Search: “harrison-chase--x”

LangChain Community Spotlight: Saving $1M in LLM Costs 💰 Gustaf, an AI Engineer, shows how he reduced a production RAG chatbot's costs by 90% and improved latency by 82% through caching, intelligent r

this is a fantastic example of a robust agentic memory system built on langgraph! there's 4 key parts 1. retrieval (rag) 2. storage, fragmented by memory type 3. reasoning* 4. learning (often called d

This is great @hwchase17 @bryonkuchML Seeing this update, I’m building a tutorial repo around LangChain + Groq + GEPA. The idea is simple: •LangChain builds the RAG/agent workflow •Groq gives fast inf

One of the better agentic AI courses I've seen Nearly 10 hours of great content. Covers LangChain, LangGraph, RAG, deepagents, guardrails, and more Any other good Lang* resources out there for folks w

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

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

retrieval is a decision problem, not just a similarity problem cool experiment from @assaf_elovic swapping embeddings for Jev in GPT Researcher - 73% vs 46% relevant context decision models will show

Didn't expect this 🤯 We replaced embeddings with Jev in GPT Researcher's RAG pipeline and tested both on 28 research tasks from SimpleQA and open ended research. Jev beat embeddings on every quality m

Ha gave this take to our team last week Will share some (very basic) stuff I vibe coded around this shortly Lots to be explored here! One of my more controversial bets: the AI feed will be the form fa

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

- deepagents is durable by design - built on langgraph with state machine primitives hardener over years - deepagents is extendable, pick the exact pieces you want or make your own - deepagents is ope

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

As I’ve been saying, LangChain is going to be the leader in composable AI because their tech is perfect for stitching together all of these smaller specialized models. And now we have another category

Coding agents (Claude code, codex) -> personal agents (instinct, muse) -> companyOS (???) after switching our approach to building a 'FirmOS', I'm increasingly confident that the future of agent colla

I just love the language of this study...it speaks of the shifting 'community language'...and that is so true...have you noticed the new 'community language' is 'harness', it was 'contextual prompting

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

Evals ~= Environments…they’re one of the best investments a team can make for improving agents Step 0: Turn On Tracing for Agents Step 1: Point compute at Traces to understand agent behavior, segment

Production traffic from frontier models is a golden data asset. If you can efficiently mine the traces, filter for quality, and fine-tune smaller models on them, you get specialized performance at a f

excited to be speaking at @aiDotEngineer World Fair next week on Improving Agents, Continual Learning, and why we think a large part of it is...Data Mining! Trace Mining is how we understand agent beh

LangChain proposes using Langsmith traces to fine‑tune open‑source models within the AI Development Lifecycle (ADLC). A tweet by Harrison Chase (@hwchase17) on July 25, 2026 invites interested users t

teams want to understand what their agents are doing but it’s hard to sift through all that trace data at scale but you can basically find any signal by fine-tuning a small, cheap model on data showin

We spent a lot of time streamlining the process to go from your agent's trajectories to an SFT'd model on your training/inference provider of choice so you don't have to stitch together the steps manu

🚀 deepagents 0.5 release 👉 Async subagents - kick off background tasks on any Agent Protocol backed server while you continue to interact with the main agent. Start multiple background tasks in parall

Strong Opinions, Loosely Held on Agent + Harness Engineering: 1. You can outperform any default harness+model (including codex & claude code) on pretty much any Task by engineering the harness around

streaming from modern agents is pretty complex! especially with - parallel tools / subagents - multimodal content - human in the loop events our new streaming primitives make all of this ergonomic! Ag

you can now build @LangChain agents w/ agentic video understanding! google is consistently at the forefront of multimodal processing; this is the video analog of 'agentic vision' token usage + cost is

deep agents just shipped a round of multimodal upgrades! 1. broader file support on OpenAI Responses: docx, pptx, xlsx, csv, json, markdown, not just pdfs 2. opt-in binary offloading to keep big files

managed deep agents 0.9 is here! things we shipped: ⏰ agents can schedule tasks OBO themselves or the user 🏭 dynamically configure your mda per run (models, tools, skills, etc.) 👀 Slack reactions show

Do agents listen to you… or themselves? While evaling subagent behavior in deep agent systems, we noticed an interesting quirk in our agents' alignment with hand-written system prompts vs. the instruc

Completely agree, @hwchase17 ! This is the meta-level breakthrough we've all been waiting for. Self-optimizing loops finally feel production-ready because LangSmith Engine turns evaluation from a manu

Almost everyone is building agent harness systems the wrong way. The default move: pick LangChain or LangGraph or the OpenAI Agents SDK, accept the loop, the tools, the memory, the orchestration, the

Spent more time learning deepagents from @LangChain with primary focus on integrating MCP servers with auth that doesn't conform completely to the OAuth standard. ( In prep of a work coming my way ) B

One of the better agentic AI courses I've seen Nearly 10 hours of great content. Covers LangChain, LangGraph, RAG, deepagents, guardrails, and more Any other good Lang* resources out there for folks w

have been thinking a bunch about model routing and related things current thoughts here, would love feedback: 1/ there is a difference between 'model routing' and 'model council' 'model routing' = rou

jes, a jev open-source guardrails project LangChain Ambassador @EdenEmarco177 has been working on It uses LangChain’s TypeSafeClassifier under the hood. The docs include a LangChain middleware example

Check out jes: An open source real-time guardrails for AI agents, powered by decision models like Jev from @typesafeai Built to guard every step in the agent trajectory: prompts, tool calls, tool resu