adk-code-mode 1.0.0

A "Code Mode" sandboxed code-execution tool for ADK agents to interact with tools, files, and custom packages with Python

ADK Code Mode

A Code Mode sandboxed code-execution tool for Agent Development Kit (ADK).

ExecuteCodeTool lets ADK agents write Python code to call tools and read and write files. Code runs inside a sandboxed container, and tools (and their credentials) are executed on the host. The base image comes with the stdlib and can be extended with any Python package you want. The sandboxed container can list, load, and save ADK Artifacts, and files it creates are returned as artifacts.

Inspired by Cloudflare's Code Mode and Anthropic's Code execution with MCP.

✨ Features

• Call ADK tools from sandbox code — imports against the tools package proxy back to the host and run through ADK's before_tool / after_tool / on_error callbacks and the plugin manager exactly as direct tool calls would.
• Bake any Python package into the image — extend the published base image with anything the model's code needs to import, no runtime pip install required.
• Cross-turn persistence via ADK Artifacts — save_artifact / load_artifact / list_artifacts are auto-injected and route through your configured ArtifactService. Files the code creates or changes are saved as artifacts automatically too.
• Tool results saved as artifacts — on by default; every tool's result is persisted as a code_mode.tool_result artifact (with optional model-supplied name/description) so hosts can forward outputs and large results stay out of the prompt. Opt out with save_tool_results_as_artifacts=False.
• Bounded stdout/stderr — overflow lands in a session artifact instead of poisoning the prompt.
• Production-ready remote sandbox — RemoteBackend connects to an isolated per-turn container over WebSocket, reused across the turn's execute_code calls. Deploy on any cloud platform (Cloud Run, Fargate, ACI, Kubernetes, Fly.io, etc.).
• Local development — UnsafeLocalDockerBackend runs the sandbox against your local Docker daemon for fast iteration. Not for production — see Safety.

	BuiltIn	AgentEngineSandbox	VertexAi	Container	Gke	CodeMode
Call ADK tools from code	no	no	no	no	no	yes (with limitations)
Extra Python packages	no	no (more than stdlib but fixed)	no (more than stdlib but fixed)	yes	yes	yes
Variables are stateful	no	yes	yes	no	no	yes (within a turn)
Input files	no	yes	yes	no	no	no (use Artifacts)
Output files	no	yes	yes	no	no	yes (as Artifacts)
Storage	no	yes (via variables)	yes (via variables)	no	no	yes (via ADK Artifacts)
Local development version available	no	no	no	yes	yes	yes
Bounded stdout/stderr	no	no	no	no	no	yes (max_output_chars)

📦 Install

pip install adk-code-mode

Or with uv:

uv add adk-code-mode

For local development with UnsafeLocalDockerBackend, install the docker extra:

pip install adk-code-mode[docker]

Requires Python 3.10+. Local development requires Docker; remote deployment only needs network access to the sandbox URL.

🚀 Usage

Build an ExecuteCodeTool, give it the tools the sandboxed code may call, and attach it to your agent. Wire release_invocation into after_agent_callback to release the turn's sandbox container when the turn ends. An idle reaper (session_idle_timeout_seconds, default 600) is a backstop.

Production (remote sandbox)

from google.adk.agents import LlmAgent
from adk_code_mode import ExecuteCodeTool, RemoteBackend

tool = ExecuteCodeTool(
    tools=[my_fn_tool, McpToolset(...), OpenAPIToolset(...)],
    backend=RemoteBackend(
        url="https://sandbox-xyz.run.app",  # your deployed sandbox URL
        token="your-secret-token",           # bearer token for auth
    ),
)

async def _release_sandbox(callback_context):
    await tool.release_invocation(callback_context.invocation_id)

root_agent = LlmAgent(
    name="assistant",
    model="gemini-3.5-flash",
    instruction="You are a helpful assistant.",
    tools=[tool],
    after_agent_callback=[_release_sandbox],
)

