New in Deep Agents: Subagents can now fork the supervisor's full conversation instead of starting from a blank context window. Everything you need to know from @t_bengre and @chester_curme on the @LangChain_OSS team ⤵️

ICYMI -- yesterday we released support for the new MCP protocol in @LangChain !! what do you need to know? 1. MCP support is now in the main langchain package! get started with `uv pip install 'langchain[mcp]'` 2. it's now built on top of FastMCP v4; FastMCP has ergonomic support for all things MCP (clients, servers, and apps). FastMCP handles things like protocol negotiation, tool name conflicts, connection lifecycle, transport specs, auth, and caching! 3. langchain now supports elicitation (MCP's human in the loop concept) via interrupts! 4. langchain now supports list_tools caching (so you don't have to re-fetch on every agent run; this was a common pain point for TTFT!) 5. the new protocol is stateless, which means the agent <-> server interactions are more efficient, and much more scale-able.

We’re looking for someone to lead SmithDB at LangChain! SmithDB is the database we’ve built from the ground up to power LangSmith. It’s optimized specifically for the access patterns that emerge when you’re storing and querying enormous volumes of agent traces. It’s serving production traffic at significant scale with great performance, but it’s still early. The problems we are solving are genuinely fun and very hard: indexing, query execution, compaction, ingestion specifically for agent observability + making all of this fast and cost-efficient at massive scale. We’re looking for an exceptional engineering leader to take ownership of this critical project. You’re likely a great fit if you’ve built database or distributed systems, led strong engineering teams through technically demanding projects, and have exceptional execution and project management skills. Apply here or DM me directly: Learn more about SmithDB here:

MongoDB now powers the virtual file system behind @LangChain's Deep Agents. 🚀 This new integration lets you implement Deep Agents' BackendProtocol against MongoDB Atlas instead of building your own storage layer. And agent code calling read_file, write, glob, and grep don’t have to change. Your retrieval and storage run through one backend instead of a maze of stitched-together tools. Vector, full-text, and hybrid search (via $rankFusion) route through Atlas; file reads and writes forward directly to S3. For builders of long-running or multi-agent workflows: Plans, intermediate outputs, and knowledge artifacts persist across sessions, deployments, and sub-agent handoffs, so file-based context survives between runs instead of resetting each time.