LangGraph
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. It models your workflow as a graph, allowing for precise control over the state and execution flow, making it ideal for complex, conditional agent systems. It is a core component of the LangChain ecosystem, designed for developers building production-grade AI applications that require state management, human-in-the-loop capabilities, and cyclical data flows.
Coding
Added on
Sep 15, 2026
Stateful graphsHuman-in-the-loopStreamingPython
Product information
Everything worth knowing about LangGraph
The complete picture — from everyday features to the technical detail builders and IT folks dig for.
Core features
What it actually does
Stateful Graph Execution
Design and run complex, multi-step workflows where the system remembers the full state at each node. Perfect for agents, chains, and custom logic.
Human-in-the-Loop Control
Pause, inspect, and manually intervene in the execution of your graph at any point for debugging, approval, or interactive tasks.
Built-in Streaming
Stream the output of each step in your graph in real-time to the UI, providing immediate feedback and progress for long-running tasks.
Technical capabilities
Persistence & Time Travel
Automatically save the full state of your graph at each step. Jump back to any previous state to debug or re-run from a specific point.
Modular Node Architecture
Build graphs from simple, composable Python functions. Each function (node) performs one task, making the system testable and maintainable.
Must watch videos
13:21
Tutorial13:21
LangGraph Explained for Beginners
🧪Try LangGraph Hands-On Labs for Free - https://kode.wiki/41WTH62
Learn how LangGraph transforms simple LangChain chatbots into powerful AI agents with StateGraph, loops, and conditional workflows!
In this comprehensive video, we'll show you exactly how to build a production-ready AI Research Assistant that searches the web, evaluates trustworthiness, extracts facts, and generates intelligent reports using nodes, edges, and shared state management.
Ready to build your own stateful AI workflows? Access our FREE interactive labs where you can experiment with real LangGraph implementations, create your own StateGraph architectures, and see agentic AI workflows in action!
🧪Try LangGraph Hands-On Labs for Free - https://kode.wiki/41WTH62
📚 What You'll Learn:
• LangChain vs LangGraph: When to use each framework
• How StateGraph works with nodes, edges, and persistent memory
• State management patterns for production AI agents
• LangGraph Workflow Demo
⏱️ Timestamps:
00:00 - Introduction to LangGraph
00:20 - LangChain vs LangGraph: What’s the real difference?
01:19 - Deep Research Assistant Use Case Example
01:53 - Traditional approach pain points
02:21 - Orchestration in LangGraph
03:08 - What is StateGraph?
03:39 - LangGraph Workflow
04:42 - LangGraph Adoption in Business Requirements
05:16 - Demo - Installing LangGraph Ecosystem
05:50 - Demo - Sequential Workflow vs Stateful Workflow
06:29 - Demo - Chunking Strategy and Embedding
07:37 - Demo - StateGraph
08:29 - Demo - Nodes, Edges and Routing
09:38 - Demo - Loops and Iterations
10:15 - Demo - Tool Integration
10:45 - Demo - Memory and State
11:27 - Demo - Build Your Own Research Assistant
12:52 - Conclusion & Free Lab Access
🔔 Subscribe for more AI tutorials!
#LangGraph #LangChain #StateGraph #AIagents #AgenticAI #AIworkflows #AIautomation #LLM #OpenAI #Python #AutonomousAgents #workflowautomation #kodekloud
9:54
Comparison9:54
LangChain vs LangGraph: A Tale of Two Frameworks
Want to learn more about AI agents and assistants? Register for Virtual Agents Day here → https://ibm.biz/BdaAVa
Download the AI model guide to learn more → https://ibm.biz/Bdab9w
Learn more about AI solutions → https://ibm.biz/Bdab9k
Get ready for a showdown between LangChain and LangGraph, two powerful frameworks for building applications with large language models (LLMs.) Master Inventor Martin Keen compares the two, taking a look at their unique features, use cases, and how they can help you create innovative, context-aware solutions.
AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/Bdab9t
#llm #largelanguagemodels #langchain
3:09:51
Tutorial3:09:51
LangGraph Complete Course for Beginners – Complex AI Agents with Python
Welcome to this video course on LangGraph, the powerful Python library for building advanced conversational AI workflows. In this course, Vaibhav Mehra will teach you how to design, implement, and manage complex dialogue systems using a graph-based approach. By the end, you'll be equipped to build robust, scalable conversational applications that leverage the full potential of large language models.