Local development only

UnsafeLocalDockerBackend is not safe for production or multi-tenant use. See Safety.

from adk_code_mode import ExecuteCodeTool, UnsafeLocalDockerBackend

tool = ExecuteCodeTool(
    tools=[my_fn_tool, McpToolset(...), OpenAPIToolset(...)],
    backend=UnsafeLocalDockerBackend(image="ghcr.io/a2anet/adk-code-mode:latest"),
)

Inside the sandbox, the model writes code like:

from tools.slack import send_message
print(send_message(channel="C123", text="hi"))

🌐 Remote Deployment

Every turn runs in its own container, which the platform destroys when the turn ends — no cross-turn or cross-tenant state. The sandbox runs as a WebSocket server (set ADK_CODE_MODE_CONTROL_HTTP=1) and accepts exactly one connection, so you must configure your platform for one container per turn (--concurrency 1 on Cloud Run, or equivalent).

Deploy to Cloud Run

# Push the sandbox image to Artifact Registry
gcloud auth configure-docker <region>-docker.pkg.dev
docker pull --platform linux/amd64 ghcr.io/a2anet/adk-code-mode:latest
docker tag  ghcr.io/a2anet/adk-code-mode:latest \
    <region>-docker.pkg.dev/<project>/<repository>/adk-code-mode-sandbox:latest
docker push <region>-docker.pkg.dev/<project>/<repository>/adk-code-mode-sandbox:latest

# Create a VPC connector with no egress routes (blocks outbound network from sandbox)
gcloud compute networks create adk-sandbox-vpc --subnet-mode=custom
gcloud compute networks subnets create adk-sandbox-subnet \
    --network=adk-sandbox-vpc \
    --region=<region> \
    --range=10.8.0.0/28
gcloud compute firewall-rules create adk-sandbox-deny-all-egress \
    --network=adk-sandbox-vpc \
    --direction=EGRESS \
    --action=DENY \
    --rules=all \
    --priority=1000
gcloud compute networks vpc-access connectors create adk-sandbox-connector \
    --region=<region> \
    --subnet=adk-sandbox-subnet

# Deploy — note --concurrency 1, --vpc-egress=all-traffic, and the /health startup probe
gcloud run deploy adk-code-mode-sandbox \
    --image <region>-docker.pkg.dev/<project>/<repository>/adk-code-mode-sandbox:latest \
    --region <region> \
    --port 8080 \
    --cpu 1 \
    --memory 1Gi \
    --concurrency 1 \
    --timeout 3600 \
    --max-instances 120 \
    --allow-unauthenticated \
    --vpc-connector=adk-sandbox-connector \
    --vpc-egress=all-traffic \
    --set-env-vars "ADK_CODE_MODE_CONTROL_HTTP=1" \
    --set-secrets "ADK_CODE_MODE_AUTH_TOKEN=<your-secret-name>:latest" \
    --startup-probe "httpGet.path=/health,httpGet.port=8080,timeoutSeconds=3,periodSeconds=3,failureThreshold=80"

These flags are recommendations to tune per deployment, not hard requirements. --timeout 3600 (Cloud Run's max) is the per-turn ceiling since the container holds the WebSocket for the whole turn; --max-instances should cover your peak concurrent turns (120 covers a 10–100 target — verify your region's Cloud Run vCPU quota). The /health startup probe avoids cold-start HTTP 503s — Cloud Run's default TCP probe opens a raw socket the WebSocket server rejects.

Then in your agent:

RemoteBackend(
    url="https://adk-code-mode-sandbox-xxxxx.run.app",
    token="<your-secret>",
)

--concurrency 1 is critical for security. It pins one turn to one container. Without this flag, Cloud Run may route multiple turns to the same container. The sandbox rejects the second connection, but the misconfiguration itself is a risk.

--vpc-egress=all-traffic with a deny-all VPC is critical for security. Without it, user code can make arbitrary outbound requests — including hitting the GCP metadata endpoint (169.254.169.254) to steal the service account token, exfiltrating data, or scanning your VPC. The sandbox only needs to accept inbound connections; it never needs outbound access.

