LangChain articles
42 articles about LangChain, newest first.
- Scheduling Managed Deep Agents with LangChain and LangSmith — Learn how to use cron syntax to automate recurring AI tasks and deliver proactive reports to Slack using LangChain's Managed Deep Agents and LangSmith.
- Building Agent Harnesses with Jev and TypeSafe AI — Learn how Jev, a System 1 decision model from TypeSafe AI, replaces heavy LLM calls for classification and routing in LangChain agent harnesses.
- Understanding AI Agent Memory: Short-Term vs Long-Term — Learn how AI agents use context windows for short-term memory and external storage for long-term memory to maintain continuity across multiple runs.
- Controlling AI Agents with LangChain Managed Deep Agents Middleware — Learn how to use LangChain's middleware for Managed Deep Agents to implement PII redaction, custom logging, and policy enforcement for secure AI agents.
- Building Complex AI Systems with LangChain Deep Agents — Learn how LangChain's Deep Agents harness provides a secure, portable framework for building complex AI agents with sandboxing and human-in-the-loop gating.
- Building AI Agents with LangChain: Deep Agents and Agent Builder — Learn how to build AI agents using LangChain's Deep Agents for developers and Agent Builder for no-code users, plus how to monitor them with LangSmith.
- Deploying LangChain Managed Deep Agents to Slack — Learn how to move AI agents from LangSmith Studio to production using Slack as the interaction layer for team collaboration and live monitoring.
- What is an Agent Harness? Moving Beyond the LLM Brain — Learn why a model alone isn't an agent and how a 'harness' provides the memory, tools, and structure needed for complex AI autonomy.
- Automate Your Quality Control: Using LLM-as-a-Judge with LangSmith CLI — Learn how to use the LangSmith CLI to deploy an LLM judge that automatically flags user frustration in production traces at scale.
- Stop Over-Engineering Your AI: Why One Agent is Often Better Than Many — Sierra's Head of Product explains why multi-agent systems can actually destroy value by killing critical context.
- Stop Breaking Your AI Agents: A Guide to Evals for Managed Deep Agents — Learn how to use Harbor and LangSmith to catch regressions and systematically improve your AI agents using Managed Deep Agents evals.
- dcode Essentials: Build Flexible AI Agents with LangChain Academy — Learn how LangChain's new dcode Essentials course teaches you to create open-source terminal agents that switch models, track costs, and stay in control.
- The Whack‑a‑Mole Problem in AI Teams: How to Break the Cycle — Discover why AI teams face a 'Whack‑a‑Mole' bug cycle and how LangChain’s Walker Ward turns user feedback into repeat‑free eval cases.
- Huawei’s Adaptive Harness: The New Boss for LLMs — Explore Huawei’s adaptive harness architecture and how it rivals Claude Code for long‑horizon AI agents.
- How to Evaluate AI Agents: A Practical Guide for Developers — Learn practical ways to evaluate AI agents, use LLMs as judges, build real‑world test sets, and keep evaluation continuous.
- Accelerate Agent Improvement with LangSmith Engine – AI Development Made Easy — LangSmith Engine automates bug detection, fix suggestions, automated testing, and regression monitoring for AI agents, boosting production reliability.
- Clay’s 300M‑Run Agent Stack: Eval‑First Scaling with LangSmith — Explore Clay’s 300M‑run agent pipeline, its eval philosophy, data‑lake architecture, and how LangSmith enables safe scaling.
- How Morningstar Mastered AI Agent Debugging with LangSmith — Discover how Morningstar’s AI team eliminated blind debugging by adopting LangSmith for tracing, prompt collaboration, and metrics.
- Stop Obsessing Over the Model: Why the 'AI Harness' is the Real Secret Sauce — Discover why the workflow and orchestration surrounding an LLM—the harness—drive performance and cost more than the model version itself.
- DeepWiki Explained: How Cognition Builds AI‑Ready Codebase Wikis at Scale — Discover how DeepWiki auto‑generates codebase documentation, evolves from v1 to agentic v2, and uses context‑engineering principles to power AI agents.
- Master LangChain with Coding Tutors: Learn AI Development Hands‑On — Discover how LangChain Academy’s coding tutors let you learn AI agent development through interactive lessons, quizzes, and customizable teaching styles.
- LangSmith Preview Builds: Test Your LLM Agents Before Production — Discover how LangSmith preview builds let you spin up temporary environments for each pull request to test LangChain agents safely before merging.
- Managed Deep Agents: Build Production AI Agents Without Server Hassle — Learn how Managed Deep Agents let you build production AI agents by writing instructions and picking a model, while LangSmith handles the runtime—no servers needed.
- Managed Deep Agents: How Instructions Power Autonomous AI Workflows — Learn how LangChain's managed deep agents sync markdown instructions to a Context Hub, letting you edit behavior live in LangSmith and auto‑sync changes to the agent.
- Managed Deep Agents: How LangChain Lets You Package Skills Like Lego Blocks — LangChain’s managed deep‑agent skills let you bundle task‑specific procedures into markdown directories that the agent auto‑loads, turning repetitive work into reusable Lego‑style components.
- Managed Deep Agents: Adding Custom Python/TypeScript Tools — Learn how to define and register custom tools for LangChain's Managed Deep Agents in just a few steps.
- Toyota's AI Factory: How LangChain and LangSmith Cut Downtime and Save Six Figures — Toyota uses LangChain and LangSmith to let technicians query machines, slash downtime costs, and achieve six-figure savings per line.
- Control LLM Costs with LangSmith LLM Gateway – Simple Cost Management for Multi‑Model Workflows — Learn how LangSmith LLM Gateway lets you set and enforce cost limits across multiple models and agents, avoiding surprise AI spend spikes.
- How Toyota’s Deep Agents Supercharge R&D with LangChain — Toyota’s R&D GPT uses Deep Agents to query internal data with one command, turning scattered research into instant answers, and LangSmith’s Polly and Insights give visibility into how agents behave.
- From Brittle Graphs to Deep Agents: How Harmonic 4x'd User Retention — Learn how Harmonic replaced complex query graphs with a flexible agentic loop to drastically improve AI reliability and user retention.