One of the benefits of Deep Agents deploy is model optionality Choose from models from @OpenAI @GeminiApp @AnthropicAI @FireworksAI_HQ @baseten @OpenRouter @ollama @nvidia and many others Another is you can bring your own sandbox – @daytonaio @modal @RunloopDev
@langchain
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LangChain Launches Deep Agents, Open Source Alternative to Claude
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Read the full blog here: blog.langchain.com/deep-agents-deploy-an-open-alternative-to-claude-managed-agents/ Documentation: docs.langchain.com/oss/python/deepagents/deploy [Translated from EN to English]
→ View original post on X — @langchain, 2026-04-09 15:49 UTC
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Deep Agents deployment with open standards configuration
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Deep Agents deploy is simple – just specify configuration like you would for a coding agent: – AGENTs.md (open standard)
– /skills (open standard)
– mcp.json (convention) and then choose your model and sandbox provider -
LangSmith Agent Deployment with Memory and Protocol Exposure
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From there – we spin up a deployment on LangSmith deployments with production ready short term and long term memory We expose your agent via MCP, A2A, and agent protocol
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Deep Agents Deploy Beta Launch for Production-Ready Models
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Deep Agents deploy Today we’re launching Deep Agents deploy in beta. Deep Agents deploy is the fastest way to deploy a model agnostic, open source agent harness in a production ready way. Open harness, open memory, model agnostic
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Building Better AI Agents with Traces and Continuous Improvement
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Join us in NY for a meetup on building better agents with @palashshah
, Applied AI Engineer @LangChain
. Palash will walk through the agent improvement loop and how teams use traces as the foundation for continuous improvement. We’ll cover how to:
– Capture traces of real agent -
SF Meetup: Building Better Agents with Improvement Loops
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🌉 Join us in SF for a meetup on building better agents with the agent and code improvement loops. You'll hear talks by @samecrowder, Head of Product at @LangChain, and @nnennahacks, AI Developer Relations Lead at @QodoAI .
— LangChain (@LangChain) 8 avril 2026
Sam will walk through the agent improvement loop and… pic.twitter.com/9e61dYvueD🌉 Join us in SF for a meetup on building better agents with the agent and code improvement loops. You'll hear talks by @samecrowder, Head of Product at @LangChain, and @nnennahacks, AI Developer Relations Lead at @QodoAI . Sam will walk through the agent improvement loop and how teams use traces as the foundation for continuous improvement. Nnenna will present how Qodo is building agents, their architecture, and how they’re enhancing them using LangSmith. 🗓️ Wed, April 29 | ⌚ 6 PM | 📍 SF (SOMA) RSVP 👉 luma.com/4nu6vpsh
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Self-Improving Agents: Systems Engineering and Evaluation Infrastructure
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Self-improving agents isn’t a single algorithm – it’s a systems engineering problem involving: – eval data curation + maintenance – experiment design to battle overfitting – an update algorithm – human review during the process & especially before prod we share practical learnings + a local research scaffold to autonomously hill-climb harness centered around evals our goal is to give everyone the tooling and infra to measure and iteratively their improve agents. Evals are training data for agents which fuels this loop let's build the future of well-designed, self-improving systems 🚀 Viv (@Vtrivedy10) x.com/i/article/204172946391… — https://nitter.net/Vtrivedy10/status/2041927488918413589#m
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LinkedIn’s AI recruiting agent with LangGraph and LangSmith
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🎤 Hiring 10x faster with LangGraph and LangSmith: Behind LinkedIn's AI recruiting agent
— LangChain (@LangChain) 8 avril 2026
Recruiting is one of the most time-intensive workflows in any organization—especially for small and mid-size businesses without dedicated hiring teams. @LinkedIn's engineering team tackled… pic.twitter.com/iK1IUPh3BE🎤 Hiring 10x faster with LangGraph and LangSmith: Behind LinkedIn's AI recruiting agent Recruiting is one of the most time-intensive workflows in any organization—especially for small and mid-size businesses without dedicated hiring teams. @LinkedIn's engineering team tackled this head-on by building an AI recruiting agent with LangGraph. At Interrupt, Senior Software Engineers Tracy He and Shang Liu will walk through how they built it: the agent architecture, the tool-calling patterns that power it, and how they keep the system observable in production with LangSmith. Catch Tracy and Shang’s talk along with all the others at Interrupt, the Agent Conference by LangChain. May 13-14 in San Francisco. Get tickets here 👉 interrupt.langchain.com
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Deploy Multi-Agent Systems with A2A Protocol
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🔌 Deploy agents with A2A
— LangChain (@LangChain) 8 avril 2026
A2A is an agent-to-agent communication protocol, useful for building multi-agent systems. With LangSmith Deployments, you get A2A support out of the box!
Watch how: https://t.co/PF2FZbOb79
Docs: https://t.co/ETQ24g3KOX
A2A Protocol:… pic.twitter.com/TJVX5v9RZu🔌 Deploy agents with A2A A2A is an agent-to-agent communication protocol, useful for building multi-agent systems. With LangSmith Deployments, you get A2A support out of the box! Watch how: piped.video/SjGPXBNH614 Docs: docs.langchain.com/langsmith… A2A Protocol: a2a-protocol.org/latest/