Deploy on other platforms

The same pattern works on any platform that runs Docker containers as HTTP services (AWS Fargate/ECS, Azure Container Instances, Kubernetes, Fly.io, etc.):

1. One container per turn. Each container handles exactly one turn (one or more execute_code calls) and exits.
2. Block all outbound network access. Without egress restrictions, user code can exfiltrate data, access cloud metadata endpoints, or scan internal networks.
3. Keep /workspace and /tools writable. The sandbox stages the working directory and materialises the tools package into /tools at connect time. If you set a read-only root filesystem (e.g., readOnlyRootFilesystem: true in Kubernetes), mount both as writable volumes (e.g., an emptyDir).
4. Authenticate connections. Set ADK_CODE_MODE_AUTH_TOKEN and layer platform-level auth (IAM, NetworkPolicy, security groups) on top.

Required env vars:

Env var	Required	Default	Purpose
ADK_CODE_MODE_CONTROL_HTTP	yes	—	Set to 1 to run the sandbox as a WebSocket server (required for remote)
ADK_CODE_MODE_AUTH_TOKEN	yes	—	Bearer token for WebSocket auth
PORT	no	8080	Listen port
ADK_CODE_MODE_MAX_UPLOAD_TOOLS	no	100 MiB	Max tools tar archive size
ADK_CODE_MODE_MAX_UPLOAD_WORKSPACE	no	100 MiB	Max workspace tar archive size

Connection tuning, retry, and the same upload limits (plus a download limit) are configurable on RemoteBackend:

RemoteBackend(
    url="...",
    token="...",
    connect_timeout=10.0,             # seconds to wait for the WS handshake (default)
    start_attempts=3,                 # connect attempts before giving up (default)
    start_retry_delay_seconds=1.0,    # linear backoff base: delay * attempt (default)
    start_retry_jitter_seconds=0.25,  # uniform jitter added per retry (default)
    max_upload_tools_bytes=100 * 1024 * 1024,       # 100 MiB (default)
    max_upload_workspace_bytes=100 * 1024 * 1024,    # 100 MiB (default)
    max_download_workspace_bytes=100 * 1024 * 1024,  # 100 MiB (default)
)

🗂️ Storage

Code Mode exposes two file surfaces:

• The working directory — the turn's workspace. It persists across the turn's execute_code calls and resets between turns. Files created or changed by a call are collected afterward and saved as session artifacts automatically, returned to the model as a list of filenames (reloadable via load_artifact) — nothing is re-hydrated into the working directory on the next turn unless the model explicitly loads it back with load_artifact.

• ADK Artifacts — persistent cross-turn storage. ExecuteCodeTool injects three tools into the sandbox:

import json
from tools import save_artifact, load_artifact, list_artifacts

save_artifact(
    filename="report.json",
    content=json.dumps({"status": "ready"}),
    mime_type="application/json",
)
print(list_artifacts())
report = load_artifact(filename="report.json")
if report is not None and report["kind"] == "text":
    payload = json.loads(report["data"])

Pass include_artifact_tools=False to opt out. To react when the model saves an artifact, pass on_artifacts_saved:

async def on_saved(invocation_context, delta):
    # delta is {filename: version} for everything the sandbox-side save_artifact
    # calls (or wrapped tool results) saved this turn.
    ...

ExecuteCodeTool(tools=..., backend=..., on_artifacts_saved=on_saved)

Tool results as artifacts

By default (save_tool_results_as_artifacts=True) every non-artifact tool is wrapped so its return value is saved as a session artifact tagged code_mode.tool_result = "true". This lets a host forward tool outputs to the user (read the marker in on_artifacts_saved) and keeps large results out of the model's context — a result whose serialised form exceeds the threshold is replaced in the reply with a short note pointing at the artifact, which the model reloads with load_artifact.

