Harrison Chase(@hwchase17)

interrupt london is next tuesday really looking forward to hearing from Orange and OVO on what's ac...

Harrison Chase (@hwchase17) — Wed, 07 Oct 2026 19:51:15 GMT

interrupt london is next tuesday

really looking forward to hearing from Orange and OVO on what's actually working with agents in production. we're also running hands-on workshops on Deep Agents and Engine

x.com/LangChain/stat…

LangChain@LangChain

Only one week to go until Interrupt London!

interrupt.langchain.com/london

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One cool thing deepagents now supports: dynamically loading tools when is a skill is loaded Some sk...

Harrison Chase (@hwchase17) — Wed, 07 Oct 2026 18:58:29 GMT

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 pair them 

With openai/anthropic models, it’s possible to do this in a way where it doesn’t break the prompt cache!

Sydney Runkle@sydneyrunkle

x.com/i/article/2107…

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We have a really strong team :) Come join us!

Harrison Chase (@hwchase17) — Wed, 07 Oct 2026 09:34:55 GMT

We have a really strong team :)

Come join us!

John Kim@0xjohnkim

Every startup says they have the best team. We looked at the data.

@paraformtalent’s Talent Density Index ranks which companies attract, develop and retain the people the market values most.

The latest edition is live.

New Top 50: paraform.com/talent-density…

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Good take on harnesses

Harrison Chase (@hwchase17) — Tue, 06 Oct 2026 04:26:37 GMT

Good take on harnesses

David Cramer@zeeg

You’re living in a bubble. You need to understand that a whole world lives beyond the constraints you have manufactured or imagined.

For example the codex harness provides very little in terms of actual functionality - the same goes for most coding harnesses. They will be generally superseded by more capable specialized harnesses. Even if you scoped codex to only a coding-focused harness - which it really isn’t - it is lacking compared to purpose built coding harnesses that compete with it. 

Right now labs have the advantage and can somewhat dictate terms but you should not believe that will be true forever. There’s seemingly a belief that we should be thankful for the (limited) capabilities we’ve been given- that we should allow a few companies in the world to dictate what we can and can’t do with technology. That’s never worked before and nothing makes this tech any more defensible. 

In five years we will almost certainly be consuming local models for a majority of daily tasks, and for the things that can’t be done locally it’s going to be entirely commoditized. Once that’s true it means vendors will have to start competing by giving up more to the customer rather than the increasing restrictions being done today.

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cool to see cognition doing "dreaming" - memory needs an offline cleanup loop, not just better retri...

Harrison Chase (@hwchase17) — Mon, 05 Oct 2026 20:28:50 GMT

cool to see cognition doing "dreaming" - memory needs an offline cleanup loop, not just better retrieval, otherwise stale records pile up

open q is how inferred memories get validated before the agent uses them

glad it's an open standard too - memory should be open

x.com/cognition/stat…

Cognition@cognition

Introducing Dreaming: Across sessions, Devin builds a memory graph of how you like to work

At night Devin self-improves its memory to remove stale records and discover latent information

We are creating an OSS standard called Agent Memory Repo: cognition.com/agent-memory-r…

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we're seeing our coding agent costs decrease significantly for second month in row longer blog on t...

Harrison Chase (@hwchase17) — Sun, 04 Oct 2026 10:39:18 GMT

we're seeing our coding agent costs decrease significantly for second month in row

longer blog on this later, but three quick steps we took

1/ cost visibility. you cant control what you cant measure. Start tracking everything to langsmith. This gives us super granular visibility into who is using what, how they are using it, etc. LangSmith supports first party integrations with all main coding harnesses langchain.com/blog/your-codi…

2/ cost controls. Through our LLM gateway, set user level cost caps. (You can increase your cap by talking to our VPE). This helps curb unexpected or accidental spend. docs.langchain.com/langsmith/llm-…

3/ optimize the harness. Obviously 3rd party harnesses are tougher to optimize, but we are doing more and more coding through OpenSWE, our open source cloud agent harness. Things like model routing help bring the cost down langchain.com/blog/how-to-bu…

feel free to reach out if you want help implementing these at your co!

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specialized agents or one mega agent? we're having this debate internally...

Harrison Chase (@hwchase17) — Sat, 03 Oct 2026 20:05:50 GMT

specialized agents or one mega agent?

we're having this debate internally...

a16z@a16z

.@OpenRouter co-founder Alex Atallah on personal agents: 10 specialized chiefs of staff beat one universal agent. One job each means you can see exactly where it's failing.

