| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1. Introduction.mp4 | 49.8 MB | ||
| 10 - Human in the loop planning & send() Execution | |||
| 11 - Subgraphs, Merged Context, and Live Tool Pipelines | |||
| 12 - Closing | |||
| 2 - LangGraph Basics | |||
| 10. Conditional Routing.mp4 | 48.8 MB | ||
| 3 - Type-Safe Graphs with Pydantic, TypedDict, and Dataclasses | |||
| 11. Introduction - Data types.mp4 | 89.1 MB | ||
| 12. Conditional Graph TypedDict.mp4 | 63.8 MB | ||
| 13. DataClass and Pydantic conditional graph.mp4 | 120.9 MB | ||
| 4 - State Updates & Reducers in LangGraph | |||
| 14. Resolving State Collisions_ Safe Merging and Reducers in LangGraph.mp4 | 176.6 MB | ||
| 15. State updates message state and annotated add reducer similarities.mp4 | 85.4 MB | ||
| 5 - Adding Conversation Memory From One-Shot Prompts to Stateful LLM Turns | |||
| 16. Evolving a One-Node Graph Into a Typed, Schema-Driven LLM Workflow.mp4 | 104.6 MB | ||
| 17. Adding Conversation Memory.mp4 | 119.5 MB | ||
| 6 - Teaching Your LLM to Use Tools | |||
| 18. Teaching Your LLM to Use Tools.mp4 | 269.9 MB | ||
| 19. LLM-Powered Text Analytics with Tools.mp4 | 38.9 MB | ||
| 20. Building a Staged Travel Agent LLM → Tools → LLM.mp4 | 165.8 MB | ||
| 7 - Managing and Optimizing Chat History in LangGraph | |||
| 21. Rewriting History Edit & Remove Messages in LangGraph.mp4 | 105.6 MB | ||
| 22. Token-Smart Conversations Auto-Trimming Chat History.mp4 | 90 MB | ||
| 23. Rolling Memory Auto-Summarizing Your Agent’s Chat History.mp4 | 127.7 MB | ||
| 8 - Clean IO & Human-in-the-Loop Refinements | |||
| 24. Clean IO, Rich Internals.mp4 | 56.4 MB | ||
| 25. Single-State Flow One Frame, Two Refinements.mp4 | 76.7 MB | ||
| 26. Clean IO, Deep Work State.mp4 | 48.4 MB | ||
| 27. Human-in-the-Loop Tight Inputs, Editable Outputs.mp4 | 120.7 MB | ||
| 9 - Memory, Persistence & Structured Extraction | |||
| 28. Saving Agent State to SQLite.mp4 | 129.4 MB | ||
| 29. Cross-Thread Memory with an Evolving Memo.mp4 | 159.4 MB | ||
| 30. Structured Extraction Meets Long-Term Memory.mp4 | 184.2 MB | ||
| 31. TrustCall Structured Extraction Superpowers for Your Agent.mp4 | 217.9 MB | ||
| 32. Closing.mp4 | 53.7 MB | ||
| 8. Hello World LangGraph.mp4 | 95.8 MB | ||
| 9. First LLM Call via graph.mp4 | 24 MB | ||
| 38. closing.mp4 | 73.7 MB | ||
| 35. Web + Weather + LLM A Merged Context Planner.mp4 | 173 MB | ||
| 36. Static Subgraph.mp4 | 181.3 MB | ||
| 37. Live Tools A Two-Subgraph Pipeline.mp4 | 161.5 MB | ||
| 33. End-to-End Guided Planning with HITL + Structured Extraction.mp4 | 140.1 MB | ||
| 34. Running Your Graph with send().mp4 | 176.3 MB | ||
| 2. Travel Bot Plan.mp4 | 49.8 MB | ||
| 3. Environment Setup.mp4 | 73.8 MB | ||
| 4. Essential API's.mp4 | 66.9 MB | ||
| 5. Core Concepts-Invoke, Roles, Temperature, Streaming.mp4 | 66.9 MB | ||
| 6. Agentic thinking.mp4 | 29.4 MB | ||
| 7. Closing.mp4 | 34.6 MB |
LangGraph Made Easy
https://WebToolTip.com
Published 12/2025
Created by Ryan Banze
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 38 Lectures ( 3h 39m ) | Size: 4 GB
A practical, hands-on guide to building real production AI apps using LangGraph
What you'll learn
Build agentic workflows in LangGraph, starting from basic LLM calls to complex multi-step graphs with conditional routing and subgraphs.
Apply TypedDict, Pydantic models, Annotated reducers, and various state-management patterns to design reliable agent state machines.
Use tools, streaming, message editing, auto-summaries, and memory trimming to build production-ready conversational agents.
Implement long-term memory using SQLite, evolving memos, structured extraction, and cross-thread memory patterns.
Design and run stateless and stateful LLM systems using lean I/O, deep-work states, refinements, and state-prompt workflows.
Integrate external APIs such as weather, web search, and custom tools into LangGraph with live executions and error-safe routing.
Build advanced agentic patterns including human-in-the-loop planning, guided execution, trust calls, and multi-stage refinement loops.
Deploy end-to-end LangGraph agents using the Send API with efficient context management and reproducible agent state flows.
Requirements
Basic familiarity with Python (functions, imports, and simple classes).
A general understanding of Large Language Models (no deep math required).
Optional: familiarity with APIs and JSON responses.
No prior experience with LangChain or LangGraph is required — everything is taught from scratch.
A computer capable of running Python 3.10+ and installing standard packages.
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