Code: https://github.com/iamvaibhavmehra/LangGraph-Course-freeCodeCamp
Vaibhav Mehra on LinkedIn: https://www.linkedin.com/in/vaibhav-mehra-main/
Vaibhav on Instagram: https://www.instagram.com/iamvaibhavmehra/
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp
⭐️ Contents ⭐️
0:00:00 Introduction
0:01:24 Type Annotations
0:08:35 Elements
0:18:39 Agent 1 Intro
0:21:08 Agent 1 Code
0:32:22 Agent 1 Exercise
0:33:07 Agent 2 Intro
0:34:02 Agent 2 Code
0:43:27 Agent 2 Exercise
0:44:29 Agent 3 Intro
0:45:18 Agent 3 Code
0:54:04 Agent 3 Exercise
0:55:36 Agent 4 Intro
0:56:37 Agent 4 Code
1:13:09 Agent 4 Exercise
1:14:14 Agent 5 Intro
1:15:45 Agent 5 Code
1:29:19 Agent 5 Exercise
1:31:01 AI Agent 1 Intro
1:32:49 AI Agent 1 Code
1:42:54 AI Agent 2 Intro
1:43:58 AI Agent 2 Code
2:03:20 AI Agent 3 Intro
2:05:00 AI Agent 3 Prerequisite
2:12:28 AI Agent 3 Code
2:28:46 AI Agent 4 Intro
2:30:59 AI Agent 4 Code
2:52:09 RAG Agent Intro
2:53:01 RAG Agent Code
3:06:15 RAG Agent Testing
3:09:04 Course Outro
🎉 Thanks to our Champion and Sponsor supporters:
👾 Drake Milly
👾 Ulises Moralez
👾 Goddard Tan
👾 David MG
👾 Matthew Springman
👾 Claudio
👾 Oscar R.
👾 jedi-or-sith
👾 Nattira Maneerat
👾 Justin Hual
--
Learn to code for free and get a developer job: https://www.freecodecamp.org
Read hundreds of articles on programming: https://freecodecamp.org/news
Who uses it
Real use cases, no hypeWhat's great
- Explicit graph-based orchestration for complex agent flows.
- Native state persistence and human-in-the-loop support.
- Ideal for long-running, multi-step agent processes.
Technical strengths
- Flexible runtime: use LangChain or custom executors.
- Durable execution with checkpointing for fault tolerance.
Where it falls short
- Steep learning curve for non-trivial graph topologies.
- Can be overkill for simple, single-prompt agent tasks.
- Debugging complex graphs requires good tooling.
Technical limitations
- Execution graph design is manual; no auto-optimization.
- Depends heavily on LangChain ecosystem components.
For technical folks
The deep-dive specs
Core Paradigm
Cyclic Graph Execution
State Management
In-memory & Persistent Backends
Primary Interface
Python (LangChain)
Deployment Model
Self-hosted or Managed Cloud
FAQ
Includes technical Q&AIt's a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows with explicit control flow.
Traffic Insights
Monthly Visits
—
October 2026
Growth Rate
0%
vs last month
Dominance
0%
in Coding
Top Country
—
Top Countries Breakdown
—
Growth Trend
Stable
Traffic has remained stable.
Honest pricing
No sneaky tiers, no “contact sales”
Here's who each plan is actually for — and where the hidden charges might hit.
View Full Plans & Pricing on LangGraph
Official pricing, checkout, and plan upgrades are handled directly on langchain.com.
Reviews
What the internet actually thinks
Data refreshed weekly. No paid placements.
Trustpilot
Not reviewed yet
G2
Not reviewed yet
Capterra
Not reviewed yet
Product Hunt
Not reviewed yet
COMMUNITY COMMENTS
Leave a Comment
Help others make informed decisions. Your honest feedback shapes the community's understanding of this tool.
Tips for commenting
- •Be specific about features you used
- •Share real use cases and results
- •Mention both pros and cons
- •Keep it honest and constructive
to leave a comment
Your comment is published immediately.
Filter by rating:
Sort by:
No comments yet
Be the first to share your experience with this tool.
Alternatives
Not quite the right fit?
Here's what else is out there.
—
Unknown
Semantic Kernel
Coding
Microsoft's open-source SDK for integrating LLMs into your C#, Python, and Java apps.
View
—
Freemium
Haystack
Coding
Open-source AI orchestration framework for building production-grade agents and RAG systems.
View
—
Paid
Text2SQL.ai
Coding
Convert Text to SQL with AI, in seconds
#SQL#AI
View 
—
Freemium
Codiga
Coding
Automated static code analysis for IDEs and CI/CD, now part of Datadog.
View
—
Freemium
DeepSeek
Coding
Explore the uncharted territories of AI with a powerful, free-to-use large language model family and API platform.
View