From 7abbc24bf7fb34abaabc1d178616cc162bf3f24c Mon Sep 17 00:00:00 2001 From: Wanli-Lee <1181451942@qq.com> Date: Tue, 30 Jun 2026 14:39:46 +0800 Subject: [PATCH 1/4] Add OSWorld-V2 hybrid GUI+CLI evaluation experiment + results Harness ablation: drive OSWorld-V2 (108 tasks) with a hybrid GUI+CLI agent (codex CLI injected in-VM + GUI channel) on the GPT-5.5 backbone, scored with OSWorld's native env.evaluate(). Same backbone gains +5.5 pt Binary (13.0->18.5%) at ~2x tool-call efficiency (149.8->77.5/task) vs the official batched row. Includes core code, launchers, and aggregated per-task results. Co-Authored-By: Claude --- README.md | 1 + experiments/osworld_v2_hybrid/.gitignore | 22 + experiments/osworld_v2_hybrid/README.md | 41 + .../osworld_v2_hybrid/aggregate_results.py | 158 ++ .../launchers/run_osworld_v2_inject_claude.sh | 91 ++ .../launchers/run_osworld_v2_inject_codex.sh | 121 ++ .../osworld_v2_hybrid/lib_run_single.py | 1303 +++++++++++++++++ .../osworld_v2_hybrid/mm_agents/__init__.py | 0 .../mm_agents/claudecode_agent.py | 302 ++++ .../mm_agents/codex_agent.py | 485 ++++++ .../mm_agents/openclaw_agent.py | 771 ++++++++++ .../codex_hybrid_gpt55/RESULT_ANALYSIS.md | 103 ++ .../results/codex_hybrid_gpt55/action_mix.csv | 105 ++ .../codex_hybrid_gpt55/per_task_scores.json | 1190 +++++++++++++++ .../results/codex_hybrid_gpt55/summary.json | 50 + .../run_osworld_v2_inject.py | 536 +++++++ 16 files changed, 5279 insertions(+) create mode 100644 experiments/osworld_v2_hybrid/.gitignore create mode 100644 experiments/osworld_v2_hybrid/README.md create mode 100644 experiments/osworld_v2_hybrid/aggregate_results.py create mode 100755 experiments/osworld_v2_hybrid/launchers/run_osworld_v2_inject_claude.sh create mode 100755 experiments/osworld_v2_hybrid/launchers/run_osworld_v2_inject_codex.sh create mode 100644 experiments/osworld_v2_hybrid/lib_run_single.py create mode 100644 experiments/osworld_v2_hybrid/mm_agents/__init__.py create mode 100644 experiments/osworld_v2_hybrid/mm_agents/claudecode_agent.py create mode 100644 experiments/osworld_v2_hybrid/mm_agents/codex_agent.py create mode 100755 experiments/osworld_v2_hybrid/mm_agents/openclaw_agent.py create mode 100644 experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md create mode 100644 experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/action_mix.csv create mode 100644 experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/per_task_scores.json create mode 100644 experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/summary.json create mode 100644 experiments/osworld_v2_hybrid/run_osworld_v2_inject.py diff --git a/README.md b/README.md index 74afe50..0e03982 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,7 @@ ## 📰 News +- **2026-06-30** — **OSWorld-V2** under a **hybrid GUI+CLI harness** (codex CLI in-VM + GUI, native grading): GPT-5.5 **+5.5 pt Binary (13.0→18.5%)** at **~2× tool-call efficiency (149.8→77.5/task)**. [`experiments/osworld_v2_hybrid/`](./experiments/osworld_v2_hybrid). - **2026-06-12** — WeaveBench hit **#4 on [Hugging Face Daily Papers](https://huggingface.co/papers/2606.09426)** (104 upvotes). 🎉 - **2026-06-10** — Evaluated **9 frontier backbones × 4 agent runtimes** (OpenClaw, Codex CLI, Claude Code, Hermes); best pairing tops out at **41.2% PassRate**. Full leaderboard in [`docs/REPRODUCE.md`](./docs/REPRODUCE.md). - **2026-06-08** — Initial preprint and [project website](https://weavebench.github.io) live. diff --git a/experiments/osworld_v2_hybrid/.gitignore b/experiments/osworld_v2_hybrid/.gitignore new file mode 100644 index 0000000..aa307eb --- /dev/null +++ b/experiments/osworld_v2_hybrid/.gitignore @@ -0,0 +1,22 @@ +# Python +__pycache__/ +*.py[cod] +*.egg-info/ +.ipynb_checkpoints/ + +# Large runtime assets — never commit (download / build separately, see README) +*.tar.gz +*.qcow2 +*.zip + +# Raw run outputs — keep only the aggregated files under results/codex_hybrid_gpt55/ +results/osworld_v2_inject/ +**/pyautogui/ +**/screenshots/ +**/agent.log +*.log + +# Secrets / local endpoints +*.env +my_api.json +litellm_api.json diff --git a/experiments/osworld_v2_hybrid/README.md b/experiments/osworld_v2_hybrid/README.md new file mode 100644 index 0000000..9c38048 --- /dev/null +++ b/experiments/osworld_v2_hybrid/README.md @@ -0,0 +1,41 @@ +# OSWorld-V2 hybrid GUI+CLI re-evaluation + +A harness ablation on **OSWorld-V2** (108 tasks): we drive each task with a +**hybrid GUI+CLI agent** — the OpenAI `codex` CLI injected inside the VM plus a +GUI action channel — on the same **GPT-5.5** backbone the paper benchmarks. +Scored with OSWorld's native grader (`env.evaluate()`). + +## Headline + +| Model / harness | Binary (%) | Partial (%) | Tool calls/task | +|---|---|---|---| +| **GPT-5.5 + codex hybrid (this work)** | **18.5** | 59.3 | 77.5 | +| GPT-5.5 batched (official Table 3) | 13.0 | 49.5 | 149.8 | + +Same backbone, swapping the official batched loop for the codex hybrid harness +lifts GPT-5.5 **13.0% → 18.5% Binary** at ~half the tool calls. Dropping 4 +infra-failure tasks (063/064/082/069) gives a 104-task cohort: **51.39% avg / +19.23% Binary**. Full breakdown in +[`results/codex_hybrid_gpt55/RESULT_ANALYSIS.md`](./results/codex_hybrid_gpt55/RESULT_ANALYSIS.md). + +## Layout + +``` +run_osworld_v2_inject.py # entrypoint: run agent on OSWorld-V2, score via native env.evaluate() +aggregate_results.py # rebuild results/*.json from per-task score.json +mm_agents/codex_agent.py # the codex CLI + GUI hybrid agent (uses openclaw_agent's VM helpers) +launchers/ # exact run commands (env-var placeholders for secrets/paths) +results/codex_hybrid_gpt55/ # aggregated scores + analysis (raw trajectories not committed) +``` + +## Running + +Drop this folder into an OSWorld-V2 checkout (needs `desktop_env`, the VM qcow2, +and an OpenAI-compatible endpoint), then set the launcher placeholders via env +vars and run: + +```bash +OSWORLD_QCOW2=/path/to/osworld-v2.qcow2 WEAVEBENCH_ASSETS_DIR=/path/to/runtime_assets \ +LITELLM_API_KEY= CODEX_REASONING_EFFORT=xhigh \ +bash launchers/run_osworld_v2_inject_codex.sh +``` diff --git a/experiments/osworld_v2_hybrid/aggregate_results.py b/experiments/osworld_v2_hybrid/aggregate_results.py new file mode 100644 index 0000000..7074e2b --- /dev/null +++ b/experiments/osworld_v2_hybrid/aggregate_results.py @@ -0,0 +1,158 @@ +#!/usr/bin/env python3 +"""Aggregate OSWorld-V2 codex-hybrid (GPT-5.5) per-task scores into +per_task_scores.json + summary.json. + +Source of truth: each task's score.json written by the inject runner under + /pyautogui/screenshot//tasks//score.json +plus the action-mix CSV (CLI vs GUI tool calls) and codex total-token counts +parsed from each task's agent.log. + +Run from this folder: + RUN_DIR=/path/to/FULL_108_gpt55_codex_xhigh_20260628_125852 \ + python aggregate_results.py + +If RUN_DIR is unset it falls back to the path used for the published numbers. +The committed JSON in results/codex_hybrid_gpt55/ was produced by this script; +re-running it against the same run reproduces those files exactly. +""" +import csv +import json +import os +import re +import statistics as st +from pathlib import Path + +RUN_DIR = Path( + os.environ.get( + "RUN_DIR", + # Point this at your inject-runner output directory, e.g. + # results/osworld_v2_inject/OSW2_codex_hybrid_ + "results/osworld_v2_inject/FULL_108_gpt55_codex_xhigh_20260628_125852", + ) +) +MODEL = os.environ.get("MODEL", "gpt-5.5") +OUT_DIR = Path(__file__).parent / "results" / "codex_hybrid_gpt55" + +# Infra-failure tasks excluded from the de-infra cohort (root cause unrelated to +# model capability): VM disk fault, network resets, framework multiphase limit. +INFRA = {"063", "064", "082", "069"} +INFRA_REASON = { + "063": "VM ext4 journal error -> root remounted read-only", + "064": "network: Connection broken / IncompleteRead during execution", + "082": "network: Connection reset by peer during task setup", + "069": "framework: multiphase_unsupported_in_inject_runner", +} + +TOK_RE = re.compile(r"^([0-9,]+)$") + + +def parse_tokens(agent_log: Path): + """Sum codex 'tokens used' totals across turns (total tok = input+reasoning+output).""" + if not agent_log.exists(): + return None + toks = [] + lines = agent_log.read_text(errors="replace").splitlines() + for i, l in enumerate(lines): + if l.strip() == "tokens used" and i + 1 < len(lines): + m = TOK_RE.match(lines[i + 1].strip()) + if m: + toks.append(int(m.group(1).replace(",", ""))) + return sum(toks) if toks else None + + +def load_action_mix(): + csv_path = OUT_DIR / "action_mix.csv" + if not csv_path.exists(): + return {} + mix = {} + with open(csv_path) as f: + for row in csv.DictReader(f): + mix[row["task_id"]] = { + "cli": int(row["cli"]), + "gui": int(row["gui"]), + "total": int(row["total"]), + } + return mix + + +def main(): + tasks_dir = RUN_DIR / "pyautogui" / "screenshot" / MODEL / "tasks" + mix = load_action_mix() + rows = [] + for td in sorted(tasks_dir.glob("*")): + sj = td / "score.json" + if not sj.exists(): + continue + s = json.loads(sj.read_text()) + tid = s["task_id"] + rows.append( + { + "task_id": tid, + "score": s.get("score") or 0.0, + "agent_done": s.get("agent_done"), + "error": s.get("error"), + "elapsed_seconds": s.get("elapsed_seconds"), + "total_tokens": parse_tokens(td / "agent.log"), + "tool_calls": mix.get(tid, {}).get("total"), + "gui_calls": mix.get(tid, {}).get("gui"), + "cli_calls": mix.get(tid, {}).get("cli"), + } + ) + + def cohort(keep): + n = len(keep) + binary = sum(1 for r in keep if r["score"] >= 0.999) + partial = sum(1 for r in keep if 0 < r["score"] < 0.999) + avg = sum(r["score"] for r in keep) / n + return { + "n": n, + "binary_pct": round(binary / n * 100, 2), + "partial_pct": round(partial / n * 100, 2), + "avg_score_pct": round(avg * 100, 2), + "binary_count": binary, + "partial_count": partial, + } + + def mean(key, src=rows): + vs = [r[key] for r in src if r.get(key) is not None] + return round(sum(vs) / len(vs), 1) if vs else None + + def median(key, src=rows): + vs = [r[key] for r in src if r.get(key) is not None] + return round(st.median(vs), 1) if vs else None + + all108 = cohort(rows) + deinfra = cohort([r for r in rows if r["task_id"] not in INFRA]) + + summary = { + "run": RUN_DIR.name, + "model": MODEL, + "harness": "codex CLI injected into VM + GUI channel (hybrid)", + "reasoning_effort": "xhigh", + "scoring": "native OSWorld env.evaluate() (paper-faithful)", + "cohorts": {"all_108": all108, "de_infra_104": deinfra}, + "efficiency": { + "tool_calls_per_task": {"mean": mean("tool_calls"), "median": median("tool_calls")}, + "gui_calls_per_task": {"mean": mean("gui_calls"), "median": median("gui_calls")}, + "cli_calls_per_task": {"mean": mean("cli_calls"), "median": median("cli_calls")}, + "total_tokens_per_task_incl_input": { + "mean": mean("total_tokens"), + "median": median("total_tokens"), + "note": "codex CLI logs only a combined token total (input+reasoning+output); " + "output-only tokens and cost were not recorded -> reported as '-' in tables", + }, + }, + "excluded_infra_tasks": INFRA_REASON, + } + + OUT_DIR.mkdir(parents=True, exist_ok=True) + (OUT_DIR / "per_task_scores.json").write_text(json.dumps(rows, indent=2, ensure_ascii=False)) + (OUT_DIR / "summary.json").write_text(json.dumps(summary, indent=2, ensure_ascii=False)) + print("wrote", OUT_DIR / "per_task_scores.json", "(", len(rows), "tasks )") + print("wrote", OUT_DIR / "summary.json") + print("all_108:", all108) + print("de_infra_104:", deinfra) + + +if __name__ == "__main__": + main() diff --git a/experiments/osworld_v2_hybrid/launchers/run_osworld_v2_inject_claude.sh b/experiments/osworld_v2_hybrid/launchers/run_osworld_v2_inject_claude.sh new file mode 100755 index 0000000..2f3386e --- /dev/null +++ b/experiments/osworld_v2_hybrid/launchers/run_osworld_v2_inject_claude.sh @@ -0,0 +1,91 @@ +#!/usr/bin/env bash +# OSWorld-V2 (108 tasks) — CLAUDE CODE injected, CLI-ONLY agent. +# In-VM agent = `claude` CLI + opus-4.8 at MAX thinking (paper Table 3 parity). +# CLI-only (AGENT_GUI=false): no computer MCP, pure shell/file tools. +set -euo pipefail + +LAUNCHER_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +EVAL_DIR="$(cd "${LAUNCHER_DIR}/.." && pwd)" +OSWORLD_DIR="$(cd "${EVAL_DIR}/../.." && pwd)" + +# === Anthropic endpoint (copilot-api on 4141) === +LITELLM_HOST_IP="${LITELLM_HOST_IP:-127.0.0.1}" +LITELLM_VM_IP="${LITELLM_VM_IP:-172.17.0.1}" +LITELLM_PORT="${LITELLM_PORT:-4141}" +LITELLM_API_KEY="${LITELLM_API_KEY:-dummy}" +MODEL="${MODEL:-claude-opus-4-8}" + +# === Run knobs === +NUM_ENVS="${NUM_ENVS:-8}" +MAX_STEPS="${MAX_STEPS:-500}" +AGENT_GUI="${AGENT_GUI:-false}" # CLI-only +AGENT_TIMEOUT="${AGENT_TIMEOUT:-7200}" +LIMIT="${LIMIT:-0}" +TASK_FILTER="${TASK_FILTER:-}" +CLIENT_PASSWORD="${CLIENT_PASSWORD:-osworld-public-evaluation}" + +# === claude-specific === +export CLAUDE_EFFORT_LEVEL="${CLAUDE_EFFORT_LEVEL:-max}" +export CLAUDE_MAX_RETRIES="${CLAUDE_MAX_RETRIES:-3}" + +# === Image / data === +OSWORLD_QCOW2="${OSWORLD_QCOW2:-/path/to/osworld_v2_images/osworld-v2-ubuntu-x86.qcow2}" +export WEAVEBENCH_ASSETS_DIR="${WEAVEBENCH_ASSETS_DIR:-/path/to/osworld_v2_images/runtime_assets}" +export WEBSITE_HOST_SUFFIX="${WEBSITE_HOST_SUFFIX:-web.hku.icu}" + +# host-side LLM-judge evaluators still route via LiteLLM 4200 (gpt judge) +export OSWORLD_EVAL_MODEL_PROVIDER="${OSWORLD_EVAL_MODEL_PROVIDER:-openai_compatible}" +export OSWORLD_EVAL_MODEL_API_KEY="${OSWORLD_EVAL_MODEL_API_KEY:-sk-litellm-azure-direct}" +export OSWORLD_EVAL_MODEL_BASE_URL="${OSWORLD_EVAL_MODEL_BASE_URL:-http://127.0.0.1:4200/v1}" + +export GITLAB_URL="${GITLAB_URL:-http://172.17.0.1}" +export GITLAB_PRIVATE_TOKEN="${GITLAB_PRIVATE_TOKEN:-REPLACE_WITH_YOUR_GITLAB_TOKEN}" + +RESULT_TAG="${RESULT_TAG:-OSW2_claude_cli_$(date +%Y%m%d_%H%M%S)}" +RESULT_DIR="${RESULT_DIR:-${OSWORLD_DIR}/results/osworld_v2_inject/${RESULT_TAG}}" +mkdir -p "${RESULT_DIR}" +PY_BIN="${PY_BIN:-python3}" + +# === Preflight === +echo "[preflight] copilot-api ${LITELLM_HOST_IP}:${LITELLM_PORT} has ${MODEL}?" +curl -sS --max-time 8 --noproxy '*' "http://${LITELLM_HOST_IP}:${LITELLM_PORT}/v1/models" \ + | grep -q "${MODEL}" || { echo "[preflight] FAIL: ${MODEL} not on 4141"; exit 1; } +echo "[preflight] opus OK" +[ -f "${OSWORLD_QCOW2}" ] || { echo "[preflight] FAIL: qcow2 missing"; exit 1; } +CCTAR="${WEAVEBENCH_ASSETS_DIR}/claudecode.tar.gz" +[ -f "$CCTAR" ] || CCTAR="/path/to/runtime_assets/claudecode.tar.gz" +[ -f "$CCTAR" ] || { echo "[preflight] FAIL: claudecode.tar.gz missing"; exit 1; } +echo "[preflight] claudecode assets OK ($CCTAR)" +[ -d "${OSWORLD_DIR}/evaluation_examples/task_class" ] || { echo "[preflight] FAIL task_class"; exit 1; } +echo "[preflight] task_class OK" + +# in-VM reaches copilot-api on host gateway:4141, no /v1 suffix (anthropic SDK adds it) +AGENT_BASE_URL="http://${LITELLM_VM_IP}:${LITELLM_PORT}" + +echo "===========================================================" +echo " OSWorld-V2 CLAUDE CODE (CLI-only) — ${MODEL} effort=${CLAUDE_EFFORT_LEVEL}" +echo "Agent: ${AGENT_BASE_URL} gui=${AGENT_GUI} envs=${NUM_ENVS} timeout=${AGENT_TIMEOUT}s" +echo "Result: ${RESULT_DIR} Start: $(date)" +echo "===========================================================" + +cd "${OSWORLD_DIR}" +HOST_PROXY="${HOST_PROXY:-http://127.0.0.1:7897}" +export http_proxy="${HOST_PROXY}" https_proxy="${HOST_PROXY}" +export HTTP_PROXY="${HOST_PROXY}" HTTPS_PROXY="${HOST_PROXY}" +export NO_PROXY="127.0.0.1,localhost,172.17.0.1,0.0.0.0"; export no_proxy="${NO_PROXY}" + +EXTRA_ARGS=() +[ -n "${TASK_FILTER}" ] && EXTRA_ARGS+=(--task_filter "${TASK_FILTER}") +[ "${LIMIT}" -gt 0 ] 2>/dev/null && EXTRA_ARGS+=(--limit "${LIMIT}") +[ -n "${TEST_META_PATH:-}" ] && EXTRA_ARGS+=(--test_all_meta_path "${TEST_META_PATH}") + +"${PY_BIN}" "${EVAL_DIR}/run_osworld_v2_inject.py" \ + --provider_name docker --headless true \ + --path_to_vm "${OSWORLD_QCOW2}" --osworld_root "${OSWORLD_DIR}" \ + --num_envs "${NUM_ENVS}" --model "${MODEL}" \ + --litellm_base_url "${AGENT_BASE_URL}" --litellm_api_key "${LITELLM_API_KEY}" \ + --max_steps "${MAX_STEPS}" --agent_gui "${AGENT_GUI}" \ + --agent_timeout "${AGENT_TIMEOUT}" --client_password "${CLIENT_PASSWORD}" \ + --result_dir "${RESULT_DIR}" --agent_harness claudecode \ + "${EXTRA_ARGS[@]}" \ + 2>&1 | tee "${RESULT_DIR}/run_$(date +%Y%m%d_%H%M%S).log" diff --git a/experiments/osworld_v2_hybrid/launchers/run_osworld_v2_inject_codex.sh b/experiments/osworld_v2_hybrid/launchers/run_osworld_v2_inject_codex.sh new file mode 100755 index 0000000..a9fe465 --- /dev/null +++ b/experiments/osworld_v2_hybrid/launchers/run_osworld_v2_inject_codex.sh @@ -0,0 +1,121 @@ +#!