We've been thinking a lot about great agent UX with Managed Deep Agents - here's some of what we've been building in this area and a bit about our product philosophy x.com/i/article/2108…
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nathan drezner
@ndrezn
LangChain
LangChainPM @LangChain building Managed Deep Agents. I post demos, code, and what breaks. prev @plotly
nathan (at) drezner (dot) xyz
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more on Restock powered by @link - here's what it looks like in action
LangChain@LangChain · Oct 8Here’s an app we built called Restock, an office supply agent that works in @SlackHQ.
A quick demo ⏯️
A quick demo ⏯️
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@ndrezn reposted
LangChain developers can now easily build agents that can pay for things with @link (which works across any checkout online or programmatic payments over @mpp).
See their in-depth guide and demo! =>
See their in-depth guide and demo! =>
LangChain@LangChain · Oct 8You can now build an agent that:
🔎 Finds real products
🛒 Prepares purchases
💳 Pays
…with a person approving every order 👨💻
Ask in @SlackHQ, review the order, approve in @stripe's Link agent wallet.
Built on MPP + Managed Deep Agents.
langchain.com/blog/agents-th…
Open quoted post →🔎 Finds real products
🛒 Prepares purchases
💳 Pays
…with a person approving every order 👨💻
Ask in @SlackHQ, review the order, approve in @stripe's Link agent wallet.
Built on MPP + Managed Deep Agents.
langchain.com/blog/agents-th…
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we built Restock, an agent to automate ordering office supplies using @link by @stripe - pens, groceries, and more. it's open source, so you can fork it, modify it, and deploy it yourself with `mda deploy`
payments are handled via @mpp. Restock is a great example of a complex use case which handles user authentication, secure secrets storage, and human-in-the-loop payments approval.
source code 👇
payments are handled via @mpp. Restock is a great example of a complex use case which handles user authentication, secure secrets storage, and human-in-the-loop payments approval.
source code 👇
LangChain@LangChain · Oct 8You can now build an agent that:
🔎 Finds real products
🛒 Prepares purchases
💳 Pays
…with a person approving every order 👨💻
Ask in @SlackHQ, review the order, approve in @stripe's Link agent wallet.
Built on MPP + Managed Deep Agents.
langchain.com/blog/agents-th…
Open quoted post →🔎 Finds real products
🛒 Prepares purchases
💳 Pays
…with a person approving every order 👨💻
Ask in @SlackHQ, review the order, approve in @stripe's Link agent wallet.
Built on MPP + Managed Deep Agents.
langchain.com/blog/agents-th…
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@ndrezn reposted
We’re announcing Cohere’s first office in Ottawa 🇨🇦
Canada is where Cohere started, and we’re proud to keep building here.
Canada is where Cohere started, and we’re proud to keep building here.
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Really sad to hear that Margaret Hamilton has passed away. Led an incredible life and was an inspiration to so, so, so many. From MIT: news.mit.edu/2026/margaret-…
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We just shipped 🦾 MDA v0.9 - it adds dynamic agent definitions 🧱, Slack reactions 👀, and...
It's now possible to build agents which can dynamically set schedules.
Build tools for your agents around the new schedules SDK to let anyone using their agent schedule updates, deep research tasks, and more.
Happy building 🏗️
It's now possible to build agents which can dynamically set schedules.
Build tools for your agents around the new schedules SDK to let anyone using their agent schedule updates, deep research tasks, and more.
Happy building 🏗️
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Really excited to try building against this protocol - there are a lot of interesting explorations in this space like web MCP and MPP.
but there's a lot of innovation that can happen in this space and PAP feels like a step in the right direction
but there's a lot of innovation that can happen in this space and PAP feels like a step in the right direction
Sierra@SierraPlatform · Oct 6Today we’re announcing Personal Agent Protocol — an open standard @Meta and @SierraPlatform are developing along with industry partners at @Genesys, @instinct, @RocketOTD, @Shopify, @stripe, and @Walmart. It will help define how personal agents interact with businesses and is open for anyone to implement.
Read more: sierra.ai/blog/introduci…
Read more: sierra.ai/blog/introduci…
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@ndrezn reposted
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. deepagents has durable execution built in and manages the context window for you
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. deepagents has durable execution built in and manages the context window for you
Josh Rosen@JoshARosen · Oct 5We should be stealing a lot more from data engineering when we build AI systems.
DAGs - does this really need to be one giant agent loop?
Lineage - which sources, artifacts, prompts, and model versions produced this?
Change detection - if a source changes, which generated artifacts are now outdated?
Incremental processing - what can we keep instead of rerunning the whole agent?
ETL - what context do we pull in, what does the model do with it, and where do we save the result?
Data engineers have been solving these problems for decades.
Open quoted post →DAGs - does this really need to be one giant agent loop?
Lineage - which sources, artifacts, prompts, and model versions produced this?
Change detection - if a source changes, which generated artifacts are now outdated?
Incremental processing - what can we keep instead of rerunning the whole agent?
ETL - what context do we pull in, what does the model do with it, and where do we save the result?
Data engineers have been solving these problems for decades.
