LangChain × LangGraph Tutorial Series

25 hands-on tutorials — from your first chain to production multi-agent systems

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What's inside

This series takes you from the very basics of LangChain all the way to cutting-edge patterns like Corrective RAG, Deep Agents, and the MCP/A2A protocols. Every tutorial is a self-contained Jupyter notebook — read the theory, run the cells, and experiment.

Phase	Tutorials	Topics
Foundations	01 – 06	LangChain core, prompts, chains, agents, RAG, memory
LangGraph	07 – 13	Graphs, complex flows, real-world apps, best practices
Advanced patterns	14 – 21	HITL, Supervisor, Swarm, Subgraphs, Parallelism, Memory, Time Travel, Functional API
Cutting-edge	22 – 25	Deep Agents, LangChain 1.0 utilities, Middleware, MCP & A2A

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Quick Start

1. Get a Groq API key

Groq provides free, blazing-fast LLM inference used throughout this series.

1. Go to console.groq.com
2. Sign up (or log in) — no credit card required for the free tier
3. Navigate to API Keys → Create API Key
4. Copy the key — you'll use it in step 4 below

2. Clone and set up

git clone https://github.com/DoomL/langchain-langgraph-tutorial.git
cd langchain-langgraph-tutorial

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate        # Linux / macOS
# .venv\Scripts\activate         # Windows

pip install -r requirements.txt

3. Configure your environment

Create a .env file in the repo root:

GROQ_API_KEY=your_groq_api_key_here

# Optional — enables LangSmith tracing
LANGCHAIN_TRACING_V2=false
LANGCHAIN_API_KEY=your_langsmith_key_here
LANGCHAIN_PROJECT=LangChainTutorial

4. Open any tutorial

jupyter notebook Tutorial01/Tutorial_1_Introduction_to_LangChain.ipynb

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Tutorial Map

Foundations (Tutorials 1–6)

#	Title	Key concepts
01	Introduction to LangChain	LCEL, chains, prompts, first app
02	Working with Language Models	ChatGroq, PromptTemplate, output parsers
03	Document Processing	Loaders, text splitting, FAISS, Corrective RAG (CRAG)
04	Agents in LangChain	create_react_agent, tool calling, multi-tool agents
05	Advanced Agent Techniques	Vector search tools, memory agents, research assistant
06	Memory Systems	MemorySaver, InMemoryStore, add_messages, summary memory

LangGraph (Tutorials 7–13)

#	Title	Key concepts
07	Introduction to LangGraph	StateGraph, nodes/edges, add_messages, ToolNode, ReAct loop
08	Complex Flows	Conditional edges, add_conditional_edges, error handling
09	Combining LangChain + LangGraph	LCEL inside graphs, async optimisation, CRAG pipeline
10	Real-world Applications	Content moderation, translation, customer support chatbot
11	Structured Data	Pydantic, .with_structured_output(), JSON queries
12	Advanced LangChain Techniques	Custom chains, RAG, sentiment analysis
13	Best Practices & Advanced Topics	Async LCEL, token tracking, FastAPI deployment, Prometheus

Advanced Patterns (Tutorials 14–21)

#	Title	Key concepts
14	Human-in-the-Loop	interrupt(), Command(resume=...), approval workflows
15	Supervisor Agent	create_supervisor(), worker delegation, multi-level hierarchy
16	Swarm Agents	create_swarm(), peer-to-peer handoffs, create_handoff_tool()
17	Subgraphs	Nested graphs, shared vs private state, Command.PARENT
18	Parallelization & Map-Reduce	Fan-out/fan-in, Send API, conditional fan-out, stable sorting
19	Long-term Memory	MemorySaver, SqliteSaver, InMemoryStore, trim_messages()
20	Time Travel	get_state_history(), forking, update_state()
21	Functional API	@entrypoint, @task, previous, parallel tasks

Cutting-edge (Tutorials 22–25)

#	Title	Key concepts
22	Deep Agents	create_deep_agent(), planning tool, sub-agents, filesystem
23	LangChain 1.0 Utilities	init_chat_model(), trim_messages(), filter_messages()
24	Agent Middleware	AgentMiddleware, before_model, after_model, wrap_model_call, HumanInTheLoopMiddleware
25	MCP & A2A Protocol	MultiServerMCPClient, Agent Cards, cross-framework interoperability

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Prerequisites

• Python 3.10+
• Basic Python knowledge — functions, classes, async/await
• Jupyter Notebooks — how to open and run cells
• A Groq API key — free at console.groq.com

No prior LangChain experience required — Tutorial 1 starts from scratch.

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Project structure

langchain-langgraph-tutorial/
├── README.md
├── requirements.txt
├── .env                    ← create this (see Quick Start above)
├── Tutorial01/
│   ├── README.md
│   └── Tutorial_1_Introduction_to_LangChain.ipynb
├── Tutorial02/ …
│   ⋮
└── Tutorial25/
    ├── README.md
    └── Tutorial_25_mcp_a2a_protocol.ipynb

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Models used

All tutorials use Groq for fast LLM inference:

Tutorial	Model
01 – 13, 15 – 21, 23 – 25	llama-3.1-8b-instant
04 (agents), 05 (research)	qwen/qwen3-32b
22 (deep agents)	qwen/qwen3-32b

Both models are available on the free Groq tier. Switch to any other Groq model by changing the model_name in the setup cell of any notebook.

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Useful resources

Official documentation

• LangChain docs · LangGraph docs · Groq docs

Community & inspiration

• langchain-ai/langchain — LangChain source
• langchain-ai/langgraph — LangGraph source
• NirDiamant/GenAI_Agents — agent patterns collection
• kyrolabs/awesome-langchain — curated resource list

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Built with ❤️ using LangChain, LangGraph, and Groq