/usr/bin/env bash +# OSWorld-V2 (108 tasks) — CODEX-injected hybrid GUI+CLI agent. +# +# Identical orchestration to run_osworld_v2_inject.sh, but the in-VM agent is +# the OpenAI `codex` CLI instead of openclaw. Selected via --agent_harness codex. +# Scoring = native env.evaluate() (paper-faithful), same as openclaw. +set -euo pipefail + +LAUNCHER_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +EVAL_DIR="$(cd "${LAUNCHER_DIR}/.." && pwd)" # experiments/osworld_v2_inject +OSWORLD_DIR="$(cd "${EVAL_DIR}/../.." && pwd)" # OSWorld-V2 repo root + +# === LiteLLM (gpt-5.5) endpoints === +LITELLM_HOST_IP="${LITELLM_HOST_IP:-127.0.0.1}" +LITELLM_VM_IP="${LITELLM_VM_IP:-172.17.0.1}" +LITELLM_PORT="${LITELLM_PORT:-4200}" +LITELLM_API_KEY="${LITELLM_API_KEY:-sk-litellm-azure-direct}" +MODEL="${MODEL:-gpt-5.5}" + +# === Run knobs === +NUM_ENVS="${NUM_ENVS:-6}" +MAX_STEPS="${MAX_STEPS:-500}" +AGENT_GUI="${AGENT_GUI:-true}" +AGENT_TIMEOUT="${AGENT_TIMEOUT:-5400}" +LIMIT="${LIMIT:-0}" +TASK_FILTER="${TASK_FILTER:-}" +CLIENT_PASSWORD="${CLIENT_PASSWORD:-osworld-public-evaluation}" + +# === codex-specific knobs === +# Reasoning effort baked into /root/.codex/config.toml. Default medium (match +# the openclaw v6 run). Override with CODEX_REASONING_EFFORT=high|xhigh|low. +export CODEX_REASONING_EFFORT="${CODEX_REASONING_EFFORT:-medium}" +# LiteLLM proxy speaks the OpenAI Responses wire on /v1/responses. If the +# gateway rejects it, set CODEX_WIRE_API=chat. +export CODEX_WIRE_API="${CODEX_WIRE_API:-responses}" +export CODEX_MAX_RETRIES="${CODEX_MAX_RETRIES:-3}" + +# === Image / data === +OSWORLD_QCOW2="${OSWORLD_QCOW2:-/path/to/osworld_v2_images/osworld-v2-ubuntu-x86.qcow2}" +# codex.tar.gz + weavebench_computer_mcp/server.py live here (alongside +# openclaw.tar.gz). codex_agent.py resolves this via WEAVEBENCH_ASSETS_DIR. +export WEAVEBENCH_ASSETS_DIR="${WEAVEBENCH_ASSETS_DIR:-/path/to/osworld_v2_images/runtime_assets}" +export WEBSITE_HOST_SUFFIX="${WEBSITE_HOST_SUFFIX:-web.hku.icu}" + +# LLM-judge evaluators (host-side env.evaluate) route through LiteLLM proxy. +export OSWORLD_EVAL_MODEL_PROVIDER="${OSWORLD_EVAL_MODEL_PROVIDER:-openai_compatible}" +export OSWORLD_EVAL_MODEL_API_KEY="${OSWORLD_EVAL_MODEL_API_KEY:-${LITELLM_API_KEY:-sk-litellm-azure-direct}}" +export OSWORLD_EVAL_MODEL_BASE_URL="${OSWORLD_EVAL_MODEL_BASE_URL:-http://${LITELLM_HOST_IP:-127.0.0.1}:${LITELLM_PORT:-4200}/v1}" + +# GitLab-backed tasks (026/041). +export GITLAB_URL="${GITLAB_URL:-http://172.17.0.1}" +export GITLAB_PRIVATE_TOKEN="${GITLAB_PRIVATE_TOKEN:-REPLACE_WITH_YOUR_GITLAB_TOKEN}" + +RESULT_TAG="${RESULT_TAG:-OSW2_codex_hybrid_$(date +%Y%m%d_%H%M%S)}" +RESULT_DIR="${RESULT_DIR:-${OSWORLD_DIR}/results/osworld_v2_inject/${RESULT_TAG}}" +mkdir -p "${RESULT_DIR}" + +PY_BIN="${PY_BIN:-python3}" + +# === Preflight === +echo "[preflight] LiteLLM ${LITELLM_HOST_IP}:${LITELLM_PORT} has ${MODEL}?" +curl -sS --max-time 8 --noproxy '*' \ + "http://${LITELLM_HOST_IP}:${LITELLM_PORT}/v1/models" \ + -H "Authorization: Bearer ${LITELLM_API_KEY}" \ + | grep -q "\"id\":\"${MODEL}\"" \ + || { echo "[preflight] FAIL: ${MODEL} not on LiteLLM ${LITELLM_PORT}"; exit 1; } +echo "[preflight] LiteLLM OK (${MODEL})" + +[ -f "${OSWORLD_QCOW2}" ] || { echo "[preflight] FAIL: qcow2 missing: ${OSWORLD_QCOW2}"; exit 1; } +echo "[preflight] qcow2 OK ($(du -h "${OSWORLD_QCOW2}" | cut -f1))" + +[ -f "${WEAVEBENCH_ASSETS_DIR}/codex.tar.gz" ] || { echo "[preflight] FAIL: codex.tar.gz missing in ${WEAVEBENCH_ASSETS_DIR}"; exit 1; } +[ -f "${WEAVEBENCH_ASSETS_DIR}/weavebench_computer_mcp/server.py" ] || { echo "[preflight] FAIL: MCP server.py missing in ${WEAVEBENCH_ASSETS_DIR}/weavebench_computer_mcp"; exit 1; } +echo "[preflight] codex assets OK" + +[ -d "${OSWORLD_DIR}/evaluation_examples/task_class" ] \ + && [ "$(ls "${OSWORLD_DIR}"/evaluation_examples/task_class/task_*.py 2>/dev/null | wc -l)" -ge 100 ] \ + || { echo "[preflight] FAIL: task_class/*.py missing"; exit 1; } +echo "[preflight] task_class OK" + +AGENT_BASE_URL="http://${LITELLM_VM_IP}:${LITELLM_PORT}/v1" + +echo "===========================================================" +echo " OSWorld-V2 CODEX INJECT (hybrid GUI+CLI) — ${MODEL}" +echo "Agent LLM (in-VM): ${AGENT_BASE_URL} model=${MODEL} gui=${AGENT_GUI}" +echo "codex effort : ${CODEX_REASONING_EFFORT} wire=${CODEX_WIRE_API}" +echo "qcow2 : ${OSWORLD_QCOW2}" +echo "num_envs : ${NUM_ENVS} max_steps=${MAX_STEPS} agent_timeout=${AGENT_TIMEOUT}s" +echo "Result dir : ${RESULT_DIR}" +echo "Start : $(date)" +echo "===========================================================" + +cd "${OSWORLD_DIR}" +HOST_PROXY="${HOST_PROXY:-http://127.0.0.1:7897}" +export http_proxy="${HOST_PROXY}" https_proxy="${HOST_PROXY}" +export HTTP_PROXY="${HOST_PROXY}" HTTPS_PROXY="${HOST_PROXY}" +export NO_PROXY="127.0.0.1,localhost,172.17.0.1,0.0.0.0" +export no_proxy="${NO_PROXY}" + +EXTRA_ARGS=() +[ -n "${TASK_FILTER}" ] && EXTRA_ARGS+=(--task_filter "${TASK_FILTER}") +[ "${LIMIT}" -gt 0 ] 2>/dev/null && EXTRA_ARGS+=(--limit "${LIMIT}") +[ -n "${TEST_META_PATH:-}" ] && EXTRA_ARGS+=(--test_all_meta_path "${TEST_META_PATH}") + +"${PY_BIN}" "${EVAL_DIR}/run_osworld_v2_inject.py" \ + --provider_name docker \ + --headless true \ + --path_to_vm "${OSWORLD_QCOW2}" \ + --osworld_root "${OSWORLD_DIR}" \ + --num_envs "${NUM_ENVS}" \ + --model "${MODEL}" \ + --litellm_base_url "${AGENT_BASE_URL}" \ + --litellm_api_key "${LITELLM_API_KEY}" \ + --max_steps "${MAX_STEPS}" \ + --agent_gui "${AGENT_GUI}" \ + --agent_timeout "${AGENT_TIMEOUT}" \ + --client_password "${CLIENT_PASSWORD}" \ + --result_dir "${RESULT_DIR}" \ + --agent_harness codex \ + "${EXTRA_ARGS[@]}" \ + 2>&1 | tee "${RESULT_DIR}/run_$(date +%Y%m%d_%H%M%S).log" diff --git a/experiments/osworld_v2_hybrid/lib_run_single.py b/experiments/osworld_v2_hybrid/lib_run_single.py new file mode 100644 index 0000000..247a132 --- /dev/null +++ b/experiments/osworld_v2_hybrid/lib_run_single.py @@ -0,0 +1,1303 @@ +import datetime +import json +import logging +import os +import shutil +import textwrap +import time +from wrapt_timeout_decorator import * +from lib_results_logger import log_task_completion + +logger = logging.getLogger("desktopenv.experiment") + +DEFAULT_USER_RESPONSE = "I have no further information to provide. " \ + "I trust you can figure it out based on the current observation and the instruction. " \ + "Please proceed with the next action. DO NOT ask me any more questions for this task." + +def _jsonl_append(path, payload): + with open(path, "a", encoding="utf-8") as file_obj: + file_obj.write(json.dumps(payload, ensure_ascii=False, default=str)) + file_obj.write("\n") + + +def _persist_evaluation_result( + result, + example_result_dir, + scores=None, + *, + result_suffix="", + append_score=True, +): + """Persist the result returned by ``env.evaluate()``. + + ``result`` may be a float (legacy) or a dict. When it is a dict, the + ``score`` field is treated as the canonical float score, and the full + dict is additionally written to ``result.json`` next to ``result.txt``. + + ``result_suffix`` is used for optional checkpoint evaluations. For example, + ``result_suffix="_step_150"`` writes ``result_step_150.txt`` and + ``result_step_150.json`` without touching the legacy final files. + + Returns the float score. + """ + result_txt_name = f"result{result_suffix}.txt" + result_json_name = f"result{result_suffix}.json" + if isinstance(result, dict): + try: + score = float(result.get("score", 0.0)) + except (TypeError, ValueError): + score = 0.0 + with open(os.path.join(example_result_dir, result_json_name), "w", encoding="utf-8") as f: + json.dump(result, f, indent=2, ensure_ascii=False, default=str) + else: + score = float(result) + if append_score and scores is not None: + scores.append(score) + with open(os.path.join(example_result_dir, result_txt_name), "w", encoding="utf-8") as f: + f.write(f"{score}\n") + return score + + +def _parse_checkpoint_steps(args, max_steps): + if getattr(args, "checkpoint_eval_mode", "off") == "off": + return [] + + raw_steps = str(getattr(args, "checkpoint_steps", "") or "").strip() + if not raw_steps: + return [] + + steps = [] + for raw_item in raw_steps.split(","): + item = raw_item.strip() + if not item: + continue + try: + step = int(item) + except ValueError: + logger.warning("Ignoring invalid checkpoint step %r", item) + continue + if step <= 0: + logger.warning("Ignoring non-positive checkpoint step %s", step) + continue + if step >= int(max_steps): + logger.info( + "Ignoring checkpoint step %s because the final max_steps=%s evaluation already covers it.", + step, + max_steps, + ) + continue + steps.append(step) + return sorted(set(steps)) + + +def _task_allows_intermediate_eval(example): + if hasattr(example, "get") and callable(getattr(example, "get")): + return bool(example.get("intermediate_eval_safe", True)) + return bool(getattr(example, "intermediate_eval_safe", True)) + + +def _read_checkpoint_records(path): + if not os.path.exists(path): + return [] + try: + with open(path, "r", encoding="utf-8") as file_obj: + data = json.load(file_obj) + return data if isinstance(data, list) else [] + except Exception: + logger.warning("Could not read existing checkpoint results at %s; rewriting it.", path) + return [] + + +def _write_checkpoint_record(example_result_dir, record): + path = os.path.join(example_result_dir, "checkpoint_results.json") + records = _read_checkpoint_records(path) + records = [ + item for item in records + if not ( + isinstance(item, dict) + and item.get("step") == record.get("step") + and item.get("mode") == record.get("mode") + ) + ] + records.append(record) + records.sort(key=lambda item: int(item.get("step") or 0)) + tmp_path = f"{path}.tmp.{os.getpid()}" + with open(tmp_path, "w", encoding="utf-8") as file_obj: + json.dump(records, file_obj, indent=2, ensure_ascii=False, default=str) + file_obj.write("\n") + os.replace(tmp_path, path) + + +def _prepare_checkpoint_cache_dir(source_cache_dir, checkpoint_cache_dir): + if not checkpoint_cache_dir: + return + + if not source_cache_dir or not os.path.isdir(source_cache_dir): + os.makedirs(checkpoint_cache_dir, exist_ok=True) + return + + source_abs = os.path.abspath(source_cache_dir) + checkpoint_abs = os.path.abspath(checkpoint_cache_dir) + if source_abs == checkpoint_abs: + raise ValueError(f"Checkpoint cache dir matches source cache dir: {checkpoint_cache_dir}") + + if os.path.lexists(checkpoint_cache_dir): + if os.path.isdir(checkpoint_cache_dir) and not os.path.islink(checkpoint_cache_dir): + shutil.rmtree(checkpoint_cache_dir) + else: + os.unlink(checkpoint_cache_dir) + + checkpoint_parent = os.path.dirname(checkpoint_cache_dir) + if checkpoint_parent: + os.makedirs(checkpoint_parent, exist_ok=True) + shutil.copytree(source_cache_dir, checkpoint_cache_dir, symlinks=True) + + +def _run_inline_checkpoint_eval(env, example, args, example_result_dir, step_num): + if not _task_allows_intermediate_eval(example): + record = { + "step": step_num, + "mode": "inline", + "status": "skipped", + "reason": "intermediate_eval_safe_false", + "timestamp": datetime.datetime.now().isoformat(), + } + _write_checkpoint_record(example_result_dir, record) + logger.info("Skipping inline checkpoint eval at step %s for task %s: marked unsafe.", step_num, example.get("id", "unknown")) + return None + + old_cache_dir = getattr(env, "cache_dir", None) + old_setup_cache_dir = getattr(env.setup_controller, "cache_dir", None) + old_raw_eval_dir = os.environ.get("OSWORLD_EVAL_SAVE_RAW_DIR") + + checkpoint_cache_dir = f"{old_cache_dir}_step_{step_num}" if old_cache_dir else None + checkpoint_raw_dir = ( + os.path.join(example_result_dir, f"model_eval_file_log_step_{step_num}") + if old_raw_eval_dir + else None + ) + + try: + if checkpoint_cache_dir: + _prepare_checkpoint_cache_dir(old_cache_dir, checkpoint_cache_dir) + env.cache_dir = checkpoint_cache_dir + env.setup_controller.reset_cache_dir(checkpoint_cache_dir) + if checkpoint_raw_dir: + os.environ["OSWORLD_EVAL_SAVE_RAW_DIR"] = checkpoint_raw_dir + + logger.info("Running inline checkpoint eval at step %s.", step_num) + result = env.evaluate() + score = _persist_evaluation_result( + result, + example_result_dir, + scores=None, + result_suffix=f"_step_{step_num}", + append_score=False, + ) + record = { + "step": step_num, + "mode": "inline", + "status": "success", + "score": score, + "result_txt": f"result_step_{step_num}.txt", + "result_json": f"result_step_{step_num}.json" if isinstance(result, dict) else None, + "cache_dir": checkpoint_cache_dir, + "model_eval_raw_dir": checkpoint_raw_dir, + "timestamp": datetime.datetime.now().isoformat(), + } + _write_checkpoint_record(example_result_dir, record) + logger.info("Inline checkpoint eval at step %s returned %.4f.", step_num, score) + return score + except Exception as exc: + record = { + "step": step_num, + "mode": "inline", + "status": "error", + "error": str(exc), + "cache_dir": checkpoint_cache_dir, + "model_eval_raw_dir": checkpoint_raw_dir, + "timestamp": datetime.datetime.now().isoformat(), + } + _write_checkpoint_record(example_result_dir, record) + logger.exception("Inline checkpoint eval at step %s failed.", step_num) + return None + finally: + if old_cache_dir is not None: + env.cache_dir = old_cache_dir + if old_setup_cache_dir is not None: + env.setup_controller.reset_cache_dir(old_setup_cache_dir) + if old_raw_eval_dir is None: + os.environ.pop("OSWORLD_EVAL_SAVE_RAW_DIR", None) + else: + os.environ["OSWORLD_EVAL_SAVE_RAW_DIR"] = old_raw_eval_dir + + +def _build_guest_memory_script(top_n: int) -> str: + smaps_limit = min(max(1, top_n), 10) + return textwrap.dedent( + f""" + import json + import os + import subprocess + + TOP_N = {int(top_n)} + SMAPS_LIMIT = {int(smaps_limit)} + warnings = [] + + def _parse_int(value): + try: + return int(value) + except Exception: + return None + + def _parse_float(value): + try: + return float(value) + except Exception: + return None + + def _kb_to_bytes(value): + return None if value is None else int(value) * 1024 + + def _read_meminfo(): + wanted = {{"MemTotal", "MemFree", "MemAvailable", "Buffers", "Cached", "SwapTotal", "SwapFree"}} + values = {{}} + try: + with open("/proc/meminfo", "r", encoding="utf-8") as meminfo_file: + for line in meminfo_file: + if ":" not in line: + continue + key, raw_value = line.split(":", 1) + key = key.strip() + if key not in wanted: + continue + first_token = raw_value.strip().split() + if not first_token: + continue + parsed = _parse_int(first_token[0]) + if parsed is None: + warnings.append(f"invalid_meminfo_{{key}}") + continue + values[key] = parsed + except Exception as exc: + warnings.append(f"meminfo_read_failed:{{exc}}") + return values + + def _read_memory_pressure(): + path = "/proc/pressure/memory" + if not os.path.exists(path): + return None + try: + with open(path, "r", encoding="utf-8") as pressure_file: + return pressure_file.read() + except Exception as exc: + warnings.append(f"memory_pressure_read_failed:{{exc}}") + return None + + def _read_loadavg(): + try: + with open("/proc/loadavg", "r", encoding="utf-8") as loadavg_file: + tokens = loadavg_file.read().strip().split() + if len(tokens) < 3: + warnings.append("loadavg_parse_failed") + return {{}} + return {{ + "loadavg_1": _parse_float(tokens[0]), + "loadavg_5": _parse_float(tokens[1]), + "loadavg_15": _parse_float(tokens[2]), + }} + except Exception as exc: + warnings.append(f"loadavg_read_failed:{{exc}}") + return {{}} + + def _read_cpu_stat(): + try: + with open("/proc/stat", "r", encoding="utf-8") as stat_file: + first_line = stat_file.readline().strip() + if not first_line.startswith("cpu "): + warnings.append("cpu_stat_missing") + return {{}} + fields = first_line.split()[1:] + while len(fields) < 10: + fields.append("0") + names = [ + "user", + "nice", + "system", + "idle", + "iowait", + "irq", + "softirq", + "steal", + "guest", + "guest_nice", + ] + return {{name: _parse_int(value) for name, value in zip(names, fields)}} + except Exception as exc: + warnings.append(f"cpu_stat_read_failed:{{exc}}") + return {{}} + + def _read_network_snapshot(): + snapshot = {{ + "interfaces": [], + "totals": {{ + "rx_bytes": 0, + "tx_bytes": 0, + "rx_packets": 0, + "tx_packets": 0, + "rx_errors": 0, + "tx_errors": 0, + "rx_drop": 0, + "tx_drop": 0, + }}, + }} + try: + with open("/proc/net/dev", "r", encoding="utf-8") as netdev_file: + lines = netdev_file.readlines()[2:] + for line in lines: + if ":" not in line: + continue + interface_name, raw_values = line.split(":", 1) + interface_name = interface_name.strip() + values = raw_values.strip().split() + if len(values) < 16: + warnings.append(f"netdev_parse_failed:{{interface_name}}") + continue + interface_info = {{ + "name": interface_name, + "rx_bytes": _parse_int(values[0]), + "rx_packets": _parse_int(values[1]), + "rx_errors": _parse_int(values[2]), + "rx_drop": _parse_int(values[3]), + "tx_bytes": _parse_int(values[8]), + "tx_packets": _parse_int(values[9]), + "tx_errors": _parse_int(values[10]), + "tx_drop": _parse_int(values[11]), + }} + snapshot["interfaces"].append(interface_info) + if interface_name != "lo": + for key in snapshot["totals"]: + snapshot["totals"][key] += interface_info.get(key) or 0 + except Exception as exc: + warnings.append(f"netdev_read_failed:{{exc}}") + return snapshot + + def _read_smaps_rollup(pid): + fields = {{ + "pss_kb": None, + "private_clean_kb": None, + "private_dirty_kb": None, + }} + path = f"/proc/{{pid}}/smaps_rollup" + try: + with open(path, "r", encoding="utf-8") as smaps_file: + for line in smaps_file: + if ":" not in line: + continue + key, raw_value = line.split(":", 1) + value_tokens = raw_value.strip().split() + if not value_tokens: + continue + value = _parse_int(value_tokens[0]) + if key == "Pss": + fields["pss_kb"] = value + elif key == "Private_Clean": + fields["private_clean_kb"] = value + elif key == "Private_Dirty": + fields["private_dirty_kb"] = value + except FileNotFoundError: + warnings.append(f"smaps_rollup_missing:{{pid}}") + except PermissionError: + warnings.append(f"smaps_rollup_permission_denied:{{pid}}") + except Exception as exc: + warnings.append(f"smaps_rollup_failed:{{pid}}:{{exc}}") + return fields + + def _read_top_processes(): + command = [ + "ps", + "-eo", + "pid=,ppid=,user=,comm=,rss=,vsz=,%mem=,%cpu=,args=", + "--sort=-rss", + ] + try: + output = subprocess.check_output(command, text=True, stderr=subprocess.STDOUT) + except Exception as exc: + warnings.append(f"ps_failed:{{exc}}") + return [] + + processes = [] + lines = [line for line in output.splitlines() if line.strip()] + for index, line in enumerate(lines[:TOP_N]): + parts = line.strip().split(None, 8) + if len(parts) < 8: + warnings.append(f"ps_parse_failed:{{line}}") + continue + while len(parts) < 9: + parts.append("") + + pid, ppid, user, comm, rss, vsz, pct_mem, pct_cpu, cmd = parts + process_info = {{ + "pid": _parse_int(pid), + "ppid": _parse_int(ppid), + "user": user, + "comm": comm, + "rss_kb": _parse_int(rss), + "rss_bytes": _kb_to_bytes(_parse_int(rss)), + "vsz_kb": _parse_int(vsz), + "vsz_bytes": _kb_to_bytes(_parse_int(vsz)), + "pct_mem": _parse_float(pct_mem), + "pct_cpu": _parse_float(pct_cpu), + "cmd": cmd, + }} + if index < SMAPS_LIMIT and process_info["pid"] is not None: + process_info.update(_read_smaps_rollup(process_info["pid"])) + processes.append(process_info) + return processes + + meminfo = _read_meminfo() + total_bytes = _kb_to_bytes(meminfo.get("MemTotal")) + free_bytes = _kb_to_bytes(meminfo.get("MemFree")) + available_bytes = _kb_to_bytes(meminfo.get("MemAvailable")) + buffers_bytes = _kb_to_bytes(meminfo.get("Buffers")) + cached_bytes = _kb_to_bytes(meminfo.get("Cached")) + swap_total_bytes = _kb_to_bytes(meminfo.get("SwapTotal")) + swap_free_bytes = _kb_to_bytes(meminfo.get("SwapFree")) + used_bytes = None + if total_bytes is not None and available_bytes is not None: + used_bytes = max(total_bytes - available_bytes, 0) + + payload = {{ + "memory_summary": {{ + "total_bytes": total_bytes, + "free_bytes": free_bytes, + "available_bytes": available_bytes, + "used_bytes": used_bytes, + "buffers_bytes": buffers_bytes, + "cached_bytes": cached_bytes, + "swap_total_bytes": swap_total_bytes, + "swap_free_bytes": swap_free_bytes, + }}, + "cpu_snapshot": {{ + "cpu_count": os.cpu_count(), + "stat_counters": _read_cpu_stat(), + **_read_loadavg(), + }}, + "network_snapshot": _read_network_snapshot(), + "top_processes": _read_top_processes(), + "collector_warnings": warnings, + "memory_pressure": _read_memory_pressure(), + }} + print(json.dumps(payload, ensure_ascii=False)) + """ + ).strip() + + +class GuestMemoryTracer: + def __init__(self, env, example, args, example_result_dir): + self.env = env + self.example = example + self.args = args + self.example_result_dir = example_result_dir + self.trace_path = os.path.join(example_result_dir, "trace.jsonl") + self.enabled = bool(getattr(args, "trace_guest", False)) + self.supported = self.enabled and getattr(env, "os_type", "") == "Ubuntu" + self.top_n = max(1, int(getattr(args, "guest_trace_top_n", 30))) + self.timeout = max(1, int(getattr(args, "guest_trace_timeout", 15))) + self.task_id = example.get("id") if hasattr(example, "get") else getattr(example, "id", None) + self.script = _build_guest_memory_script(self.top_n) if self.supported else None + self.previous_sample_time = None + self.previous_cpu_counters = None + self.previous_network_totals = None + + if self.enabled and not self.supported: + logger.warning( + "Guest memory tracing is enabled but skipped for os_type=%s on task %s", + getattr(env, "os_type", None), + self.task_id, + ) + + @staticmethod + def _counter_total(counter_dict): + if not isinstance(counter_dict, dict): + return None + total = 0 + found = False + for value in counter_dict.values(): + if isinstance(value, (int, float)): + total += value + found = True + return total if found else None + + def _augment_cpu_summary(self, cpu_snapshot, sample_time): + summary = dict(cpu_snapshot or {}) + counters = summary.get("stat_counters") or {} + interval_seconds = None if self.previous_sample_time is None else max(sample_time - self.previous_sample_time, 0.0) + summary["interval_seconds_since_prev"] = round(interval_seconds, 3) if interval_seconds is not None else None + summary["usage_pct_since_prev"] = None + summary["idle_pct_since_prev"] = None + summary["iowait_pct_since_prev"] = None + + if self.previous_cpu_counters and counters: + total_prev = self._counter_total(self.previous_cpu_counters) + total_cur = self._counter_total(counters) + idle_prev = (self.previous_cpu_counters.get("idle") or 0) + idle_cur = (counters.get("idle") or 0) + iowait_prev = (self.previous_cpu_counters.get("iowait") or 0) + iowait_cur = (counters.get("iowait") or 0) + + if total_prev is not None and total_cur is not None: + delta_total = total_cur - total_prev + delta_idle = idle_cur - idle_prev + delta_iowait = iowait_cur - iowait_prev + delta_active = delta_total - delta_idle - delta_iowait + if delta_total > 0: + summary["usage_pct_since_prev"] = round(max(delta_active, 0) * 100.0 / delta_total, 2) + summary["idle_pct_since_prev"] = round(max(delta_idle, 0) * 100.0 / delta_total, 2) + summary["iowait_pct_since_prev"] = round(max(delta_iowait, 0) * 100.0 / delta_total, 2) + + self.previous_cpu_counters = counters + return summary + + def _augment_network_summary(self, network_snapshot, sample_time): + summary = dict(network_snapshot or {}) + totals = dict(summary.get("totals") or {}) + interval_seconds = None if self.previous_sample_time is None else max(sample_time - self.previous_sample_time, 0.0) + summary["interval_seconds_since_prev"] = round(interval_seconds, 3) if interval_seconds is not None else None + summary["delta_since_prev"] = None + summary["throughput_bytes_per_sec"] = None + summary["throughput_packets_per_sec"] = None + + if self.previous_network_totals and totals and interval_seconds and interval_seconds > 0: + delta = {} + for key in [ + "rx_bytes", + "tx_bytes", + "rx_packets", + "tx_packets", + "rx_errors", + "tx_errors", + "rx_drop", + "tx_drop", + ]: + delta[key] = (totals.get(key) or 0) - (self.previous_network_totals.get(key) or 0) + summary["delta_since_prev"] = delta + summary["throughput_bytes_per_sec"] = { + "rx": round(delta["rx_bytes"] / interval_seconds, 2), + "tx": round(delta["tx_bytes"] / interval_seconds, 2), + } + summary["throughput_packets_per_sec"] = { + "rx": round(delta["rx_packets"] / interval_seconds, 2), + "tx": round(delta["tx_packets"] / interval_seconds, 2), + } + + self.previous_network_totals = totals + return summary + + def capture( + self, + sample_type, + *, + phase_index=None, + phase_name=None, + step_num=None, + action_index_in_step=None, + global_action_index=None, + action_timestamp=None, + action=None, + step_wall_time_ms=None, + ): + if not self.supported: + return + + trace_record = { + "task_id": self.task_id, + "sample_type": sample_type, + "trace_timestamp": datetime.datetime.now().isoformat(), + "phase_index": phase_index, + "phase_name": phase_name, + "step_num": step_num, + "action_index_in_step": action_index_in_step, + "global_action_index": global_action_index, + "action_timestamp": action_timestamp, + "action": action, + "step_wall_time_ms": step_wall_time_ms, + "collector_wall_time_ms": None, + "collector_error": None, + "collector_warnings": [], + "memory_summary": None, + "cpu_summary": None, + "network_summary": None, + "top_processes": [], + "memory_pressure": None, + } + + sample_time = time.time() + collector_started = time.perf_counter() + try: + result = self.env.controller.run_python_script( + self.script, + timeout=self.timeout, + ) + trace_record["collector_wall_time_ms"] = round((time.perf_counter() - collector_started) * 1000.0, 2) + + if not isinstance(result, dict): + trace_record["collector_error"] = "collector returned a non-dict result" + elif result.get("status") != "success": + trace_record["collector_error"] = result.get("error") or result.get("message") or "collector returned error status" + else: + raw_output = result.get("output") or "" + if not raw_output: + trace_record["collector_error"] = "collector returned empty output" + else: + parsed = json.loads(raw_output) + trace_record["collector_warnings"] = parsed.get("collector_warnings", []) or [] + trace_record["memory_summary"] = parsed.get("memory_summary") + trace_record["cpu_summary"] = self._augment_cpu_summary(parsed.get("cpu_snapshot"), sample_time) + trace_record["network_summary"] = self._augment_network_summary(parsed.get("network_snapshot"), sample_time) + trace_record["top_processes"] = parsed.get("top_processes", []) or [] + trace_record["memory_pressure"] = parsed.get("memory_pressure") + except Exception as exc: + trace_record["collector_wall_time_ms"] = round((time.perf_counter() - collector_started) * 1000.0, 2) + trace_record["collector_error"] = str(exc) + + self.previous_sample_time = sample_time + _jsonl_append(self.trace_path, trace_record) + + +def _get_task_phases(example): + getter = getattr(example, "get_phases", None) + if not callable(getter): + return [] + phases = getter() or [] + if not isinstance(phases, list): + raise TypeError("get_phases() must return a list") + return phases + + +def _configure_agent_for_task(agent, example): + task_current_date = None + if hasattr(example, "get") and callable(getattr(example, "get")): + task_current_date = example.get("task_current_date") + if task_current_date is None: + task_current_date = getattr(example, "task_current_date", None) + setattr(agent, "task_current_date", task_current_date) + + +def _run_multi_phase_task_example(agent, env, example, max_steps, args, example_result_dir, scores, runtime_logger): + phases = _get_task_phases(example) + if not phases: + raise ValueError("No phases found for multi-phase task") + + use_proxy = bool(getattr(example, "proxy", False) and getattr(env, "enable_proxy", False)) + phase_results = [] + total_score = 0.0 + + env.reset(task_config=example) + memory_tracer = GuestMemoryTracer(env, example, args, example_result_dir) + global_action_index = 0 + time.sleep(60) + env.controller.start_recording() + + try: + for phase_index, phase in enumerate(phases, start=1): + phase_name = phase.get("name", f"Phase {phase_index}") + phase_instruction = phase["instruction"] + + if phase_index > 1: + env._step_no = 0 + env.action_history.clear() + env._traj_no += 1 + logger.info("Starting phase %d in trajectory %d", phase_index, env._traj_no) + + phase["setup"](env.setup_controller, use_proxy=use_proxy) + env.is_environment_used = True + pause_after_setup = phase.get("pause_after_setup_seconds", 5) + if pause_after_setup: + time.sleep(pause_after_setup) + + env.instruction = phase_instruction + _configure_agent_for_task(agent, example) + try: + agent.reset(runtime_logger) + except Exception: + agent.reset() + + obs = env._get_obs() + memory_tracer.capture( + "initial_after_reset", + phase_index=phase_index, + phase_name=phase_name, + ) + done = False + step_idx = 0 + + while not done and step_idx < max_steps: + response, actions = agent.predict( + phase_instruction, + obs + ) + + if not actions: + answer = DEFAULT_USER_RESPONSE + if env.user_simulator is not None: + answer = env.user_simulator.respond(response if response else "") + + logger.info( + "Phase %d User simulator Q: %s | A: %s", + phase_index, + response, + answer, + ) + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S%f") + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "phase_index": phase_index, + "phase_name": phase_name, + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": "ASK_USER", + "question": response, + "user_answer": answer, + "screenshot_file": None + })) + f.write("\n") + obs["user_response"] = answer + step_idx += 1 + continue + + for action_index_in_step, action in enumerate(actions, start=1): + global_action_index += 1 + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S%f") + logger.info("Phase %d Step %d: %s", phase_index, step_idx + 1, action) + action_started = time.perf_counter() + obs, reward, done, info = env.step(action, args.sleep_after_execution) + step_wall_time_ms = round((time.perf_counter() - action_started) * 1000.0, 2) + memory_tracer.capture( + "post_action", + phase_index=phase_index, + phase_name=phase_name, + step_num=step_idx + 1, + action_index_in_step=action_index_in_step, + global_action_index=global_action_index, + action_timestamp=action_timestamp, + action=action, + step_wall_time_ms=step_wall_time_ms, + ) + + screenshot_name = f"phase_{phase_index}_step_{step_idx + 1}_{action_timestamp}.png" + with open(os.path.join(example_result_dir, screenshot_name), "wb") as _f: + _f.write(obs["screenshot"]) + + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "phase_index": phase_index, + "phase_name": phase_name, + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": action, + "response": response, + "reward": reward, + "done": done, + "info": info, + "screenshot_file": screenshot_name + })) + f.write("\n") + if done: + logger.info("Phase %d is done.", phase_index) + break + step_idx += 1 + + memory_tracer.capture( + "final_before_evaluate", + phase_index=phase_index, + phase_name=phase_name, + ) + phase_score = float(phase["evaluate"](env)) + total_score += phase_score + phase_results.append( + { + "phase_index": phase_index, + "phase_name": phase_name, + "instruction": phase_instruction, + "score": phase_score, + } + ) + + gate_min_score = phase.get("gate_min_score") + if gate_min_score is not None and phase_score < float(gate_min_score): + logger.info( + "Stopping after phase %d returned %.4f below required %.4f", + phase_index, + phase_score, + float(gate_min_score), + ) + break + if phase.get("gate") and phase_score <= 0.0: + logger.info("Stopping after gated phase %d returned %.4f", phase_index, phase_score) + break + + final_score = round(max(0.0, min(1.0, total_score)), 4) + logger.info("Multi-phase result: %.4f", final_score) + scores.append(final_score) + with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f: + f.write(f"{final_score}\n") + with open(os.path.join(example_result_dir, "phase_results.json"), "w", encoding="utf-8") as f: + json.dump(phase_results, f, indent=2, ensure_ascii=False) + + log_task_completion(example, final_score, example_result_dir, args) + finally: + env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4")) + + +def run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores): + runtime_logger = setup_logger(example, example_result_dir) + phases = _get_task_phases(example) + if phases: + if getattr(args, "checkpoint_eval_mode", "off") != "off": + logger.warning( + "Checkpoint evaluation is not implemented for multi-phase task %s; running final-only evaluation.", + example.get("id", "unknown") if hasattr(example, "get") else getattr(example, "id", "unknown"), + ) + _run_multi_phase_task_example( + agent, + env, + example, + max_steps, + args, + example_result_dir, + scores, + runtime_logger, + ) + return + + try: + _configure_agent_for_task(agent, example) + agent.reset(runtime_logger) + except Exception as e: + agent.reset() + + env.reset(task_config=example) + memory_tracer = GuestMemoryTracer(env, example, args, example_result_dir) + + time.sleep(60) # Wait for the environment to be ready + obs = env._get_obs() # Get the initial observation + memory_tracer.capture("initial_after_reset") + done = False + step_idx = 0 + global_action_index = 0 + checkpoint_steps = set(_parse_checkpoint_steps(args, max_steps)) + env.controller.start_recording() + while not done and step_idx < max_steps: + response, actions = agent.predict( + instruction, + obs + ) + + # If no actions returned, treat response as a question to the user + if not actions: + answer = DEFAULT_USER_RESPONSE + + if env.user_simulator is not None: + answer = env.user_simulator.respond(response if response else "") + + logger.info("User simulator Q: %s | A: %s", response, answer) + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S%f") + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": "ASK_USER", + "question": response, + "user_answer": answer, + "screenshot_file": None + })) + f.write("\n") + obs["user_response"] = answer + step_idx += 1 + if step_idx in checkpoint_steps: + _run_inline_checkpoint_eval(env, example, args, example_result_dir, step_idx) + continue + + for action_index_in_step, action in enumerate(actions, start=1): + global_action_index += 1 + # Capture the timestamp before executing the action + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S%f") + logger.info("Step %d: %s", step_idx + 1, action) + action_started = time.perf_counter() + obs, reward, done, info = env.step(action, args.sleep_after_execution) + step_wall_time_ms = round((time.perf_counter() - action_started) * 1000.0, 2) + memory_tracer.capture( + "post_action", + step_num=step_idx + 1, + action_index_in_step=action_index_in_step, + global_action_index=global_action_index, + action_timestamp=action_timestamp, + action=action, + step_wall_time_ms=step_wall_time_ms, + ) + + logger.info("Reward: %.2f", reward) + logger.info("Done: %s", done) + + # only save for last action if multiple actions, to avoid saving too many screenshots + if action_index_in_step == len(actions) or done: + # Save screenshot and trajectory information + with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"), + "wb") as _f: + _f.write(obs['screenshot']) + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": action, + "response": response, + "reward": reward, + "done": done, + "info": info, + "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png" + })) + f.write("\n") + if done: + logger.info("The episode is done.") + break + else: + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": action, + "response": response, + "reward": reward, + "done": done, + "info": info, + "screenshot_file": None + })) + f.write("\n") + + step_idx += 1 + if step_idx in checkpoint_steps: + _run_inline_checkpoint_eval(env, example, args, example_result_dir, step_idx) + memory_tracer.capture("final_before_evaluate") + result = env.evaluate() + score = _persist_evaluation_result(result, example_result_dir, scores) + logger.info("Result: %.2f", score) + + log_task_completion(example, score, example_result_dir, args) + env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4")) + + +def setup_logger(example, example_result_dir): + runtime_logger = logging.getLogger(f"desktopenv.example.{example['id']}") + runtime_logger.setLevel(logging.DEBUG) + runtime_logger.addHandler(logging.FileHandler(os.path.join(example_result_dir, "runtime.log"))) + return runtime_logger + +def run_single_example_human(env, example, max_steps, instruction, args, example_result_dir, scores): + runtime_logger = setup_logger(example, example_result_dir) + env.reset(task_config=example) + time.sleep(60) # Wait for the environment to be ready + obs = env._get_obs() # Get the initial observation + + # Save initial screenshot + with open(os.path.join(example_result_dir, "initial_state.png"), "wb") as _f: + _f.write(obs['screenshot']) + + # Save trajectory information + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "instruction": instruction, + "initial_state": "initial_state.png" + })) + f.write("\n") + + # Evaluate the result + result = env.evaluate() + score = _persist_evaluation_result(result, example_result_dir, scores) + logger.info("Result: %.2f", score) + + + +def run_single_example_openaicua(agent, env, example, max_steps, instruction, args, example_result_dir, scores): + runtime_logger = setup_logger(example, example_result_dir) + agent.reset(runtime_logger) + env.reset(task_config=example) + time.sleep(60) # Wait for the environment to be ready + obs = env._get_obs() # Get the initial observation + done = False + step_idx = 0 + env.controller.start_recording() + while not done and step_idx < max_steps: + response, actions = agent.predict( + instruction, + obs + ) + + done = not response.get('state_correct', False) + + for action in actions: + # Capture the timestamp before executing the action + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S") + logger.info("Step %d: %s", step_idx + 1, action) + obs, reward, done, info, step_info = agent.step(action) + + if not done: + if not response.get('state_correct', False): + done = True + + logger.info("Reward: %.2f", reward) + logger.info("Done: %s", done) + # Save screenshot and trajectory information + with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"), + "wb") as _f: + _f.write(obs['screenshot']) + + # Remove pending checks if they exist which will cause issues with json serialization + if action.get('pending_checks', None): + del action['pending_checks'] + + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": action, + "reward": reward, + "done": done, + "info": info, + "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png" + })) + f.write("\n") + if done: + logger.info("The episode is done.") + break + step_idx += 1 + result = env.evaluate() + score = _persist_evaluation_result(result, example_result_dir, scores) + logger.info("Result: %.2f", score) + env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4")) + +def run_single_example_opencua(agent, env, example, max_steps, instruction, args, example_result_dir, scores): + runtime_logger = setup_logger(example, example_result_dir) + agent.reset(runtime_logger) + env.reset(task_config=example) + time.sleep(60) # Wait for the environment to be ready + obs = env._get_obs() # Get the initial observation + done = False + step_idx = 0 + env.controller.start_recording() + while not done and step_idx < max_steps: + response, actions, info_dict = agent.predict(instruction, obs) + + logger.info(f"Got Action: {actions}") + # Breack if no actions + if not actions or len(actions)==0 or actions[0]=="" or actions[0].lower().startswith("error"): + break + + for action in actions: + # Capture the timestamp before executing the action + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S") + logger.info("Step %d: %s", step_idx + 1, action) + + obs, reward, done, info = env.step(action, args.sleep_after_execution) + + logger.info(f"Action {action} executed, reward: {reward}, done: {done}") + # Save screenshot and trajectory information + with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"), + "wb") as _f: + _f.write(obs['screenshot']) + + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": action, + "response": response, + "reward": reward, + "done": done, + "info": info, + "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png" + })) + f.write("\n") + if done: + logger.info("The episode is done.") + break + step_idx += 1 + + result = env.evaluate() + score = _persist_evaluation_result(result, example_result_dir, scores) + logger.info("Result: %.2f", score) + env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4")) + +def run_single_example_autoglm(agent, env, example, max_steps, instruction, args, example_result_dir, scores): + runtime_logger = setup_logger(example, example_result_dir) + try: + agent.reset(runtime_logger) + except Exception as e: + agent.reset() + + env.reset(task_config=example) + + time.sleep(60) # Wait for the environment to be ready + obs = env._get_obs() # Get the initial observation + done = False + step_idx = 0 + env.controller.start_recording() + while not done and step_idx < max_steps: + response, actions = agent.predict( + instruction, + obs + ) + for action in actions: + # Capture the timestamp before executing the action + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S") + logger.info("Step %d: %s", step_idx + 1, action) + obs, reward, done, info = env.step(action, args.sleep_after_execution) + + logger.info("Reward: %.2f", reward) + logger.info("Done: %s", done) + # Save screenshot and trajectory information + with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"), + "wb") as _f: + _f.write(obs['screenshot']) + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": action, + "response": response, + "reward": reward, + "done": done, + "info": info, + "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png" + })) + f.write("\n") + + if done: + logger.info("The episode is done.") + break + + # Invalid Action + if not actions: + obs = env._get_obs() # update observation + + step_idx += 1 + + if not done: # not completed the task yet + env.action_history.append('FAIL') + + result = env.evaluate() + score = _persist_evaluation_result(result, example_result_dir, scores) + logger.info("Result: %.2f", score) + env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4")) + +def run_single_example_mano(agent, env, example, max_steps, instruction, args, example_result_dir, scores): + runtime_logger = setup_logger(example, example_result_dir) + agent.reset(runtime_logger) + env.reset(task_config=example) + time.sleep(60) # Wait for the environment to be ready + obs = env._get_obs() # Get the initial observation + done = False + step_idx = 0 + env.controller.start_recording() + + with open(os.path.join(example_result_dir, f"step_0.png"), + "wb") as _f: + _f.write(obs['screenshot']) + while not done and step_idx < max_steps: + response, actions = agent.predict( + instruction, + obs + ) + if len(actions) > 1: + if (("pyautogui.hotkey('shift')" in actions[0] or "pyautogui.hotkey('ctrl')" in actions[0]) + and "pyautogui.click" in actions[1]): + hotkey_type = 'shift' if "shift" in actions[0] else 'ctrl' + action = f"pyautogui.keyDown('{hotkey_type}')\n{actions[1]}\npyautogui.keyUp('{hotkey_type}')" + actions = [action] + + for action in actions: + # Capture the timestamp before executing the action + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S") + logger.info("Step %d: %s", step_idx + 1, action) + obs, reward, done, info = env.step(action, args.sleep_after_execution) + + logger.info("Reward: %.2f", reward) + logger.info("Done: %s", done) + # Save screenshot and trajectory information + with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"), + "wb") as _f: + _f.write(obs['screenshot']) + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": action, + "reward": reward, + "done": done, + "info": info, + "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png", + "response":response + })) + f.write("\n") + if done: + logger.info("The episode is done.") + break + step_idx += 1 + result = env.evaluate() + score = _persist_evaluation_result(result, example_result_dir, scores) + logger.info("Result: %.2f", score) + env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4")) + +def run_single_example_uipath(agent, env, example, max_steps, instruction, args, example_result_dir, scores): + runtime_logger = setup_logger(example, example_result_dir) + try: + agent.reset(runtime_logger) + except Exception as e: + agent.reset() + + env.reset(task_config=example) + + time.sleep(60) # Wait for the environment to be ready + obs = env._get_obs() # Get the initial observation + done = False + step_idx = 0 + env.controller.start_recording() + while not done and step_idx < max_steps: + response, actions = agent.predict( + instruction, + obs, + args, + step_idx + ) + for action in actions: + # Capture the timestamp before executing the action + action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S") + logger.info("Step %d: %s", step_idx + 1, action) + obs, reward, done, info = env.step(action, args.sleep_after_execution) + + logger.info("Reward: %.2f", reward) + logger.info("Done: %s", done) + # Save screenshot and trajectory information + with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"), + "wb") as _f: + _f.write(obs['screenshot']) + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write(json.dumps({ + "step_num": step_idx + 1, + "action_timestamp": action_timestamp, + "action": action, + "response": response, + "reward": reward, + "done": done, + "info": info, + "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png" + })) + f.write("\n") + if done: + logger.info("The episode is done.") + break + step_idx += 1 + result = env.evaluate() + score = _persist_evaluation_result(result, example_result_dir, scores) + logger.info("Result: %.2f", score) + env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4")) diff --git a/experiments/osworld_v2_hybrid/mm_agents/__init__.py b/experiments/osworld_v2_hybrid/mm_agents/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/experiments/osworld_v2_hybrid/mm_agents/claudecode_agent.py b/experiments/osworld_v2_hybrid/mm_agents/claudecode_agent.py new file mode 100644 index 0000000..3647a69 --- /dev/null +++ b/experiments/osworld_v2_hybrid/mm_agents/claudecode_agent.py @@ -0,0 +1,302 @@ +"""ClaudeCode (in-VM) agent for OSWorld-V2 — drop-in sibling of CodexAgent. + +Mirrors the bootstrap/configure/run interface of mm_agents.codex_agent.CodexAgent +so the inject runner can select it with `--agent_harness claudecode`. Runs the +Anthropic `claude` CLI INSIDE the OSWorld qcow2 VM; the host runner calls +agent.run() once per task and scores with native env.evaluate(). + +Ported from the private WeaveBench dev tree +but rewired onto OSWorld-V2's openclaw vm helpers + asset dir, and effort=max +(paper Table 3: opus uses max thinking). +""" +from __future__ import annotations + +import json +import logging +import os +import shlex +import time +from pathlib import Path +from typing import Optional + +from mm_agents.openclaw_agent import ( + WCB_ASSETS_DIR, + _vm_exec, + _vm_launch, + _vm_upload, + _vm_upload_bytes, + _vm_fetch, + _wait_file, +) + +logger = logging.getLogger("claudecode_agent") + +# claudecode.tar.gz next to codex/openclaw tarballs; fall back to repro cache. +_CC_CANDS = [ + WCB_ASSETS_DIR / "claudecode.tar.gz", + Path("/path/to/runtime_assets/claudecode.tar.gz"), +] +CLAUDECODE_TARBALL = next((p for p in _CC_CANDS if p.is_file()), _CC_CANDS[0]) +MCP_SERVER_DIR = WCB_ASSETS_DIR / "weavebench_computer_mcp" + +CLAUDE_BIN_IN_VM = "/usr/local/bin/claude" +CLAUDE_JS_PATH = "/usr/lib/node_modules/@anthropic-ai/claude-code/cli.js" +CLAUDE_HOME_IN_VM = "/home/user/.claude" +CLAUDE_PROJECTS_DIR = f"{CLAUDE_HOME_IN_VM}/projects" +MCP_SERVER_DIR_IN_VM = "/usr/local/lib/weavebench_computer_mcp" +MCP_SERVER_PATH_IN_VM = f"{MCP_SERVER_DIR_IN_VM}/server.py" + +SCRATCH_DIR = "/tmp/weavebench_claudecode_install" +CLAUDE_PROMPT_PATH = f"{SCRATCH_DIR}/prompt.txt" +CLAUDE_RUN_LOG = f"{SCRATCH_DIR}/run.log" +CLAUDE_RUN_DONE = f"{SCRATCH_DIR}/run.done" +CLAUDE_RUN_SH = f"{SCRATCH_DIR}/run.sh" +BOOTSTRAP_MARKER = "/home/user/.weavebench_claudecode_bootstrap.done" + +DEFAULT_EFFORT = os.environ.get("CLAUDE_EFFORT_LEVEL", "max").strip() or "max" + +_CC_TOOL_MAP = ( + "\n=== TOOL NAME MAPPING (THIS HARNESS = claudecode) ===\n" + "The system prompt above uses openclaw's tool names. In Claude Code:\n" + "- `__computer__` (GUI control) -> mcp__weavebench_computer__computer (same schema)\n" + "- `bash`/`exec` -> Bash; read/write/edit -> Read/Write/Edit\n" + "USE the computer tool aggressively for desktop apps; verify deliverables " + "with Bash before declaring done.\n" + "=== END TOOL NAME MAPPING ===\n" +) + +_CC_CLI_NOTE = ( + "\n=== HARNESS = claudecode, CLI-ONLY ===\n" + "There is NO GUI/computer tool here. Use Bash for everything; Read/Write/" + "Edit for files. For web tasks drive the already-open Chrome over CDP " + "(localhost:1337/9222) from Bash. Verify deliverables with Bash before done.\n" + "=== END ===\n" +) + + +class ClaudeCodeAgent: + """In-VM claude-cli delegate agent (CodexAgent-compatible interface).""" + + def __init__(self, + model: str = "claude-opus-4-8", + litellm_base_url: str = "http://172.17.0.1:4141", + litellm_api_key: str = "dummy", + client_password: str = "password", + timeout: int = 900, + gui: bool = True, + max_steps: int = 100): + self.model = model + self.litellm_base_url = litellm_base_url.rstrip("/") + self.litellm_api_key = litellm_api_key + self.client_password = client_password + self.timeout = int(timeout) + self.gui = bool(gui) + self.max_steps = int(max_steps) + self.max_refusal_retries = int(os.environ.get("CLAUDE_MAX_RETRIES", "3")) + self._bootstrapped_envs: set[int] = set() + + def _sudo_wrap(self, body: str) -> str: + escaped = body.replace("'", "'\"'\"'") + return f"echo '{self.client_password}' | sudo -S -p '' bash -c '{escaped}'" + + # ---------------- bootstrap (once per VM) ---------------- + def _marker_present(self, env) -> bool: + try: + out = _vm_exec(env, ["bash", "-c", + f"test -f {BOOTSTRAP_MARKER} && which claude >/dev/null 2>&1 && echo YES || echo NO"]) + return "YES" in (out.get("output") or "") + except Exception: + return False + + def _upload_mcp_server(self, env) -> None: + if not (MCP_SERVER_DIR / "server.py").is_file(): + raise FileNotFoundError(f"MCP server missing at {MCP_SERVER_DIR}/server.py") + _vm_exec(env, ["bash", "-c", f"mkdir -p {SCRATCH_DIR}"]) + tmp = f"{SCRATCH_DIR}/server.py" + _vm_upload(env, (MCP_SERVER_DIR / "server.py").read_text(encoding="utf-8"), tmp) + body = (f"mkdir -p {MCP_SERVER_DIR_IN_VM} && cp -f {tmp} {MCP_SERVER_PATH_IN_VM} && " + f"chmod +x {MCP_SERVER_PATH_IN_VM}") + _vm_exec(env, ["bash", "-c", self._sudo_wrap(body)], timeout=60) + + def _register_mcp(self, env) -> None: + if not self.gui: + logger.info("_register_mcp: gui=False, skip MCP") + return + add = (f"{CLAUDE_BIN_IN_VM} mcp remove weavebench_computer 2>/dev/null; " + f"{CLAUDE_BIN_IN_VM} mcp add -s user weavebench_computer " + f"-- /usr/bin/python3 {MCP_SERVER_PATH_IN_VM}") + out = _vm_exec(env, ["bash", "-c", add], timeout=60) + logger.info("registered weavebench_computer MCP rc=%s", out.get("returncode")) + + def bootstrap(self, env) -> None: + if self._marker_present(env): + logger.info("claude already bootstrapped — refresh MCP only.") + self._upload_mcp_server(env) + self._register_mcp(env) + self._bootstrapped_envs.add(id(env)) + return + if not CLAUDECODE_TARBALL.is_file(): + raise RuntimeError(f"Missing claudecode tarball at {CLAUDECODE_TARBALL}.") + logger.info("Bootstrapping claude inside VM (one-time)...") + host_epoch = int(time.time()) + prep = (f"date -s '@{host_epoch}' >/dev/null 2>&1 || true; " + "systemctl stop packagekit.service unattended-upgrades.service " + "apt-daily.service apt-daily-upgrade.service 2>/dev/null || true; " + "pkill -9 -f packagekitd 2>/dev/null || true; " + 'echo "user ALL=(ALL) NOPASSWD: ALL" > /etc/sudoers.d/99-claude-user; ' + "chmod 440 /etc/sudoers.d/99-claude-user") + _vm_exec(env, ["bash", "-c", self._sudo_wrap(prep)], timeout=300) + logger.info("Uploading claudecode tarball (%.1f MB)...", CLAUDECODE_TARBALL.stat().st_size / 1e6) + _vm_upload_bytes(env, CLAUDECODE_TARBALL, "/tmp/claudecode.tar.gz") + install = ("set -e; tar xzf /tmp/claudecode.tar.gz -C / && " + f"ln -sf {CLAUDE_JS_PATH} {CLAUDE_BIN_IN_VM} && test -x {CLAUDE_JS_PATH} && " + f"{CLAUDE_BIN_IN_VM} --version 2>&1") + out = _vm_exec(env, ["bash", "-c", self._sudo_wrap(install)], timeout=300) + combined = (out.get("output") or "") + "\n" + (out.get("error") or "") + if not any("claude" in ln.lower() for ln in combined.splitlines()): + raise RuntimeError(f"claude install verify failed; tail: {combined[-400:]!r}") + logger.info("claude installed -> %s", combined.strip().splitlines()[-1]) + self._upload_mcp_server(env) + self._register_mcp(env) + _vm_exec(env, ["bash", "-c", self._sudo_wrap( + f"mkdir -p $(dirname {BOOTSTRAP_MARKER}) && date -u +%FT%TZ > {BOOTSTRAP_MARKER} && " + f"chown user:user {BOOTSTRAP_MARKER}")]) + self._bootstrapped_envs.add(id(env)) + logger.info("claude bootstrap done.") + + # ---------------- configure (every task) ---------------- + def configure(self, env) -> None: + body = (f"{self._render_auth_setup()} && mkdir -p {CLAUDE_PROJECTS_DIR} && " + f"rm -rf {CLAUDE_PROJECTS_DIR}/* 2>/dev/null; mkdir -p {CLAUDE_PROJECTS_DIR}") + _vm_exec(env, ["bash", "-c", body], timeout=60) + logger.info("configure: wrote litellm_env.sh (model=%s, gui=%s, effort=%s)", + self.model, self.gui, DEFAULT_EFFORT) + + def _render_auth_setup(self) -> str: + env_file = f"{CLAUDE_HOME_IN_VM}/litellm_env.sh" + raw = self.litellm_base_url.rstrip("/") + if raw.endswith("/v1"): + raw = raw[:-3] + return (f"mkdir -p {CLAUDE_HOME_IN_VM} && umask 077 && " + "cat > " + env_file + " < dict: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + self.bootstrap(env) + self.configure(env) + (output_dir / "auth_setup.sh").write_text(self._render_auth_setup(), encoding="utf-8") + + full_prompt = self._build_prompt(instruction, system_prompt_override) + _vm_exec(env, ["bash", "-c", f"mkdir -p {SCRATCH_DIR} /tmp_workspace && chmod 777 /tmp_workspace 2>/dev/null || true"]) + _vm_upload(env, full_prompt, CLAUDE_PROMPT_PATH) + _vm_exec(env, ["bash", "-c", f"rm -f {CLAUDE_RUN_DONE} {CLAUDE_RUN_LOG} {CLAUDE_RUN_SH}"]) + _vm_upload(env, self._render_run_script(), CLAUDE_RUN_SH) + _vm_exec(env, ["bash", "-c", f"chmod +x {CLAUDE_RUN_SH}"]) + + logger.info("Launching claude exec (timeout=%ds, gui=%s, effort=%s)...", + self.timeout, self.gui, DEFAULT_EFFORT) + t0 = time.perf_counter() + _vm_launch(env, ["bash", "-c", f"bash {CLAUDE_RUN_SH}"]) + ok = _wait_file(env, CLAUDE_RUN_DONE, timeout=self.timeout + 180) + elapsed = time.perf_counter() - t0 + + exit_code = None + if ok: + out = _vm_exec(env, ["bash", "-c", f"cat {CLAUDE_RUN_DONE}"]) + try: + exit_code = int(((out.get("output") or "").strip() or "0").splitlines()[-1]) + except (ValueError, IndexError): + exit_code = None + else: + logger.warning("claude did not finish within %ds (elapsed=%.0fs)", self.timeout, elapsed) + _vm_exec(env, ["bash", "-c", self._sudo_wrap( + "pkill -TERM -f '/usr/local/bin/claude' 2>/dev/null; " + "pkill -TERM -f '@anthropic-ai/claude-code' 2>/dev/null; " + "pkill -TERM -f 'weavebench_computer_mcp/server.py' 2>/dev/null; true")], timeout=30) + + if not _vm_fetch(env, f"{CLAUDE_RUN_LOG}.retry", output_dir / "agent.log"): + _vm_fetch(env, CLAUDE_RUN_LOG, output_dir / "agent.log") + _vm_fetch(env, CLAUDE_RUN_LOG, output_dir / "claude_stream.jsonl") + try: + sb = env.controller.get_screenshot() + if sb: + (output_dir / "final_screenshot.png").write_bytes(sb) + except Exception: + pass + + return {"agent_done": ok, "elapsed_seconds": round(elapsed, 2), + "timed_out": not ok, "exit_code": exit_code} + + # ---------------- helpers ---------------- + def _build_prompt(self, task_prompt: str, system_override: Optional[str]) -> str: + if not system_override: + return task_prompt + sep = "\n\n=== END OF SYSTEM CONTEXT — TASK INSTRUCTION BELOW ===\n\n" + footer = _CC_TOOL_MAP if self.gui else _CC_CLI_NOTE + return system_override.rstrip() + footer + sep + task_prompt + + def _render_run_script(self) -> str: + model_arg = shlex.quote(self.model) + display = 'export DISPLAY=:0' if self.gui else 'unset DISPLAY' + return f'''#!/usr/bin/env bash +# osw2 claudecode runner — retry-hardened (effort={DEFAULT_EFFORT}) +set -u +mkdir -p $(dirname {CLAUDE_RUN_LOG}) +source {CLAUDE_HOME_IN_VM}/litellm_env.sh +{display} +export XAUTHORITY=/run/user/1000/gdm/Xauthority +export DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/1000/bus +export XDG_RUNTIME_DIR=/run/user/1000 +export HOME=/home/user +export PATH=/usr/local/bin:/usr/bin:/bin +export DISABLE_PROMPT_CACHING=1 +export CLAUDE_CODE_EFFORT_LEVEL={DEFAULT_EFFORT} +export IS_SANDBOX=1 +PROMPT_FILE={CLAUDE_PROMPT_PATH} +RUN_LOG={CLAUDE_RUN_LOG} +RETRY_LOG={CLAUDE_RUN_LOG}.retry +DONE_FILE={CLAUDE_RUN_DONE} +MAX_RETRIES={self.max_refusal_retries} +mkdir -p /tmp_workspace && cd /tmp_workspace +DISALLOWED_TOOLS="WebFetch,WebSearch" +: > "$RUN_LOG"; : > "$RETRY_LOG" +TRY=0; RC=0; RETRIES_USED=0; SESSION_ID="" +while : ; do + if [ -n "$SESSION_ID" ]; then + echo "=== claudecode turn (try=$TRY resume) ===" >> "$RETRY_LOG" + echo "Resume and continue the task." | {CLAUDE_BIN_IN_VM} --print --output-format stream-json \\ + --verbose --dangerously-skip-permissions --setting-sources user \\ + --effort {DEFAULT_EFFORT} --model {model_arg} --resume "$SESSION_ID" \\ + --disallowedTools "$DISALLOWED_TOOLS" >> "$RUN_LOG" 2>> "$RETRY_LOG" + else + echo "=== claudecode turn (try=0 first) ===" >> "$RETRY_LOG" + {CLAUDE_BIN_IN_VM} --print --output-format stream-json \\ + --verbose --dangerously-skip-permissions --setting-sources user \\ + --effort {DEFAULT_EFFORT} --model {model_arg} \\ + --disallowedTools "$DISALLOWED_TOOLS" < "$PROMPT_FILE" >> "$RUN_LOG" 2>> "$RETRY_LOG" + fi + RC=$? + echo "AGENT_TURN_EXIT=$RC try=$TRY" >> "$RETRY_LOG" + [ "$RC" -ne 0 ] && break + [ "$TRY" -ge "$MAX_RETRIES" ] && break + LAST=$(tac "$RUN_LOG" | grep -m1 '"type":"result"' || true) + [ -z "$LAST" ] && break + INIT=$(tac "$RUN_LOG" | grep -m1 '"subtype":"init"' || true) + [ -n "$INIT" ] && SESSION_ID=$(echo "$INIT" | python3 -c 'import sys,json;print(json.loads(sys.stdin.read()).get("session_id",""))' 2>/dev/null || true) + echo "$LAST" | grep -qE 'is_error.