Module 08 LangChain and LangGraph

🦜 Module 08 · LangChain and LangGraph

The other ecosystem — and an honest side-by-side with Strands.

Module 08 of 16 · track Build · Estimated time: 7–8 hours · Prerequisites: Module 05. Module 06 recommended.

LangChain is the framework you will meet in most existing codebases. This module builds it up from first principles — why it exists, what it actually does — then puts it directly against Strands so you can choose with evidence rather than allegiance.

Open the module on GitHub ↗: the slides, notebooks, exercises and solutions are there. This page is the reading order, with the tutorial and the workbench alongside.

Read first, on the site

The lesson that frames this module is How AI-DLC, BMAD, Spec Kit and Scrum Fit One Lifecycle (7 min): why the framework changes and the lifecycle does not.

What you will be able to do

• Explain what LangChain abstracts and why chains exist at all
• Build chains, tools, memory and structured output in LangChain
• Model a stateful workflow as a LangGraph graph
• Compare LangChain and Strands on the same task and justify a choice

Concepts in this module

Portable GenAI concepts	AWS-specific surface
Chains and composition	langchain-aws
Runnables	Bedrock as a LangChain provider
Structured output	LangGraph on Bedrock
Graph state machines
Middleware

Portable concepts transfer to any stack. The AWS column is where this module touches the cloud — see docs/concepts/portability-matrix.md.

Run it in this order

#		Step	What it is
1	📖	slides/LangChain-from-Zero.pptx	LangChain from zero
2	💻	notebooks/01_why_langchain_context_and_history.ipynb	Why LangChain — context and history
3	💻	notebooks/02_essentials_how_langchain_works.ipynb	How LangChain actually works
4	✏️	exercises/exercise_1_foundations.md	Exercise 1 — foundations
5	💻	notebooks/03_basic_langchain.ipynb	Basic LangChain
6	✏️	exercises/exercise_2_tools.md	Exercise 2 — tools
7	💻	notebooks/04_intermediate_langchain.ipynb	Intermediate
8	✏️	exercises/exercise_3_memory_and_structured_output.md	Exercise 3 — memory and structured output
9	💻	notebooks/05_advanced_langchain.ipynb	Advanced
10	✏️	exercises/exercise_4_middleware_and_graphs.md	Exercise 4 — middleware and graphs
11	💻	notebooks/PierPoint_LangGraph_Chains_to_Swarms.ipynb	LangGraph: chains to swarms
12	💻	notebooks/06_langchain_vs_strands_side_by_side.ipynb	LangChain vs Strands, side by side
13	✏️	exercises/exercise_5_multiagent_capstone.md	Exercise 5 — multi-agent capstone

📖 read   💻 run   ✏️ practise   📊 workbook   🔖 reference

Worked solutions live in solutions/. Attempt every exercise closed-book first — the solutions are written to be read after you have a wrong answer to compare against.

Common mistakes

• Cargo-culting chains. If a function would do, use a function.
• LangGraph state that grows without bound — checkpointing is not free.

When it breaks

Every error anyone has hit in this course is in the Error Index, by the exact string. Model access and region problems are in Model and Region Notes. If it is a question rather than an error, check the answered ones first, then ask in Q&A.

Field guide for this module

Frameworks

• Handoff Multiplier — Cost the topology, whichever framework
• Context Budget Ledger — Checkpointed state is not free

Quick reference

• LangChain and LangGraph — Runnables, state machines, checkpointing
• Strands — The other side of the comparison

Recipes and procedures

• Migrate LangChain ↔ Strands — And when not to

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