"The worst part about doing cross-domain joins with your personal agent is that the more work you give it, the more understanding of what's going on you're sacrificing."

"And no one is taking responsibility for that sacrifice. Agents don't have any responsibility."

"With a universal agent that's doing all things, I can't adjust how much understanding I'm sacrificing."

"It points me towards: maybe down the road, the sub-agents people use will be very vertically focused, and we have a chief of staff type agent that coordinates between them."

"You need vertically focused agents where this agent is more responsible, psychologically, for these things. I want quality checks that make sure it's doing those things correctly. It doesn't need to focus on anything else, it has one focus area."

"I wonder if that's going to help people get at least a loose sense of responsibility on top of agents."

@alexatallah

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cool project by @dbreunig in langchain this is ModelRouterMiddleware - jev reads the first message,...

Harrison Chase (@hwchase17) — Sat, 03 Oct 2026 20:05:10 GMT

cool project by @dbreunig

in langchain this is ModelRouterMiddleware - jev reads the first message, picks the model, and that model handles the whole run

jev is cheap enough that you can also re-pick after each tool result with a custom hook

docs: docs.langchain.com/oss/python/int…x.com/dbreunig/statu…

Drew Breunig@dbreunig

A quick example of a model router, using DSPy and Jev, based on your initial prompt. 

Sketched this out in ~15 min, by request, and am surprised at how well it works! gist.github.com/dbreunig/949b4…

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good pattern from this google paper the verifier starts from a clean context, so the explorer's rea...

Harrison Chase (@hwchase17) — Fri, 02 Oct 2026 17:42:10 GMT

good pattern from this google paper

the verifier starts from a clean context, so the explorer's reasoning can't talk it into a bad proof

so the explorer can try wild ideas and the verifier tosses the wrong ones

same reason deepagents subagents get their own context

x.com/omarsar0/statu…

elvis@omarsar0

Banger paper from Google Research on multi-agent proof discovery.

(bookmark it)

It's really interesting to see this emerging multi-agent pattern: not enforcing too much execution structure and pairing it with dedicated agents for advising and verification.

I think it is generally applicable as well. Great read.

Here is how it works:

Cogentic runs on Gemini and works on open problems in theoretical computer science, starting from the problem statement with no expert hints.

The system works in rounds, and the orchestrator decides how many provers to run in each round. Every prover gets one direction to work on, such as a specific bound or a counterexample search, plus a short briefing that a summarizer agent writes from earlier attempts and verifier feedback. Each summarizer writes its briefing independently, so provers in the same round read different summaries of the same history.

Each draft goes through two adversarial verifiers. One checks the draft on its own, and the other reads all of the round's drafts side by side to catch shared mistakes. A draft is accepted only if both pass it.

The agents share state through two disk documents. A record logs every attempt with the objection it failed on, and a ledger stores verified lemmas and ruled-out directions. An auditor extracts correct lemmas from rejected proofs, verifies them again independently, and adds them to the ledger.

A separate process advisor reads the verification logs across rounds and updates the instructions given to provers and verifiers. The orchestrator and the advisor can't give mathematical opinions, so the provers provide all the math.

It produced new results on five open problems in online learning, auction theory, and mechanism design, each checked by domain experts. Most problems took around 100 Gemini calls, and the hardest took around 1,000.

Paper: arxiv.org/abs/2609.40324

Chat with Paper: academy.dair.ai/papers/cogenti…

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every agent team ends up wanting its own review ui over their traces - LangSmith Custom Apps lets yo...

Harrison Chase (@hwchase17) — Fri, 02 Oct 2026 17:33:45 GMT

every agent team ends up wanting its own review ui over their traces - LangSmith Custom Apps lets you build it on your own data and ship it right into the workspace

x.com/LangChain/stat…

LangChain@LangChain

💻 Build the interface you want w/ your LangSmith data
✅ Publish it into your workspace
🙅 Skip hosting, auth, + permissions work

Get started with Custom Apps today.langchain.com/blog/langsmith…nW

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Memory is hard to get working well, especially in a company setting Making some improvements to how...

Harrison Chase (@hwchase17) — Fri, 02 Oct 2026 16:13:18 GMT

Memory is hard to get working well, especially in a company setting

Making some improvements to how we memory in managed deepagents!

