Blog
Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.
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Agents1 min read
LangSmith now allows users to create, publish, and run custom user interfaces around their agent data without hosting or permissions work.
From LangChain blog
Agents1 min read
LangSmith now displays agent session trajectories, making it easier to see what an agent did and find issues faster.
From LangChain blog
How this blog is made
Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.
Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.
LangSmith Engine v2 introduces red teaming and automatic testing of fixes. It helps find issues early and speeds up agent development.
From LangChain blog
Agents1 min read
LangSmith now offers smithtune, a CLI that turns agent trajectories into custom fine-tuned models. It handles data, training, and evaluation.
From LangChain blog
Agents1 min read
See how LangSmith helps healthcare AI teams turn clinical review into reusable evaluators, datasets, and release gates for safer AI in production.
From LangChain blog
Agents1 min read
Use Jev as a judge for LangSmith evals to evaluate agent traces with faster, cheaper structured feedback across production runs, datasets, and regression tests.
From LangChain blog
Agents2 min read
Companies across Europe and the Middle East are shifting from individual agent prototypes to centralized agent platforms to manage a growing number of AI agents. This approach emphasizes observability, governance, and a standardized infrastructure layer to support operational efficiency and compliance.
From LangChain blog
Agents1 min read
This month’s LangChain newsletter announces the public beta of Managed Deep Agents and LLM Gateway, alongside significant improvements to the LangSmith Engine and Tuned Evaluators. Teams can now deploy Deep Agents with one command and implement robust controls for LLM usage in production.
From LangChain blog
Agents2 min read
The LangSmith LLM Gateway provides runtime controls for agents, enabling teams to manage costs, prevent outages, and protect sensitive data across model providers and agents. This centralized layer simplifies governance and avoids vendor lock-in for production agent deployments.
From LangChain blog
Agents1 min read
Podium decreased engineering intervention by 90% using LangSmith for agent testing and observability. The platform’s testing approach, including dataset curation and model fine-tuning, improved agent performance and enabled rapid issue resolution.
From LangChain blog
Agents2 min read
The LangChain State of AI 2024 report reveals key trends in LLM app development, including increased open-source model adoption, a shift to agentic workflows, and growing application complexity. Developers are utilizing LangSmith to trace and optimize these increasingly sophisticated AI systems.
From LangChain blog
Agents1 min read
LangSmith’s improvements, including large trace handling, search capabilities, and thread views, address the challenges of debugging Replit Agent’s high-complexity workflows, enabling faster agent development and scaling.
From LangChain blog
Agents1 min read
LangSmith’s product homepage has been redesigned to better align with developer workflows focused on observability, evaluation, and prompt engineering. New resource tags provide flexible grouping and filtering capabilities within workspaces, improving resource management and accessibility.
From LangChain blog
Agents1 min read
LangSmith’s new LLM-as-a-Judge evaluators incorporate self-improvement through stored human corrections, reducing prompt engineering and enabling adaptive alignment with user preferences. This streamlines evaluation for generative LLM applications.
From LangChain blog
Agents1 min read
Factory, a provider of AI-powered SDLC automation, utilized LangSmith to automate its feedback loop, achieving a 2x improvement in iteration speed. The self-hosted platform enabled robust observability and data privacy for complex LLM workflows.
From LangChain blog
Agents1 min read
The EU AI Act requires specific operational infrastructure for high-risk AI systems. LangSmith and LangChain OSS provide observability, tracing, and evaluation tools to meet these requirements, including full execution capture, continuous quality scoring, and human oversight mechanisms.
From LangChain blog
Agents1 min read
Reducing AI agent latency involves identifying bottlenecks, leveraging streaming responses, and strategically minimizing LLM calls. Tools like LangSmith and LangGraph offer visibility and control for optimizing agent performance and user experience.
From LangChain blog
Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.
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