This release is a pre-release and may not be stable for production use.
Python authoring package and CLI launcher for Managed Deep Agents.
managed-deepagents is the PyPI package for authoring Managed Deep Agents in
Python. It includes:
define_deep_agent, the Python authoring contract for managed agents.define_schedule, the Python contract for managed cron schedules.mda, the CLI used to build and deploy your agent to LangSmith.managed_deepagents.runtime, the runtime helper used by generated managed entry modules.
Install
uv tool install managed-deepagents
This package requires Python 3.9 or newer. Each platform wheel bundles the
prebuilt mda binary for its OS and CPU architecture and exposes it through the
mda console script. This PyPI-installed CLI scaffolds and compiles Python
projects only and vendors the Python runtime bundled with this wheel.
To start a new project, run mda init. In a terminal, the CLI asks you to name
the agent:
mda init
Run mda init -i to initialize your agent interactively and optionally hand it
off to a coding agent to build:
mda init -i
Choose a coding agent to continue in the new project, or select View raw prompt to copy the setup instructions.
For coding agents and other headless use, pass the project name:
mda init my-agent
mda init can shape the project up front — --instructions "..." (or
--instructions-file <path>) writes the system prompt, --model <spec> picks
the model, --memory agent opts into deployment-shared durable memory,
--no-sandbox leaves out the sandbox, and -c slack (also --channel /
--channels) writes channels/slack.py. Every new project includes an
identity.py that explicitly selects auth.langsmith_api_key() authentication.
Evaluate
Managed Deep Agent evals run in Harbor. Install uv and Docker before running
them.
-
From the project root, initialize the eval workspace:
mda evals init -i
-
Follow the coding-agent prompt to author tasks directly under
evals/<task>/. The CLI creates the user-ownedevals/harbor-job.jsononce and preserves later edits..mda/evals/is generated. A task may include an authoredevals/<task>/identity.jsonfixture. It requires a non-emptyuser.id;user.kind,user.email, and top-levelgroups,claims, andsource.providerare optional. Keep it with the task, not in generated.mda/evals/. -
Export
LANGSMITH_API_KEY,LANGSMITH_WORKSPACE_IDwhen your credentials require it, and the model or tool credential variables used by the agent. -
From the same project root, run the pinned Harbor 0.21.0 command included at the end of the coding-agent prompt. The command loads
MDAJobPluginandLangSmithPlugin. It uses POSIX syntax on macOS/Linux and PowerShell on native Windows. -
From the same project root, inspect results:
uv run --python 3.12 --with 'harbor[langsmith]==0.21.0' harbor view .mda/evals/jobs
MDAJobPlugin compiles a fresh eval artifact at every Harbor job start.
This POC keeps MDA's custom Harbor adapter; migration to Harbor's built-in
LangGraph agent is deferred.
Define an Agent
Create an agent.py that defines an agent:
from managed_deepagents import define_deep_agent
# The system prompt comes from instructions.md next to this file.
agent = define_deep_agent(
name="research-assistant",
model="openai:gpt-5.5",
tools=[query_db],
)
define_deep_agent requires a static name (LangGraph assistant id and default LangSmith deployment name) and otherwise accepts the create_deep_agent keyword surface minus the managed keys: backend, store, checkpointer, memory, skills, and system_prompt. Those are provided by the managed runtime when your agent is deployed. Write the system prompt in instructions.md next to agent.py; the CLI embeds it at deploy time.
To authenticate SDK and API requests with a LangSmith workspace key while retaining MDA's thread and store authorization, declare it explicitly:
identity = define_identity(auth=auth.langsmith_api_key())
Clients send the key as x-api-key. LangSmith Cloud supplies the verification
endpoint and tenant configuration; do not add those platform-owned values to
the project .env.
On deploy, Context Hub stores harness files (instructions.md, skills/**). A
root memory.py enables agent and user memory independently:
from managed_deepagents import MemoryLayer, define_memory
memory = define_memory(
agent=MemoryLayer(),
user=MemoryLayer(),
)
Omit a layer to disable it. A layer with no allow callback uses its default
policy. Agent memory is shared across callers and is available by default.
User memory requires a trusted person and defaults to managed Slack one-to-one
DMs (event.channel_type == "im"). Shared conversations, missing event data,
and HTTP runs get no user mount unless an authored policy allows it.
Either layer can use a sync or async allow(context) callback to replace its
default policy:
from typing import TypedDict
from managed_deepagents import MemoryLayer, define_memory
class Context(TypedDict):
remember: bool
def allow_memory(context: Context) -> bool:
return context.get("remember") is True
memory = define_memory(
agent=MemoryLayer(),
user=MemoryLayer(allow=allow_memory),
)
Each callback runs once before memory mounts on each run, including interrupt
resumes. Returning false removes only that layer's mount and hot memory for
the run. If the context cannot satisfy the policy schema, MDA denies that layer
without calling its policy. An error from the callback still fails the run.
User policies cannot grant access to another
person's memory or grant user memory to a service principal. Build, schema, and
state inspection do not call either policy.
