mda CLI compiles, what a deploy creates, and which parts the runtime owns helps you reason about behavior, secrets, and state.
Managed Deep Agents is in private beta, available on LangSmith Cloud in the US region only. Join the waitlist to request access.
Compilation
mda dev and mda deploy compile your project into a runnable LangGraph app in a .mda/build directory. Your agent entry and the modules it imports are copied without rewriting, so imports behave the same as in a normal Python or TypeScript project. The build leaves out secrets and generated files such as .env and node_modules. For the full ignored-path list, see the CLI reference.
Deploy lifecycle
You author and test your project locally, then deploy it to LangSmith with one command.mda dev runs the compiled app in LangSmith Studio so you can test it. mda deploy validates the project, syncs deploy-owned context to Context Hub, uploads the build, triggers a hosted build, and reconciles any cron schedules once the deployment is live. For secrets routing, deploy flags, and operational tips, see Deploy an agent. For the full step list and flags, see the CLI reference.
When you deploy a Managed Deep Agent, LangSmith creates or updates a hosted LangGraph deployment, creates a Context Hub agent repo for managed context, and reconciles any managed cron schedules declared under schedules/. Open the deployment page in LangSmith to inspect build status and revisions. Open traces to inspect user inputs, final responses, model calls, tool calls, sandbox activity, files, and runtime state created during runs.
What the managed runtime owns
The managed runtime ownsbackend, store, checkpointer, memory, skills, and the system prompt. Do not set those fields in the agent definition.
| Concern | Owner | Where you configure it |
|---|---|---|
backend, store, checkpointer | Managed runtime | Not configurable. |
memory | Managed runtime, backed by Context Hub | disableMemory / disable_memory to turn off agent-scoped memory. |
skills | Managed runtime, backed by Context Hub | skills/** in the project. |
| System prompt | Managed runtime, backed by Context Hub | instructions.md in the project. |
| Model, tools, middleware, subagents, interrupts | You | The agent definition and imported modules. |
Context Hub
Each deployment has a Context Hub repo that stores deploy-owned context and runtime-created memory:/instructions.md: the managed system prompt, synced from your project on deploy./skills/**: deploy-owned skills, synced from your project on deploy./memories/AGENTS.md: durable agent memory, written by the agent at runtime.
Threads and memory
The managed runtime owns the checkpointer and store, so each thread’s state persists across runs without any setup. Durable memory persists in Context Hub and is available to the agent across threads. Scheduled runs choose their thread behavior explicitly. An ephemeral thread is cleaned up after the run, while a persistent thread reuses a stable thread ID so state accumulates. For the thread modes and when to use each, see Schedules.Sandboxes
A sandbox gives the agent an isolated environment for code execution and filesystem work. Configure one by exportingsandbox from sandbox/index.ts or sandbox/__init__.py, and use sandbox/setup.sh to provision it the first time it is created. Each thread gets its own sandbox. For configuration options and examples, see Configure a sandbox.
See also
- Overview: when to use Managed Deep Agents and beta limits.
- Deploy an agent: the full deploy workflow, secrets, and troubleshooting.
- CLI reference: every
mdacommand, flag, and project file rule.
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