73 stories
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.
LangChain introduces LangSmith Fine-Tuning and SmithTune, a CLI built for post-training models. Train specialized models without building data pipelines by hand.
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.
This is a first for the “Apple of China” phone maker-turned-frontier lab: “The MiMo-V2.6 series includes two natively omnimodal models: MiMo-V2.6-Pro is our most capable model to date, while MiMo-V2.6-Flash strikes the best balance between intelligence, efficiency, and cost.
Still guessing if your prompts work? Learn how to build a systematic prompt testing framework that catches regressions before they reach production.
We tested using Jev-as-a-Judge against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation.
See how Included Health used Deep Agents, LangGraph, and LangSmith to build Dot, a federated healthcare navigation agent with human handoff and clinical oversight.
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.
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.
A guide on scaling agents in Europe & the Middle East to see how Schneider Electric, Vodafone, and monday.com are approaching production AI at scale, from establishing shared agent platforms and LLMOps practices to designing multi-agent architectures with stronger observability, evaluation, and…
How LangChain built a paid media agent to analyze campaign performance, optimize ads, propose changes, and turn marketing data into action.
Learn how we built a GTM agent that increased lead conversion by 250% while saving each sales rep 40 hours per month
Learn how Connections in Managed Deep Agents securely manage credentials, support per-user OAuth, and let agents act with each caller’s identity.
Senior Fullstack Engineer, AI Observability & Evals Platform — Boston, MA · full-time · Hybrid · $175k-$240k
Senior Backend Software Engineer, AI Observability & Evals Platform (LangSmith) — Boston, MA · full-time · Hybrid · $180k-$240k
An agent is autonomous until it hits something it cannot buy. In this cookbook, we show how Nevermined empowers an agent built on LangChain, mid-task, to acquire web search credits, and then - after exhausting those credits - top up with more.
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