The legacy scope option remains supported. Do not combine it with agent or
user.
User hot memory and agent hot memory with an allow policy are loaded for each
model call. They are not saved in thread state. This keeps the graph structure
stable and prevents a later denied run from loading saved memory. A missing
hot file stays empty until the first memory write.
The agent layer is mounted at /memories/agent/. The user layer requires
identity.py and is mounted from an opaque per-user Context Hub repo at
/memories/user/, keyed on the trusted caller principal. Each mount's
AGENTS.md is injected every turn; other files are read on demand. Deploy never
overwrites existing memories. A project without memory.py mounts no durable
memory.
Run context
The managed channel handler passes the provider and original event as run context:
{
"provider": "slack",
"event": {
"type": "message",
"channel": "D123",
"channel_type": "im",
"user": "U123",
"text": "Hello",
"ts": "1712345678.000002"
}
}
event is ChannelEvent.raw_event, preserved as received. For Slack, it is
the inner event object, including extra provider fields; it excludes the outer
webhook envelope and HTTP headers. Interrupt resumes currently omit the raw
event, so their context contains only provider and the default memory policy
does not mount user memory. The handler verifies its service credential and
caller identity before starting a run.
An authored allow(context) replaces the default Slack rule. It can inspect
context.provider and context.event for provider-specific decisions.
Custom frontends use the same Agent Server context parameter:
await client.runs.create(
thread_id,
assistant_id,
input={"messages": [{"role": "user", "content": "Hello"}]},
context={"remember": True},
)
HTTP callers define their own context and use allow(context) to decide when
user memory is available. The example above works with the remember policy
shown earlier. Authentication still determines the memory owner. A policy that
always returns true is suitable only when every reply destination is appropriate
for the caller's personal memory.
ManagedRunContext types the managed channel fields. Keep your own context_schema
if needed; include provider and event if your policy or tools use them,
because Agent Server can remove undeclared fields. MDA does not replace your
schema. Tools and middleware read the same supplied data through runtime.context.
The default Slack DM policy reads the original run context, before MDA applies
the authored schema. Your schema does not need to declare provider or event
for that default policy.
Deployments use Agent Server 0.16.0.dev1. Python mda dev passes run context
to graph factories and can evaluate both agent and user memory policies. User
memory still requires a trusted person and an allowed policy.
Project Shape
my-agent/
agent.py # named `agent` variable
identity.py # managed authentication (included by `mda init`)
memory.py # optional durable-memory declaration
instructions.md # managed system prompt
pyproject.toml
.env # local deploy secrets, never committed
schedules/ # optional managed cron schedules
tools/ # optional custom tools and MCP declaration
mcp.py # optional MCP server declaration
middleware/ # optional middleware
skills/ # optional skills synced to Context Hub
sandbox/ # LangSmith sandbox (`mda init` includes this; delete to opt out)
The CLI copies your project files into the managed build and generates the entry module that connects your definition to the hosted runtime.
The agent entry must live at the project root as agent.py.
Define a Schedule
Create one file per schedule under schedules/ and define a named schedule:
# schedules/daily_digest.py
from managed_deepagents import define_schedule
schedule = define_schedule(
cron="0 8 * * 1-5",
timezone="America/Los_Angeles",
prompt="Write the daily digest.",
)
mda deploy reconciles schedules as LangSmith cron jobs after the deployment is
live. Declarations must be statically serializable literals or top-level
constants; prompt schedules become user-message input, and stateless runs clean
up their temporary thread after completion.
Sandbox
mda init scaffolds sandbox/__init__.py with a LangSmith sandbox. MDA only
enables the sandbox when that declaration is present — delete sandbox/ to opt
out:
from managed_deepagents import define_sandbox
sandbox = define_sandbox(
idle_ttl_seconds=600,
)
If sandbox/setup.sh exists, mda deploy / mda dev bake it into a recipe
snapshot once; thread sandboxes clone that snapshot and do not re-run setup.
MDA owns sandbox naming, image/snapshot selection, reuse, and lifecycle.
Authored tools and middleware can use the Deep Agents backend interface at
runtime.backend. It is None when the project has no sandbox:
from managed_deepagents import ManagedDeepAgentRuntime
def write_report(runtime: ManagedDeepAgentRuntime) -> None:
if runtime.backend is None:
raise RuntimeError("This tool requires a sandbox")
result = runtime.backend.write("/workspace/report.txt", "Report ready")
if result.error:
raise RuntimeError(result.error)
The backend uses the standard Deep Agents file arguments and results: ls,
read, write, edit, grep, and glob. delete is optional and depends on
the installed backend. Use upload_files and download_files for binary data.
Python also has async methods such as aread, awrite, and adelete.
All paths refer to the current sandbox. Context Hub routes for skills and
memory are not part of this backend.
