Connections offer a simple, elegant way to implement agent auth. We support agent and user (OBO) credentials in mda 0.7. Try it out, it’s magic https://t.co/pnmD06la9i https://t.co/DLPNtQD4K3

Managed Deep Agents - Docs by LangChain

Source: https://docs.langchain.com/langsmith/python/managed-deep-agents-overview Managed Deep Agents (MDA) is the simplest way to build and deploy production agents. You focus on what your agent does. MDA runs it. There are no servers to run and no infrastructure to wire together.You write the agent’s intelligence: its instructions, the tools it can call, the skills it follows, and you select the model that drives it. MDA provides everything underneath:

• The Deep Agents harness: The agent loop that plans, calls tools, manages a filesystem, and delegates to subagents. SeeDeep Agents.
• A managed runtime:LangSmith Deployment’s Agent Serverhosts and operates the agent, and keeps sessions running across restarts.

Example agent

A managed deep agent consists of a project folder that contains the business logic for its behavior:

• Model & configuration
• Instructions
• Skills
• Tools
• Middleware
• MCP Connectors

When you upload this folder with themdaCLI, it will automatically run on managed LangSmith infrastructure. You provide the business logic, and Managed Deep Agents provides the agent harness and production infrastructure.To get started, see theManaged Deep Agents quickstart.

Core capabilities

Each part of the agent maps to a file or directory. Add the ones your agent needs:

For the full layout, seeProject structure.

Next steps

LangChain (@LangChain): New in Managed Deep Agents 0.7.0+: Connections.

Agent-owned secrets for tools everyone shares, user-owned OAuth for anything that should carry a real person’s identity.

Store your credentials in your LangSmith workspace instead of .env.