Caspar@caspar_br

User memory in Managed Deep Agents 0.8: your agent can remember the people it works with langch.in/mda

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congrats @AravSrinivas! more entrants in the decision model class, and this one is open weights che...

Harrison Chase (@hwchase17) — Thu, 01 Oct 2026 23:45:36 GMT

congrats @AravSrinivas! more entrants in the decision model class, and this one is open weights

cheap typed answers for the small calls inside a harness (routing, approvals, judging), big model for the rest

x.com/AravSrinivas/s…

Aravind Srinivas@AravSrinivas

We’re open sourcing a state of the art multimodal Decision Model, pplx-decider-27b, and are offering it in a new Decisions API at 4 cents per million input tokens and free output tokens. We intend to bring down the price even further over the coming days. Enjoy!

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pretty clear every agent harness needs a durable runtime pi :: pi-durable deepagents :: langgraph

Harrison Chase (@hwchase17) — Thu, 01 Oct 2026 22:45:58 GMT

pretty clear every agent harness needs a durable runtime

pi :: pi-durable

deepagents :: langgraph

Pi@pidotdev

People of Pi: We've shipped Pi 1.0 with Pi Durable. Go make them yours.

earendil.com/posts/pi-1-0/

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decision model season indeed said there'd be more entrants before long - now open weights too, nice...

Harrison Chase (@hwchase17) — Thu, 01 Oct 2026 17:37:40 GMT

decision model season indeed

said there'd be more entrants before long - now open weights too, nice work @ritakozlov + the cloudflare team

your harness should be able to swap its decision model as easily as its main model

 x.com/ritakozlov/sta…

rita kozlov 🐀@ritakozlov

it's decision model season 🍂

today, we open sourced clef and clef-flash, our first homegrown models — both now available on @cloudflare workers ai! 

demo / leaderboard:clef-evals.workers-ai-mle.workers.devk

blog:blog.cloudflare.com/clef-decision-…z

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model routing - "no one knows what it means, but its provocative, gets the people going" we try to ...

Harrison Chase (@hwchase17) — Thu, 01 Oct 2026 17:24:18 GMT

model routing - "no one knows what it means, but its provocative, gets the people going"

we try to shed light on how to do it properly

1. understand tasks
2. understand the models
3. build the router **in the harness**
4. track outcomes

lower costs, with no performance hit

Sydney Runkle@sydneyrunkle

x.com/i/article/2105…

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great writeup by @pirroh harness doesn't disappear as models get smarter - it shifts from deciding ...

Harrison Chase (@hwchase17) — Tue, 29 Sep 2026 21:25:27 GMT

great writeup by @pirroh

harness doesn't disappear as models get smarter - it shifts from deciding to offering

in deepagents you give each subagent its own model, tools, and permissions - the main loop picks which to spawn and when

docs: docs.langchain.com/oss/python/dee…

Michele Catasta@pirroh

x.com/i/article/2104…

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listen is a great company, alfred and florian are awesome founders the problem space listen is tack...

Harrison Chase (@hwchase17) — Tue, 29 Sep 2026 21:24:48 GMT

listen is a great company, alfred and florian are awesome founders

the problem space listen is tackling is also an amazing use case for AI, very bullish on what they will still build here!

Alfred Wahlforss@itsalfredw

Listen Labs is joining @Salesforce!

A year and a half ago we launched Listen, an AI platform for understanding customers, and it’s been a crazy ride ever since.

Today, we work with some of the largest companies in the world, including Microsoft, Anthropic, and Sweetgreen.

What started as an AI interviewer is now a full platform for understanding customers. Listen finds the right people, interviews them, analyzes what they say, and even simulates how they'll behave.

Listen’s growth quickly accelerated and while we were raising our next round, we met @Benioff.

Marc is a hero of mine. I've taken countless ideas from Behind the Cloud and watched him framemog every AI CEO on the planet. We're honored to get framemogged next.

With Salesforce, we can bring Listen to every company in the world, much faster.

To our customers, our mission remains the same.I stay CEO, @Florian stays CTO and the same team will keep building the product you rely on, now inside Salesforce AI Labs. Thank you for believing in us early.

To our team, families, and everyone who believed in us before there was much to believe in, thank you.

These years have been the most fun years of my life, and it’s still only just the beginning for Listen.

