Search: “harrison-chase--x”

We’re hiring! https://www.langchain.com/careers Companies I'd consider going to if I ever had the urge to try something new. 1 Thinking Machines Lab 2 OpenAI 3 Anthropic 4 Cursor 5 Applied Intuition 6

Come and join us, we're hiring across almost all roles 🚀https://www.langchain.com/careers Companies I'd consider going to if I ever had the urge to try something new. 1 Thinking Machines Lab 2 OpenAI

This is harder to build than it looks. Preserving full conversational context while swapping underlying model providers mid-flight is a surprisingly deep systems problem. Most tools drop state or forc

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

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

openwiki 🤝 cursor we recently made openwiki more accessible than ever with coding agent integrations. our newest integration is cursor! two commands and you're ready to go: 1⃣ npm install -g openwiki@

This is very true, and something we still try to do for all products (and even features) we launch. Find a small group of customers who we think will push us the most on product and then build for the

The hardest part of and “org harness” or “enterprise ai” (as opposed to personal ai” is auth, and managing auth in a multiplayer channel (like slack) We’re thinking a lot about how to do this right fo

Without a doubt evals are the biggest blocker in most agent development pipelines. We’re going to be changing that for everyone in LangSmith in a few short weeks… Hot take: evals are the #1 thing keep

let me introduce you to the langchain stack 1. langgraph gives you this level of control 2. traces give you the complete run lineage 3. version your skills and instructions in context hub 4 & 5. deepa

One cool thing deepagents now supports: dynamically loading tools when is a skill is loaded Some skills require specific tools that you may not always want to have available. This now allows you to pa

good framing from @ch3nweiii - a lot of agent steps are yes/no calls, not generation Jev answers those as typed questions with calibrated probabilities, so the frontier model sticks to the hard work w

deepagents just hit 30k stars on github! deepagents is great for building agents that do complex work. they manage context well and use a durable runtime. though inspired by coding agent design, deepa

router lives in the harness, not a gateway, bc the harness knows the task blog: https://www.langchain.com/blog/how-to-build-a-model-router-in-the-harness https://x.com/neil_xbt/status/2109979966859510

Much of the valuable information in agent traces isn’t directly filterable with existing techniques like full text search or metadata filtering. AI Search in LangSmith closes that gap. You describe wh

🔎 Announced at Interrupt: AI Search in LangSmith You used to need an evaluator or read hours of conversations to find traces that answer questions such as: 💬 Does the user seemed frustrated?' 💬 Where

LangChain launched **Deep Agents Deploy** in beta as an open-source, model-agnostic alternative to Anthropic's Claude Managed Agents, allowing developers to deploy production-ready agents via a sin...

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

Love seeing this open-sourced. Had a great chat with @nicoalbanese10 some weeks ago where he hinted to something like this. Great reference architecture for cloud coding agents. Open Agents gives you

Thoth v3.15.0 is live. Talk to your AI from anywhere. It talks back from everywhere. 5 channels, full parity: WhatsApp, Discord, Slack, SMS, Telegram Streaming, reactions, media, slash commands New: X

DeepAgents Sandbox — a lightweight native Linux sandbox for deepagents It gives full isolation for safely running LLM/agent-generated code — no Docker, no VMs, no cloud dependency. Super fast and secu

deepagents-cli is quietly becoming the best place to start coding with open weight models. we've been investing heavily in making it a harness that's truly model-agnostic, without compromising perform

+1 to this. I was recently on a cross-continental flight without wifi, so I brought up Qwen3.6 & Gemma 4 (via @ollama) in Deep Agents on my laptop. admittedly, they fell over on some more involved/com

Vercel is positioning itself as a full-stack 'Agentic Infrastructure' platform, recognizing that weekly deployments have doubled in three months, with over 30% now initiated by coding agents — up ...

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 seeing this open-sourced. Had a great chat with @nicoalbanese10 some weeks ago where he hinted to something like this. Great reference architecture for cloud coding agents. Open Agents gives you

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.

langgraph <3 New category drop on the Arena: ✨AI agent frameworks✨ Agent Experience (AX) update as of 06/26: 1/ @LangChain LangGraph 2/ @vercel AI SDK 3/ @crewAIInc 4/ @mastra For context: we evaluate

(deepagents existed ~10 months before EVE, but...) yes - the agent industry has shifted from: ~agent frameworks~ (langchain, ai sdk, llama index) to ~agent harnesses~ (deepagents, claude agent SDK, EV

The LangChain podcast where @hwchase17 interviews agent builders is full of alpha. Recent one with @EnoReyes was the best one yet. Sharing my unstructured notes: Eno keeps bringing back some core conc

@hwchase17 @LangChain I built an open-source CLI that scaffolds, runs, and deploys production LangChain agents — frontend and backend in one command. Every agent project starts the same way: wire up a

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 (

every agent will need a sandbox and the harness should live outside it - separate the brains from the hands great to see nvidia open sourcing tools here, agree with Andrew this is part of the harness