Search: “harrison-chase--x”

great question! knowledge (system prompt, skills) and tools (apis, clis, whatnot) will always be needed, even as the best way to build agents changes i would focus on those and processes around those

Help us grow our list of community middlewares! https://docs.langchain.com/oss/python/integrations/middleware#community-integrations @IeloEmanuele is on a roll! you can now use claude code's advisor s

LangChain's Middleware abstraction, introduced by Harrison Chase (@hwchase17) in LangChain 1.0 Alpha, addresses context engineering in AI agents by providing clean `before_model`, `after_model`, an...

A developer expressed enthusiasm for the emerging convergence of `AGENTS.md`, agent skills (SKILL.md), and tool configuration toward portable, cross-tool interfaces rather than siloed, tool-specifi...

@IeloEmanuele is on a roll! you can now use claude code's advisor strategy with @LangChain agents because langchain/deepagents are provider agnostic, you can use different providers for your advisor a

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im excited about agent harnesses because i think are the first stable agent abstractions we can build on top (which is why we're investing so much in deepagents) we always wanted to run llms in a loop

Just shipped advisor-middleware: an open-source implementation of @AnthropicAI’s Advisor Strategy for @langchain DeepAgents. Pair a cheap executor (Haiku) with a powerful advisor (Opus): execute end-t

LangSmith for Startups Spotlight: @tryflint Flint is an autonomous website platform that generates on brand landing pages. Flint’s marketing agent works behind the scenes to get your message out to th

Love this new podcast format ❤️ super insightful! 🎙️Introducing Max Agency Max Agency is a new podcast where we go deep on how the best agents are actually being built: architecture decisions, tradeof

Max volume for Max Agency 📣🗣️ 🎙️Introducing Max Agency Max Agency is a new podcast where we go deep on how the best agents are actually being built: architecture decisions, tradeoffs, evals, and every

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Multi-Agent Orchestration is a great tool in building agentic products thankfully @sydneyrunkle put together a guide with runnable code for each pattern earlier this year 🙏 (link below) choose any mod

My X bookmarks have an 'intellectual shape' Apparently mine is: Technique-heavy, Tool-obsessed, Light on Opinion I didn't know until I ran the numbers OSS I've been building w/@LangChain to explore Ge

New Guide: Incorporating human judgment in the agent improvement loop Building agents is hard. Everyone talks about the code. What gets less attention is how to capture domain expert knowledge and act

An **Open Harness** is a unified architectural layer that sits between AI agents and model providers, abstracting away provider-specific APIs and patterns. Because every AI agent harness has its o...

Open swe uses deepagents under the hood Deepagents is general purpose, openswe is focused on coding @hwchase17 @LangChain This is a very interesting comparison. Now my question is: how to compare Deep

Portable agents @hwchase17 Ngl I really like this direction. The more AGENTS.md, skills, and tool config start looking like portable interfaces instead of app-specific hacks, the more usable this whol

Speed of open source! Anthropic adds advisor strategy yesterday -> Emanuele adds an implementation as middleware less than 24 hours later! Just shipped advisor-middleware: an open-source implementatio

The ‘agent harness’ is essentially the new runtime. It’s like a lightweight OS around an LLM—handling memory, tool calls, retries, and control flow. @hwchase17 im excited about agent harnesses because

The challenge with agent harnesses is not only building them but distribution. If we want agent products to scale like saas, the interfaces and ecosystem still need a fundamental breakthrough. im exci

tldr > evals are the new training data. instead of updating weights, you're updating the agent harness > problem is agents are famous cheaters. they will reward-hack your evals and overfit just to mak

we are doing this podcast specifically to highlight the innermost details of building agents in production may be a little niche, but its a niche i like 🤷‍♂️ @hwchase17 Great content, really appreciat

What does it actually take to make agents better over time? A system that starts with a trace. You capture traces of agent behavior, enrich them with evaluations and human feedback, identify what’s fa

what he said 🗣️ the very best agents today obsessively tailor the harness layer around the model I’m looking at you “5 things I learned from the Claude code leak” bros 👀 orchestration patterns, tool d

why not? what in the current harnesses are not bitter lesson-pilled? off top of head: will go away: - planning tools - compaction (or at least will matter less) not going away: - sub agents - skills -

1️⃣✅❌ 2️⃣✅❌ 3️⃣✅❌ One of the benefits of Deep Agents deploy is model optionality Choose from models from @OpenAI @GeminiApp @AnthropicAI @FireworksAI_HQ @baseten @OpenRouter @ollama @nvidia and many o

a useful mental model on how teams can think about good data design to improve their models/agents: Evals ~= Training Data ~= Environments - in Classical Deep Learning, we learn from each training exa

And that is what Thoth uses for its agents in the backend. Thanks for your contributions to oss @LangChain @hwchase17 https://github.com/siddsachar/Thoth AI that doesn't just work for you, it knows yo

Announcing the @LangChain podcast -- Max Agency. Deep context to give you the edge while building and iterating on agents. Watch the full episode on: - Youtube: https://www.youtube.com/watch?v=Xyh1Eqc

calling all of my fellow podcast nerds! 🎙️Introducing Max Agency Max Agency is a new podcast where we go deep on how the best agents are actually being built: architecture decisions, tradeoffs, evals,

Can’t get enough of Harrison and his AI takes? Lucky for you, we’re announcing Max Agency where you can hear him & other industry experts talk agents, harnesses, models, evals, and more!! 🎙️Introducin

Checkout our new Max Agency podcast - the first episode is already out! 🎙️Introducing Max Agency Max Agency is a new podcast where we go deep on how the best agents are actually being built: architect

Claude Agent SDK tracing in LangSmith just got an upgrade. Now you can trace: → Subagents → Child runs inside MCP tools → Cost tracking + more Update to the latest Python SDK to try it out. Docs: http

Deep Agents deploy gets you: - Deep Agents harness - Sandbox of your choice (@daytonaio , @modal , @RunloopDev ) - Short and long term memory - Agents exposed via MCP and A2A Production ready and open

🚀Deep Agents deploy Today we’re launching Deep Agents deploy in beta. Deep Agents deploy is the fastest way to deploy a model agnostic, open source agent harness in a production ready way. Open harnes

Deep Agents or @LangChain? Just use the right tool for the right job great q! deepagents has more 'batteries included', which langchain v1 is a very minimalistic agent harness if you are doing more co

LangChain co-founder Harrison Chase (@hwchase17) highlighted the ability to deploy a production-ready agent to the web using the same configuration setup — such as an `AGENTS.md` file — used to def...

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extremely fun jamming with Harrison on all things analytics, agents and evals! I have like 20 more hours worth of takes and opinions in this space and going on the pod uncorked them, more to come for

Gotta love @LangChain! 🥹 Providing high quality open source agent infra DAILY Single handedly advancing the trajectory of AI Open Harness, Model Choice, Open Memory (take it wherever you need), Open P

great q! deepagents has more 'batteries included', which langchain v1 is a very minimalistic agent harness if you are doing more complex workflows (eg claude code for X) -> deepagents if you want some

'Harnesses are intimately tied to memory, which means that by choosing an open harness you are choosing to own your memory, and not have it be locked into a proprietary harness or tied to a single mod

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Rob Schaper posted a reply on X (Twitter) praising Harrison Chase (@hwchase17) and the LangChain team for their work on agent harnesses, predicting the concept would become widespread within days. ...

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