[frontend] UI/UX is a surprisingly big part of building agents! It takes a while to hook up streaming, thread history, all the endpoints We do it for you! Get a frontend you can expose to users out of the box
AGENTS
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LangChain DeepAgents Deployment Now Available with Weekly Updates
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Checkout DeepAgents deploy here: https://
docs.langchain.com/oss/python/dee
pagents/deploy
… We're shipping updates every week (almost every day). What would you like to see next? -
DeepAgents Sandbox Integration with Modal, Daytona, and Runloop
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[sandbox] DeepAgents can connect to a sandbox to run! We integrate with @modal @daytonaio @RunloopDev Each provider has its own set of parameters, so be sure to check that! Docs: https://
docs.langchain.com/oss/python/dee
pagents/deploy#sandbox
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Multi-Tenant Auth for AI Agents in Production with Clerk and Supabase
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[auth] Part of deploying agents to production is deploying them in a multi-tenant way Auth is the way to do that! We support integrations with @clerk and @supabase
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LangSmith Agent Deployment with Multi-Provider Model Support
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[agent] This is where you specify the name of the agent – this is the deployment name in LangSmith This is also where you specify the model name. We support a lot of models! Not only ones from OpenAI, Anthropic, Google, but also @OpenRouter @FireworksAI_HQ @baseten @nvidia and
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DeepAgents Deploy: Configuration-Driven Cloud Agent Harness Setup
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DeepAgents deploy is a simple, configuration driven way to get an agent harness deployed to the cloud deepagents.toml is the file that configures it. It has four sections:
– agent
– sandbox
– auth
-frontend Here's what each one does -
Hassabis: AGI Is Close, Missing Continual Learning and Memory
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Demis Hassabis: We're on the right track to AGI; we probably have all the components. We're just missing a few things like continual learning and solving the memory problem. pic.twitter.com/zuIKOiKnB7
— Chubby♨️ (@kimmonismus) 30 avril 2026Demis Hassabis: We're on the right track to AGI; we probably have all the components. We're just missing a few things like continual learning and solving the memory problem.
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Small Models Handle Repetitive Agent Task Layers Efficiently
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Any repetitive agent task layer – routing, validation, retrieval – small models can handle it
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AI-Powered Dispute Responder Wins First $1,199 Stripe Case
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For the first time in a decade on @Stripe I've started winning disputes with my vibe coded dispute responder I used to ignore disputes so I almost always lost them, now I've started winning, this one is the first big dispute for $1,199 USD! Whenever a dispute comes in, my
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Glean Releases Waldo 30B Agentic Search Model for Enterprise AI
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Small Language Models are the Future of Agentic AI!
— Sumanth (@Sumanth_077) 30 avril 2026
Glean just released Waldo – a 30B agentic search model that runs before frontier LLMs.
Search is where most agentic work begins. You ask about a project, customer, process, or decision. The agent searches internal docs, reads… pic.twitter.com/8yy5Dj99lvSmall Language Models are the Future of Agentic AI! Glean just released Waldo – a 30B agentic search model that runs before frontier LLMs. Search is where most agentic work begins. You ask about a project, customer, process, or decision. The agent searches internal docs, reads