Metadata-Version: 2.4
Name: managed-deepagents
Version: 0.5.0
Summary: Managed Deep Agents — the define_deep_agent authoring interface plus the CLI that compiles and deploys a code-first Deep Agent repository to a managed LangGraph runtime.
Author: LangChain
License: MIT
Project-URL: Homepage, https://github.com/langchain-ai/managed-deepagents-sdk
Project-URL: Repository, https://github.com/langchain-ai/managed-deepagents-sdk
Keywords: langchain,langgraph,deepagents,agents,cli
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pydantic>=2.0.0
Provides-Extra: test
Requires-Dist: cryptography>=48.0.1; extra == "test"
Requires-Dist: langchain-core>=1.0.0; extra == "test"
Requires-Dist: pyjwt[crypto]>=2.8.0; extra == "test"
Requires-Dist: pytest>=9.0.3; extra == "test"
Requires-Dist: starlette>=1.3.1; extra == "test"
Dynamic: license-file

<!-- markdownlint-disable MD033 MD041 -->

<div align="center">
  <a href="https://www.langchain.com/langsmith-managed-deep-agents-waitlist">
    <img alt="Managed Deep Agents logo" src="https://raw.githubusercontent.com/langchain-ai/managed-deepagents-sdk/main/.github/assets/logo.png" width="70%">
  </a>
</div>

<div align="center">
  <h3>Python authoring package and CLI launcher for Managed Deep Agents.</h3>
</div>

> [!IMPORTANT]
> **Active development / private beta.** Managed Deep Agents is in active
> development and currently in private beta. The PyPI package API and managed
> runtime contract may change. [Join the
> waitlist](https://www.langchain.com/langsmith-managed-deep-agents-waitlist)
> for access and updates.

`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

```bash
uv tool install managed-deepagents
```

> [!NOTE]
> **Private beta: dev releases only.** We currently publish only PEP 440
> pre-release (dev) versions and no stable version yet. uv skips pre-releases
> by default unless they are allowed explicitly:
>
> ```bash
> uv tool install --prerelease allow 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:

```bash
mda init my-agent
cd my-agent
uv sync
```

`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, and
`--no-sandbox` leaves out the sandbox. Every new project includes an
`identity.py` that explicitly selects `auth.langsmith_api_key()` authentication.
Managed Deep Agent evals are Harbor tasks under `evals/tasks/`. Optionally create
a minimal source task under `evals/scaffold/` with `mda evals init <name>`, then
package the managed agent and scaffolded tasks with `mda evals compile .`.
Nothing prompts, so these commands also work in coding agents and CI scripts.

`mda init my-agent --gateway` runs the agent on
[LangSmith Gateway](https://docs.langchain.com/langsmith/gateway) — a model
LangSmith hosts, billed to your workspace's Gateway Credits, authenticated with
a LangSmith API key instead of a model provider key of your own. It is mutually
exclusive with `--model`, which names a provider you hold the key for.

## Define an Agent

Create an `agent.py` that defines an `agent`:

```python
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`](https://reference.langchain.com/python/deepagents/graph/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:

```python
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` declaring `define_memory(scope="agent")` additionally mounts one
deployment-shared memory tree at `/memories/agent/`.
`/memories/agent/AGENTS.md` is injected every turn; other files are read on
demand. Deploy never overwrites existing memories. Memory is independent of
identity, and a project without `memory.py` mounts no durable memory.

## Project Shape

```text
my-agent/
  agent.py              # named `agent` variable
  identity.py           # managed authentication (included by `mda init`)
  instructions.md       # managed system prompt
  pyproject.toml
  .env                  # local deploy secrets, never committed
  schedules/            # optional managed cron schedules
  tools/                # optional custom tools
  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`:

```python
# 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:

```python
from managed_deepagents import define_sandbox

sandbox = define_sandbox(
    scope="thread",
    idle_ttl_seconds=600,
)
```

If `sandbox/setup.sh` exists, MDA embeds it and runs it once when the sandbox is
first provisioned. MDA owns sandbox naming, image/snapshot selection, reuse, and
lifecycle.

## CLI

Create a new project:

```bash
mda init my-agent
```

Build locally:

```bash
mda build ./my-agent
```

Run on the local LangGraph dev server:

```bash
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:

```bash
mda deploy ./my-agent
```

The generated build is written to `<root>/.mda/build` by default.

Common deploy options:

```bash
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:

```bash
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:

```bash
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 including the
per-user memory repos beneath it, 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}`, which also lets a restarted deployment re-adopt its
existing sandbox instead of stranding it. The digest covers the sandbox scope
plus how it was provisioned, so editing `setup.sh` or switching snapshots gives
the next run a fresh sandbox instead of one built from the previous recipe.
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`, the process
environment, or LangSmith workspace secrets. For LangSmith itself, set
`LANGSMITH_API_KEY` the same way, 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.
