LangChain Blog

See how LangGraph orchestrates Jev, TypeSafe AI's decision model, to build faster, cheaper production agents.

LangChain announced new updates to LangSmith. Updates include Engine v2 with red teaming and automatic testing, a new version of Managed Deep Agents, trajectories and more.

LangSmith Custom Apps lets you build the interface you want with your LangSmith data, publish it into your workspace, and skip the hosting, auth, and permissions work. Learn more.

LangChain introduces LangSmith Fine-Tuning and SmithTune, a CLI built for post-training models. Train specialized models without building data pipelines by hand.

Trajectories in LangSmith provide a conversational view of an agent session. Trajectories make trace data easy to navigate and speed up debugging for long-running agents.

LangSmith Engine now includes Red Teaming to proactively detect agent issues and automated agent testing. Learn more about the Engine v2 release.

Managed Deep Agents is the simplest way to build, deploy, and run agents in production. The 0.8 release adds support for user-owned credentials, user-level memory, HTTP channels, file transfer in Slack and a pre-built tool for web search.

See how LangSmith helps healthcare AI teams turn clinical review into reusable evaluators, datasets, and release gates for safer AI in production.

Use Jev as a judge for LangSmith evals to evaluate agent traces with faster, cheaper structured feedback across production runs, datasets, and regression tests.

We tested Jev against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation.

Open Source Agent Architecture LangChain Building a Harness with Jev September 17, 2026 5 min Go back to blog Create agents Agents run in a loop: an LLM decides what to do, a tool executes, a model evaluates the results…

Deep Life Sci is LangChain's open source agentic assistant for clinical and lab scientists. It pulls from 600K+ ClinicalTrials.gov studies, 29M PubMed abstracts, and 12M PubMed Central full-text articles, with sandboxed sub-agents for real data analysis.

See how Included Health used Deep Agents, LangGraph, and LangSmith to build Dot, a federated healthcare navigation agent with human handoff and clinical oversight.

Agent programs in healthcare and life sciences are being built under a different set of constraints than those in most industries. There’s plenty of upside if the constraints can be resolved. Success can mean hours of manual review compressed into minutes, data spread across a dozen systems finally queryable in one place, and clinicians getting time back from documentation. At the same time, the cost of a wrong answer can be higher here than almost anywhere else, which changes how teams build.

How LangChain built a paid media agent to analyze campaign performance, optimize ads, propose changes, and turn marketing data into action.

See how Credit Genie uses OpenWiki to automate repo documentation, reduce tribal knowledge, and give engineers and coding agents searchable codebase context.

Learn how Connections in Managed Deep Agents securely manage credentials, support per-user OAuth, and let agents act with each caller’s identity.

deepagents introduces context modes that decide whether subagents inherit a supervisor’s conversation (fork) or start clean (isolated). Fork can be faster and cheaper by reusing prompt caching, while isolated is ideal for independent review or parallel research. The post maps worker, verifier, researcher, and memory agents to the right mode.

Managed Deep Agents and LLM Gateway hit public beta, plus Deep Agents v0.7, Tuned Evaluators, Bring Your Own Cloud on AWS, and LangSmith Engine upgrades.

Dive into LangSmith product usage patterns that show how the AI ecosystem and the way people are building LLM apps is evolving.

Reflections on how LangChain has evolved — including our products, ecosystem, and community — over the past two years, and where we're headed next.

See how Podium tests across the lifecycle development of their AI employee agent, using LangSmith for dataset curation and finetuning. They improved agent F1 response quality to 98% and reduced the need for engineering intervention by 90%.

Our new infrastructure for running agents at scale, LangGraph Cloud, is available in beta. We also have a new stable release of LangGraph.

Build, deploy, and monitor production-grade AI agents at scale with LangChain's enterprise agentic AI platform integrated with NVIDIA.

Learn proven strategies to speed up your AI agent: reduce latency, optimize LLM calls, enable parallelism, and improve UX. Expert tips from LangChain.

LangGraph Platform, our infrastructure for deploying and managing agents at scale, is now generally available. Learn how to deploy

We raised $125M at a $1.25B valuation to build the platform for agent engineering.

We built WikiBench to test whether generated wikis help coding agents. Pairing a wiki with source code scored higher than source alone, at lower cost.

Why LangChain believes in open, customizable cognitive architectures over closed systems. Build reliable LLM agents with OpenGPTs and LangSmith.

Discover Connery: open-source plugin infrastructure for LLM apps. Secure integrations, personalization, and human-in-the-loop control for AI agents.