Search: “harrison-chase--x”

@hwchase17 Love the idea of using a wiki for everything for agentic work! Recently Google created an open format for this which helps a ton creating your own skills out of it - it’s called OKF: https:

@hwchase17 s “wiki memory” really resonates with how we’ve been thinking about Enterprise Knowledgebases. Agent memory is still early, but one pattern we observe consistently: enterprises don’t need a

Harrison Chase suggests using a wiki structure as the foundation for building AI agents with memory systems, citing its simplicity and extensibility as key advantages. This approach would organize age

if you want agents to do work at scale (like security triage, trace analysis, document parsing), they need structured workflows enforced w/ code map reduce is a great example -- exactly the kind of pa

Imagine if, as an engineer, your working memory of a codebase was wiped every time you started a new ticket Sounds ridiculous, but this is functionally what happens to agents whenever we start a new t

Interrupt 2026 is coming to New York City and London this fall. → Hear from engineers + teams shipping agents in production → Keynotes from @hwchase17 + industry leaders on what's next for agents → Ha

Introducing Computer in WorkPods. Computer lets your agents use a browser. Every task that lives on the web, behind a login, a form, a button, now runs inside the same session you're already watching.

Just published a YouTube video walking through OpenWiki. Setup and configuration, how it works, what it targets, and how to automatically keep your docs up to date. Check it out, and see the OSS GitHu

More and more developers turning to glm5.2 as a daily driver for coding Here’s how to use it with deepagents code You can start building with GLM 5.2 in minutes: 1️⃣ Download dcode 2️⃣ Select your mod

Open models are the future, and that future starts now GLM 5.2 has become my daily driver. here’s how and why: You can start building with GLM 5.2 in minutes: 1️⃣ Download dcode 2️⃣ Select your model

OpenWiki is the easiest way to document your codebase, built specifically for agents to consume. OpenWiki can generate repo docs, automatically update them as your codebase evolves, and do Q&A over yo

This video provides a quick explanation of Retrieval-Augmented Generation Models (RLMs), outlining their benefits and demonstrating their implementation with DeepAgents. The content likely covers how

Some of LangSmith's biggest and most engaged customers trace with: - @aisdk - Claude Agents SDK - OpenAI Agent SDK - No framework at all And I'm sure we'll see tons of http://pi.dev in the near future

The Harness should be Model agnostic. The Knowledge it builds around you is your moat. You should not have to migrate or give that up just because a model is banned or just not good. Right now in my R

this video goes into more detail on how our integrations with @harborframework actually work! if the blog yesterday caught your eye definitely watch this! thanks for all the help @kobe0938 @alexgshaw!

we built the @hyperspell filesystem around this principle every source in your company feeds into a context graph where we resolve conflicts and assign permissions there’s a filesystem on top of the g

We call ours Operational Language Wiki, essentially a pattern for building memory to fill in the delta between the “dialect” of a team vs the “world language” that the LLM is trained on. Use it to cap

Wiki!!! One unexpected outcome of this is that I'm now using the wiki as the ONLY place I run Claude Code I use it as a master controller for all of my repos, kicking off cross-repo tasks and using To

This post announces the release of an open source tool or example demonstrating how to implement wikis for memory management, following up on a previous discussion about this trending approach. The re

you can now build RLMs with Deep Agents! the more context agents accumulate, the worse they perform, a phenomenon called context rot. RLMs, proposed by @a1zhang from MIT, help: instead of working mode

Recursive Language Model (RLM) workflows are now available as a feature in Deep Agents, allowing AI systems to iteratively call language models within multi-step processes. This capability enables mor

You can start building with GLM 5.2 in minutes: 1️⃣ Download dcode 2️⃣ Select your model (GLM 5.2) 3️⃣ Enter your API key …and you're ready to go with frontier performance on open weights. Great demo

This post describes a common architectural pattern for integrating memory components into AI systems, illustrated through examples like DeepWiki and AutoWiki that automatically generate and maintain k