*true|response.failed|Too Many Requests' || break + TRY=$((TRY+1)); RETRIES_USED=$TRY; sleep 15 +done +echo "RETRIES_USED=$RETRIES_USED" >> "$RETRY_LOG" +echo "AGENT_EXIT=$RC" >> "$RETRY_LOG" +echo $RC > "$DONE_FILE" +exit $RC +''' diff --git a/experiments/osworld_v2_hybrid/mm_agents/codex_agent.py b/experiments/osworld_v2_hybrid/mm_agents/codex_agent.py new file mode 100644 index 0000000..5db9a57 --- /dev/null +++ b/experiments/osworld_v2_hybrid/mm_agents/codex_agent.py @@ -0,0 +1,485 @@ +"""Codex (in-VM) agent for OSWorld-V2 — drop-in sibling of OpenClawAgent. + +Mirrors the bootstrap/configure/run interface of +`mm_agents.openclaw_agent.OpenClawAgent` so the inject runner can select it +with `--agent_harness codex` without any other change. Instead of openclaw, +it runs the OpenAI `codex` CLI INSIDE the OSWorld qcow2 VM. The whole +multi-step loop happens in-VM; the host runner calls `agent.run()` once per +task and scores with the native `env.evaluate()` (same as openclaw). + +Per task: + 1. bootstrap(env) — once per VM: + a. upload + `tar xzf codex.tar.gz -C /` (lays down + /usr/lib/node_modules/@openai/codex/bin/codex.js) + b. ensure Node >= 18 (codex.js needs a node runtime); reuse the + openclaw Node-22 install path if node is absent. + c. symlink /usr/local/bin/codex -> codex.js + d. upload the weavebench-computer MCP stdio server (the `computer` + GUI tool, equivalent to openclaw's `__computer__`). + 2. configure(env) — write /root/.codex/config.toml (litellm provider + + optional [mcp_servers.weavebench_computer]) and the LITELLM_API_KEY + env file. Re-run every task (endpoint/model/gui may change). + 3. run(env, instruction, output_dir, system_prompt_override=...) — + `codex exec --skip-git-repo-check --cd /tmp_workspace - < prompt.txt` + launched as root in the background; poll a done-file up to timeout. + Fetch agent.log + raw rollout.jsonl into output_dir. + +Browser suppression: same policy-prompt approach as openclaw (the system +prompt + tool-name footer tells the agent to drive the already-open desktop +Chrome, never a headless browser). GUI is gated by `gui`: when False, the +[mcp_servers] block is omitted and DISPLAY is unset, so the model physically +has no GUI tool (CLI-ablation parity). +""" +from __future__ import annotations + +import json +import logging +import os +import shlex +import time +from pathlib import Path +from typing import Optional + +# Reuse the exact in-VM Flask REST helpers + asset-dir resolver the openclaw +# agent uses, so both harnesses talk to the VM identically. +from mm_agents.openclaw_agent import ( + BOOTSTRAP_SH as _OPENCLAW_BOOTSTRAP_SH, # noqa: F401 (kept for parity/debug) + WCB_ASSETS_DIR, + _vm_exec, + _vm_launch, + _vm_upload, + _vm_upload_bytes, + _vm_fetch, + _wait_file, +) + +logger = logging.getLogger("codex_agent") + + +# --------------------------------------------------------------------------- +# Asset locations (codex.tar.gz + MCP server live next to openclaw.tar.gz) +# --------------------------------------------------------------------------- +CODEX_TARBALL = WCB_ASSETS_DIR / "codex.tar.gz" +MCP_SERVER_DIR = WCB_ASSETS_DIR / "weavebench_computer_mcp" + +# In-VM fixed paths — contract between this class and the tarball layout. +CODEX_BIN_IN_VM = "/usr/local/bin/codex" +CODEX_JS_PATH = "/usr/lib/node_modules/@openai/codex/bin/codex.js" +CODEX_HOME_IN_VM = "/root/.codex" +CODEX_CONFIG_PATH = f"{CODEX_HOME_IN_VM}/config.toml" +CODEX_SESSIONS_DIR = f"{CODEX_HOME_IN_VM}/sessions" +CODEX_ENV_FILE = f"{CODEX_HOME_IN_VM}/litellm_env.sh" +MCP_SERVER_DIR_IN_VM = "/usr/local/lib/weavebench_computer_mcp" +MCP_SERVER_PATH_IN_VM = f"{MCP_SERVER_DIR_IN_VM}/server.py" + +CODEX_INSTALL_DIR = "/tmp/weavebench_codex_install" +CODEX_PROMPT_PATH = f"{CODEX_INSTALL_DIR}/prompt.txt" +CODEX_RUN_LOG = f"{CODEX_INSTALL_DIR}/run.log" +CODEX_RUN_DONE = f"{CODEX_INSTALL_DIR}/run.done" +CODEX_RUN_SH = f"{CODEX_INSTALL_DIR}/run.sh" + +BOOTSTRAP_MARKER = "/home/user/.weavebench_codex_bootstrap.done" + +DEFAULT_REASONING_EFFORT = os.environ.get("CODEX_REASONING_EFFORT", "medium").strip() or "medium" + + +# Tool-name mapping footer. The OSWorld inject system prompt is written in +# openclaw's `__computer__`/`bash` vocabulary; translate to codex's actual +# tool names so the model picks the right tool. +_CODEX_TOOL_NAME_MAPPING = ( + "\n=== TOOL NAME MAPPING (THIS HARNESS = codex) ===\n" + "The system prompt above uses openclaw's tool names. In codex the " + "equivalents are:\n" + "- `__computer__` (GUI control) -> MCP tool **computer** (model sees " + "bare `computer`). Call with the SAME schema: `{actions: [{action:" + "\"click\", x, y, button:1}, ...]}` or `{action: {type:\"screenshot\"}}`. " + "Action types: screenshot, click, double_click, move, keypress, scroll, " + "drag, type, wait, cursor_position. Returns a fresh full-screen " + "screenshot after every call.\n" + "- `bash` / `exec` (shell) -> codex's built-in **exec_command** " + "(or **shell**) tool. Use it with `cat`/`sed`/`tee` for reading and " + "editing files; codex has no separate read/write/edit tools.\n" + "- background jobs -> **exec_command** + `nohup ... &`.\n" + "- view an image -> **view_image** (pass a local path).\n" + "- web fetch -> not available; use exec_command with `curl`/`wget`.\n" + "\n" + "codex also exposes a built-in `update_plan` for plan tracking; it " + "CANNOT see, click, or type.\n" + "\n" + "USE `computer` AGGRESSIVELY for any task involving desktop apps " + "(LibreOffice, GIMP, Chrome, file manager, settings UI) or any " + "deliverable judged by looking at the screen. Do NOT end the turn after " + "a single `actions:[]` screenshot observation — plan the concrete click/" + "type sequence and call `computer` AGAIN with real actions. Make several " + "concrete GUI actions before declaring done, then verify with " + "exec_command.\n" + "=== END TOOL NAME MAPPING ===\n" +) + +_CODEX_CLI_NOTE = ( + "\n=== HARNESS = codex, CLI-ONLY ===\n" + "There is NO GUI/computer tool here. Use exec_command (bash) for everything; " + "cat/sed/tee for file edits. For web tasks drive the already-open Chrome over " + "CDP (localhost:1337/9222) via curl from exec_command. Verify deliverables " + "with exec_command before declaring done.\n" + "=== END ===\n" +) + + +# Node-ensure snippet: codex.js needs a node runtime. The OSWorld-V2 qcow2 +# may not ship one, so install Node 22 to /opt/node22 (same source the +# openclaw bootstrap uses) when `node` is absent. Idempotent. +_ENSURE_NODE_SH = r""" +set -uo pipefail +if command -v node >/dev/null 2>&1 && [ "$(node --version 2>/dev/null | sed -E 's/^v([0-9]+).*/\1/' || echo 0)" -ge 18 ]; then + echo "NODE_OK $(node --version)" + exit 0 +fi +export DEBIAN_FRONTEND=noninteractive +cd /tmp +NODE_TAR=node-v22.13.0-linux-x64.tar.xz +if [ ! -f "$NODE_TAR" ]; then + curl -fsSL "https://nodejs.org/dist/v22.13.0/$NODE_TAR" -o "$NODE_TAR" \ + || curl -fsSL "https://npmmirror.com/mirrors/node/v22.13.0/$NODE_TAR" -o "$NODE_TAR" \ + || { echo "NODE_DL_FAIL"; exit 11; } +fi +rm -rf /opt/node22 && mkdir -p /opt/node22 +tar -xJf "$NODE_TAR" -C /opt/node22 --strip-components=1 || { echo "NODE_EXTRACT_FAIL"; exit 12; } +ln -sf /opt/node22/bin/node /usr/local/bin/node +ln -sf /opt/node22/bin/npm /usr/local/bin/npm +ln -sf /opt/node22/bin/npx /usr/local/bin/npx +hash -r +echo "NODE_INSTALLED $(/usr/local/bin/node --version)" +""" + + +class CodexAgent: + """In-VM codex-cli delegate agent (OpenClawAgent-compatible interface).""" + + def __init__(self, + model: str = "gpt-5.5", + litellm_base_url: str = "http://172.17.0.1:4200/v1", + litellm_api_key: str = "sk-litellm-azure-direct", + client_password: str = "password", + timeout: int = 900, + gui: bool = True, + max_steps: int = 100): + self.model = model + self.litellm_base_url = litellm_base_url.rstrip("/") + self.litellm_api_key = litellm_api_key + self.client_password = client_password + self.timeout = int(timeout) + self.gui = bool(gui) + self.max_steps = int(max_steps) + self.max_refusal_retries = int(os.environ.get("CODEX_MAX_RETRIES", "3")) + self._bootstrapped_envs: set[int] = set() + + # ---------------- sudo helper ---------------- + def _sudo_wrap(self, body: str) -> str: + """Run a shell body as root via `sudo -S`. The in-VM Flask + /setup/execute runs commands as the unprivileged `user`; writes to + /usr/*, /root/* need root. Mirrors openclaw_agent's sudo idiom.""" + escaped = body.replace("'", "'\"'\"'") + return f"echo '{self.client_password}' | sudo -S -p '' bash -c '{escaped}'" + + # ---------------- bootstrap (once per VM) ---------------- + def _marker_present(self, env) -> bool: + try: + out = _vm_exec(env, ["bash", "-c", + f"test -f {BOOTSTRAP_MARKER} && which codex >/dev/null 2>&1 && echo YES || echo NO"]) + return "YES" in (out.get("output") or "") + except Exception: + return False + + def _upload_mcp_server(self, env) -> None: + """Refresh the weavebench-computer MCP server (small, every bootstrap).""" + if not (MCP_SERVER_DIR / "server.py").is_file(): + raise FileNotFoundError(f"MCP server missing at {MCP_SERVER_DIR}/server.py") + _vm_exec(env, ["bash", "-c", f"mkdir -p {CODEX_INSTALL_DIR}"]) + tmp_path = f"{CODEX_INSTALL_DIR}/server.py" + _vm_upload(env, (MCP_SERVER_DIR / "server.py").read_text(encoding="utf-8"), tmp_path) + body = ( + f"mkdir -p {MCP_SERVER_DIR_IN_VM} && " + f"cp -f {tmp_path} {MCP_SERVER_PATH_IN_VM} && " + f"chmod +x {MCP_SERVER_PATH_IN_VM}" + ) + _vm_exec(env, ["bash", "-c", self._sudo_wrap(body)], timeout=60) + + def bootstrap(self, env) -> None: + # Re-probe the VM each task (docker provider recreates the container + # on env.reset, so an in-process cache alone is unsafe). + if self._marker_present(env): + logger.info("codex already bootstrapped in VM — refresh MCP server only.") + self._upload_mcp_server(env) + self._bootstrapped_envs.add(id(env)) + return + + if not CODEX_TARBALL.is_file(): + raise RuntimeError(f"Missing codex tarball at {CODEX_TARBALL}.") + + logger.info("Bootstrapping codex inside VM (one-time)...") + # 1) clock fix + quiesce apt daemons + ensure node (reuse openclaw's + # HOST_EPOCH clock trick by setting the clock first). + host_epoch = int(time.time()) + prep = ( + f"date -s '@{host_epoch}' >/dev/null 2>&1 || true; " + "hwclock --systohc >/dev/null 2>&1 || true; " + "systemctl stop packagekit.service unattended-upgrades.service " + " apt-daily.service apt-daily-upgrade.service 2>/dev/null || true; " + "pkill -9 -f packagekitd 2>/dev/null || true; " + "apt-get install -y -qq curl ca-certificates xz-utils 2>/dev/null || true; " + + _ENSURE_NODE_SH + ) + out = _vm_exec(env, ["bash", "-c", self._sudo_wrap(prep)], timeout=600) + node_msg = (out.get("output") or "") + logger.info("codex bootstrap node: %s", node_msg.strip().splitlines()[-1] if node_msg.strip() else "?") + + # 2) upload tarball + extract + symlink + verify + logger.info("Uploading codex tarball (%.1f MB)...", + CODEX_TARBALL.stat().st_size / 1e6) + _vm_upload_bytes(env, CODEX_TARBALL, "/tmp/codex.tar.gz") + install = ( + "set -e; " + "tar xzf /tmp/codex.tar.gz -C / && " + f"ln -sf {CODEX_JS_PATH} {CODEX_BIN_IN_VM} && " + f"test -x {CODEX_JS_PATH} && " + f"{CODEX_BIN_IN_VM} --version 2>&1" + ) + out = _vm_exec(env, ["bash", "-c", self._sudo_wrap(install)], timeout=300) + combined = (out.get("output") or "") + "\n" + (out.get("error") or "") + ver = [ln for ln in combined.splitlines() if "codex" in ln.lower()] + if not ver: + raise RuntimeError(f"codex install verify failed; tail: {combined[-400:]!r}") + logger.info("codex installed -> %s", ver[-1].strip()) + + # 3) MCP server + self._upload_mcp_server(env) + + # 4) passwordless sudo for `user` (so MCP `sudo -n -u user` works and + # so the agent can sudo without the password in the prompt). + _vm_exec(env, ["bash", "-c", self._sudo_wrap( + 'echo "user ALL=(ALL) NOPASSWD: ALL" > /etc/sudoers.d/99-codex-user; ' + 'chmod 440 /etc/sudoers.d/99-codex-user' + )]) + + # 5) marker + _vm_exec(env, ["bash", "-c", self._sudo_wrap( + f"mkdir -p $(dirname {BOOTSTRAP_MARKER}) && date -u +%FT%TZ > {BOOTSTRAP_MARKER} && " + f"chown user:user {BOOTSTRAP_MARKER}" + )]) + self._bootstrapped_envs.add(id(env)) + logger.info("codex bootstrap done.") + + # ---------------- configure (every task) ---------------- + def configure(self, env) -> None: + config_toml = self._render_config_toml() + _vm_exec(env, ["bash", "-c", f"mkdir -p {CODEX_INSTALL_DIR}"]) + tmp_cfg = f"{CODEX_INSTALL_DIR}/config.toml" + _vm_upload(env, config_toml, tmp_cfg) + body = ( + f"mkdir -p {CODEX_HOME_IN_VM} && " + f"cp -f {tmp_cfg} {CODEX_CONFIG_PATH} && chmod 0644 {CODEX_CONFIG_PATH} && " + f"{self._render_auth_setup()} && " + f"rm -rf {CODEX_SESSIONS_DIR}/* 2>/dev/null; mkdir -p {CODEX_SESSIONS_DIR}" + ) + _vm_exec(env, ["bash", "-c", self._sudo_wrap(body)], timeout=60) + logger.info("configure: wrote %s (model=%s, gui=%s, effort=%s)", + CODEX_CONFIG_PATH, self.model, self.gui, DEFAULT_REASONING_EFFORT) + + def _render_config_toml(self) -> str: + lines = [ + 'model_provider = "litellm"', + f'model_reasoning_effort = "{DEFAULT_REASONING_EFFORT}"', + 'model_reasoning_summary = "none"', + 'model_supports_reasoning_summaries = false', + 'hide_agent_reasoning = true', + f'model = "{self.model}"', + 'approval_policy = "never"', + 'sandbox_mode = "danger-full-access"', + '', + '[model_providers.litellm]', + 'name = "LiteLLM"', + f'base_url = "{self.litellm_base_url}"', + f'wire_api = "{os.environ.get("CODEX_WIRE_API", "responses").strip()}"', + 'env_key = "LITELLM_API_KEY"', + ] + if self.gui: + # MCP server must run as `user` (pyautogui + live X/dbus session + # live in the user account; codex itself runs as root). + lines += [ + '', + '[mcp_servers.weavebench_computer]', + 'command = "/usr/bin/sudo"', + ('args = ["-n", "-u", "user", "env", ' + '"DISPLAY=:0", ' + '"XAUTHORITY=/run/user/1000/gdm/Xauthority", ' + '"DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/1000/bus", ' + '"XDG_RUNTIME_DIR=/run/user/1000", ' + '"PYTHONUNBUFFERED=1", ' + f'"/usr/bin/python3", "{MCP_SERVER_PATH_IN_VM}"]'), + ] + return "\n".join(lines) + "\n" + + def _render_auth_setup(self) -> str: + return ( + f"mkdir -p {CODEX_HOME_IN_VM} && umask 077 && " + f"echo 'export LITELLM_API_KEY={shlex.quote(self.litellm_api_key)}' > {CODEX_ENV_FILE} && " + f"chmod 0600 {CODEX_ENV_FILE}" + ) + + # ---------------- run (per task) ---------------- + def run(self, env, instruction: str, output_dir: Path, + system_prompt_override: Optional[str] = None, + **_ignored) -> dict: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + self.bootstrap(env) + self.configure(env) + (output_dir / "config.toml").write_text(self._render_config_toml(), encoding="utf-8") + + full_prompt = self._build_prompt(instruction, system_prompt_override) + _vm_exec(env, ["bash", "-c", f"mkdir -p {CODEX_INSTALL_DIR} /tmp_workspace && chmod 777 /tmp_workspace 2>/dev/null || true"]) + _vm_upload(env, full_prompt, CODEX_PROMPT_PATH) + + _vm_exec(env, ["bash", "-c", f"rm -f {CODEX_RUN_DONE} {CODEX_RUN_LOG} {CODEX_RUN_SH}"]) + _vm_exec(env, ["bash", "-c", self._sudo_wrap( + f"rm -rf {CODEX_SESSIONS_DIR}/* 2>/dev/null; mkdir -p {CODEX_SESSIONS_DIR}")]) + + run_sh = self._render_run_script() + _vm_upload(env, run_sh, CODEX_RUN_SH) + _vm_exec(env, ["bash", "-c", f"chmod +x {CODEX_RUN_SH}"]) + + logger.info("Launching codex exec (timeout=%ds, gui=%s)...", self.timeout, self.gui) + t0 = time.perf_counter() + # Launch as root so codex can read /root/.codex and spawn the MCP + # server via `sudo -n -u user`. + _vm_launch(env, ["bash", "-c", self._sudo_wrap(f"bash {CODEX_RUN_SH}")]) + + ok = _wait_file(env, CODEX_RUN_DONE, timeout=self.timeout + 180) + elapsed = time.perf_counter() - t0 + + exit_code = None + if ok: + out = _vm_exec(env, ["bash", "-c", f"cat {CODEX_RUN_DONE}"]) + try: + exit_code = int(((out.get("output") or "").strip() or "0").splitlines()[-1]) + except (ValueError, IndexError): + exit_code = None + else: + logger.warning("codex did not finish within %ds (elapsed=%.0fs)", self.timeout, elapsed) + _vm_exec(env, ["bash", "-c", self._sudo_wrap( + "pkill -TERM -f '/usr/local/bin/codex' 2>/dev/null; " + "pkill -TERM -f '@openai/codex' 2>/dev/null; true")], timeout=30) + + # Artifacts: run log -> agent.log, raw rollout for debugging. + _vm_fetch(env, CODEX_RUN_LOG, output_dir / "agent.log") + self._fetch_rollout(env, output_dir) + try: + sb = env.controller.get_screenshot() + if sb: + (output_dir / "final_screenshot.png").write_bytes(sb) + except Exception: + pass + # Per-step screenshots written by the MCP server into the workspace. + self._fetch_screenshots(env, output_dir) + + return {"agent_done": ok, "elapsed_seconds": round(elapsed, 2), + "timed_out": not ok, "exit_code": exit_code} + + # ---------------- helpers ---------------- + def _build_prompt(self, task_prompt: str, system_override: Optional[str]) -> str: + if not system_override: + return task_prompt + sep = "\n\n=== END OF SYSTEM CONTEXT — TASK INSTRUCTION BELOW ===\n\n" + footer = _CODEX_TOOL_NAME_MAPPING if self.gui else _CODEX_CLI_NOTE + return system_override.rstrip() + footer + sep + task_prompt + + def _render_run_script(self) -> str: + display_export = 'export DISPLAY=:0' if self.gui else 'unset DISPLAY' + return f'''#!