# Dockerfile.ace — OpenClaw + ACE (self-improving agent)
#
# Extends the OpenClaw image with Python 3.12 and the ACE framework
# pre-installed, so the agent can learn from past sessions automatically.
#
# Build (two-step, from the openclaw repo root):
#
#   # 1. Build the base OpenClaw image
#   docker build -t openclaw:base .
#
#   # 2. Extend with ACE
#   docker build -t openclaw:local -f Dockerfile.ace .
#
# Then set OPENCLAW_IMAGE=openclaw:local in your .env file.
#
# IMPORTANT: You must pass your LLM API key through docker-compose so
# ace-learn can call the reflection model. Add to docker-compose.yml
# under the gateway service's environment section:
#
#   ACE_MODEL: "bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0"
#   # Plus one of: AWS_BEARER_TOKEN_BEDROCK, ANTHROPIC_API_KEY,
#   #              OPENROUTER_API_KEY, or LITELLM_API_KEY

ARG OPENCLAW_IMAGE=openclaw:base
FROM ${OPENCLAW_IMAGE}

USER root

# ---------------------------------------------------------------------------
# Python 3.12 + uv
# ---------------------------------------------------------------------------

# uv — fast Python package & version manager
RUN curl -LsSf https://astral.sh/uv/install.sh | env UV_INSTALL_DIR=/usr/local/bin sh

# Install Python 3.12 to a shared location
# (bookworm ships 3.11; uv downloads an official standalone build)
ENV UV_PYTHON_INSTALL_DIR=/opt/python
RUN uv python install 3.12

# ---------------------------------------------------------------------------
# ACE framework
# ---------------------------------------------------------------------------

RUN rm -rf /opt/ace \
    && git clone --depth 1 https://github.com/Kayba-ai/agentic-context-engine.git /opt/ace \
    && cd /opt/ace \
    && uv sync --no-dev --extra claude-code --python 3.12 \
    && uv pip install boto3 --python .venv/bin/python \
    && chown -R node:node /opt/ace

# Wrapper script: writes output to the persistent workspace volume
# so the skillbook survives container restarts.
RUN printf '#!/bin/bash\n\
set -euo pipefail\n\
export OPENCLAW_HOME="${OPENCLAW_HOME:-$HOME/.openclaw}"\n\
OUTPUT_DIR="$OPENCLAW_HOME/workspace/skills/kayba-ace"\n\
mkdir -p "$OUTPUT_DIR"\n\
cd /opt/ace\n\
exec .venv/bin/python examples/openclaw/kayba-ace/learn_from_traces.py \\\n\
    --output "$OUTPUT_DIR" \\\n\
    "$@"\n' > /usr/local/bin/ace-learn \
    && chmod +x /usr/local/bin/ace-learn

# Note: Do NOT set OPENCLAW_HOME here — the base image derives it from
# $HOME (~/.openclaw). Setting it explicitly causes double-nesting.

# ---------------------------------------------------------------------------
# Restore original user and working directory
# ---------------------------------------------------------------------------

USER node
WORKDIR /app