Knowledge catalogue
On the latest episode of Max Agency, @hwchase17 sat down with @nlarusstone, Head of AI at @benchling for a conversation on building agents for scientific work. ⏯️ YouTube: https://www.youtube.com/watc
This is the enterprise-agent security frame I like: treat agents like untrusted developers. They need the ability to call an API, not possession of the credential. Control plane outside runtime. Fail
transitions like this are why we think it's helpful to have a provider-agnostic harness we used to talk more about swapping models when the latest and greatest came out -- but the latest and greatest
We have a Fleet agent called @docs_plz in our Slack that's made a very noticeable impact on our velocity of docs changes. In the chart below, you can clearly see that after it was added, the amount of
we're taking an early bet on open models specifically, because they're SO much cheaper one point of reference: an app outputting 10M tokens/day costs roughly 250/day on Opus 4.8 versus ~24/day for Min
The `create_agent` function in LangChain provides a minimal, lightweight agent framework designed for easy customization through middleware, enabling developers to build task-specific agent harnesses
langsmith! ✅ Sandbox: https://docs.langchain.com/langsmith/sandboxes ✅ Gateway: https://docs.langchain.com/langsmith/llm-gateway ✅ Observability: https://docs.langchain.com/langsmith/observability eve
latest iteration of middleware is rad: there was a subagent that we had that was taking some pretty wild trajectories and was costing wayyyy too much We adapted a 60 line middleware from a different a
Managed Deep Agents combine open-source Deep Agents with LangSmith's infrastructure to provide both the flexibility of open-source tools and the reliability of hosted execution with persistent context
LangSmith's LLM Gateway provides real-time cost visibility and spend tracking capabilities, allowing users to monitor their language model usage costs aggregated by workspace. This feature helps elimi
Startup discovery platform @harmonic_ai rebuilt Scout, their AI platform using Deep Agents and LangSmith. Deep Agents: One frontier model + two tool sets (global company data and firm-specific context
Thanks @hwchase17 @bryonkuchML for the inspiration and the nudge from @MEGAcodePaul - took your feedback seriously. I have developed a boilerplate that covers the full optimization loop that productio
the best agents aren't just built with the best models: they're built with harnesses purpose-built for the task at hand here's a guide on how to build a harness that's really good at feeding the model
Quarq's team has developed an AI agent built on LangGraph that currently leads the LongMemEval-S benchmark, demonstrating advances in long-context memory evaluation. The work represents a notable achi
we are seeing costs start to matter! uber just set limits of $1500 in tokens per developer per month i think we're going to start seeing more of this, and LangSmith Gateway is a great way to implement
With LangSmith Engine, systemic issues get surfaced automatically instead of getting buried in traces. @ollieelmgren from @ListenLabs on how LangSmith Engine changed the way his team evaluates their a
Can we design legal agent verifiers that are up to 1,000x cheaper? Verifiers are LLM judges that check an agent’s work against rubric criteria: they're used both in agent benchmarking and as reward si
🎉 Congratulations to @OdessiaTravel on their public launch! Odessia is an AI-powered travel agent that allows users to plan and book entire trips in one conversation. The team used LangSmith and LangG
Continual Learning involves engineering whole systems including Efficient Verifiers to make RL/fine-tuning and running evaluations much cheaper at scale! some initial work we’re releasing from LangCha
LangChain Labs is conducting research into developing more efficient verification systems for validating the outputs and work products of AI agents. This research likely focuses on improving the speed
Efficient verification is what makes scaling legal agents practical. Excited to partner with @hwchase17 and the @LangChain Labs team on designing efficient verifiers - sharing early results showing op
Fleet offers the most secure computer use experience, ensuing your can let your agent access private resources, without worrying about it mishandling or leaking secrets Giving agents access to sensiti
Giving agents access to sensitive systems shouldn’t mean handing them your secrets. With Fleet access profiles, agents running in sandboxes can utilize sensitive credentials without you needing to set
LangSmith Studio added a new Deploy button feature that enables users to deploy agents directly to production with a single click, streamlining the workflow from prototype development to production de
New in Deep Agents: Agent Rubrics! Attach a rubric to your agent invocation, and a grader evaluates and self-corrects output until it satisfies all requirements. This is helpful for long/complex tasks
new in deepagents: agent rubrics! you define a rubric, and the agent self-evaluates and iterates until it satisfies every rubric criterion. this is similar to /goal in claude code or codex, but more f
Prompting agents to 'do better' is unreliable 🙅 Giving them a rubric, a grader, and a correction loop is much closer to how you get your agent do what you want! Similar to /goal in Claude Code or othe
This post likely provides a brief explanation of a managed deepagents offering, describing what deepagents are, how the managed service works, and its key benefits or use cases. As a post from Harriso
RubricMiddleware is a LangChain feature that enables agents to verify task completion by delegating grading to a specialized subagent using predefined rubrics. This approach parallels the goal-verific