Now back to work.  🚀

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No

Harrison Chase (@hwchase17) — Tue, 29 Sep 2026 18:19:39 GMT

No

Rhys@RhysSullivan

it's crazy how far out the labs are building at the application layer

on the one hand it makes sense, on the other hand do you really want all of your company knowledge, docs, workflows built so locked in to a single model?

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company OS (org harness) vs personal agent, similarities and differences: differences: - Many princ...

Harrison Chase (@hwchase17) — Tue, 29 Sep 2026 14:25:29 GMT

company OS (org harness) vs personal agent, similarities and differences:

differences:
- Many principals (users), one actor (agent): will need to be multiplayer, handle multiple users (sometimes even in same thread), and as a result handle auth/memory correctly
- governance: observability, auditability, admin control plane matter way more
- interaction pattern: more event driven and async

similarities:
- should be able to write/execute code
- browser use likely very important! (key difference from a coding harness)
- skills/MCP as standards
- core agent loop/logic

what am i missing?

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coding harness -> personal harness -> org harness (company harness? enterprise harness? still...

Harrison Chase (@hwchase17) — Mon, 28 Sep 2026 22:36:10 GMT

coding harness -> personal harness -> org harness

(company harness? enterprise harness? still figuring out right name, open to suggestions)

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every agent will need a sandbox and the harness should live outside it - separate the brains from t...

Harrison Chase (@hwchase17) — Mon, 28 Sep 2026 22:13:33 GMT

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 not a bolt on

x.com/AndrewYNg/stat…

Andrew Ng@AndrewYNg

The OpenAI-Hugging Face hack was enabled by weak sandboxing. It is great that Nvidia is releasing open source tools for sandboxing AI agents. OpenWorker, our open-source agent harness supporting cybersecurity workflows, is proud to support this.

A sandbox gives an agent limited permissions. OpenWorker is building on Nvidia OpenShell and will support running each agent's commands inside a sandbox. Only the files relevant to the task go in. Secret API keys, your web browser login credentials, the ability to access arbitrary websites, are inaccessible to the agent by default. These restrictions are implemented in deterministic code rather than by prompting an LLM, which can make mistakes or be susceptible to prompt injections. Further, all actions are logged for monitoring and audit.

I'm grateful for @JensenHuang's leadership making AI agents more secure. OpenWorker (which @rohitcprasad and I are working on) will continue to improve security for agents.

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retrieval is a decision problem, not just a similarity problem cool experiment from @assaf_elovic s...

Harrison Chase (@hwchase17) — Mon, 28 Sep 2026 16:32:40 GMT

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 up all over the harness

x.com/assaf_elovic/s…

Assaf Elovic@assaf_elovic

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 measure we ran:
- 59% more relevant context (73% vs 46%)
- Reports preferred 15 to 3 in blind comparisons
- Same cost per report

GPT Researcher now runs on Jev by default, and no longer needs embeddings at all.

All you need is @LangChain + @tavilyai +Jev for the perfect RAG system.

Check out the repo here:github.com/assafelovic/gp…q
Research:docs.gptr.dev/docs/gpt-resea…q

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> feels way more shippable than "the LLM decided" probabilities are the underrated part of decis...

Harrison Chase (@hwchase17) — Sun, 27 Sep 2026 04:39:20 GMT

> feels way more shippable than "the LLM decided"

probabilities are the underrated part of decision models

you can threshold them, log them, write evals against them

 https://t.co/k4eRrMgNV0

Hiếu Nguyễn Trung@Hiumonday

@hwchase17 honestly the probabilities are the best part. you can set a threshold, log it, write tests for it. feels way more shippable than "the LLM decided"

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jev and langgraph are a great combo! modeling agents as complex systems, with AI imbued in them, ma...

Harrison Chase (@hwchase17) — Fri, 25 Sep 2026 19:07:11 GMT

jev and langgraph are a great combo!

modeling agents as complex systems, with AI imbued in them, makes a ton of sense

langgraph is the best way to model them as such, and using Jev inside langgraph to turbo charge all the small decisions that need to be made is awesome combo

Sydney Runkle@sydneyrunkle

x.com/i/article/2103…

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Proactive agents run in the background, schedules are (one way) how you achieve that!

Harrison Chase (@hwchase17) — Wed, 23 Sep 2026 16:47:23 GMT

Proactive agents run in the background, schedules are (one way) how you achieve that!

LangChain@LangChain

You can add schedules to Managed Deep Agents.

