@LangChain
Pinned post
events.langchain.com Agent Memory 102: Context Retrieval & Storage Agents face the same problem you do in an open-book exam: the answers are all there, but finding the right one without wasting time is the hard part. Clumsy searches, oversized results, and stale or... we've been focused on supporting more autonomous, proactive agents; here's what we shipped in deepagents this week:View quoted post on X
1. browser use with @Stagehanddev, so your agent can work in a real browser, sometimes on your behalf!
* blog: langchain.com/blog/browser-u…
* deep dive: langchain.com/blog/how-we-op…
* docs: docs.langchain.com/oss/python/dee…
2. async subagents, so the main agent remains available to a user while background tasks run
* blog: langchain.com/blog/agents-ar…
* docs: docs.langchain.com/oss/python/dee…
www.langchain.com Context Engineering (Agent Memory) for AI Agents Starts with Retrieval Agents waste time and context on clumsy searches, oversized results and stale data. Learn the retrieval techniques LangChain's Deep Agents harness uses to get the right context in. 