A great read from @LangChain Best framing I've seen for agent architecture. Every non-model piece like the filesystem, sandbox, compaction, Ralph loops exists to patch a specific model limitation. htt

a very cool Harbor x LangSmith flow I love to help you “look at the data”: 1. you do evals or rollouts for RL 2. all reward metrics and traces and rollouts get automatically propulates into Experiment

Code-writing AI agents can solve complex problems more effectively than agents using other approaches, but deploying them requires executing potentially untrusted generated code within sandboxed or is

agree a lot with this - the actual storage layer for memory (eg which database you use) matters less than the process around that data (retrieval, ingestion, etc) @hwchase17 what if memory is not stor

Building voice agents can come with tradeoffs. 💬 Better convos w/ speech to speech models Vs 🥪 More reliable harnesses w/ sandwich architecture How to build a voice research agent with both: ✅ Gemini

This post likely discusses how to evaluate AI agents built with LangChain using Harbor, a tool for testing and monitoring. It covers practical approaches for assessing agent performance, reliability,

Giving agents the ability to write code makes them dramatically more capable. It also makes security a lot harder. At LangChain, we have spent a lot of time this year figuring out how to do both. http

good description of how someone has done wiki memory in the wild @hwchase17 @cognition @FactoryAI @karpathy We implemented wiki idea by hosting the 'files' as doc db objects, added an API for db query

harbor is a great framework for running evals for long running, stateful agents its becoming industry standard, powering benchmarks like terminal bench 2 we've integrated deeply with harbor: across La

.@harborframework can now integrate directly with Deep Agents, LangSmith Sandboxes, and LangSmith Observability. You need to run agents in a real, reproducible, isolated environment, many times in par

Harrison Chase discusses techniques for executing agent code safely without requiring a complete sandbox environment, highlighting hardening measures implemented in their code interpreter runtime. The

human in the loop is very nice for these wiki memory systems @hwchase17 My rule is that nothing gets added to the knowledge system without me reviewing it first, which has served me well so far, along

@hwchase17 very spot-on! for our (http://qontext.ai) customers, the journey is usually: local .md files -> shared github repo (-> gbrain) before these approaches break down at a) multiplayer (incl acc

This post expresses enthusiasm for command-line interface (CLI) loading spinners, highlighting the aesthetic and user experience satisfaction they provide during software operations. The author apprec

joined @LangChain almost three months ago and have spent a ton of time thinking about evals! i think that harbor is the future of eval frameworks so really enjoyed being able to bring it into tighter

LangChain 今天连发两记，直接把 agent reliability 往前推了一步：一套统一评估栈，一个不用完整沙箱的代码执行方案。 先说评估。长运行、有状态的 agent 怎么测？Harbor + LangSmith 的组合给出一个 runner，能挂载 sandbox、接入 observability，终于不是靠单元测试糊弄了。 再看代码执行。Deep Agents 需要跑不受信代码，

Memory has somehow consistently been the most exciting area of agent development over the last 3 years (imo), and it's still a largely unsolved problem!! Wiki's are the biggest advancement I've seen i

Row-Bot's approach to memory is hybrid. We have taken the best of both worlds. A knowledge graph for the agent + bi-directionaly synced Wiki for human readability/audit. With in-app graph visualizatio

This post likely discusses technical decisions and design patterns for dynamically displaying subagents within an AI system, documenting the alignment the team reached after encountering implementatio

this may be ai generated, but is true - the hardest part of wikis (and memory in general) is the process that condenses raw data into learnings/memory @hwchase17 @cognition @FactoryAI @karpathy wiki m

Tutorial on using Gemini live to build a voice agent Uses deepagents as a tool: offload complex work to this subagent, use Gemini live for the naturalness/latency Building voice agents can come with t

we launched code interpreters for deep agents last month. Basic idea is to let agents plan, delegate, and organize context using code instead of chained tool calls Code interpreters don't need a sandb

Deep Agents now supports dynamic subagents. Instead of invoking subagents with tool calls, the main agent writes orchestration code to coordinate work at scale. This enables workflows like processing