Naming is transparent: the filename is derived from the tool name and call id. Each wrapped tool also gains two optional parameters — artifact_name and artifact_description — that the model may pass to name/describe the saved artifact; both land in the artifact's custom_metadata (code_mode.artifact_name / code_mode.artifact_description) alongside the marker. Set save_tool_results_as_artifacts=False to return tool results inline without persisting them.

ExecuteCodeTool(tools=..., backend=..., save_tool_results_as_artifacts=True)

🐳 Sandbox Image

The published base image (ghcr.io/a2anet/adk-code-mode) works as-is for tools whose execution is fully host-side. To bake in extra Python packages:

FROM ghcr.io/a2anet/adk-code-mode:latest
RUN pip install --no-cache-dir pandas==2.2.*

The same image works for both RemoteBackend and UnsafeLocalDockerBackend. To build directly from this repo, run make docker-image.

⚙️ Configuration

All settings are ExecuteCodeTool constructor arguments:

Argument	Default	Purpose
append_function_stubs_to_system_instruction	True	Appends a <code-mode> block listing available function signatures and docstrings to the system instruction on every model turn. If the rendered catalog would exceed max_catalog_chars, the model can still discover functions by listing /tools/ and reading the generated stubs.
max_catalog_chars	50_000	Maximum size of the appended function catalog.
max_output_chars	50_000	Caps stdout/stderr handed back to the model. Overflow is saved as a session artifact at code_mode/stdout/<call-id>.txt and the model sees a head-and-tail view pointing to it.
max_code_chars	1_000_000	Rejects oversized code payloads before starting a container.
timeout_seconds	None	Caps overall execution time of one execute_code call. Defaults to the platform request timeout (e.g. Cloud Run --timeout); set explicitly for defense in depth.
per_tool_timeout_seconds	None	Caps each individual tool call made from within the sandbox.
session_idle_timeout_seconds	600	Idle reaper: closes a turn's container once it goes untouched this long. Backstop for turns that never call release_invocation.

The model can read spilled stdout back from the overflow artifact:

from tools import load_artifact
spilled = load_artifact(filename="code_mode/stdout/<call-id>.txt")
print(spilled["data"][-2000:])

Turn-scoped sessions

A sandbox container is held open for one turn (one ADK invocation) and reused across that turn's execute_code calls, so cold start is paid at most once per turn. Python globals and the working directory persist across a turn's calls and reset between turns — use ADK Artifacts for cross-turn persistence. The container is released when the turn ends (via await tool.release_invocation(...), wired in Usage) and destroyed by the platform, so no state survives into another turn or tenant.

🏗️ Architecture

Host wheel (adk-code-mode). Lives in the same process as your LlmAgent. ExecuteCodeTool.process_llm_request resolves tools and, when append_function_stubs_to_system_instruction is enabled, renders the catalog and appends it to the system instruction. At execution time (run_async), it generates a tools/ Python package of thin stubs, stages the working directory, and opens (or reuses) the turn's sandbox connection — the container spans the whole turn.

Sandbox wheel (adk-code-mode-sandbox). Pre-installed in the container image. When model code calls a stub, it sends a JSON-Lines frame over the control connection; the host runs the real tool (with callbacks and plugins) and sends the result back.

The only things crossing the boundary are: code, tool call arguments, tool return values, and log frames.

Backend	Transport	Multi-tenant safe?	When to use
RemoteBackend	WebSocket over HTTPS	Yes	Production — any cloud platform
UnsafeLocalDockerBackend	TCP over Docker bridge	No	Local development only

What the model sees

execute_code has a single code: string parameter. With append_function_stubs_to_system_instruction enabled, the system instruction also gets a <code-mode> reference catalog on every turn:

…your instruction…

Reference catalog of the Python functions available inside the execute_code sandbox.
Import these functions from the `tools` package in the code you pass to execute_code.

