Knowledge catalogue
This is actually useful. LangChain just released OpenWiki. It's an open-source agent that creates a wiki for your codebase, connects it to your coding agent, and keeps it updated as your repo changes.
This post discusses using WorkPods agent memory systems organized as wikis, likely exploring how LLM agents can maintain and access persistent knowledge repositories in wiki format for improved contex
1/ OpenWiki First, we wrote about why 'wikis' are interesting as a form of memory: https://www.langchain.com/blog/wiki-memory Cites examples from @FactoryAI, @cognition, @karpathy Then @BraceSproul op
3/ Harbor integration Harbor is a framework for running long running, stateful agent evals We integrate with it in multiple different ways. We wrote a blog on those integrations: https://www.langchain
4/ programatic subagents in deepagents Deep Agents is our open source, model agnostic agent harness: https://github.com/langchain-ai/deepagents We added the ability to programatically call subagents.
🎧A look at how we built LangSmith Engine with @hwchase17 + @bentannyhill Special episode with @bentannyhill on the Max Agency podcast. A month ago, his team shipped LangSmith Engine, our agent that hu
big week at langchain, with a lot of launches: 1/ OpenWiki - auto generate a wiki of a github repo 2/ two different voice agent tutorials 3/ Harbor integration and tutorial for long running, stateful
Fun chat with @hwchase17 talking about our work on Engine! Special episode with @bentannyhill on the Max Agency podcast. A month ago, his team shipped LangSmith Engine, our agent that hunts through yo
huge week of really exciting launches at langchain! big week at langchain, with a lot of launches: 1/ OpenWiki - auto generate a wiki of a github repo 2/ two different voice agent tutorials 3/ Harbor
I gave OpenWiki a spin last night and was very impressed. I've had Claude hand roll wikis for my personal projects/agents, but I threw OpenWiki at our @subtextdev repo and it handled the whole thing p
my fave question, talked about this coding agent Eval+Improvement loop infra + UX in my AIE talk yesterday! biased but LangSmith is the best spot to Eval + continuously improve your coding agents, and
OpenWiki is designed to run in the background, without you needing to think about it It'll generate docs, update your AGENTS.md so your agent automatically knows how to read the docs, and update itsel
so proud to be working with a bunch of people who are absolutely crushing it! check out all these new things if you haven’t had a chance to yet. huge week for us here @LangChain!! big week at langchai
Special episode with @bentannyhill on the Max Agency podcast. A month ago, his team shipped LangSmith Engine, our agent that hunts through your agent's failures, prioritizes issues, and drafts the fix
Harrison Chase expressed strong interest in developing open wiki functionality, indicating plans to prioritize wiki development in the near term. The post solicits community input on wiki features and
wikis! LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and
You shouldn’t need to think about your memory or agent docs! It should *just work* which was the thesis we went into this with gonna try this out, it seems really cool. banking on this being a better
Your coding agent bill doubled and nobody can tell you why. Here's the actual reason: Claude Code, Cursor, and Copilot all log activity in different formats. The second your team uses more than one (t
This post by Harrison Chase, creator of LangChain, likely discusses the concept of building specialized wikis or knowledge bases tailored to specific domains using AI and language models. The idea sug
'agentic map reduce' is a nice name spawning subagents programatically allows you to do these advanced agent patterns, where you want some determinism to control how agents are created/run check out d
Build a in-browser debug mode sort of tool for @LangChain deepagents that resides a shortcut away. Helpful to debug there and then as the conversation is going on without switching tabs, like seeing a
Excited to release OpenWiki! OpenWiki is the easiest way to generate and maintain documentation in your codebase, built specifically for agents to consume Get started with a single command: `openwiki
'For agents within an actual product, I have gravitated to using LangChain DeepAgents. I feel like their base harness is less heavy, and their abstraction for building agents that use skills and tools
get started with deep agents code, our open source coding agent with first class open model support You can start building with GLM 5.2 in minutes: 1️⃣ Download dcode 2️⃣ Select your model (GLM 5.2) 3
good post on the value of knowing whats going on in your harness 'For agents within an actual product, I have gravitated to using LangChain DeepAgents' The frontier model lab harnesses are amazing - d
harvey is ripping!! honor to work with them Q2 recap for @harvey - +$100M NNARR - 53% DAU/MAU Key hires (including Q1) - Anique (CPO) - prev VP of Product at Rippling - Rachel (CMO) - prev CMO at Noti
@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