Day 0 support for Nvidia's Nemotron 3 Super! We're excited to support open source models that push the frontier of model intelligence, cost, and latency Try it out in deepagents today!
@langchain
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Agents in Production: Unpredictability Beyond Traditional Software Testing
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New Conceptual Guide: You don’t know what your agent will do until it’s in production With traditional software, you ship with reasonable confidence. Test coverage handles most paths. Monitoring catches errors, latency, and query issues. When something breaks, you read the
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LangGraph Deploy: Ship Agents to Production in Minutes
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Introducing `langgraph deploy`
— LangChain (@LangChain) 10 mars 2026
Deploy an agent to LangSmith Deployment with a single command.
$ uvx –from langgraph-cli@latest langgraph deploy
Go from prototype → production in minutes.
Try it today: https://t.co/wxXQ9TXORe pic.twitter.com/D0PQFlGHfQIntroducing `langgraph deploy` Deploy an agent to LangSmith Deployment with a single command. $ uvx –from langgraph-cli@latest langgraph deploy Go from prototype → production in minutes. Try it today: https://
docs.langchain.com/langsmith/cli#
deploy
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Andrew Ng Joins LangChain to Discuss the Future of AI Agents
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@AndrewYNg is joining us at Interrupt to talk about the future of AI agents. Hear what he's learned from building DeepLearning.AI and AI Fund. May 13-14 in SF: https://interrupt.langchain.com
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Agent Builder Launches Central Inbox for Task Management
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Agent Builder now has a central inbox for managing every agent task.
— LangChain (@LangChain) 9 mars 2026
One place to:
→ See active and completed tasks
→ Approve or reject actions
→ Manage agents running in parallel
→ Act on what matters without context-switching
Try it free: https://t.co/VdxNX6Mefk pic.twitter.com/hZhgzAPexXAgent Builder now has a central inbox for managing every agent task. One place to:
→ See active and completed tasks
→ Approve or reject actions
→ Manage agents running in parallel
→ Act on what matters without context-switching Try it free: https://
smith.langchain.com/agents?skipOnb
oarding=true/?utm_medium=social&utm_source=twitter&utm_campaign=q1-2026_inbox-launch_co
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LangSmith Launches Multi-Modal Support for Evaluators
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We just launched multi-modal support for evaluators in LangSmith! You can now pass attachments and base64 multi-modal content directly into evaluators with flexible mapping, allowing you to measure quality, safety, and performance across the full interaction end to end. Docs:
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LangSmith Insights Agent: Scheduling and Recurring Usage Patterns
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🌟 LangSmith Insights Agent 🌟
— LangChain (@LangChain) 22 février 2026
Use LangSmith Insights to group traces and find emergent usage patterns of your agents 🔎
Now with the ability to set a schedule and run recurring jobs!
Docs: https://t.co/IjrWYOBUBK pic.twitter.com/5f7fsajRXOLangSmith Insights Agent Use LangSmith Insights to group traces and find emergent usage patterns of your agents Now with the ability to set a schedule and run recurring jobs! Docs:
https://docs.langchain.com/langsmith/insights
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Improving Deep Agents Performance Through Harness Engineering
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Improving Deep Agents with harness engineering Our coding agent went from Top 30 to Top 5 on Terminal Bench 2.0. We only changed the harness. The goal of a harness is to mold the inherently spiky intelligence of a model for tasks we care about. Harness Engineering is about
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LangChain Hosts SF Tech Talk on the Future of Agents
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We’re thrilled to be hosting and speaking at @contrary 's next SF Tech Talk, featuring eng leads from @cognition_labs
, @elevenlabs
, and @andocorporation
. We'll be speaking to the future of agents. Each company will live demo their latest product features for leading builders -
Three Ways to Optimize LangSmith Agent Memory and Performance
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LangSmith Agent Builder uses memory to improve with feedback. Three practical ways to get the most out of memory: → Tell your agent to remember what works
→ Use skills to give it specialized context when needed
→ Edit its instructions directly when that's faster Full