The agent development lifecycle has been manual for too long. We’re building a future where it runs continuously, without manual triggers. Where well-understood issue types resolve without human revie
The Quarq agent is constructed using LangGraph, a framework that simplifies the development of complex memory systems. Quarq has achieved a top ranking on the LongMemEval benchmark, demonstrating the
Verification is the hidden bottleneck for knowledge work agents, especially in legal AI — complex, long-horizon work is graded by rubrics with dozens of strict criteria. In new research with @LangChai
Verifiers are important for scaling evals/RL But costs add up! So can we make them cheaper? Some great work by @Vtrivedy10 @jakebroekhuizen in conjunction with @nikogrupen @gabepereyra and the Harvey
all agents in the future are going to need to write and execute code LangSmith Sandboxes are GA - try them out today https://docs.langchain.com/langsmith/sandboxes .@MukilLoganathan’s Interrupt keynot
Harrison Chase compares LangSmith Engine to Tesla's 'Full Self-Driving' moment, suggesting it represents a significant advancement in AI engineering capabilities and automation. The post indicates Lan
Big day for American open models... Nemotron 3 Ultra is now the strongest US open-weight model tested, while apparently serving 300+ tok/s 🤯 Comparable large DeepSeek/Kimi models are usually 50-100 to
This post outlines the agent development lifecycle, consisting of four key phases: Build, Test, Deploy, and Monitor. The content covers best practices and methodologies for developing AI agents using
LangSmith's APAC instance now supports Fleet computer use, enabling Fleet agents to access virtual computers for enhanced functionality. This feature allows users in the Asia-Pacific region to leverag
great look into how Rippling built RipplingAI .@Rippling AI runs on Deep Agents and LangSmith. Here’s how they shipped to millions of users in 6 months. https://www.langchain.com/blog/how-rippling-wen
great overview of deepagents! what it is, how we made it great at complex tasks, how to take it to production here's a quick overview of a) what is deepagents b) what makes deepagents good at complex
have manually read 1000s traces since joining LangChain! it’s a great way to learn and understand your agent but completely infeasible to do at agent scale 🙃 engine helps us automate that process so h
DeepAgents are AI systems designed to handle complex tasks through advanced reasoning and planning capabilities. The post outlines the key features that enable DeepAgents to excel at complicated probl
LangSmith is a development platform designed to build, test, and monitor LLM applications, offering tools for debugging and evaluation to improve AI application quality and reliability. The statement
This post likely discusses how the LangChain framework (which Harrison Chase co-founded) can assist developers in constructing more effective AI agents by providing tools, abstractions, and patterns f
Managed Deep Agents keeps the project shape you already know: ↳ AGENTS.md, skills/, subagents/, + tools.json Context Hub gives your agent a managed place to retain and update this context across sessi
.@MukilLoganathan’s Interrupt keynote on Sandboxes. https://youtu.be/IIchUA5T3gs In 20 minutes, you’ll learn how to run agent code safely. Isolated from your runtime, with network controls, persistent
Open models! Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency
.@Rippling AI runs on Deep Agents and LangSmith. Here’s how they shipped to millions of users in 6 months. https://www.langchain.com/blog/how-rippling-went-ai-native-across-every-product-in-6-months-w
I cannot provide an accurate summary of this post as the tweet content itself is not provided, only a reference indicating Jay Farei's engagement with an idea shared by Harrison Chase. Without access
LangSmith Engine is a tool designed to automatically diagnose and resolve agent failures, eliminating the need for manual troubleshooting and triage. The feature appears to leverage automated analysis
This is great @hwchase17 @bryonkuchML Seeing this update, I’m building a tutorial repo around LangChain + Groq + GEPA. The idea is simple: •LangChain builds the RAG/agent workflow •Groq gives fast inf
This macroeconomic research agent powered by Deep Agents, LangSmith, and the @youdotcom Finance Research API: ✅ Analyzes GDP data ✅ Detects anomalies ✅ Investigates structural & cyclical drivers at th
🧑⚖️Evaluating Deep Agents with LangSmith on AWS Great deep dive blog with our friends at AWS on evaluating DeepAgents with LangSmith Covers datapoint and evaluator design for longer horizon agents ht
This article from LangChain explores the architectural components and design principles of agent harnesses, which are systems that manage the execution and behavior of AI agents. The piece likely cove
LangChain🤝GEPA shout out to @bryonkuchML for contributing a PR to the GEPA repo to make it work for LangChain! You can now optimize your LangChain chains Docs: https://gepa-ai.github.io/gepa/tutorials
the market is speaking The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 nine months ago. The overall
agent builder! Start creating agents using everyday language with LangSmith Fleet. Learn how to build no-code agents for real work. Take our free LangChain Academy course today: https://academy.langch
Deep Agents v0.6 makes harness profiles a first-class abstraction. Now, you can get production-grade performance from models like @Kimi_Moonshot, @Alibaba_Qwen, and @DeepSeek_ai at 20x+ lower cost tha
Different models need different prompts, sometimes tools “Harness profiles” are how we do that in deepagents Deep Agents v0.6 makes harness profiles a first-class abstraction. Now, you can get product
LangSmith Sandboxes include an automatic pause feature that activates when the environment is idle, preventing users from incurring costs for unused resources. This functionality helps optimize cloud