LangChain v1.x Core Series
A 10-part developer's guide to mastering LangChain v1.x — from agent fundamentals and LangGraph stateful pipelines to RAG, MCP integrations, production guardrails, multi-agent systems, structured output, context engineering, event streaming, and deep long-horizon agents.
Learning Path & Timeline
- Part 1•2026-06-11•Editorial
From Generative AI to Autonomous Agents: LangChain v1.x Core, Part 1
Learn how to transition from simple LLM wrappers to autonomous AI agents in LangChain v1.x. Build a fully runnable ReAct tool-calling agent with Gemini, understand the reasoning loop, and see why agents outperform raw LLM calls for real-world tasks.
- Part 2•2026-06-11•Editorial
Building Stateful Workflows & Graph Pipelines with LangGraph — Part 2
LangGraph tutorial 2025: design stateful AI agent workflows as explicit directed graphs. Learn typed state, conditional routing, cyclic execution, and MemorySaver checkpointers with full Python code examples.
- Part 3•2026-06-11•Editorial
Enterprise Knowledge Retrieval: Traditional RAG vs. Vectorless RAG — Part 3
Compare traditional vector RAG with vectorless RAG in LangChain. Learn ChromaDB embeddings, text chunking strategies, and structural index navigation — with full Python code for both approaches and when to choose each.
- Part 4•2026-06-11•Editorial
Deep Research Agents & Decentralized Integrations with MCP — Part 4
Build a deep research agent in LangChain that autonomously queries multiple sources, then connect it to any tool microservice using Model Context Protocol (MCP). Includes full FastMCP server setup and LangChain adapter integration.
- Part 5•2026-06-11•Editorial
Production Engineering: Guardrails, Gateways & AI Evaluation — Part 5
Ship LangChain agents to production with confidence. Learn to add safety guardrails, configure LLM failover routing with LiteLLM, and run automated quality evaluations with LangSmith — with full Python code and real evaluation datasets.
- Part 6•2026-06-12•Editorial
Multi-Agent Systems: Supervisor Routing & Shared State Channels — Part 6
Build collaborative multi-agent systems in LangGraph with supervisor routing and shared TypedDict state channels. Learn when to use multi-agent architecture vs. a single agent, how to prevent state conflicts, and wire up specialist agents for real business workflows.
- Part 7•2026-06-13•Editorial
Prompt Templates, Structured Output & Output Parsers — Part 7
Master LangChain's prompt engineering stack: build reusable ChatPromptTemplates, extract structured JSON with Pydantic via with_structured_output(), and add auto-retry output parsers that self-correct on validation failures.
- Part 8•2026-06-14•Editorial
Short-Term Memory, Message Trimming & Context Engineering — Part 8
Master LangChain's context engineering toolkit: use trim_messages() to enforce token budgets, implement conversation summarization to fight context rot, and allocate context window space across system prompts, history, and content.
- Part 9•2026-06-15•Editorial
Event Streaming & Real-Time Agent UX — Part 9
Build real-time streaming agent interfaces in LangChain using astream_events(). Learn to differentiate token streams, tool call events, and node transitions — then render them in a live dashboard for multi-agent workflows.
- Part 10•2026-06-16•Editorial
Deep Agents: Planning, Context Files & Long-Horizon Tasks — Part 10
Build production-ready long-horizon planning agents in LangGraph that write structured task plans to files, persist state across sessions, spawn subagent children, and resume exactly where they left off after crashes.