Knowledge catalogue
What are loops, and how do you build one? A 'loop' is the repeated process where some event or input kicks off an action. For example: 1. CI fails -> you fix it 2. CI fails again -> you fix it 3. CI p
Deep Agents are an AI concept that Sydney Runkle explains concisely in under 90 seconds, likely covering how agents can be designed to operate with deeper reasoning and decision-making capabilities. T
deepagent for competitive analysis Introducing 'Signals of AI' - DeepAgent powered by - @nebiustf for Inference - @NVIDIAAI Nemotron 3 Ultra - @tavilyai for Web Search - @LangChain for the core deepag
DeepAgents is a framework or tool designed to enable the creation of intelligent agents, likely demonstrated or explained in a brief 90-second format by Harrison Chase, a co-founder of LangChain. The
This post likely explains the concept of loops in programming or automation workflows, covering how they function and their practical applications for automating repetitive tasks. It probably includes
Introducing 'Signals of AI' - DeepAgent powered by - @nebiustf for Inference - @NVIDIAAI Nemotron 3 Ultra - @tavilyai for Web Search - @LangChain for the core deepagent architecture! An ideal engine f
LangSmith Fleet introduces file handling capabilities that enable users to create and manipulate documents, presentations, and webpages directly within conversations, as well as upload existing files
Rubrics are even more flexible than /goal You can define a custom subagent for the grading, including custom tools, prompt, and iteration limits. Try it out and let us know what you think! we just shi
we just shipped support for rubrics in deepagents ✅ give your agent a clear definition of what 'done' looks like, and force it to run in a loop until said goal is complete this is similar to /goal in
imo there’s a pretty solid default recipe that everyone should use to optimize a system of Agent = Model + Harness you should “train” both 1. Build v1 agent using a sensible base harness and some task
We're building this at LangChain Fleet lets you create and manage a fleet of agents. Each agent specializes in a workflow, e.g. inbox management, blog writing, competitor research, candidate recruitin
Fleet is a messaging platform that includes a feature allowing users to pin important conversations for quick access and easy retrieval later. This functionality helps users maintain organized convers
Open-source AI Agent Security resource , fully on the @LangChain 🦜 stack. It shows real attacks (prompt injection, Indirect prompt injection, tenant data exfiltration, memory poisoning) and the securi
Spent more time learning deepagents from @LangChain with primary focus on integrating MCP servers with auth that doesn't conform completely to the OAuth standard. ( In prep of a work coming my way ) B
This post likely discusses the trend toward headless architecture becoming standard across software platforms, where the backend logic and frontend presentation are decoupled, allowing greater flexibi
Sandboxes are already helping teams move from agents that answer questions to agents that can do work safely. At @mondaydotcom, that means giving Sidekick a secure environment to write and run code fo
LangSmith Engine, a tool by LangChain, helped Odessa Travel improve their development workflow by converting execution traces into actionable bug fixes, making debugging more accessible to their team,
Witan Labs is developing a headless Office stack designed specifically for AI agents, combining productivity tools and capabilities without a traditional user interface. This post appears to be a week
Harrison Chase, co-founder of LangChain, shares insights on the future development and evolution of AI agents in this Twitter thread. The post likely covers emerging trends, technical capabilities, an
New in the LangSmith Sandboxes GA Release: Sandbox CLI ✅Build snapshots from Dockerfiles ✅Manage sandboxes ✅Open interactive consoles ✅Tunnel raw TCP ✅Use standard tools (ssh, scp, rsync, sftp) agains
nice post from @Harvey and @LangChain Labs, worth a read. improve agent feedback loop without setting $$ on fire Can we design legal agent verifiers that are up to 1,000x cheaper? Verifiers are LLM ju
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