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@ndrezn reposted
time to level up langch.in/mda
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free web search! for your agents! backed by Parallel with ZDR. Take it for a spin today.
LangChain@LangChain · Oct 6Add web search to your Managed Deep Agents for free.
🌐 Powered by @p0
💻 No separate vendor accounts, managing extra PI keys, or wiring search tools necessary
A quick demo from @ndrezn
🌐 Powered by @p0
💻 No separate vendor accounts, managing extra PI keys, or wiring search tools necessary
A quick demo from @ndrezn
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morning reading today is Lifestreams (1996) which Scott Jenson referenced in his excellent talk on innovating the desktop OS -
lots of interesting ideas in this paper.
- implicit organization: our interactions with the computer say more about intention than we might realize in terms of how information should be organized
- filesystem management as a first stem is a poor abstraction: we never write the name of the project on a piece of paper nor are we required to stuff it in a folder in real life, so why do we require this in our digital lives?
- document-first & compression-first design: our interactions with computers consist of the production of documents. let's optimize the computer to sort and find information out of this mind map
I think Obsidian might be the closest modern human-first implementation of a system like this but there are a ton of ideas in here I think are fascinating when thinking specifically about agentic memory and retrieval systems that are possible today with llms
lots of interesting ideas in this paper.
- implicit organization: our interactions with the computer say more about intention than we might realize in terms of how information should be organized
- filesystem management as a first stem is a poor abstraction: we never write the name of the project on a piece of paper nor are we required to stuff it in a folder in real life, so why do we require this in our digital lives?
- document-first & compression-first design: our interactions with computers consist of the production of documents. let's optimize the computer to sort and find information out of this mind map
I think Obsidian might be the closest modern human-first implementation of a system like this but there are a ton of ideas in here I think are fascinating when thinking specifically about agentic memory and retrieval systems that are possible today with llms
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huge congrats to Polars team - looking forward to using v2!
polars data@DataPolars · Oct 6We just released Polars 2.0.
It removed many of our legacy decisions makes the streaming engine our default and promotes SQL to a first class citizen within Polars.
It comes with initial out-of-core (spill to disk) support, a new Map data type and a lot of performance improvements. In fact, we think Polars is now one of the fastest analytical SQL engines on a single node. See benchmarks in the post: pola.rs/posts/release-…
It removed many of our legacy decisions makes the streaming engine our default and promotes SQL to a first class citizen within Polars.
It comes with initial out-of-core (spill to disk) support, a new Map data type and a lot of performance improvements. In fact, we think Polars is now one of the fastest analytical SQL engines on a single node. See benchmarks in the post: pola.rs/posts/release-…
Open quoted post →
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good take on HN about the value of durable harnesses re Pi Durable - very excited to try out the new Pi harness
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great piece from @sydneyrunkle on moving model routing upstream into the harness -
great way to think about managing routing more efficiently and building your harness to avoid context bloat.
great way to think about managing routing more efficiently and building your harness to avoid context bloat.
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I had some folks asking for the source code for this demo!
Link in 🧵 if you want to fork/build off of it in any direction. Our documentation for HTTP channels is also fantastic.
Link in 🧵 if you want to fork/build off of it in any direction. Our documentation for HTTP channels is also fantastic.
LangChain@LangChain · Sep 30Managed Deep Agents 0.8 ships HTTP channels: agents can take requests from any webhook, not just Slack.
@ndrezn built an expense agent that lives in text messages, backed by @PhotonHQ.
Text a receipt, get it logged automatically.
More on Managed Deep Agents: langchain.com/blog/langsmith…
@ndrezn built an expense agent that lives in text messages, backed by @PhotonHQ.
Text a receipt, get it logged automatically.
More on Managed Deep Agents: langchain.com/blog/langsmith…
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We've just shipped a new terminal interface 💻 for Managed Deep Agents. It rips.
Use it to instantly test drive your agent locally. Studio isn't going anywhere; the TUI is handy for lightweight development.
Take it for a spin with `mda chat`.
Personally I'm a big fan of the theming the team put together 🌈
Use it to instantly test drive your agent locally. Studio isn't going anywhere; the TUI is handy for lightweight development.
Take it for a spin with `mda chat`.
Personally I'm a big fan of the theming the team put together 🌈
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UCI Worlds. Cycling is easily one of the most insane sports to watch live. Every weekend here this september!
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🔢 at @LangChain we've been putting a lot of thought into safe storage of user memories.
🔐 We wanted a secure way for a distributed agent to manage memory without it leaking outside of its intended context.
Here's a quick overview of the system and how to add user and agent memory to your managed deep agent - it's a single line of Python or TypeScript
🔐 We wanted a secure way for a distributed agent to manage memory without it leaking outside of its intended context.
Here's a quick overview of the system and how to add user and agent memory to your managed deep agent - it's a single line of Python or TypeScript
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Are you as jazzed about Managed Deep Agents as @SlackHQ is?? Get building!!
Slack@SlackHQ · Sep 24Smarter permissions, seamless file drops, and built-in search. This is shaping up to be a beautiful workflow. 🎨
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