Chat LangChain is now embedded directly in our docs You can ask questions grounded in:
• Full docs (LangSmith + OSS)
• Knowledge base
• OSS code We've been investing heavily in developer experience. This is one step toward making everything easier and more accessible.
@langchain
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Chat LangChain Now Embedded Directly in Our Documentation
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LangChain Partners with MongoDB for AI Stack Integration
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Announcing our partnership with @MongoDB
: The AI Stack that runs on the database you already trust Atlas Vector Search as a drop-in retriever. MongoDB Checkpointer for durable agent state in LangSmith Deployment. Text-to-MQL for natural-language queries over operational data. -
LangChain Posts Humorous Ad About Token Spending
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Great ad by @LangChain "token spend higher than your rent?" [Translated from EN to English]
→ View original post on X — @langchain, 2026-03-31 18:10 UTC
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Agent Improvement Loop: Tracing as Foundation for Enhancement
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New conceptual guide: The agent improvement loop starts with a trace Tracing is the foundational primitive for improving agents. A trace gives you the full behavioral record of what an agent actually did. From there, teams can enrich traces with evals and human feedback,
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LangChain Academy: Monitoring Production Agents Course
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New LangChain Academy Course Launch: Monitoring Production Agents
— LangChain (@LangChain) 31 mars 2026
Shipping agents to production is hard. Unlike traditional software, agents are non-deterministic. Users can say anything, and the same input can produce different outputs.
You can’t rely on pre-launch testing… pic.twitter.com/rbj9Ror8OgNew LangChain Academy Course Launch: Monitoring Production Agents Shipping agents to production is hard. Unlike traditional software, agents are non-deterministic. Users can say anything, and the same input can produce different outputs. You can’t rely on pre-launch testing
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LangSmith Experiments Detail View Redesigned for Better Debugging
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The hardest part of debugging an AI agent isn't knowing it failed–it's knowing why.
— LangChain (@LangChain) 30 mars 2026
We rebuilt the detail view in LangSmith Experiments from the ground up to answer that question faster.
Next time you click and inspect any experiment results, you will find:
* Less clutter
*… pic.twitter.com/x50OxCJxnWThe hardest part of debugging an AI agent isn't knowing it failed–it's knowing why. We rebuilt the detail view in LangSmith Experiments from the ground up to answer that question faster. Next time you click and inspect any experiment results, you will find:
* Less clutter
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LangSmith Prompt Hub Environments: Streamlined Prompt Promotion Workflow
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Environments in LangSmith Prompt Hub
— LangChain (@LangChain) 27 mars 2026
Environments give you a proper promotion workflow for your prompts:
– Assign any commit to Staging or Production
– Promote between environments instantly
– Roll back with a single click from a full deployment history
– Reference reserved tags… pic.twitter.com/u1DOC6hVLWEnvironments in LangSmith Prompt Hub Environments give you a proper promotion workflow for your prompts:
– Assign any commit to Staging or Production
– Promote between environments instantly
– Roll back with a single click from a full deployment history
– Reference reserved tags -
Agent Evaluation Readiness Checklist: Testing and Shipping Agents
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The Agent Evaluation Readiness Checklist Starting to think through how to test your agents? We put together a step-by-step checklist for building, running, and shipping agent evals. We walk through:
→ How to read traces in LangSmith and analyze errors, before building evals -
Kensho Multi-Agent Data Retrieval System on LangGraph
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See how @Kensho built a multi-agent data retrieval system on LangGraph to give S&P Global customers a single entry point into verified financial data — equity research, macro, ESG, and more. The architecture consists of a central router + specialized Data Retrieval Agents, one
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LangSmith for Startups: Free Credits and Tools to Scale
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LangSmith for Startups gives early stage teams the tooling and community they need to iterate faster and win bigger.
— LangChain (@LangChain) 26 mars 2026
Observe, evaluate, and deploy your agents — now with free credits to help you get started.
Our Scale program offers:
• $10,000 in LangSmith credits
•… pic.twitter.com/52o39fkyl6LangSmith for Startups gives early stage teams the tooling and community they need to iterate faster and win bigger. Observe, evaluate, and deploy your agents — now with free credits to help you get started. Our Scale program offers: • $10,000 in LangSmith credits •