Search: “harrison-chase--x”

fun read on real tradeoffs & design decisions we debated when designing Engine for the data scale that customers produce one common thread is that we’re pretty strong supporters of just giving the age

Hunt Lovell from the LangChain open-source team provides an explanation of interpreters, likely covering how interpreters function within the LangChain framework for executing code or processing langu

ICYMI: LangSmith Sandboxes are GA ✅ Agents get a real filesystem, shell, and package manager. Isolated from your infra. ✅ Works with Deep Agents, Open SWE, or your own code. ✅ Auth with the same API k

no trace left unread, no issue left unfound 👀 With Engine we’re pointing agentic compute across every trace across every agent so every team can find product feedback, bugs, issues 24/7 langsmith engi

Personally incredibly exciting at LangChain Labs to lead a focused, applied research effort into Continual Learning. A big prior is in treating the long horizon agent optimization problem as one that

Stoked to work closely with @Vtrivedy10 on this. I've spoken about it before but an emerging trend i'm seeing with many of the ai-native companies I work with (shoutout @larsen_weigle_ ) is a focus on

the first step to building evals is often times figuring out what you want to eval (sounds simple, in practice it has a lot of nuance) you can condense long horizon evals with agents into smaller subs

Exa, a search lab focused on organizing web data for AI agents, announced a Series C funding round of 250 million at a 2.2 billion valuation, led by venture capital firm Andreessen Horowitz (a16z). Th

We’re hiring for Labs! 🧪 If you’re interested in working with us to push forward Continual Learning, pls DM me with a blurb + link to the best Applied Research you’ve done (or even better shipped!) yo

working on some hex dashboards -- this is a bummer! if you use langchain/deepagents as your agent harness, you can swap models with fallback middleware when your first choice provider is down https://

you can condense long horizon evals with agents into smaller subsets that still let you test intended behavior. i'm currently evaluating an agent that runs for 30+ minutes, and analyzes thousands of t

This post likely documents the technical architecture and development process behind Engine, a software component or platform, shared by Palash Shah. The content presumably covers key design decisions

Curious finding while creating evals and benchmarks for long-horizon (100+ turn) agents While it’s generally thought that a direct swap to open source models can bring immediate cost savings, that’s n

Deep Agents now integrates with @nebiusai Token Factory. Now, you can run agent workloads on production-grade AI infrastructure with open-source models, dedicated endpoints, real-time search, and full

deepagents v0.6 is about performance the first level at which we can control that is the model layer: how can you squeeze perf out of a model? tweaking prompts, tool names, and tool descriptions in ac

Engine is a sophisticated production agent system developed by a team that Harrison Chase recommends for understanding advanced agent architecture and implementation. The post suggests it represents n

Excited for this conversation with @hwchase17 , Co-Founder & CEO of @LangChain , on June 2 at the @traversal_ai office in NYC. We’ll be talking about what it actually takes to operate AI agents in pro

Harrison Chase provides a detailed technical overview of the architecture and development process behind LangSmith Engine, sharing key learnings and practical tips accumulated during its construction.

This post likely discusses techniques and methodologies for building AI agents capable of performing AI engineering tasks, such as code generation, model optimization, or system design. It probably co

I've been thinking a lot about the two different groups of evals you need in general agents/agents which handle broad tasks: 1. Benchmark evals - this is a suite of up to 100 eval cases which test the

LangSmith Engine is a tool within the LangChain ecosystem that provides visibility and debugging capabilities for AI applications built with LangChain. This technical deep-dive likely covers how the e

LangGraph.js Long-Term Memory Store is now generally available. This integration brings long-term memory across sessions, across users, and across time, with Atlas as the unified backend for checkpoin

Most teams building agents are tracing their agent and reviewing the outputs, but the flow from error identification to merged fix is manual and slow. Engine is built to close that gap. https://www.la