1️⃣ Add a schedule file to schedules/ with a cron expression + a prompt
2️⃣ Deploy
...And the agent runs itself

💻 Docs:docs.langchain.com/langsmith/pyth…M

⏯️ Demo from @ndrezn ⤵️

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🖼️ Improved UI for Jev models in LangSmith agents are going to complex systems of many llms calls ...

Harrison Chase (@hwchase17) — Tue, 22 Sep 2026 18:49:55 GMT

🖼️ Improved UI for Jev models in LangSmith

agents are going to complex systems of many llms calls

many llm calls will be jev-like (decision models)

we've updated tracing view in LangSmith to display these types of calls (state, questions, choices, output) more clearly!

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this is cool kind of like "goal" but with multiple in a row you need to hit?

Harrison Chase (@hwchase17) — Mon, 21 Sep 2026 20:47:09 GMT

this is cool

kind of like "goal" but with multiple in a row you need to hit?

Josh Rosen@JoshARosen

x.com/i/article/2102…

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try out jev in langsmith today! can use it to score traces cheap and accurately

Harrison Chase (@hwchase17) — Mon, 21 Sep 2026 17:30:00 GMT

try out jev in langsmith today! can use it to score traces cheap and accurately

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the reason i'm excited about decision models: agentic systems are just good engineering around mode...

Harrison Chase (@hwchase17) — Mon, 21 Sep 2026 17:17:01 GMT

the reason i'm excited about decision models:

agentic systems are just good engineering around models. "build prod, not god"

these types of fast, cheap & accurate models are super important for that

the excitement around jev shows that most people think this way as well!

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semif is pretty close to jev (75.4 to 74.7) on JevBench from @airesearch12 lots of other open sour...

Harrison Chase (@hwchase17) — Mon, 21 Sep 2026 16:50:24 GMT

semif is pretty close to jev (75.4 to 74.7) on JevBench from @airesearch12 

lots of other open source options as well!

x.com/airesearch12/s…

Harrison Chase@hwchase17

jev is a new type of model - "decision models"

there are a few open source alternatives, one is SemIf (based on qwen3.5)

we're hosting SemIf and offering it for free through LangSmith Gateway for the next week!

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jev is a new type of model - "decision models" there are a few open source alternatives, one is Sem...

Harrison Chase (@hwchase17) — Mon, 21 Sep 2026 15:59:16 GMT

jev is a new type of model - "decision models"

there are a few open source alternatives, one is SemIf (based on qwen3.5)

we're hosting SemIf and offering it for free through LangSmith Gateway for the next week!

Jacob Lee@Hacubu

🆓 Free Open-Source Jev 🤝 LangSmith Gateway

Decision models are becoming a first-class part of the LangSmith Gateway!

We're serving SemIf, an open-source decision model, free for the next week.

Compatible with the TypeSafe SDK - just change a string to try it!

Docs in 🧵

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Decision Model is a great name, and I expect other entrants in this new model class before long

Harrison Chase (@hwchase17) — Sun, 20 Sep 2026 16:31:25 GMT

Decision Model is a great name, and I expect other entrants in this new model class before long

Maggie Appleton@Mappletons

Can we agree to call Jev a "Decision Model"?
I feel like they buried this in the explainations and docs.
Language models output language. Decision models output decisions.

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"jev as a judge" cheap and fast semantic verifiers! great for evals - especially online evals where...

Harrison Chase (@hwchase17) — Sun, 20 Sep 2026 13:53:23 GMT

"jev as a judge"

cheap and fast semantic verifiers! great for evals - especially online evals where you want to grade LOTS of traces

LangChain@LangChain

We tested Jev against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation. x.com/i/article/2101…

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we've been exploring how to use Jev to build a better harness doing a webinar on it next week with ...

Harrison Chase (@hwchase17) — Sun, 20 Sep 2026 13:52:15 GMT

we've been exploring how to use Jev to build a better harness

doing a webinar on it next week with @sydneyrunkle@huntlovell and Allie from @typesafeai 

come join us!

events.langchain.com/webinar/buildi…

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Semantic triggers Semantic crons

Harrison Chase (@hwchase17) — Sat, 19 Sep 2026 00:38:52 GMT

Semantic triggers

Semantic crons

Sajid Mehmood@smehmood

Alright I think I'm excited to ship "natural language triggers" for niteshift.dev

Lets you pipe a super high-volume triggers (Slack messages, Datadog alerts, etc) into a natural language filter powered by Jev, that then dispatches the right agent/model/sandbox

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jev has sparked more internal demos and exploration of how we can use it in our product than any oth...

