Metadata-Version: 2.4
Name: adk-code-mode
Version: 0.2.0
Summary: A "Code Mode" Code Executor for ADK for agents to interact with tools, files, and custom packages with Python
Project-URL: Documentation, https://github.com/A2ANet/adk-code-mode#readme
Project-URL: Issues, https://github.com/A2ANet/adk-code-mode/issues
Project-URL: Source, https://github.com/A2ANet/adk-code-mode
Author-email: A2A Net <hello@a2anet.com>
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: a2a,adk,agents,code-executor,code-mode
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.10
Requires-Dist: google-adk<2.0.0,>=1.0.0
Requires-Dist: google-genai>=1.0.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: websockets>=14.0
Provides-Extra: docker
Requires-Dist: docker>=7.0.0; extra == 'docker'
Description-Content-Type: text/markdown

# ADK Code Mode

A [Code Mode](https://blog.cloudflare.com/code-mode/) code executor for [Agent Development Kit (ADK)](https://github.com/google/adk-python).

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](https://blog.cloudflare.com/code-mode/) and Anthropic's [Code execution with MCP](https://www.anthropic.com/engineering/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](#-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

```bash
pip install adk-code-mode
```

Or with uv:

```bash
uv add adk-code-mode
```

For local development with `UnsafeLocalDockerBackend`, install the `docker` extra:

```bash
pip install adk-code-mode[docker]
```

Requires Python 3.10+. Local development requires [Docker](https://docs.docker.com/get-docker/); remote deployment only needs network access to the sandbox URL.

## 🚀 Usage

Build a `CodeModeCodeExecutor`, wire `code_mode_before_model_callback` into the agent, and put `CODE_MODE_SYSTEM_INSTRUCTION` in the agent's `instruction`. The callback injects the tool catalog into the system prompt — skip it and the model has no idea what tools exist.

### Production (remote sandbox)

```python
from google.adk.agents import LlmAgent
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,
    before_model_callback=code_mode_before_model_callback(executor),
)
```

### Local development only

> **`UnsafeLocalDockerBackend` is not safe for production or multi-tenant use.** See [Safety](#-safety).

```python
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:

```python
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:

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

### Deploy to Cloud Run

```bash
# Push the sandbox image to Artifact Registry
gcloud auth configure-docker <region>-docker.pkg.dev
docker pull ghcr.io/a2anet/adk-code-mode:latest
docker tag  ghcr.io/a2anet/adk-code-mode:latest \
    <region>-docker.pkg.dev/<project>/adk-code-mode/sandbox:latest
docker push <region>-docker.pkg.dev/<project>/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>/adk-code-mode/sandbox:latest \
    --region <region> \
    --port 8080 \
    --cpu 1 \
    --memory 1Gi \
    --concurrency 1 \
    --no-allow-unauthenticated \
    --vpc-connector=adk-sandbox-connector \
    --vpc-egress=all-traffic \
    --set-env-vars "ADK_CODE_MODE_CONTROL_HTTP=1,ADK_CODE_MODE_AUTH_TOKEN=<your-secret>"
```

Then in your agent:

```python
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.):

1. **One container per request.** Each container handles exactly one execution 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. **Set a read-only root filesystem** where the platform supports it (e.g., `readOnlyRootFilesystem: true` in Kubernetes). The sandbox only writes to `/workspace`.
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 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`:

```python
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:

```python
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`:

```python
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:

```dockerfile
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.

```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.

```python
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:

```python
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 `<tools>` block appended by the callback:

```
…your instruction…

<tools>

# 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
…

</tools>
```

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](#-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](#-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

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

Docker integration tests are opt-in:

```bash
uv run pytest -m docker
```

## 📄 License

`adk-code-mode` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.

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