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LangchainMental ModelTradeoffsFailure ModesInterview ReasoningLangChain Tools and Agents
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LangChain Tools and Agents gives you the cleanest entry point before branching into constraints, failures, and related systems.
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1LangChain Tools and Agents: The Classic Agent Loop🎯 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 — Thought → Action → Obser21 min2LangChain RAG: Retrieval-Augmented Generation in Practice⚡ 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 min3LangChain Memory: Conversation History and SummarizationTLDR: LLMs are stateless — every API call starts fresh. LangChain memory classes (Buffer, Window, Summary, SummaryBuffer) explicitly inject history into each call, and RunnableWithMessageHistory is th18 min4LangChain 101: Chains, Prompts, and LLM IntegrationTLDR: LangChain's LCEL pipe operator (|) wires prompts, models, and output parsers into composable chains — swap OpenAI for Anthropic or Ollama by changing one line without touching the rest of your c19 min5From LangChain to LangGraph: When Agents Need State MachinesTLDR: LangChain's AgentExecutor is a solid starting point — but it has five hard limits (no branching, no pause/resume, no parallelism, no human-in-the-loop, no crash recovery). LangGraph replaces the18 min6Skills vs LangChain, LangGraph, MCP, and Tools: A Practical Architecture GuideTLDR: 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 outcomes15 min7Mastering Prompt Templates: System, User, and Assistant Roles with LangChainTLDR: A production prompt is not a string — it is a structured message list with system, user, and optional assistant roles. LangChain's ChatPromptTemplate turns this structure into a reusable, testab14 min8How to Develop Apps Using LangChain and LLMsTLDR: 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 raw17 min9Guide to Using RAG with LangChain and ChromaDB/FAISSTLDR: 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 chu14 min10Mastering Prompt Templates: System, User, and Assistant Roles with LangChainTLDR: Prompt templates are the contract between your application and the LLM. Role-based messages (System / User / Assistant) provide structure. LangChain's ChatPromptTemplate and MessagesPlaceholder 13 min11LLM Observability: Tracing, Logging, and Debugging Production AI SystemsTLDR: 🔍 LLM observability is radically different from traditional APM—non-deterministic outputs, variable token costs, and multi-step reasoning chains require specialized tracing. LangSmith provides 19 min12Context Window Management: Strategies for Long Documents and Extended ConversationsTLDR: 🧠 Context windows are LLM memory limits. When conversations grow past 4K-128K tokens, you need strategies: sliding windows (cheap, lossy), summarization (balanced), RAG (selective), map-reduce 20 min
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