Read more on what we've learned while developing long-horizon agent evals: Curious finding while creating evals and benchmarks for long-horizon (100+ turn) agents While it’s generally thought that a d

this has probably been the most interesting project i've ever worked on. we created the best agent at finding problems with your agent. and we integrated it with the best observability platform to spi

This release is absolutely stacked. I want to call out a sleeper feature: Interpreters Imagine your cloud agent gets a 10k-row CSV of support tickets. Without a code runtime, the model is stuck reason

two years ago we started building agents to automate work. turns out these are really useful, so there’s a LOT of runs and long traces that are hard to reason about now, use an ambient agent (engine)

a lot of stuff to be excited about in this release! We're centering on two things to make deepagents great: - how do we make the most performant harness - how do we make it a joy to work with and buil

announcing deepagents v0.6, our biggest release yet! it’s all about performance: at the model layer w harness profiles, agent layer w code interpreter, and at scale w streaming and delta channels cont

Behind the scenes of SmithDB with @ankush_gola11 ⤵️ We leveraged two amazing open source projects when building SmithDB. One is @ApacheDataFusio: an extensible Rust based query engine. We built custom

ICYMI: SmithDB is our purpose-built data layer for agent observability + eval workloads. Supporting increasingly complex query patterns at low latency, over large traces, with self-hosting + multi-clo

LangChain released Deep Agents v0.6, described as their largest release to date. The announcement was made by Harrison Chase on X (formerly Twitter), indicating a significant update to the Deep Agents

In case you're not following the @LangChain_OSS team, here's who to follow! @masondrxy and @Vtrivedy10 are doing an awesome push on harness profiles @huntlovell doing cool stuff w code interpreter / R

LangSmith Engine automates the full agent fix loop — detecting failures, diagnosing causes and drafting PRs. But multi-model enterprises say a neutral observability layer still wins. http://venturebea

LLM apps fail in ways normal logs can’t explain. Same input, different outputs. Subtle drift. Silent hallucinations. That’s the problem space LangSmith by @LangChain was built for: observability + eva

The post discusses positive features and improvements included in version 0.6 of DeepAgents, with Harrison Chase praising a write-up by Sydney that explains these updates. This likely covers new capab

@masondrxy and @Vtrivedy10 are doing an awesome push on harness profiles @huntlovell doing cool stuff w code interpreter / REPL @bromann leading the charge on streaming our OSS team is stacked! lots o

Nebius and @LangChain have partnered to integrate Nebius Token Factory with LangChain's Deep Agents. The integration, combined with LangChain's existing Tavily integration, gives teams building on Lan

Today in AI Engineering (May 17) • Nous Research ships Hermes Agent v0.14.0: Grok subs, Codex runtime, Windows beta • LangSmith Engine releases trace issue clustering, drafts PRs and evals from prod t

when evaluating long running agents, all of your evals don't need to be end to end. i'm working on a proper blog about this, but in our evals for our agents that run for 30-60 minutes, we have two set

This post outlines a development roadmap for an AI agent system, prioritizing initial version building with internal testing (dogfooding), implementing tracing and observability for data collection, a

Harness profiles! https://docs.langchain.com/oss/python/deepagents/profiles I like what Langchain recently released in their deepagents harness, which is an adapter to modify the syntax of primitive f

LangSmith Engine feels like the missing CI/CD loop for AI agents , automatically detecting failures, clustering issues, proposing fixes, and generating evals from production traces. Agent engineering

LangSmith Engine is how we’re spinning the always-on, self-improvement loop for every agent - Tracing is on for every single agent - Purpose built infra with SmithDB to handle data at agent scale (mor

This is one of the first real continual learning systems for agents in production. Not just monitoring. Actually getting better over time. LangSmith Engine is how we’re spinning the always-on, self-im

This post likely discusses how developers often create unnecessarily complex evaluation environments when testing AI agents, and suggests simplifying these setups for more effective testing. The advic