ADK Code Mode

A Code Mode code executor for Agent Development Kit (ADK).

The CodeModeCodeExecutor allows ADK agents to 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. It also supports input_files and output_files, and the sandboxed container can list, load, and save ADK 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.
Bounded stdout/stderr — overflow lands in a session artifact instead of poisoning the prompt.
Production-ready remote sandbox — RemoteBackend connects to an isolated, single-use container over WebSocket. 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	no
Input files	no	yes	yes	no	no	yes
Output files	no	yes	yes	no	no	yes
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 a CodeModeCodeExecutor, then wire three things into the agent:

CODE_MODE_SYSTEM_INSTRUCTION — append to the agent's instruction. Teaches the model how to write code blocks and use artifacts.
code_mode_before_model_callback — set as before_model_callback. Injects the tool catalog (<code-mode> block) into the system prompt on every model turn.
generate_content_config with function_calling_config.mode="NONE" — disables native function calling so the model writes Python instead of attempting tool calls that fail with MALFORMED_FUNCTION_CALL (since tools=[]).

Production (remote sandbox)

from google.adk.agents import LlmAgent
from google.genai import types as genai_types
from adk_code_mode import (
    CODE_MODE_SYSTEM_INSTRUCTION,
    CodeModeCodeExecutor,
    RemoteBackend,
    code_mode_before_model_callback,
)

executor = CodeModeCodeExecutor(
    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
    ),
)

root_agent = LlmAgent(
    name="assistant",
    model="gemini-2.5-pro",
    instruction=f"You are a helpful assistant.\n\n{CODE_MODE_SYSTEM_INSTRUCTION}",
    tools=[],  # do NOT also bind tools here; the executor owns them.
    code_executor=executor,
    generate_content_config=genai_types.GenerateContentConfig(
        tool_config=genai_types.ToolConfig(
            function_calling_config=genai_types.FunctionCallingConfig(mode="NONE"),
        ),
    ),
    before_model_callback=code_mode_before_model_callback(executor),
)

Local development only

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

from adk_code_mode import (
    CODE_MODE_SYSTEM_INSTRUCTION,
    CodeModeCodeExecutor,
    UnsafeLocalDockerBackend,
    code_mode_before_model_callback,
)

executor = CodeModeCodeExecutor(
    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 execution runs in its own container. The container accepts exactly one WebSocket connection, executes the user's code, returns results, and exits. The hosting platform destroys the container after each request — no cross-tenant data leakage, no residual state. You must configure your platform for one container per request (--concurrency 1 on Cloud Run, or equivalent).

Setting ADK_CODE_MODE_CONTROL_HTTP=1 activates HTTP mode. The container:

Starts a WebSocket server on port 8080 (configurable via PORT)
Accepts exactly one connection (rejects further connections with 503)
Receives tools and workspace as tar archives over binary WebSocket frames
Sanitizes the environment (strips all env vars except a safe allowlist)
Executes user code with tools proxied back to the host over the same WebSocket
Returns stdout/stderr and updated workspace files
Exits

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 and --vpc-egress=all-traffic
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 \
    --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"

Then in your agent:

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

--concurrency 1 is critical for security. Without this flag, Cloud Run may route multiple requests 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.):

One container per request. Each container handles exactly one execution and exits.
Block all outbound network access. Without egress restrictions, user code can exfiltrate data, access cloud metadata endpoints, or scan internal networks.
Set a read-only root filesystem where the platform supports it (e.g., readOnlyRootFilesystem: true in Kubernetes). The sandbox only writes to /workspace.
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 activate HTTP mode
`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

The same upload limits (plus a download limit) are configurable on RemoteBackend:

RemoteBackend(
    url="...",
    token="...",
    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:

/workspace — per-run working directory. ADK input_files are staged here before code runs (open("input.csv") works). Files created or modified under /workspace are returned as CodeExecutionResult.output_files but are not re-hydrated next turn unless persisted via save_artifact.
ADK Artifacts — persistent cross-turn storage. CodeModeCodeExecutor injects three tools into the catalog:

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 saved this turn.
    ...

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

🐳 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

Catalog overflow

max_catalog_chars (default 50_000) is a soft cap on the rendered tool catalog in the system prompt. When exceeded, the per-tool sections are replaced with a short note telling the model how to navigate /tools/ from Python.

CodeModeCodeExecutor(tools=..., backend=..., max_catalog_chars=20_000)

Output truncation

max_output_chars (default 50_000) caps stdout and stderr handed back to the model. Overflow is saved as a session artifact at code_mode/stdout/<execution-id>.txt, and the model sees a head-and-tail view with a marker pointing to it.

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

Code size limit

max_code_chars (default 1_000_000) rejects oversized code payloads before starting a container.

Timeouts

timeout_seconds caps overall execution time; per_tool_timeout_seconds caps each individual tool call. Both default to None (relying on platform timeouts). Set them explicitly for defense in depth:

CodeModeCodeExecutor(
    tools=...,
    backend=...,
    timeout_seconds=30,
    per_tool_timeout_seconds=10,
)

🏗️ Architecture

Host wheel (adk-code-mode). Lives in the same process as your LlmAgent. The before_model_callback resolves tools, renders the catalog, and appends it to the system prompt. At execution time, it generates a tools/ Python package of thin stubs, stages input_files into /workspace, and launches the sandbox.

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

Your instruction (containing CODE_MODE_SYSTEM_INSTRUCTION) followed by a <code-mode> block appended by the callback:

…your instruction…

# How to execute code and use tools
Code you write in a fenced Python block (i.e. ```python) will be executed in a sandbox.
The Python Standard Library and a custom set of tools are available to you.
To see the result of your code, you need to print it.

For example, if you had the following tool:

from tools.slack import send_message

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


To call the tool, you should write:

"""
```python
from tools.slack import send_message

print(send_message(channel="C123", text="hi"))

"""

How to use files and variables in between executions

Code is executed in a new environment each time. To list available Artifacts, use the list_artifacts tool. To save an Artifact, use the save_artifact tool, and to load an Artifact, use the load_artifact tool.

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 …

```

When the rendered catalog exceeds max_catalog_chars, the per-tool sections are replaced with:

<code-mode>
A `tools` package is available in the sandbox. List `/tools/` with
`pathlib.Path('/tools').iterdir()`. Each entry is either a `.py` file
(a top-level tool, importable as `from tools import <name>`) or a
subdirectory (a namespace, with tools importable as
`from tools.<namespace> import <name>`). To see a tool's signature and
docstring, read its `.py` file with `open(...).read()`.
</code-mode>

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 execution. Fresh container per request — no shared filesystem, memory, or processes.
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.
No state across executions. Variables don't survive between turns. Use save_artifact / load_artifact to persist, or /workspace within a single run.
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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LICENSE		LICENSE
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ADK Code Mode

✨ Features

📦 Install

🚀 Usage

Production (remote sandbox)

Local development only

🌐 Remote Deployment

Deploy to Cloud Run

Deploy on other platforms

🗂️ Storage

🐳 Sandbox Image

⚙️ Configuration

Catalog overflow

Output truncation

Code size limit

Timeouts

🏗️ Architecture

What the model sees

How to use files and variables in between executions

tools.slack

tools

🛡️ Safety

RemoteBackend (production)

UnsafeLocalDockerBackend (development only)

What this does NOT protect against

⚠️ Limitations

🛠️ Development

📄 License

🤝 Join the A2A Net Community

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