[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
@hwchase17
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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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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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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 -
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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Madrigal Pharma: Pioneering Agentic AI in Biopharma
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@MadrigalPharma is living in the (agentic) future. If you haven't heard of Madrigal, they are a pioneering biopharma company. Some months ago, they set out to solve the problem of integrating, searching, and synthesizing information from diverse datasets at scale. This lead -
OSS Models Key to Cost-Effective DeepAgents in 2026
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big theme of 2026 – cost of closed models is too high! really excited to make deepagents work exceptionally well with OSS models
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Building and Deploying Agents with DeepAgents Deploy
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That's a quick tour through building and deploying a simple agent with DeepAgents deploy Was just a markdown file, a json file, and toml file Easy to set up, but robust deployment by the end Try out deepagents deploy! Full docs here:
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Agent Deployment via MCP, A2A and Remote Graph API
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As part of this deployment, you can interact with the agent in several ways We expose the agent via MCP and A2A, as well as with as a "remote graph" API, which means you can interact with as you would a normal in-memory graph See streaming docs here: https://
docs.langchain.com/langsmith/stre
aming
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User-Friendly Agent Interaction in LangSmith Studio
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Interacting with the agent in the studio is the easiest way to get a user friendly UI! I can share it with others in my LangSmith org, and they can chat with it there