#LangChain
174 related articles
Bilibili's 748-Episode Agent Development Course: A Complete Breakdown from Zero to Enterprise-Level Practice
An in-depth look at Bilibili's 748-episode AI Agent beginner course: three-part structure covering foundational concepts, LangChain and AutoGen practice, and enterprise projects, assessing how it bridges the gap between theory and hands-on work.
Text-to-SQL Agent in Practice: Building Read-Only Safe Queries with LangChain and Gemini Free Tier
Explore a Text-to-SQL agent built with LangChain and Gemini free tier, featuring three custom tools, database-level read-only protection, and defense against prompt injection attacks.
LangChain in Practice: A Complete Path to Building a Production-Ready Agent from Scratch
Based on a Bilibili hands-on video, this article systematically covers building a production-ready Agent from scratch with LangChain: model integration, system prompts, tools and internet search, short-term and long-term memory, RAG knowledge base, FastAPI service exposure, and the key pitfall of memory persistence in multi-worker deployments.
LangChain Agent in Practice: A Zero-Base Introduction to AI Agent Development
Based on the project practice chapter of Bilibili's 348-episode AI large model course, this article breaks down the LangChain Agent development learning path: virtual project demo, product and technical architecture design, resource preparation, and environment setup — a reference for zero-base learners starting AI Agent development.
RAG in Production: Should You Really Use Frameworks Like LangChain?
A developer shares production-grade RAG engine experience: when to use LangChain/LlamaIndex frameworks and when to write native code. Document parsing, retrieval fusion, and observability each have their best choices.
LangChain Tutorial for Beginners: A Complete Guide from Principles to Practice in AI Application Development
What is LangChain? This article starts from principles: unified model interfaces, private data integration, context and memory management, agents and tool calling, plus comparisons with Ollama and Coze to help you quickly build an overall understanding of the AI application development framework.
LangChainJS Quickly Adapts to Gemini 4 Argon: Current Status and Future Plans
LangChainJS is now compatible with Gemini 4 Argon without requiring a new library. This article covers the current capabilities, the minimal thinking level, the 64k output limit, and follow-up support plans for Long Decode and the Interactions API.
AI Agent for Beginners: Building a Commercial-Grade Coding Agent from Scratch
A hands-on AI Agent tutorial covering the perception-decision-action loop, LangChain, LangGraph, and MCP, with a Vue project case study showing full commercial-grade coding agent development.
Agent Beginner's Roadmap: Don't Be Intimidated by New Buzzwords—Build an Enterprise-Grade Agent in Three Steps
How to get started with AI agents? A Bilibili tutorial proposes a three-step roadmap: build a knowledge map to cut learning in half, let real projects drive you, and choose high-ROI paths. Core formula: Agent = LLM + Memory + Tools.
Building Multimodal AI Customer Support with LangChain: Images, Text, and Voice in One Go
A complete tutorial on building a multimodal AI customer support Agent with LangChain: troubleshoot app errors using image, text, and voice, covering Base64 encoding, thread_id conversation memory, model media support boundaries, and privacy considerations.
The Truth About Lagging LangSmith Evals: How Do You Reconcile After CI Passes?
How should LangSmith evals handle delayed ground truth? This article breaks down the two-stage async reconciliation plan after CI passes, the historical attribution challenge, and the trade-offs of three practical paths: updating original results, creating separate reconciliation runs, and maintaining an external validation ledger.
LangChain and LangGraph in Practice: End-to-End Implementation of a Production-Grade RAG System
A complete guide to building a production-grade RAG system with LangChain and LangGraph: semantic chunking, hybrid retrieval, RRF fusion, Cross-Encoder reranking, guardrails, orchestration, FastAPI deployment, and monitoring.
LangChain or LangGraph? Let create_age…
LangChain or LangGraph? Let create_agent's Return Value Decide
In LangChain 1.4.3, create_agent actually returns a LangGraph graph. Five runnable scripts compare create_agent, StateGraph, and agent-as-node, with selection rules and common error fixes.
Getting Started with DeepAgents: Sub-Agents and Its Place in the LangChain Ecosystem
DeepAgents is a LangChain team framework for building stable, long-running complex agents. Learn how it relates to LangChain and LangGraph, and what sub-agents are.
LangChain-Fireworks 1.7.1 Released: Fix for Streaming Reasoning Preservation
LangChain-Fireworks 1.7.1 fixes reasoning content loss in streaming and tool call loops, refreshes model profile data, and upgrades langgraph-sdk to 0.4.6.
LangChain Core 1.6.8 Released: Fixes IPv6 SSRF Security Vulnerability
LangChain Core 1.6.8 is released with a key security fix hardening SSRF checks for scoped and IPv6-compatible addresses. Learn why this matters and how to upgrade.
LangChain 1.4.4 Released: SummarizationMiddleware Fix and Dependency Upgrades
LangChain 1.4.4 fixes SummarizationMiddleware context overflow retry logic and upgrades fsspec, urllib3, pyjwt, langgraph-sdk, and more. A breakdown of what's changed.
LangChain Core 1.6.9 Released: with_retry Now Supports Callable Arguments
LangChain Core 1.6.9 is out. The key improvement: with_retry now accepts callable arguments, enabling dynamic and programmable retry strategies for LLM apps.
AI Agent Development for Absolute Beginners: A 3-Step Learning Path to Get Started Fast
Intimidated by Python, LLMs, and LangChain? This beginner's guide offers a 3-step path to AI Agent development: basic Python in days, hands-on LLM practice, and your first LangChain Agent.
Getting Started with LangChain: Build Your First AI Agent in Just a Few Lines with create_agent()
LangChain tutorial: compare bare OpenAI SDK vs. create_agent() with a weather example. Learn to build your first AI Agent and master hidden agent loop mechanics.