Knowledge catalogue
pretty sick that i get to work with Jake every day on making continual learning + memory accessible at scale for every single agent one common thread here is...the Trace a large part of Continual Lear
Sleep-time compute is the next scaling axis for intelligence 🧠LangSmith Engine as Sleep Time Compute Memory for agents is often described as “sleep time compute” or “dreaming” This involves running a
Tracing functionality can be utilized to monitor and analyze memory usage patterns in applications, helping developers identify memory leaks, optimize allocation, and understand runtime behavior. This
We built Claude for outbound sellers. AEs & SDRs can harness GTM engineering through chat across 40+ data sources, no technical skills required. We’ve had 57,548 queries in our first few weeks of beta
auto-research style proposal loops should be data driven! they largely work best only when Data/Evals/Feedback give a useful gradient to hill-climb against increasingly auto-research is a very good to
Harrison Chase suggests that evaluations (evals) are becoming as critical to AI/LLM development as Product Requirements Documents (PRDs) traditionally were, implying a shift in how teams validate and
GLM 5.2 is already top 10 on OpenRouter this week, sitting right next to Opus 4.8. it's also been really strong in Deep Agents across long-horizon coding, big context, tool use, and verifying work bef
i was already using GLM-5.2 but WOW it's even better through baseten! give it a try with deepagents code (dcode), our open harness built to work with any model/provider We have the fastest GLM-5.2 dep
interesting point here: loops amplify behavior, making them a double edged sword but we know loops are the future, so how do we avoid amplifying bad patterns? you need an *engaged* human in the (stack
Let the smart horses build their own harness. See my previous article on this: https://x.com/Kangwook_Lee/status/2052925157606568217 🧠Self-Harness: Harnesses that improve themselves New paper on agent
Model council as a tool to push performance definitely works, I think the interesting next frontier is trying to scale this at the agent level have been thinking a bunch about model routing and relate
One thing we’ve seen pretty consistently: shipping the first version is only a small part of the work A key part of building reliable agents is having a repeatable lifecycle for improving them over ti
🧠Self-Harness: Harnesses that improve themselves New paper on agents shaping their own harnesses to improve over time. Not from LangChain, but builds on top of DeepAgents! Three key steps: 1/ Weakness
self-harness: harnesses that improve themselves spoiler alert, it's all loop engineering! 1. weakness mining - run agent and observe failures 2. propose improvements to the harness 3. confirm improvem
you need docs built for agents Docs are the eyes and ears of Agents. But we're moving so fast that they are always outdated. So hence an outdated doc also confuses the models. But it's also hard keep
context engineering docs for agentic engineering - plans, research, etc SHOULD NOT be stored in version control: A good docs management system keeps them: > outside your repo > accesible to agent via
Deep Agents v0.6 feature spotlight: a code interpreter. Agents can now call tools from inside a runtime, keep intermediate results out of model context, and only pass the relevant output back. Fewer r
have been thinking a bunch about model routing and related things current thoughts here, would love feedback: 1/ there is a difference between 'model routing' and 'model council' 'model routing' = rou
If you are building agents, Max Agency with @hwchase17 is THE best behind the scenes of how the top agents are built at companies like @SierraPlatform @RampLabs @benchling @ListenLabs @_hex_tech @coge
Something we’re thinking a bunch about as well Would love thoughts! context engineering docs for agentic engineering - plans, research, etc SHOULD NOT be stored in version control: A good docs managem
There are basically two skillsets that matter more in the age of coding agents. The first is the one everyone talks about: product sense and taste. Code is cheap now, so knowing exactly what to build
you can try out GLM 5.2 with deepagents code (dcode) today!! dcode is general purpose and provider agnostic, so you can take any of the new oss models for a spin! docs: https://docs.langchain.com/oss/
'Build your own Claude Code with Deep Agents' Good article by the community showing how to build a Claude Code-like agent using Deep Agents Especially relevant given how strong GLM-5.2 seems to be! ht
people want to build agents, and they want it to be easy even more important, it should be easy for your agents to improve over time that's what the 4th loop ('hill climbing') enables. automating the
Harrison Chase confirms that 'Yes' (likely referring to LangChain or a related tool/framework) is model-agnostic and designed as a general-purpose solution, meaning it can work across different AI mod
One of the better agentic AI courses I've seen Nearly 10 hours of great content. Covers LangChain, LangGraph, RAG, deepagents, guardrails, and more Any other good Lang* resources out there for folks w
Very cool work from @jit_infinity: 🔥Leve: filesystem-first, durable agent framework built on LangGraph. You describe an agent as a directory of files. Leve compiles that directory into an agent and ru
Deepagents code is sick cuz you can just use the best model as it comes out it is indeed quite good! don't try it in claude code/codex - those harnesses are overly tuned for their proprietary models d
it is indeed quite good! don't try it in claude code/codex - those harnesses are overly tuned for their proprietary models dcode (deepagents code) is a model agnostic harness - try it there with @Fire
Try it out! We are seeing amazing results with GLM 5.2! it is indeed quite good! don't try it in claude code/codex - those harnesses are overly tuned for their proprietary models dcode (deepagents cod
AI agents have become surprisingly good at talking to servers 🤖 The next challenge is getting them to interact with the environment users actually live in. 🧑💻 Browsers. 🖥️ Apps. 📱 Devices. 🌐 Local st
Harrison Chase announced a LangChain meetup event in Chicago on June 22 featuring discussion of deepagents, in collaboration with focused.io. The event was promoted on X (formerly Twitter) as an oppor
first in a series of technical blogs of how we build llm infra How do you support full-text search JSON filtering over agent traces that span up to hundreds of MBs, while keeping a median (P50) latenc
future of travel! We raised $6M led by Sequoia to build the future of travel. Watch me plan a perfect trip to Mexico City in 3 minutes. Flights, hotels and a full itinerary that matches my preferences
How do you support full-text search JSON filtering over agent traces that span up to hundreds of MBs, while keeping a median (P50) latency of 400ms? Here’s an inside look at how we built a custom inve
Introducing @PoeticHQ: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised 50M at 500M from Kleiner Perkins, Founders Fund, First Harm
it’s actually so cool to work at LangChain the…database company (??) yup, the cracked team that built SmithDB is doing a cool blog series on “How to build the internals of a database” —> for agent sca
We raised $6M led by Sequoia to build the future of travel. Watch me plan a perfect trip to Mexico City in 3 minutes. Flights, hotels and a full itinerary that matches my preferences. All bookable on
loops are great because its just ai running in the background 'triggers' are the way we kick those off in fleet What are loops, and how do you build one? A 'loop' is the repeated process where some ev
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