deployment-io/agentbox

agentbox

Run AI coding agents in a Docker container with a predictable contract.

Give agentbox a prompt and credentials via environment variables; it installs the agent, runs it against a bind-mounted working directory, streams output to stdout, and writes a structured result to /result.json. You don't have to think about stream-json parsing, subprocess lifecycle, signal handling, or version pinning — agentbox handles it so the same invocation shape works across CI, a managed platform, or a local terminal.

Built by deployment.io but designed to stand alone — useful to anyone running agents headlessly on their own infrastructure.

Quick Start

You'll need:

• Docker
• A working directory (any git repo or folder for the agent to work on)
• An Anthropic API key (see Credentials)

# Pull the image
docker pull deploymenthq/agentbox:latest

# Create a scratch output dir (agentbox writes result.json there)
mkdir -p /tmp/agentbox-out
chmod 777 /tmp/agentbox-out

# Run the agent on a local directory
docker run --rm \
  -e ANTHROPIC_API_KEY="sk-ant-..." \
  -e STEP_PROMPT="Add a README.md if missing, summarizing the project." \
  -e RESULT_PATH="/scratch/result.json" \
  -v "$(pwd):/work" \
  -v /tmp/agentbox-out:/scratch \
  deploymenthq/agentbox:latest

# Inspect the outcome
cat /tmp/agentbox-out/result.json

On exit:

• Files the agent created or modified are in your working directory
• /tmp/agentbox-out/result.json has the structured outcome (status, changes_summary, files_changed, `token_u