Knowledge catalogue
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
I like what Langchain recently released in their deepagents harness, which is an adapter to modify the syntax of primitive file system commands depending on the model Claude likes “Bash”, Gemini likes
just got back from my second langchain interrupt conf and honestly it's probably the best AI engineering conference right now. what makes it different is that it's deeply practical. less 'AI will chan
“Dependably for LLM agent failures” @hwchase17 Started on this and finding it awesome; also LangSmith engine sparked an idea. The 'Dependabot like for LLM agent failures'. LangSmith Engine gives you t
This appears to be a humorous exchange from X/Twitter creator Harrison Chase referencing train cars named after AI/ML tools and services (Devin, Langsmith, and Mistral), likely making a joke about the
Excited to see SmithDB announcement at Interrupt, our purpose-built distributed database for agent observability! SmithDB is built on top object storage, written in Rust and leverages Apache DataFusio
Had the honor of sharing the stage with the one and only @sydneyrunkle at Interrupt 2026 to talk about Deep Agents. Sign-up for the Managed Deep Agents waitlist today to get early access! https://www.
@hwchase17 Started on this and finding it awesome; also LangSmith engine sparked an idea. The 'Dependabot like for LLM agent failures'. LangSmith Engine gives you the smoke detector. The natural next
ICYMI: 1️⃣ LangSmith Engine 2️⃣ SmithDB 3️⃣ Managed Deep Agents 4️⃣ LangSmith Sandboxes: Now Generally Available 5️⃣ Context Hub 6️⃣ LangSmith LLM Gateway 7️⃣ Sandboxes, Prebuilt agents, + free model
it almost never makes sense to use real api's for your evals. with how good coding agents have become, i will pretty much always opt to create a fake mock server for my agent to hit. the workflow is u
it’s kind of awesome that continual learning has now come to the agent & harness level. i remember when online learning was the big craze in traditional ml a couple of years ago, that transitioned int
Key ideas I picked up at the @LangChain conference this week: 'AI engineering is data science. Look at your data.' — @sh_reya & @HamelHusain 'Data strategy *before* agents.' — @AndrewYNg 'Coding agent
@LangChain’s Interrupt 2026 conference was a blast!! such a pleasure to with @VictorMoreira16 about deep agents! ICYMI: we just dropped v0.6, which is focused on performance at the model, harness, and
SmithDB lets you see traces in seconds instead of minutes. This quote from our friends at @cogent_security says a lot: “At Cogent, our background agents can produce a huge volume of traces all at once
This quote from our friends at @cogent_security says a lot: “At Cogent, our background agents can produce a huge volume of traces all at once. We need live observability into those systems, and SmithD
turns out that building evals is super super challenging even now. i thought a lot of it was table stakes but turns out it has only become harder since agents are now more complex than ever! going to
Wow! What a week! Lots of new things to learn - new ideas to form The complete Agent Development Life Cycle 🎯 ❤️ SF but can’t wait to get home and back to building! Thanks for the warm welcome and fun
Abridge: 100M+ medical conversations, real-time prior auth, and the clinical intelligence layer https://www.latent.space/p/abridge @AbridgeHQ is building the clinical intelligence layer for healthcare
Agent observability is a means to an end: making your agent better. But observability and evals tools have traditionally failed to connect traces to meaningful actions. Agent engineering teams are lef
big launches today! 🚀Launching: LangSmith Engine LangSmith Engine is an agent that sits on top of your traces It runs in the background and automatically identifies issues It then proactively suggests
deepagents v0.6 is our biggest release yet!!! it’s all about perf - at the model layer w harness profiles, agent layer w code interpreter, and at scale w streaming and delta channels context hub backe
DID SOMEONE SAY FREE TOKENS IN FLEET???? Yes it's true. Fleet now has a built in model powered by @FireworksAI_HQ that's free for all Developer & Plus plan users. Try it out today (or before @hwchase1
everything we build at LangChain is provider/model agnostic for these reasons! our harness is open source and easily customizable! On topic: there is a mistake I saw large companies make in late 2025/
great agents need great infrastructure. proud to be @LangChain's Deep Agents Inference Partner at Interrupt 2026 in SF. great to spend time with builders at our day 1 happy hour. excited for day 2 tod
Great move. If you’re building agents, so much time is spent engineering your way through traces. And then comes the hard to rapidly iterate and figure out the changes needed. 🚀Launching: LangSmith En