/usr/bin/env bash +# osw2 codex runner — retry-hardened +set -u +{display_export} +source {CODEX_ENV_FILE} +export PATH=/usr/local/bin:/usr/bin:/bin +mkdir -p $(dirname {CODEX_RUN_LOG}) + +PROMPT_FILE={CODEX_PROMPT_PATH} +RUN_LOG={CODEX_RUN_LOG} +DONE_FILE={CODEX_RUN_DONE} +MAX_RETRIES={self.max_refusal_retries} + +TRY=0 +RC=0 +RETRIES_USED=0 +while : ; do + echo "=== codex turn (try=$TRY) ===" >> "$RUN_LOG" + {CODEX_BIN_IN_VM} exec --skip-git-repo-check --cd /tmp_workspace - \\ + < "$PROMPT_FILE" >> "$RUN_LOG" 2>&1 + RC=$? + echo "AGENT_TURN_EXIT=$RC try=$TRY" >> "$RUN_LOG" + [ "$TRY" -ge "$MAX_RETRIES" ] && break + + TAIL=$(tail -200 "$RUN_LOG" 2>/dev/null) + REASON="" + if echo "$TAIL" | grep -qE 'response\\.failed|Connection error|stream interrupted'; then + REASON="UPSTREAM" + elif echo "$TAIL" | grep -qE 'Too Many Requests|status[: ]*429|rate.?limit'; then + REASON="RATE" + elif echo "$TAIL" | grep -qE '401|Unauthorized|authentication.*fail'; then + REASON="AUTH" + elif [ "$RC" -ne 0 ]; then + REASON="OTHER" + fi + [ -z "$REASON" ] && break + + TRY=$((TRY+1)); RETRIES_USED=$TRY + case "$REASON" in + AUTH) echo "$REASON — sleep 60" >> "$RUN_LOG"; sleep 60 ;; + RATE) echo "$REASON — sleep 90" >> "$RUN_LOG"; sleep 90 ;; + *) echo "$REASON — sleep 15" >> "$RUN_LOG"; sleep 15 ;; + esac +done + +echo "RETRIES_USED=$RETRIES_USED" >> "$RUN_LOG" +echo "AGENT_EXIT=$RC" >> "$RUN_LOG" +echo $RC > "$DONE_FILE" +exit $RC +''' + + def _fetch_rollout(self, env, output_dir: Path) -> None: + """Copy the latest codex rollout.jsonl to host for offline debugging. + Scoring uses native env.evaluate(), so this is best-effort only.""" + try: + out = _vm_exec(env, ["bash", "-c", self._sudo_wrap( + f"ls -1t {CODEX_SESSIONS_DIR}/*/rollout.jsonl 2>/dev/null | head -1")]) + path = (out.get("output") or "").strip().splitlines() + path = path[-1].strip() if path else "" + if not path: + return + # copy to a user-readable tmp, then fetch + _vm_exec(env, ["bash", "-c", self._sudo_wrap( + f"cp -f {path} {CODEX_INSTALL_DIR}/rollout.jsonl && " + f"chmod 0644 {CODEX_INSTALL_DIR}/rollout.jsonl")]) + _vm_fetch(env, f"{CODEX_INSTALL_DIR}/rollout.jsonl", output_dir / "codex_rollout.jsonl") + except Exception as e: + logger.warning("rollout fetch failed: %s", e) + + def _fetch_screenshots(self, env, output_dir: Path) -> None: + try: + listing = _vm_exec(env, ["bash", "-c", + "ls -1 /tmp_workspace/_screenshots/*.png 2>/dev/null || true"]) + names = [ln.strip() for ln in (listing.get("output") or "").splitlines() + if ln.strip().endswith(".png")] + if not names: + return + shots = output_dir / "screenshots" + shots.mkdir(parents=True, exist_ok=True) + for remote in names: + try: + _vm_fetch(env, remote, shots / Path(remote).name) + except Exception: + pass + except Exception: + pass diff --git a/experiments/osworld_v2_hybrid/mm_agents/openclaw_agent.py b/experiments/osworld_v2_hybrid/mm_agents/openclaw_agent.py new file mode 100755 index 0000000..e2bc78b --- /dev/null +++ b/experiments/osworld_v2_hybrid/mm_agents/openclaw_agent.py @@ -0,0 +1,771 @@ +"""OpenClaw agent for OSWorld. + +This agent does NOT use OSWorld's per-step prediction loop. Instead, it +delegates the whole multi-step reasoning to the `openclaw` CLI running +INSIDE the VM (which has its own internal step loop). The host-side +runner just calls `agent.run(env, instruction)` once per task. + +Workflow per task: + 1. ensure node + openclaw are installed inside the VM (cached across tasks) + 2. write provider config (`~/.openclaw/openclaw.json`) pointing at LiteLLM + 3. start `openclaw gateway --port 18789` in background (idempotent) + 4. run `openclaw agent --session-id chat --timeout T --message ""` + synchronously, with optional DISPLAY=:0 for GUI tasks + 5. fetch chat.jsonl + agent.log into the per-task results dir +""" +from __future__ import annotations + +import json +import logging +import os +import time +from pathlib import Path +from typing import Optional + +import requests + +logger = logging.getLogger("openclaw_agent") + + +# --------------------------------------------------------------------------- +# Bootstrap script: installs node + openclaw inside the Ubuntu VM +# Runs once per VM (gated by /home/user/.openclaw_bootstrap.done) +# --------------------------------------------------------------------------- +BOOTSTRAP_SH = r"""#!/bin/bash +set -uo pipefail +exec > /tmp/openclaw_bootstrap.log 2>&1 + +PASS="${CLIENT_PASSWORD:-password}" +export DEBIAN_FRONTEND=noninteractive +APT_OPTS='-y -qq -o Dpkg::Options::=--force-confdef -o Dpkg::Options::=--force-confold' +SUDO() { echo "$PASS" | sudo -S -p '' env DEBIAN_FRONTEND=noninteractive "$@"; } + +if [ -f /home/user/.openclaw_bootstrap.done ]; then + echo "Bootstrap already done." + exit 0 +fi + +# -1) Fix VM clock. The OSWorld-V2 qcow2 boots with a stale RTC, so its clock is +# often BEHIND real time — HTTPS then fails with "certificate is not yet +# valid" (curl 60) and apt with "Release file is not valid yet", killing the +# Node download. The runner passes the host epoch via HOST_EPOCH; set the +# clock from it (fallback: bump 1 year if unset) before any TLS op. +if [ -n "${HOST_EPOCH:-}" ]; then + SUDO date -s "@${HOST_EPOCH}" >/dev/null 2>&1 || true +fi +SUDO hwclock --systohc >/dev/null 2>&1 || true +echo "[clock] VM time now: $(date -u)" + +# 0) Quiesce background package daemons that race us for the apt/dpkg lock. +# The OSWorld-V2 image runs packagekitd + unattended-upgrades which grab +# /var/lib/apt/lists/lock and /var/lib/dpkg/lock-frontend right after boot; +# our apt-get then fails ("Could not get lock ... held by packagekitd"), +# cascading into a bootstrap timeout. Stop them and wait for the locks. +SUDO systemctl stop packagekit.service unattended-upgrades.service apt-daily.service apt-daily-upgrade.service 2>/dev/null || true +SUDO systemctl kill packagekit.service 2>/dev/null || true +SUDO pkill -9 -f packagekitd 2>/dev/null || true +SUDO pkill -9 -f unattended-upgrade 2>/dev/null || true +for i in $(seq 1 30); do + if SUDO fuser /var/lib/dpkg/lock-frontend /var/lib/apt/lists/lock >/dev/null 2>&1; then + echo "[apt-lock] still held, waiting ($i/30)..."; sleep 2 + else + break + fi +done +SUDO dpkg --configure -a 2>/dev/null || true + +# 1) apt deps +SUDO apt-get update -qq || true +SUDO apt-get install $APT_OPTS curl ca-certificates xdotool wmctrl python3-pip scrot gnome-screenshot imagemagick || true + +# 2) Node 22 — direct binary install to /opt/node22 (avoids apt repo flakiness) +NODE_MAJ=$(/opt/node22/bin/node --version 2>/dev/null | sed -E 's/^v([0-9]+).*/\1/' || echo 0) +if [ "${NODE_MAJ:-0}" -lt 22 ]; then + cd /tmp + NODE_TAR=node-v22.13.0-linux-x64.tar.xz + if [ ! -f "$NODE_TAR" ]; then + curl -fsSL "https://nodejs.org/dist/v22.13.0/$NODE_TAR" -o "$NODE_TAR" \ + || curl -fsSL "https://npmmirror.com/mirrors/node/v22.13.0/$NODE_TAR" -o "$NODE_TAR" \ + || { echo "FAILED to download node tarball"; exit 11; } + fi + SUDO rm -rf /opt/node22 + SUDO mkdir -p /opt/node22 + SUDO tar -xJf "$NODE_TAR" -C /opt/node22 --strip-components=1 || { echo "node extract failed"; exit 12; } + SUDO ln -sf /opt/node22/bin/node /usr/local/bin/node + SUDO ln -sf /opt/node22/bin/npm /usr/local/bin/npm + SUDO ln -sf /opt/node22/bin/npx /usr/local/bin/npx +fi +hash -r +/usr/local/bin/node --version || exit 14 +/usr/local/bin/npm --version || exit 15 + +# 3) openclaw: extract the WCB tarball uploaded to /tmp/openclaw.tar.gz +if [ ! -f /tmp/openclaw.tar.gz ]; then + echo "openclaw.tar.gz not uploaded yet"; exit 16 +fi +SUDO rm -rf /usr/lib/node_modules/openclaw /usr/bin/openclaw /usr/local/bin/openclaw +SUDO mkdir -p /usr/lib/node_modules +SUDO tar xzf /tmp/openclaw.tar.gz -C /usr/lib/node_modules || { echo "extract failed"; exit 17; } +ls -la /usr/lib/node_modules/openclaw/openclaw.mjs || { echo "openclaw.mjs missing after extract"; exit 18; } +SUDO chmod +x /usr/lib/node_modules/openclaw/openclaw.mjs +SUDO ln -sf /usr/lib/node_modules/openclaw/openclaw.mjs /usr/bin/openclaw +SUDO ln -sf /usr/lib/node_modules/openclaw/openclaw.mjs /usr/local/bin/openclaw +hash -r +/usr/bin/openclaw --version || { echo "openclaw not runnable"; exit 19; } + +# 4) python deps for warmup/postconfig pyautogui (already mostly present in OSWorld VM) +# Only install what's missing; hard-bound by `timeout` so a slow PyPI mirror +# can't hang bootstrap forever (pip3 install opencv-python alone can take 10+ min). +MISSING=$(python3 - <<'PY' 2>/dev/null || true +mods = ["pyautogui","pygetwindow","pyperclip","PIL","requests","docx","pptx","openpyxl","pandas","fpdf","reportlab","fitz","pypdf","bs4","lxml"] +miss=[] +for m in mods: + try: __import__(m) + except Exception: + if m=="PIL": miss.append("Pillow") + elif m=="docx": miss.append("python-docx") + elif m=="pptx": miss.append("python-pptx") + elif m=="fpdf": miss.append("fpdf2") + elif m=="fitz": miss.append("PyMuPDF") + elif m=="bs4": miss.append("beautifulsoup4") + else: miss.append(m) +print(" ".join(miss)) +PY +) +if [ -n "${MISSING:-}" ]; then + echo "Installing missing python modules: $MISSING" + timeout 240 pip3 install --user --quiet $MISSING 2>&1 | tail -5 || echo "[pip install timed out / failed; continuing]" +else + echo "All required python modules already present." +fi + +# 5) computer-tool plugin (native CUA via patched pi-ai) +# The plugin registers a `__computer__` tool which the patched pi-ai +# surfaces to OpenAI Responses as the native computer_use_preview tool. +SUDO mkdir -p /home/user/.openclaw/extensions/computer-tool +SUDO cp /tmp/computer_tool_plugin/openclaw.plugin.json /home/user/.openclaw/extensions/computer-tool/openclaw.plugin.json +SUDO cp /tmp/computer_tool_plugin/index.ts /home/user/.openclaw/extensions/computer-tool/index.ts +SUDO chown -R user:user /home/user/.openclaw + +# 6) Patch @mariozechner/pi-ai to add native CUA support. +# We replace dist/providers/openai-responses-shared.js with the patched +# copy. Idempotent: keep a .orig backup so subsequent installs can re-patch. +PIAI_DIR=/usr/lib/node_modules/openclaw/node_modules/@mariozechner/pi-ai/dist/providers +if [ -d "$PIAI_DIR" ]; then + if [ ! -f "$PIAI_DIR/openai-responses-shared.orig.js" ]; then + SUDO cp "$PIAI_DIR/openai-responses-shared.js" "$PIAI_DIR/openai-responses-shared.orig.js" + fi + SUDO cp /tmp/openclaw_patches/openai-responses-shared.patched.js "$PIAI_DIR/openai-responses-shared.js" + echo "Patched pi-ai openai-responses-shared.js" + if [ ! -f "$PIAI_DIR/openai-responses.orig.js" ]; then + SUDO cp "$PIAI_DIR/openai-responses.js" "$PIAI_DIR/openai-responses.orig.js" + fi + SUDO cp /tmp/openclaw_patches/openai-responses.patched.js "$PIAI_DIR/openai-responses.js" + echo "Patched pi-ai openai-responses.js" +else + echo "WARN: pi-ai not found at $PIAI_DIR — patch skipped" +fi + +# Drop any stale GUI-related skills/plugins from prior bootstraps so the +# computer-tool plugin is the SOLE GUI entry point. +SUDO rm -rf /home/user/.openclaw/skills/desktop-control || true +SUDO rm -rf /home/user/.openclaw/extensions/use-gui || true +SUDO rm -f /usr/local/bin/use_gui.py /usr/local/bin/use_gui || true + +# 7) Passwordless sudo for `user` so the agent doesn't need to know the +# sudo password (and so we can drop credentials from the system prompt, +# which Azure gpt-5.4's safety filter currently flags as "I cannot assist"). +SUDO bash -c 'echo "user ALL=(ALL) NOPASSWD: ALL" > /etc/sudoers.d/99-openclaw-user; chmod 440 /etc/sudoers.d/99-openclaw-user' + +touch /home/user/.openclaw_bootstrap.done +echo "BOOTSTRAP_DONE" +""" + +# Injection assets (491MB openclaw.tar.gz + computer-tool plugin + pi-ai +# patches) live in the WeaveBench checkout's cache/runtime_assets. Override the +# location with the WEAVEBENCH_ASSETS_DIR env var. +def _wcb_assets_dir() -> Path: + env_dir = os.environ.get("WEAVEBENCH_ASSETS_DIR") + if env_dir: + return Path(env_dir).expanduser().resolve() + # Default to the local-NVMe copy (faster parallel reads, carries the + # OSW2-patched openai-responses.patched.js). Falls back to NAS if absent. + local = Path("/path/to/osworld_v2_images/runtime_assets") + if (local / "openclaw.tar.gz").exists(): + return local + return Path("/path/to/runtime_assets") + +WCB_ASSETS_DIR = _wcb_assets_dir() +_OPENCLAW_TARBALL_NFS = WCB_ASSETS_DIR / "openclaw.tar.gz" + +OPENCLAW_TARBALL = _OPENCLAW_TARBALL_NFS + + +# --------------------------------------------------------------------------- +# Per-task configure script: write LiteLLM provider config + auth profiles +# --------------------------------------------------------------------------- +def _configure_sh(model: str, base_url: str, api_key: str, gui: bool, + thinking_level: str = "medium") -> str: + full_model = f"litellm/{model}" if not model.startswith("litellm/") else model + computer_enabled = "true" if gui else "false" + enable_or_disable = "enable" if gui else "disable" + return rf"""#!