Deep Coding Agents

​

Overview

Deep coding agents — long-running AI sessions that clone a repo, explore the codebase, and write code — need more compute than a laptop and a stable environment that survives disconnects. Chalk Compute lets you deploy a scaling group, install your agent, and connect from a browser or terminal.

This tutorial walks through deploying OpenCode in a Chalk scaling group. The same pattern works for any agent that runs as a server process (Aider, Continue, etc.).

​

Define the image

Build an image with curl, git, and opencode baked in. Chalk caches the built image, so subsequent launches skip the install step entirely.

from chalkcompute import Image, ScalingGroup

opencode_image = (
    Image.base("python:3.12-slim")
    .run_commands(
        "apt-get update -qq && apt-get install -y -qq curl git",
        "curl -fsSL https://opencode.ai/install | bash",
    )
)

​

Write the deploy script

Create a file called deploy_opencode.py:

import time
from chalkcompute import Image, ScalingGroup

opencode_image = (
    Image.base("python:3.12-slim")
    .run_commands(
        "apt-get update -qq && apt-get install -y -qq curl git",
        "curl -fsSL https://opencode.ai/install | bash",
    )
)

OPENCODE_PORT = 4096

scaling_group = ScalingGroup(
    image=opencode_image,
    name="opencode-server",
    env={
        "OPENAI_API_KEY": "sk-...",        # your LLM provider key
        "GH_TOKEN": "ghp_...",             # for private repos
    },
    port=OPENCODE_PORT,
    min_replicas=0,
    max_replicas=1,
    entrypoint=[
        "bash", "-c",
        "git clone --depth 1 https://github.com/your-org/your-repo.git /root/code"
        " && /root/.opencode/bin/opencode serve"
        "    --hostname=0.0.0.0"
        f"   --port={OPENCODE_PORT}",
    ],
).deploy().wait_ready()

print(f"Web UI: {scaling_group.web_url}")
print("Press Ctrl-C to stop.")

try:
    time.sleep(43200)  # 12 hours
except KeyboardInterrupt:
    pass
finally:
    scaling_group.delete()

​

Deploy it

python deploy_opencode.py

The script builds the image (first run only), starts the scaling group, and prints a URL you can open in your browser. The coding agent is now running with full cloud compute behind it.

​

Manage a running scaling group

You can reconnect to a deployed scaling group by name:

from chalkcompute import ScalingGroup

# Reconnect to the deployed scaling group
scaling_group = ScalingGroup.from_name("opencode-server")

print(scaling_group.web_url)

# Delete when done
scaling_group.delete()

​

Pass files with volumes

Use a Volume to share configuration or model files with the scaling group without baking them into the image:

from chalkcompute import Image, ScalingGroup, Volume

vol = Volume(name="agent-config")
vol.put_file("opencode.json", '{"model": "claude-sonnet-4-20250514", "provider": "anthropic"}')

scaling_group = ScalingGroup(
    image=Image.base("python:3.12-slim").run_commands(
        "apt-get update -qq && apt-get install -y -qq curl git",
        "curl -fsSL https://opencode.ai/install | bash",
    ),
    name="opencode-with-config",
    port=4096,
    min_replicas=0,
    max_replicas=1,
    volumes=[("agent-config", "/root/.config/opencode")],
    entrypoint=["bash", "-c", "/root/.opencode/bin/opencode serve --hostname=0.0.0.0 --port=4096"],
).deploy().wait_ready()

The volume is mounted at /root/.config/opencode so the agent picks up your configuration on startup.