Harrison Chase (@hwchase17) — Fri, 18 Sep 2026 23:47:40 GMT

jev has sparked more internal demos and exploration of how we can use it in our product than any other model launch

its not even close

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we've been diving into how Jev can help with agents @sydneyrunkle will do a deep dive on this next ...

Harrison Chase (@hwchase17) — Fri, 18 Sep 2026 23:03:14 GMT

we've been diving into how Jev can help with agents

@sydneyrunkle will do a deep dive on this next week - come join!

LangChain@LangChain

Hot topic livestream: Learn about Jev

A buzzy new model Jev, by @typesafeai, reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification tasks. It's aiming to solve the problem of slow and costly agent loops.

Next Tuesday, we’re bringing @sydneyrunkle from our engineering team at @LangChain, for a live convo on how TypeSafe AI’s model makes fast, structured decisions, where it fits in the agent loop, and how to build a harness with Jev.

Join here: events.langchain.com/webinar/buildi…

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browser use is the best application of Jev ive seen yet

Harrison Chase (@hwchase17) — Fri, 18 Sep 2026 21:02:37 GMT

browser use is the best application of Jev ive seen yet

nathan drezner@ndrezn

Browser use w/ @LangChain  + @typesafeai's Jev! Really fun to build.

... I found it's excellent at playing the Wikipedia Game. (But, it's also great at "folding laundry" type tasks, like finding cheap flights.)

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Jev isn’t meant for text generation, it’s for simpler constrained output Turns out, that can actual...

Harrison Chase (@hwchase17) — Fri, 18 Sep 2026 02:25:19 GMT

Jev isn’t meant for text generation, it’s for simpler constrained output

Turns out, that can actually be very useful when building a harness!

Sydney Runkle@sydneyrunkle

x.com/i/article/2100…

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Models may not actually benefit from a native harness

Harrison Chase (@hwchase17) — Thu, 17 Sep 2026 10:00:49 GMT

Models may not actually benefit from a native harness

Melissa Pan@melissapan

Does your Claude model really need Claude Code…? 🤔

We evaluate 7 models on Claude Code, Codex, and Pi. Three surprising findings emerge:

1️⃣Harness choice has little effect on task success rate, but can significantly affect the cost
2️⃣A simple harness can be competitive
3️⃣The native harness isn’t always the best.

Millions of people are using coding agents, but the impact of harness choice remains unclear.

(1/n) More details in the thread. 🧵

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The hardest part of and “org harness” or “enterprise ai” (as opposed to personal ai” is auth, and ma...

Harrison Chase (@hwchase17) — Wed, 16 Sep 2026 02:55:24 GMT

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 for managed deepagents

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We will be following this strictly for all LangChain dinners going forward 🫡

Harrison Chase (@hwchase17) — Wed, 16 Sep 2026 02:53:29 GMT

We will be following this strictly for all LangChain dinners going forward 🫡

Barry McCardel@barrald

this made it into the WSJ lol

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memory has been a hot topic for the past ~2 years every time we bring it up or do anything there, g...

Harrison Chase (@hwchase17) — Tue, 15 Sep 2026 13:50:43 GMT

memory has been a hot topic for the past ~2 years

every time we bring it up or do anything there, gets ton of interest, but nothing sticks

few takes on why below:

- how to update and then use memory needs to be pretty tightly integrated with the harness. so its hard to build a standalone memory product

- the hardest part of memory is the logic of deciding what to remember. storing and even querying memory is not necessarily hard. the logic of deciding what to remember is often (a) application specific, (b) just prompting, which makes it hard to productize

- memory is not that useful in a general purpose agent, but more for when you are doing repeated tasks. more high usage ai agent are not just doing the same task repeatedly

- memory hasnt really proven to be useful for coding agents, and most agent things that take off happen first in coding agents

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slack is where agents are doing work managed deepagents has a very tight slack integration - check ...

Harrison Chase (@hwchase17) — Mon, 14 Sep 2026 16:59:41 GMT

slack is where agents are doing work

managed deepagents has a very tight slack integration - check out the tutorial on it below

LangChain@LangChain

PSA: You can connect a Managed Deep Agent to Slack.