Private published images can declare registry credentials by environment variable name; MDA creates or updates the deployment-owned Host registry:
sandbox = define_sandbox(
docker_image="ghcr.io/acme/agent-base:1",
registry={
"url": "ghcr.io",
"username": "octocat",
"password_env": "GHCR_TOKEN",
},
)
Put GHCR_TOKEN in the project .env or process environment. Its value is
used only to reconcile the registry and never enters the build or snapshot.
MCP Servers
Add tools/mcp.py to attach MCP servers. The file must define a module-level
mcp. By default, MDA exposes every tool loaded from each
declared server. In mda dev, an agent-owned connection reads from
MDA_DEV_<SLUG> with the slug uppercased and hyphens changed to underscores.
Hosted deployments resolve workspace connections from Agent Auth:
The old connectors/mcp.py file API remains available with a warning during
0.7.x. It will be removed in 0.8.0.
from managed_deepagents import define_mcp, connections
mcp = define_mcp(
servers={
"langchainDocs": {
"transport": "http",
"url": "https://docs.langchain.com/mcp",
"include_tools": ["search", "fetch"],
"connection": connections.get("docs-token", {"type": "agent"}),
},
},
)
For this example, set MDA_DEV_DOCS_TOKEN.
A user-owned connection — connections.get(slug, {"type": "user"}) — resolves
per caller (OAuth or opaque). Any runtime access interrupts the run when a grant
is missing, including access from a custom tool or middleware. Connector
declarations use the same behavior in a pre-run gate, so all known grants can be
requested before the first model call. This path works in mda deploy and
mda dev when LANGSMITH_API_KEY and LANGSMITH_WORKSPACE_ID are set and
the workspace connection rows exist. Signed-in Studio users are identified by
their LangSmith ls_user_id. Local development uses separate user connections
from deployed Studio. OAuth completes on
LangSmith’s platform callback URL. The local UI must show
credential_authorization_required and resume after the user connects.
Deploy the project first, then provision its connections with
mda connections create. Agent-owned opaque secrets are scoped to that
deployment, so the command refuses to create one before mda deploy.
mda deploy fails when a slug a project declares is missing from the
workspace.
Use include_tools or exclude_tools inside a server config to select a
subset. Tool names are raw MCP tool names before the managed {server}__ prefix
is applied, so "include_tools": ["search"] on server langchainDocs exposes
langchainDocs__search when prefixing is enabled.
CLI
Create a new project:
mda init my-agent
Build locally:
mda build ./my-agent
Run on the local LangGraph dev server:
mda dev ./my-agent
mda dev requires uv on PATH, but it resolves the local LangGraph dev
server automatically; you do not need to install a global langgraph command.
Deploy to LangSmith:
mda deploy ./my-agent
The generated build is written to <root>/.mda/build by default.
Common deploy options:
mda deploy ./my-agent --name my-agent-dev --deployment-type dev
mda deploy ./my-agent --workspace-id "$LANGSMITH_WORKSPACE_ID"
mda deploy ./my-agent --no-wait
Read the deployed agent's server logs:
mda logs ./my-agent
mda logs ./my-agent --lines 200 --level error
mda logs ./my-agent > agent.log
In a terminal mda logs streams new output until you press Ctrl-C. When the
output is piped or redirected it prints the most recent lines (1000 by default)
and exits.
Tear it down again:
mda delete ./my-agent
mda delete (alias mda destroy) removes the LangSmith deployment, the tracing
project created alongside it, the deployment's Context Hub repo (plus any legacy
per-user or org child memory repos left from older runtimes), and the managed
sandboxes the deployment created. It asks for confirmation first; pass --yes
to skip the prompt in scripts. Agent memory and thread history are not
recoverable afterwards.
Sandboxes are matched by name: the runtime names each one
{deployment}--{digest} of the thread id, which also lets a restarted
deployment re-adopt its existing sandbox instead of stranding it. Recipe changes
(setup.sh or bake base) produce a new deploy-time snapshot; live threads keep
their boxes until reclaim. Sandboxes created before this behavior existed are
unnamed and are left to
LangSmith's idle-stop and retention window.
Before deploying, make sure your model provider key such as OPENAI_API_KEY
or ANTHROPIC_API_KEY is available in the project .env or LangSmith
workspace secrets; a value exported in your shell is not deployed. For
LangSmith itself, set LANGSMITH_API_KEY in .env or your shell, or run
interactively and press Enter at the prompt to sign in with your browser (the
CLI creates a key and writes it to .env). Use LANGSMITH_WORKSPACE_ID or
--workspace-id when your credentials require a workspace selection.
1 maintainer
Release files for managed-deepagents 0.8.0.dev1
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| managed_deepagents-0.8.0.dev1-py3-none-manylinux2014_x86_64.whl | Python 3 | none | Linux glibc 2.17+ x86-64 | Details |
| managed_deepagents-0.8.0.dev1-py3-none-manylinux2014_aarch64.whl | Python 3 | none | Linux glibc 2.17+ ARM64 | Details |
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| managed_deepagents-0.8.0.dev1-py3-none-macosx_10_12_x86_64.whl | Python 3 | none | macOS 10.12+ x86-64 | Details |
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