up, OpenClaw reads learnings.md at every startup and applies your stored preferences without you having to repeat yourself.
β± Chapters:
0:00 Intro β Self-Improving Agent in Action
0:51 The Problem β Agent Forgets Your Corrections
1:56 Install the Self-Improving Skill from ClawHub
3:02 Explore the .learning Directory Structure
4:31 Configure openclaw.json Hooks
5:29 Restart Gateway and Verify the Setup
6:14 Test β Agent Now Remembers Your Preferences
7:55 Outro
π Resources:
Self-Improving Agent Skill (ClawHub): [SHARE THE CLAWFHUB URL AND I WILL REPLACE THIS]
OpenClaw Tutorials Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQMZf3rlYZZ8vwUBcm6jv4d
πΊ Watch next:
OpenClaw Memory Setup with Mem0 β https://www.youtube.com/watch?v=c157x7Pc8xw&list=PLc2rvfiptPSQMZf3rlYZZ8vwUBcm6jv4d
π Go deeper β my Udemy course:
Agentic AI: Deploy LangChain v1 Agent Projects to Production β https://kgptalkie.com/ai-agent-projects
If this helped, like the video and subscribe for more OpenClaw tutorials. Drop a comment if you get stuck on the hook configuration β I read and reply to every comment.
#OpenClaw #SelfImprovingAgent #AIAgent OpenClaw Self Improving Agent Setup - Full Tutorial (2026)](https://i.ytimg.com/vi/zBqbodcj8Rc/mqdefault.jpg)


