Best of Langchain — October 2025

  1. 1
    Article
    Avatar of langchainLangChain·32w

    Not Another Workflow Builder

    LangChain's CEO explains why they haven't built a visual workflow builder despite frequent requests. The argument centers on workflow builders being squeezed from two directions: simple use cases are better served by no-code agents (prompt + tools), while complex scenarios require code-based workflows like LangGraph. As AI models improve, the middle ground for visual workflow builders shrinks—agents handle more complexity reliably, and code generation lowers the barrier for building sophisticated workflows. The focus should shift to making no-code agents more reliable and improving code generation for LLM-powered systems.

  2. 2
    Article
    Avatar of langchainLangChain·29w

    Introducing DeepAgents CLI

    DeepAgents CLI is a new command-line tool for building AI agents with persistent memory that can code, research, and execute tasks. The tool supports file operations, shell command execution with approval, web search, API requests, and cross-session memory retention. Agents store knowledge in local memory files and follow a memory-first protocol to recall information across sessions. Users can create multiple specialized agents for different projects, with the default using Anthropic's Claude Sonnet 4 model.

  3. 3
    Article
    Avatar of langchainLangChain·29w

    Introducing LangSmith’s No Code Agent Builder

    LangSmith introduces Agent Builder, a no-code platform that enables non-developers to create AI agents without writing code. Unlike visual workflow builders, it focuses on agent-based decision-making through four core components: prompts, tools (via MCP integration), triggers, and subagents. The platform simplifies prompt creation through guided conversations and includes built-in memory that learns from corrections over time. Built on the deepagents package and informed by LangChain and LangGraph development, it targets internal productivity use cases like email assistants, chat automation, and Salesforce integrations.

  4. 4
    Article
    Avatar of langchainLangChain·30w

    LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones

    LangChain and LangGraph have reached their 1.0 stable releases, marking a commitment to no breaking changes until 2.0. LangChain 1.0 introduces the create_agent abstraction with middleware support for customization, standardized content blocks across providers, and a streamlined package focused on core agent functionality. LangGraph 1.0 provides production-ready features including durable state, built-in persistence, and human-in-the-loop patterns for complex workflows. Both frameworks are backward compatible, with LangChain built on top of LangGraph's runtime, allowing developers to start with high-level abstractions and drop down to lower-level control when needed.

  5. 5
    Article
    Avatar of bytebytegoByteByteGo·29w

    The Evolution of LinkedIn’s Generative AI Tech Stack

    LinkedIn evolved its GenAI infrastructure from fragmented experiments to a unified platform supporting multi-agent systems. The company shifted from Java to Python for both offline and online development, adopted LangChain as its primary framework, and built centralized systems for prompt management, skill registries, and memory. The platform leverages existing messaging infrastructure for agent orchestration, implements strict privacy controls, and uses OpenTelemetry for production observability. Key architectural decisions include keeping abstractions thin for flexibility, using human-in-the-loop controls for critical actions, and building reusable components that enable teams to ship AI features faster while maintaining consistency and trust.

  6. 6
    Video
    Avatar of TechWithTimTech With Tim·33w

    Python Web Scraping: A Million Dollar Project Idea - FULL Build/Tutorial

    A comprehensive tutorial demonstrating how to build an Amazon competitor analysis tool using Python. The project combines web scraping via OxyLabs API, Streamlit for the frontend, LangChain for AI-powered insights, and TinyDB for local data storage. The application scrapes Amazon product details, identifies competing products, and generates AI-driven competitive analysis reports. The tutorial covers setting up dependencies, implementing the scraping client, building database operations, creating a UI with product cards, and normalizing scraped data into a structured format.

  7. 7
    Article
    Avatar of langchainLangChain·30w

    Reflections on Three Years of Building LangChain

    Harrison Chase reflects on LangChain's evolution from an 800-line Python side project in 2022 to a $1.25 billion company. The journey includes launching LangSmith for observability and evaluations, creating LangGraph to address controllability concerns, and releasing LangChain 1.0 with breaking changes focused on production-ready agent development. The company raised $125 million to expand its agent engineering platform, which now powers applications at companies like Rippling, Vanta, and Cloudflare. LangChain maintains framework and model neutrality while addressing early criticism about customization, documentation, and package bloat.

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