Search: “harrison-chase--x”

Completely agree, @hwchase17 ! This is the meta-level breakthrough we've all been waiting for. Self-optimizing loops finally feel production-ready because LangSmith Engine turns evaluation from a manu

completing the loop is paramount most unrecognized step in the development lifecycle is iteration on the process itself everyone is talking about self-optimizing loops in software & agents. but what d

Computer use in Fleet is now in public beta! You can now give your agents in Fleet access to secure virtual machines allowing them to own engineering tasks for you. Use these via Fleet agents in the U

This post highlights an open-source project built using LangChain and Langgraph that implements thoughtful memory management and organization features. The project demonstrates best practices for stru

Cost controls are one of the main benefits of langsmith LLM gateway We’re seeing in a bunch of use cases adoption is getting to a point where cost really starts to matter Your agents can burn through

Deep Agents v0.6 brings Delta channels, reducing checkpoint storage by up to 100x for long-running agents, without sacrificing observability or resilience. Here’s a 200-turn coding agent session. With

Excellent talk on engine The interest and adoption of engine has been far greater than we anticipated If you don’t know what it is, this is a great starting point Improving your agent has been a manua

This post likely announces or discusses an open-source AI agent framework that incorporates memory and continual learning capabilities, enabling the system to retain information across interactions an

Fleet agents can now securely write and run code. With computer use in LangSmith Fleet, agents get isolated execution environments. Analyze data, transform files, generate & write code, and run shell

Fleet agents now come with a computer! They can write and execute code, which is helpful for general purpose tasks beyond coding Fleet agents can now securely write and run code. With computer use in

📹 How to manage context the right way with LangSmith Context Hub Context Hub gives your team and your agent a shared place to store, edit, version, and retrieve context from It can be used for skills,

LangSmith has introduced automated tools to streamline agent improvement, eliminating the manual workflow of reading execution traces, identifying patterns, writing evaluations, and implementing fixes

Boston Tech Week featured a session hosted by Mason Dry and Sydney Runkle with the Blitzy AI team focused on building deep agents. The event was promoted via Luma Events and appears to be an education

Managed Deep Agents is built for agents that need to work over long time horizons, use tools, preserve context, and produce artifacts. A few examples of what teams are building: ✅ Support + triage age

Managed deep agents is the easiest way to build and deploy long horizon agents Private preview, dm me if you want access Managed Deep Agents is built for agents that need to work over long time horizo

Model-Harness-Task fit! it’s clear that RL post-training produces a model-harness fit via tool shapes and prompting as models are trained with the harness in the loop. Mentioned this in a previous Lan

the future of Continual Learning will rely on building to systems to ingest, understand, & apply knowledge from Agent Traces at scale had a blast presenting LangSmith Engine with @bentannyhill to show

this is a fantastic example of a robust agentic memory system built on langgraph! there's 4 key parts 1. retrieval (rag) 2. storage, fragmented by memory type 3. reasoning* 4. learning (often called d

We launched Context Hub as a way to manage skills, AGENTS.md files, and other context files an agent might need You can easily use it as a virtual filesystem in deepagents See this video for more info

LangSmith's LLM Gateway is a cost control and monitoring tool designed to prevent unexpected high spending on LLM API calls by providing observability and likely rate-limiting or budget-cap features.

.@AdamRLucek on how we use traces to build evals for production agents. Trace data is literally worth its weight in gold these days, if you know what to do with it! As has been established, creating e

As an upcoming parent, I keep imagining all the tiny routines ahead 😊👼 One I am especially excited for: using AI for making up bedtime stories. So I built an app that creates one on demand: story, ill

Building agents (like software) is a deeply iterative process --> this is why we try to supply as much easy to use tooling and infra as possible so builders can start on their agent improvement journe

create_agent is the easiest way to get started building an agent! it provides an incredibly flexible interface for building agents designed around your use case! we just revamped the create_agent docs

everyone is talking about self-optimizing loops in software & agents. but what does that actually mean? in my mind, it's a system that observes it's own outputs, evaluates them, and uses that signal t

LangChain Academy Course: LangSmith Fleet Essentials Learn how to build your own agents with LangSmith Fleet. Anyone can now build, use, and manage an agent fleet for complex daily tasks, without writ

langsmith engine is an agent that helps you spin the optimization loop for your own agents everyone is talking about self-optimizing loops in software & agents. but what does that actually mean? in my

Ridiculously cool open source example using @LangChain_JS The Multi-Modal children’s bedtime story generator is an excellent example of LangChain streaming UI & stream namespace subscriptions - great

the importance of traces! Trace data is literally worth its weight in gold these days, if you know what to do with it! As has been established, creating effective agents requires shipping early, obser

Trace data is literally worth its weight in gold these days, if you know what to do with it! As has been established, creating effective agents requires shipping early, observing behavior, and iterati

we just revamped the create_agent docs! the new agents page shows how to build a custom harness for your use case w/ create_agent as an easy entrypoint start w/ your prompt and tools, then add middlew

nice write up from the HuggingFace folks aggregating works on defining agents, harnesses, environments, RL, etc. The more we can roughly have a shared vocabulary the better…I still find it confusing (

Agents shouldn’t have direct visibility into env vars or credentials that can expose sensitive systems and data. Keeping secrets outside the agent’s context helps secure the env while still allowing a

“It is built in Rust and leverages the Apache DataFusion query engine” Any new database these days We built SmithDB: the database purpose built for agent observability workloads that now powers many p

This post by Harrison Chase emphasizes the importance of mastering fundamental business and career concepts that may seem unglamorous but are critical for long-term success and risk mitigation. The co

to everyone that's using ai to generate their tweets. we know. i know you think you're being lowkey. i know you might be switching around some words, and removing em dashes. but it's so obvious. anyon

Always enjoy chatting with @hwchase17 . Excited to sit down with him at our @traversal_ai HQ in a couple of weeks and discuss all things agents. im going to be in NYC in ~1 week, and am doing a firesi

This talk from an AWS User Group Malaysia event covers practical experience and insights on building an AI Agent Harness, likely discussing architecture patterns, implementation challenges, and best p

Brand new episode of Max Agency ⤵️ Great conversation on the Max Agency podcast with @cogent_security Co-Founder + CTO Geng Sng on building agents for autonomous cyber defense. Check out the full epis

Great conversation on the Max Agency podcast with @cogent_security Co-Founder + CTO Geng Sng on building agents for autonomous cyber defense. Check out the full episode: ⏯️ YouTube: https://youtu.be/D

im going to be in NYC in ~1 week, and am doing a fireside chat with one of the top agent companies in new york - @_anish_agarwal of @traversal_ai come join us! https://partiful.com/e/MLzglw6al1KrUSXSr

Join @hwchase17 + @traversal_ai founder @_anish_agarwal for a technical fireside chat in NYC on June 2nd. Learn how Traversal builds, ships and improves their agents. Enjoy networking, drinks, and a l

Managing and monitoring agents will require us to rethink infrastructure since most systems were not designed to support agent scale. Very cool to see how @LangChain leveraged @SpiralDB and other tool

one of the best decisions i made was to start tracing my claude code sessions into LangSmith. Has been a game changer to be able to share my conversations, track usage patterns, monitor cost. And w ne

software security is a hot topic right now timely podcast with @cogent_security on building agents for cyber defense Great conversation on the Max Agency podcast with @cogent_security Co-Founder + CTO

This is one of the things I dislike about managed agents. Is it the best DX? yes. Is it now much, much more usable because it's bring your own sandbox? yes (most startups now have Sandbox credits and