#LangChain architecture
1194 related articles
·2 min
LangSmith Gateway Explained: Core Capabilities and Architecture of LangChain's Enterprise AI Model Gateway
Deep dive into LangSmith Gateway's core features including cost control, rate limiting, PII redaction, coding agent integration, and open-source model access for enterprise AI infrastructure.
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·3 min
Complete Guide to LangChain 1.3: From Models and Agents to DeepAgent Architecture
A complete guide to LangChain 1.3: LLM invocation, Agent tool calling, Harness architecture, LangGraph, RAG, and DeepAgent — build a clear, modern Agent development knowledge base.
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·3 min
Langchain.js Agent Development: A Guide to Deconstructing the OpenClaw Engine Architecture
An in-depth analysis of AI agent development based on Langchain.js—comparing workflow agents and Agent Loops, deconstructing the TypeScript implementation path of an OpenClaw-like engine, covering structured output, MCP, and LangGraph.
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·3 min
Deep Dive into LangChain's Three Core Modules: A Complete Guide to Agent Architecture Thinking
Deep dive into LangChain's three modules: Chain pipelines, LangGraph state graphs, and autonomous planning Agents. From RAG to ReAct — build your AI architecture thinking.
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·6 min
LangChain 1.3 in Practice: A Complete Guide to DeepAgent Architecture and the Harness Philosophy
An in-depth look at LangChain 1.3's core modules and DeepAgent architecture—covering the Harness philosophy, LangGraph internals, HITL, memory management, and guardrails to master production-grade AI Agent development.
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·3 min
Complete Guide to LangChain 0.3: Module Architecture & Core Development in Practice
A deep dive into LangChain 0.3's module architecture, message abstraction, prompt templates, output parsers, LCEL chains, LangSmith tracing, and LangGraph for mastering LLM application development.
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·3 min
Deep Dive into LangChain's Social Media Agent Open-Source Project: Architecture, Features, and Practical Value
In-depth analysis of LangChain's open-source social-media-agent: content sourcing, AI curation, scheduled publishing, Human-in-the-Loop design, and LangGraph architecture.
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·3 min
NestJS + LangChain: A Practical Guide for Frontend Engineers Transitioning to AI Full-Stack Architecture
How can frontend engineers transition to AI full-stack? This guide covers NestJS + LangChain, TypeScript fundamentals, AI Agent development, local model deployment, and cross-language architecture skills.
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Tutorials·2 min
LangChain 1.0 Complete Guide: Architecture, Core Modules, and Learning Path
Deep dive into LangChain 1.0's three-layer architecture (LangChain, LangGraph, Deep Agents), core components like Models, Tools, and Memory, plus a complete learning path from semantic search to multi-agent collaboration.
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Tutorials·2 min
OpenClaw Agent Architecture in Practice: Skills System Deployment & LangChain Reproduction Guide
Deep dive into OpenClaw's industrial-grade Agent architecture with its three-layer design, pluggable Skills system, and memory management. Includes a step-by-step LangChain reproduction guide with an enterprise HR assistant example.
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Tutorials·2 min
LangChain Agent Executor Architecture: Building a ReAct Agent from Scratch
Deep dive into LangChain Agent Executor internals, covering the ReAct reasoning-action-observation loop, tool choice strategies, and building a custom Agent Executor from scratch.
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Deep Dives·2 min
LangChain Core Architecture Explained: A Practical Guide to Its Five Key Modules
Deep dive into LangChain's five core modules: Model I/O, Data Connection & Retrieval, Chains, Agents, and Memory. Learn RAG workflows, Agent decision-making, and practical AI app development.
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Tutorials·4 min
Agentic RAG in Practice: Architecture Deep Dive and LangChain Implementation Guide
Compare traditional RAG vs Agentic RAG architectures, explore planning, tool use, and multi-step iteration capabilities, with full LangChain/LangGraph ReAct Agent code and ChatBoss project examples.
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Tutorials·3 min
Agentic RAG in Practice: Architecture Deep Dive and LangChain Implementation
Deep dive into Agentic RAG vs traditional RAG, covering planning, tool calling, and multi-step iteration capabilities with complete LangChain and LangGraph code implementation.
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·2 min
Harvey Labs: An Open-Source Benchmark Framework for Legal AI Agent Evaluation
Harvey Labs is Harvey's open-source benchmark framework for legal AI agent evaluation, assessing AI performance in contract review, case research, legal reasoning, and other real legal workflows.
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·3 min
ScrapeOps Proxy Tester: Choose the Right Proxy for Your Scraper with Real-World Data
ScrapeOps Proxy Tester benchmarks 20+ proxy configurations against your specific target URL, measuring success rate, latency, and cost to help scrapers and AI Agents choose the optimal proxy.
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·2 min
Omniwork Tops ProductHunt: A Proactive Desktop AI Agent Reshaping Creative Workflows
Omniwork tops ProductHunt as a proactive desktop AI agent OS, using Research, Create, Monitor, and Automate agents to reshape the full creative workflow for marketers and creators.
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·3 min
Agent DevTools: A Local Debugger Designed Specifically for AI Agents
Agent DevTools is an open-source local debugger for AI Agents that lets you inspect prompts, memory, retrieval, and tool calls, with good vs. bad run comparison to quickly find root causes.
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·2 min
DeepSeek V4 Flash Tool Calling Doom Loop: Analysis and Solutions
DeepSeek V4 Flash on Ollama Cloud frequently enters reasoning doom loops where the model fails to properly call tools. This article analyzes causes and provides practical detection and mitigation solutions.
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·3 min
Building an AI Agent Memory Layer with Go's Standard Library: A Zero-Dependency Minimalist Approach
A deep dive into building an AI agent memory layer using only Go's standard library, covering vector similarity, memory storage/retrieval, and concurrency safety in a zero-dependency approach.
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