Mentions, DMs, and thread replies all start a run. Your agent posts its response back in the same conversation.

Here’s how to get started ⤵️

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in case you want to build a domain specific harness: https://t.co/Y8QoykQ8Ex great blog by @sydney...

Harrison Chase (@hwchase17) — Sat, 12 Sep 2026 20:09:29 GMT

in case you want to build a domain specific harness:

langchain.com/blog/how-to-bu…

great blog by @sydneyrunkle

Garry Tan@garrytan

Either you die a system of record or you live long enough to become a domain-specific harness

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great insight into how companies are adopting and using open wiki in practice

Harrison Chase (@hwchase17) — Thu, 10 Sep 2026 20:01:27 GMT

great insight into how companies are adopting and using open wiki in practice

LangChain@LangChain

Credit Genie's coding agents used to guess at how a repo worked. Now they just check their OpenWiki.

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i expect computer use to get a lot better and more popular over the next few months! llms becoming ...

Harrison Chase (@hwchase17) — Wed, 09 Sep 2026 21:09:10 GMT

i expect computer use to get a lot better and more popular over the next few months!

llms becoming great at writing code is actually a big catalyst for this

Kyle Jeong@kylejeong

x.com/i/article/2097…

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I like this framing a lot: agent improvement is harness improvement, not model improvement The inte...

Harrison Chase (@hwchase17) — Wed, 09 Sep 2026 16:15:11 GMT

I like this framing a lot: agent improvement is harness improvement, not model improvement

The interesting interventions are often at the tool boundary:
- what context you pass
- when tools are provided
- how you recover from failure
- what gets measured afterward

That’s the loop we’re building around deepagents (the orchestration logic) + LangSmith (how you measure)

Arky Yang@arkyyang

Beyond Prompts: Measuring and Optimizing LLM Tool-Agent Harnesses — new paper (arXiv, 4 Sep 2026; accepted EMNLP 2026). arxiv.org/abs/2609.05736

Treats harness optimization around a fixed model as budgeted selection: edits are guarded intercepts at the tool boundary, not rewrites of execution logic. PRISM clusters failures, routes each repair to a prompt, middleware, or joint surface, and picks candidates by gate pass-rate AND reliability (RelLift95), not score alone.

3 distinct takeaways for product builders:
- Report selected-harness reliability, not just mean lift. BH found the best individual harnesses but had negative reliable lift on both tau-bench domains — a strong explorer, an unreliable selector.
- Constrain middleware to tool-boundary edit patterns (silent correction, error blocking, prerequisite blocking). Unconstrained middleware (BH-MW) fell below its own prompt-only variant on Retail; the constraint, not the surface, is what makes middleware safely searchable.
- Route each failure to the right surface: prompts for global policy, middleware for locally-checkable execution invariants. Removing routing collapsed lift first-order on all three tasks; prompts alone miss runtime failures, unconstrained middleware adds selection risk.

Experiment setup (from the paper): fixed inner execution model gpt-5.4-mini, outer proposer claude-opus-4.7. Benchmarks: BFCL multi-round (600-case scorecard) and tau2-Retail / tau2-Telecom (74-case scorecard each of 114 tasks). Baselines BH, GEPA, MIPROv2 + PRISM; disjoint repair/gate/scorecard splits, 4 seeds, up to 16 runs. Baseline = native Deep Agents (v0.5.6). Metrics: mean held-out lift (pp), WorstLift, repeatability, RelLift95(B) by 5,000 bootstrap. PRISM-MW mean lifts 14.2 / 14.9 / 10.1 pp and positive RelLift95 on all three.

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Open wiki is cool

Harrison Chase (@hwchase17) — Wed, 09 Sep 2026 01:15:44 GMT

Open wiki is cool

Zak Raicik@Zraic

I am OpenWiki's number 1 super fan @LangChain

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memory comes built into deepagents!

Harrison Chase (@hwchase17) — Tue, 08 Sep 2026 22:08:33 GMT

memory comes built into deepagents!

Gipp 🦅@gippp69

holy sh*t. this is f**king insane.

langchain just documented the missing memory layer for agents that are supposed to improve after every run.

instead of stuffing everything into one giant prompt, deep agents can keep long-term memory, reusable skills and lessons from previous tasks.

[it takes a few minutes to understand the loop]

1/ run the task
2/ save what actually worked
3/ load that lesson into the next run

that’s how an agent stops starting from zero every single time.

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