<code-mode>

# tools.slack

from tools.slack import list_channels, send_message

def list_channels() -> Any:
    """List Slack channels."""
    ...

def send_message(*, channel: str, text: str, thread_ts: str | None = ...) -> Any:
    """Send a message to a Slack channel."""
    ...

# tools

from tools import save_artifact, load_artifact, list_artifacts
…

</code-mode>

With save_tool_results_as_artifacts enabled (the default), each non-artifact tool above — e.g. list_channels and send_message — also carries two optional artifact_name: str | None = ... / artifact_description: str | None = ... parameters for naming its saved result.

If the rendered catalog would exceed max_catalog_chars, nothing is appended to the system instruction that turn — not even a fallback note. The model can still navigate the sandbox from Python:

import pathlib
print(list(pathlib.Path("/tools").iterdir()))
print(open("/tools/slack/send_message.py").read())  # signature + docstring

Text and JSON-like MIME types travel as plain strings in artifact tools; binary content is base64-encoded. load_artifact returns {"kind": "text" | "bytes", "data": str, "mime_type": str | None}.

🛡️ Safety

RemoteBackend (production)

RemoteBackend is designed for multi-tenant production use where untrusted users submit arbitrary Python code:

• One container per turn (one tenant, one invocation). Within a turn the process/filesystem are reused across that turn's execute_code calls; the container is destroyed at turn end, with no cross-turn or cross-tenant sharing.
• Environment sanitization. All env vars are stripped except a safe allowlist (PATH, HOME, USER, locale vars, Python config) before user code runs.
• Credentials never enter the sandbox. API keys, OAuth tokens, and connection strings stay in the host process. The container only receives tool results.
• Bearer token authentication. WebSocket connections without a valid token are rejected. Always set ADK_CODE_MODE_AUTH_TOKEN and layer platform-level auth on top.
• Hardened tar extraction. Path traversal (../), symlinks, hardlinks, and absolute paths are rejected.
• Non-root user. The sandbox runs as sandbox, not root.
• Tool dispatch runs ADK's guard callbacks. before_tool, after_tool, on_error, and the plugin manager all fire normally.
• Bounded inputs and outputs. See Configuration for max_code_chars, max_output_chars, timeout_seconds, per_tool_timeout_seconds, and upload/download size limits.

UnsafeLocalDockerBackend (development only)

Do not use in production or for multi-tenant workloads.

Named "Unsafe" intentionally: it binds a TCP listener on 0.0.0.0, communicates over unencrypted TCP, and relies on the local Docker daemon. It does still sanitize env vars, run as non-root, drop all Linux capabilities (cap_drop=["ALL"]), and mount the root filesystem read-only — but it is not a security boundary for untrusted users.

What this does NOT protect against

• Network egress (if you skip egress restrictions). The sandbox does NOT block outbound network by itself — configure this at the platform level. Without it, user code can exfiltrate data, access cloud metadata endpoints (169.254.169.254), or scan internal networks. See Remote Deployment.
• Container runtime escapes. Keep your container runtime patched.
• Exfiltration through legitimate tool calls. If your tool surface includes send_email, a prompt-injected payload could use it. Keep your tool surface least-privilege.
• Denial of service within resource limits. User code can consume its full CPU/memory allocation. Set platform-level limits.

⚠️ Limitations

• No credential-requesting tools. Tools that need ADK to request credentials, confirmations, UI widgets, agent transfer, escalation, or that yield without an immediate response are rejected with a structured error.
• State is turn-scoped. Variables and the working directory persist across execute_code calls within a turn, but reset between turns. Use save_artifact / load_artifact to persist across turns.
• No runtime package installation. The sandbox ships with the Python Standard Library and the runtime's own dependencies only. Extra packages must be baked into the image at build time.

🛠️ Development

make install       # uv sync --group dev
make ci            # ruff + mypy + pytest

Docker integration tests are opt-in:

uv run pytest -m docker

📄 License

adk-code-mode is distributed under the terms of the Apache-2.0 license.

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