Series
Agentic AI: LangChain and LangGraph
A structured path through AI engineering, retrieval, evaluation, and production guardrails.
16
Articles
5h 8m
Estimated reading
Intermediate to Advanced
Knowledge level
1,072
Readers
About this series
A structured path through AI engineering, retrieval, evaluation, and production guardrails.
Series Progress
0% Complete0 of 16 articles viewed
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Who is this for?
Software engineers and developers learning this topic.
Knowledge Level
Intermediate to Advanced
Last Updated
May 25, 2026
Created by
Abstract Algorithms
All Articles
Article 1
Guide to Using RAG with LangChain and ChromaDB/FAISS
TLDR: RAG (Retrieval-Augmented Generation) gives an LLM access to your private documents at query time. You chunk and embed documents into a vector store (ChromaDB or FAISS), retrieve the relevant chu
14 min read
Article 2
How to Develop Apps Using LangChain and LLMs
TLDR: LangChain is a framework that simplifies building LLM applications. It provides abstractions for Chains (linking steps), Memory (remembering chat history), and Agents (using tools). It turns raw
17 min read
Article 3
Skills vs LangChain, LangGraph, MCP, and Tools: A Practical Architecture Guide
TLDR: These are not competing ideas. They are layers. Tools do one action. MCP standardizes access to actions and resources. LangChain and LangGraph orchestrate calls. Skills package business outcomes
15 min read
Article 4
LangGraph 101: Building Your First Stateful Agent
TLDR: LangGraph adds state, branching, and loops to LLM chains ā build stateful agents with graphs, nodes, and typed state. š The Stateless Chain Problem: Why Your Agent Forgets Everything You buil
18 min read
Article 5
Deploying LangGraph Agents: LangServe, Docker, LangGraph Platform, and Production Observability
TLDR: Swap InMemorySaver ā PostgresSaver, add LangServe + Docker, trace with LangSmith. š The Demo-to-Production Gap: Why Notebook Agents Fail at Scale Your LangGraph agent works perfectly in the d
26 min read
Article 6
Human-in-the-Loop Workflows with LangGraph: Interrupts, Approvals, and Async Execution
TLDR: Pause LangGraph agents mid-run with interrupt(), get human approval, resume with Command. š The Autonomous Agent Risk: When Acting Without Permission Goes Wrong Your autonomous coding agent r
18 min read
Article 7
LangGraph Memory and State Persistence: Checkpointers, Threads, and Cross-Session Memory
TLDR: Checkpointers + thread IDs give LangGraph agents persistent memory across turns and sessions. š The Amnesia Problem: Why Stateless Agents Frustrate Users Your customer support agent is on its
18 min read
Article 8
Multi-Agent Systems in LangGraph: Supervisor Pattern, Handoffs, and Agent Networks
TLDR: Split work across specialist agents ā supervisor routing beats one overloaded generalist every time. š The Context Ceiling: Why One Agent Can't Do Everything Your research agent is writing a
27 min read
Article 9
The ReAct Agent Pattern in LangGraph: Think, Act, Observe, Repeat
TLDR: ReAct = Think + Act + Observe, looped as a LangGraph graph ā prebuilt or custom. š The Single-Shot Failure: Why One LLM Call Isn't Enough for Complex Tasks Your agent is supposed to write a f
22 min read
Article 10
Streaming Agent Responses in LangGraph: Tokens, Events, and Real-Time UI Integration
TLDR: Stream agents token by token with astream_events; wire to FastAPI SSE for zero-spinner UX. š The 25-Second Spinner: Why Streaming Is a UX Requirement, Not a Nice-to-Have Your agent takes 25 s
20 min read
Article 11
LangGraph Tool Calling: ToolNode, Parallel Tools, and Custom Tools
TLDR: Wire @tool, ToolNode, and bind_tools into LangGraph for agents that call APIs at runtime. š The Stale Knowledge Problem: Why LLMs Need Runtime Tools Your agent confidently tells you the curren
18 min read
Article 12
From LangChain to LangGraph: When Agents Need State Machines
TLDR: LangChain's AgentExecutor is a solid starting point ā but it has five hard limits (no branching, no pause/resume, no parallelism, no human-in-the-loop, no crash recovery). LangGraph replaces the
18 min read
Article 13
LangChain 101: Chains, Prompts, and LLM Integration
TLDR: LangChain's LCEL pipe operator (|) wires prompts, models, and output parsers into composable chains ā swap OpenAI for Anthropic or Ollama by changing one line without touching the rest of your c
19 min read
Article 14
LangChain Memory: Conversation History and Summarization
TLDR: LLMs are stateless ā every API call starts fresh. LangChain memory classes (Buffer, Window, Summary, SummaryBuffer) explicitly inject history into each call, and RunnableWithMessageHistory is th
18 min read
Article 15
LangChain RAG: Retrieval-Augmented Generation in Practice
ā” TLDR: RAG in 30 Seconds TLDR: RAG (Retrieval-Augmented Generation) fixes the LLM knowledge-cutoff problem by fetching relevant documents at query time and injecting them as context. With LangChain
19 min read
Article 16
LangChain Tools and Agents: The Classic Agent Loop
šÆ Quick TLDR: The Classic Agent Loop TLDR: LangChain's @tool decorator plus AgentExecutor give you a working tool-calling agent in about 30 lines of Python. The ReAct loop ā Thought ā Action ā Obser
21 min read
Agentic AI: LangChain and LangGraph Learning Roadmap
You want to build AI agents, but there are too many tutorials and no clear learning path. You discover LangGraph through a multi-agent post, get confused by StateGraph syntax, backtrack to find LangChain basics, and end up reading about RAG when you just wanted tool calling. Sound familiar?
Most AI agent learning failures aren't comprehension problems ā they're sequencing problems. This series has dependencies: LCEL chains make LangGraph's node model intuitive, the classic agent loop explains what LangGraph's ReAct pattern improves, and the bridge post explains why stateful workflows need graphs at all. Without the right order, concepts accumulate chaotically and nothing sticks.
TLDR: This roadmap gives you three clear learning paths based on your experience: complete beginner (start with LangChain foundations), knows LangChain (jump to LangGraph), or production-focused (advanced deployment track).
What You'll Learn
Understand Agentic AI: LangChain and LangGraph through real published examples
Follow a sequence of 16 articles from fundamentals to deeper topics
Connect related concepts: AI, chromadb, langchain
Practice explaining trade-offs and implementation decisions
Prerequisites
FAQs
How should I read this series?
Start from the first article if you are new, or use the article list to jump into the most relevant topic.
Is progress automatic?
Progress is based on articles opened from this browser using the local learning history.