/bin/bash +set -e +mkdir -p $HOME/.openclaw/agents/main/agent $HOME/.openclaw/agents/main/sessions + +cat > $HOME/.openclaw/openclaw.json <<'JSON' +{{ + "models": {{ + "providers": {{ + "litellm": {{ + "baseUrl": "{base_url}", + "apiKey": "{api_key}", + "api": "openai-responses", + "models": [{{"id": "{model}", "name": "{model} (LiteLLM)", "input": ["text", "image"], "reasoning": true}}] + }} + }} + }} +}} +JSON + +cat > $HOME/.openclaw/agents/main/agent/auth-profiles.json <<'JSON' +{{ + "version": 1, + "profiles": {{ + "litellm:default": {{"type": "api_key", "provider": "litellm", "key": "{api_key}"}}, + "openrouter:default": {{"type": "api_key", "provider": "openrouter", "key": "{api_key}"}} + }} +}} +JSON + +openclaw models set "{full_model}" >/dev/null +openclaw config set agents.defaults.imageModel.primary "{full_model}" >/dev/null +openclaw config set tools.web.search.enabled false >/dev/null +openclaw config set gateway.mode local >/dev/null +# Reasoning effort. The paper runs GPT-5.5 at `xhigh`. openclaw silently clamps +# xhigh -> high for non-Claude models, so high is the effective max for gpt-5.5 +# here; we still pass xhigh so the intent is explicit and survives if clamping +# ever changes. (env-overridable via OSW_THINKING) +openclaw config set agents.defaults.thinkingDefault {thinking_level} >/dev/null 2>&1 || true +echo "thinkingDefault now: $(openclaw config get agents.defaults.thinkingDefault 2>/dev/null || echo '?')" + +# Toggle the computer-tool plugin per-mode. cli mode disables it so the +# agent physically cannot see the native CUA tool. +openclaw config set plugins.entries.computer-tool.enabled {computer_enabled} >/dev/null 2>&1 || true +openclaw plugins {enable_or_disable} computer-tool >/dev/null 2>&1 || true + +# Disable openclaw's built-in `browser` tool so the agent interacts with web +# pages through the REAL desktop Chrome (via `__computer__`) or CLI, NOT through +# openclaw's headless browser session. The headless `profile:openclaw` session +# is a SEPARATE browser context: state written there (cookies, form submits, +# localStorage) never reaches the desktop Chrome / setup-cookie session that the +# OSWorld evaluator inspects → systematic false-0 on web tasks. +# NOTE: `browser` is a built-in command in openclaw 2026.3.x, NOT a plugin, so +# `plugins disable browser` is a no-op ("plugin not found: browser"). The real +# switch is the top-level `browser.enabled` config key (default true). +openclaw config set browser.enabled false >/dev/null 2>&1 || true +echo "browser.enabled now: $(openclaw config get browser.enabled 2>/dev/null || echo '?')" + +echo CONFIGURED +""" + + +def _vm_url(env, path: str) -> str: + return f"http://{env.vm_ip}:{env.server_port}{path}" + + +def _vm_exec(env, cmd: list[str], shell: bool = False, timeout: int = 120) -> dict: + r = requests.post(_vm_url(env, "/setup/execute"), + json={"command": cmd, "shell": shell}, timeout=timeout) + r.raise_for_status() + return r.json() + + +def _vm_launch(env, cmd: list[str], shell: bool = False) -> str: + r = requests.post(_vm_url(env, "/setup/launch"), + json={"command": cmd, "shell": shell}, timeout=30) + r.raise_for_status() + return r.text + + +def _vm_upload(env, content: str, remote: str) -> None: + files = {"file_data": ("payload", content.encode("utf-8"), "text/plain")} + data = {"file_path": remote} + r = requests.post(_vm_url(env, "/setup/upload"), files=files, data=data, timeout=120) + r.raise_for_status() + + +def _vm_upload_bytes(env, path: Path, remote: str, timeout: int = 1800) -> None: + with open(path, "rb") as fh: + files = {"file_data": ("payload", fh, "application/octet-stream")} + data = {"file_path": remote} + r = requests.post(_vm_url(env, "/setup/upload"), files=files, data=data, timeout=timeout) + r.raise_for_status() + + +def _vm_fetch(env, remote: str, local: Path) -> bool: + r = requests.post(_vm_url(env, "/file"), data={"file_path": remote}, timeout=120) + if r.status_code != 200: + return False + local.parent.mkdir(parents=True, exist_ok=True) + local.write_bytes(r.content) + return True + + +def _wait_file(env, remote: str, timeout: int) -> bool: + t0 = time.time() + while time.time() - t0 < timeout: + try: + out = _vm_exec(env, ["bash", "-c", f"test -f {remote} && echo YES || echo NO"]) + if "YES" in (out.get("output") or ""): + return True + except Exception: + pass + time.sleep(5) + return False + + +# --------------------------------------------------------------------------- +# OpenClawAgent +# --------------------------------------------------------------------------- +class OpenClawAgent: + """OpenClaw delegate agent. + + Args: + model: Model name (e.g. "gpt-5.4"). Will be wrapped as "litellm/". + litellm_base_url: LiteLLM proxy base URL (reachable from inside the VM). + litellm_api_key: API key. + client_password: Sudo password inside the VM (default "password"). + timeout: Per-task max wall time in seconds. + gui: True for GUI mode (passes DISPLAY=:0), False for CLI-only. + """ + + def __init__(self, + model: str = "gpt-5.5", + litellm_base_url: str = "http://172.29.0.1:4000/v1", + litellm_api_key: str = "sk-litellm-local", + client_password: str = "password", + timeout: int = 900, + gui: bool = True, + max_steps: int = 100): + self.model = model + self.litellm_base_url = litellm_base_url + self.litellm_api_key = litellm_api_key + self.client_password = client_password + self.timeout = timeout + self.gui = gui + self.max_steps = max_steps + self._bootstrapped_envs: set[int] = set() + + # ---------------- bootstrap (once per VM) ---------------- + def _ensure_plugin_installed(self, env) -> None: + """Ensure the computer-tool plugin + patched pi-ai are present. + + The pi-ai patch is applied lazily (idempotent — only needed once per + VM image build), but the plugin .ts itself is ALWAYS re-uploaded so + that local edits to wcb_assets/computer_tool_plugin/index.ts (e.g. + per-step screenshot persistence) take effect on the next eval run + without rebuilding the docker image. + """ + out = _vm_exec(env, ["bash", "-c", + "test -f /usr/lib/node_modules/openclaw/node_modules/@mariozechner/pi-ai/dist/providers/openai-responses-shared.orig.js && " + "test -f /usr/lib/node_modules/openclaw/node_modules/@mariozechner/pi-ai/dist/providers/openai-responses.orig.js " + "&& echo YES || echo NO"]) + pi_ai_patched = "YES" in (out.get("output") or "") + logger.info("Refreshing computer-tool plugin from local wcb_assets (pi-ai patched=%s)...", pi_ai_patched) + wcb_dir = WCB_ASSETS_DIR + plugin_dir = wcb_dir / "computer_tool_plugin" + patch_dir = wcb_dir / "openclaw_patches" + _vm_exec(env, ["bash", "-c", "mkdir -p /tmp/computer_tool_plugin /tmp/openclaw_patches"]) + for fname in ("openclaw.plugin.json", "index.ts"): + _vm_upload(env, (plugin_dir / fname).read_text(), + f"/tmp/computer_tool_plugin/{fname}") + _vm_upload(env, (patch_dir / "openai-responses-shared.patched.js").read_text(), + "/tmp/openclaw_patches/openai-responses-shared.patched.js") + _vm_upload(env, (patch_dir / "openai-responses.patched.js").read_text(), + "/tmp/openclaw_patches/openai-responses.patched.js") + sh = ( + f"echo '{self.client_password}' | sudo -S -p '' bash -c '" + "mkdir -p /home/user/.openclaw/extensions/computer-tool && " + "cp /tmp/computer_tool_plugin/openclaw.plugin.json /home/user/.openclaw/extensions/computer-tool/ && " + "cp /tmp/computer_tool_plugin/index.ts /home/user/.openclaw/extensions/computer-tool/ && " + "chown -R user:user /home/user/.openclaw && " + "rm -rf /home/user/.openclaw/extensions/use-gui /home/user/.openclaw/skills/desktop-control || true && " + "rm -f /usr/local/bin/use_gui.py /usr/local/bin/use_gui || true && " + "PIAI_DIR=/usr/lib/node_modules/openclaw/node_modules/@mariozechner/pi-ai/dist/providers && " + "if [ -d \"$PIAI_DIR\" ]; then " + " if [ ! -f \"$PIAI_DIR/openai-responses-shared.orig.js\" ]; then " + " cp \"$PIAI_DIR/openai-responses-shared.js\" \"$PIAI_DIR/openai-responses-shared.orig.js\"; " + " fi; " + " cp /tmp/openclaw_patches/openai-responses-shared.patched.js \"$PIAI_DIR/openai-responses-shared.js\"; " + " if [ ! -f \"$PIAI_DIR/openai-responses.orig.js\" ]; then " + " cp \"$PIAI_DIR/openai-responses.js\" \"$PIAI_DIR/openai-responses.orig.js\"; " + " fi; " + " cp /tmp/openclaw_patches/openai-responses.patched.js \"$PIAI_DIR/openai-responses.js\"; " + "fi" + "'" + ) + _vm_exec(env, ["bash", "-c", sh], timeout=180) + self._apply_sandbox_patch(env) + + def _apply_sandbox_patch(self, env) -> None: + """WCB: extend openclaw media sandbox whitelist to include /tmp_workspace, + and create a miniconda-eval shim so task warmups that hardcode + `~/miniconda3/envs/eval/bin/{python,pip}` (a path that exists in the + wildclawbench docker image but NOT in the OSWorld VM image) succeed. + + The 5 hashed local-roots-*.js bundles in dist/plugin-sdk/ share the exact + same buildMediaLocalRoots body. We idempotently patch each one by + appending `/tmp_workspace` to the roots array so the built-in `image` + and `pdf` tools can read task-bundled files placed there. + Safe to call repeatedly (grep guard makes it a no-op if already done). + """ + sh = ( + f"echo '{self.client_password}' | sudo -S -p '' bash -c '" + "OC_DIST=/usr/lib/node_modules/openclaw/dist/plugin-sdk; " + "if [ -d \"$OC_DIST\" ]; then " + " for f in $OC_DIST/local-roots-*.js; do " + " [ -f \"$f\" ] || continue; " + " if ! grep -q /tmp_workspace \"$f\"; then " + " sed -i \"s#path.join(resolvedStateDir, \\\"sandboxes\\\")#path.join(resolvedStateDir, \\\"sandboxes\\\"),\\n\\t\\t\\\"/tmp_workspace\\\"#\" \"$f\"; " + " fi; " + " done; " + " echo SANDBOX_PATCHED; " + "else " + " echo NO_OC_DIST; " + "fi'" + " && " + # WCB: create miniconda-eval shim (tasks like task_1_sam3_inference + # and task_2_sam3_debug hardcode ~/miniconda3/envs/eval/bin/pip from + # the docker layout). Symlink to system python3/pip3 so warmup + + # subsequent agent commands work out of the box. + "MC_BIN=/home/user/miniconda3/envs/eval/bin && " + "mkdir -p \"$MC_BIN\" && " + "ln -sf \"$(command -v python3)\" \"$MC_BIN/python\" && " + "ln -sf \"$(command -v python3)\" \"$MC_BIN/python3\" && " + "ln -sf \"$(command -v pip3 || command -v pip)\" \"$MC_BIN/pip\" && " + "ln -sf \"$(command -v pip3 || command -v pip)\" \"$MC_BIN/pip3\" && " + "echo MINICONDA_SHIM_OK" + ) + out = _vm_exec(env, ["bash", "-c", sh], timeout=60) + msg = out.get("output") or "" + if "SANDBOX_PATCHED" not in msg: + logger.warning("Sandbox patch did not run cleanly: %s", msg.strip()[:300]) + if "MINICONDA_SHIM_OK" not in msg: + logger.warning("Miniconda shim setup did not complete: %s", msg.strip()[:300]) + + def bootstrap(self, env) -> None: + # NOTE: do NOT cache by id(env) — the docker provider recreates the VM + # container on every env.reset(), so we must re-probe the in-VM done + # flag each task. The on-VM check is cheap (~50ms). + out = _vm_exec(env, ["bash", "-c", + "test -f /home/user/.openclaw_bootstrap.done " + "&& which openclaw >/dev/null 2>&1 && echo DONE || echo MISSING"]) + if "DONE" in (out.get("output") or ""): + logger.info("Openclaw already bootstrapped in VM.") + self._ensure_plugin_installed(env) + return + + logger.info("Bootstrapping openclaw inside VM (one-time, ~3-5 min)...") + if not OPENCLAW_TARBALL.exists(): + raise RuntimeError(f"Missing openclaw tarball at {OPENCLAW_TARBALL}. " + f"Extract it from wildclawbench-ubuntu image first.") + logger.info("Uploading openclaw tarball (%.1f MB)...", + OPENCLAW_TARBALL.stat().st_size / (1024 * 1024)) + _vm_upload_bytes(env, OPENCLAW_TARBALL, "/tmp/openclaw.tar.gz") + + # Upload the computer-tool plugin (manifest + index.ts) and the pi-ai + # patch (openai-responses-shared.patched.js) needed for native CUA. + wcb_dir = WCB_ASSETS_DIR + plugin_dir = wcb_dir / "computer_tool_plugin" + patch_dir = wcb_dir / "openclaw_patches" + _vm_exec(env, ["bash", "-c", "mkdir -p /tmp/computer_tool_plugin /tmp/openclaw_patches"]) + for fname in ("openclaw.plugin.json", "index.ts"): + _vm_upload(env, (plugin_dir / fname).read_text(), + f"/tmp/computer_tool_plugin/{fname}") + _vm_upload(env, (patch_dir / "openai-responses-shared.patched.js").read_text(), + "/tmp/openclaw_patches/openai-responses-shared.patched.js") + _vm_upload(env, (patch_dir / "openai-responses.patched.js").read_text(), + "/tmp/openclaw_patches/openai-responses.patched.js") + _vm_upload(env, BOOTSTRAP_SH, "/tmp/openclaw_bootstrap.sh") + _vm_exec(env, ["bash", "-c", "chmod +x /tmp/openclaw_bootstrap.sh"]) + # Run synchronously via launch + wait_file (bypass 120s execute timeout) + _vm_exec(env, ["bash", "-c", "rm -f /home/user/.openclaw_bootstrap.done"]) + host_epoch = int(time.time()) + _vm_launch(env, ["bash", "-c", f"HOST_EPOCH={host_epoch} CLIENT_PASSWORD={self.client_password} /tmp/openclaw_bootstrap.sh"]) + if not _wait_file(env, "/home/user/.openclaw_bootstrap.done", timeout=1500): + log = _vm_exec(env, ["bash", "-c", "tail -100 /tmp/openclaw_bootstrap.log"]) + raise RuntimeError(f"Bootstrap timeout. Tail:\n{log.get('output')}") + # Verify openclaw binary actually got installed (bootstrap script uses + # `set -uo pipefail` not `-e`, so a silent failure could still touch the + # done flag). + verify = _vm_exec(env, ["bash", "-c", + "which openclaw && openclaw --version 2>&1 | head -3"]) + if "openclaw" not in (verify.get("output") or ""): + tail = _vm_exec(env, ["bash", "-c", "tail -80 /tmp/openclaw_bootstrap.log"]) + raise RuntimeError( + f"Bootstrap done flag set but openclaw missing.\nTail:\n{tail.get('output')}" + ) + logger.info("Openclaw bootstrap OK: %s", (verify.get("output") or "").strip()) + # WCB: also apply sandbox whitelist patch on fresh VM bootstrap path. + self._apply_sandbox_patch(env) + + # ---------------- per-task configure ---------------- + def configure(self, env) -> None: + thinking_level = os.environ.get("OSW_THINKING", "medium").strip() or "medium" + if thinking_level not in ("minimal", "low", "medium", "high", "xhigh"): + thinking_level = "xhigh" + sh = _configure_sh(self.model, self.litellm_base_url, self.litellm_api_key, + self.gui, thinking_level) + _vm_upload(env, sh, "/tmp/openclaw_configure.sh") + out = _vm_exec(env, ["bash", "-c", + "chmod +x /tmp/openclaw_configure.sh && /tmp/openclaw_configure.sh"], timeout=60) + if "CONFIGURED" not in (out.get("output") or ""): + raise RuntimeError(f"openclaw configure failed: {out}") + + # ---------------- run agent for one task ---------------- + def run(self, env, instruction: str, output_dir: Path, + system_prompt_override: str | None = None) -> dict: + """Run openclaw agent inside VM for the given instruction. + + Returns metadata dict with at least {agent_done, elapsed_seconds}. + If `system_prompt_override` is provided, it replaces the default OSWorld + system prompt entirely (useful for cross-bench drivers like + run_wildclaw_in_osworld.py that supply their own prompt scaffolding). + """ + output_dir.mkdir(parents=True, exist_ok=True) + self.bootstrap(env) + self.configure(env) + + # Prepare: clear previous chat session, write prompt + _vm_exec(env, ["bash", "-c", + "rm -f /home/user/.openclaw/agents/main/sessions/chat.jsonl /tmp/openclaw_run.done /tmp/openclaw_run.log"]) + + gui_hint = ( + "You have two equally-available tools and may use whichever fits " + "best at each step (you can also mix them freely): (1) the `bash` " + "tool for shell commands, file edits, gsettings/dconf, package " + "installs, git, etc.; (2) a NATIVE OpenAI computer-use tool that " + "lets you emit a `computer_call` (click / type / key / scroll / " + "drag / screenshot / wait) targeting on-screen pixel coordinates — " + "after every computer action a fresh full-screen screenshot is " + "returned automatically to ground your next step. Neither tool is " + "preferred; choose based on what is most direct for the current " + "sub-step. If you decide to use the computer tool and need visual " + "context, take a screenshot first. The display is `:0`. An initial " + "screenshot is at /tmp/init_screenshot.png.\n" + if self.gui else + "Use the `bash` tool to read and edit files, change configuration, " + "and run command-line utilities to satisfy the request. GUI tools " + "are not available in this run — finish the task with shell only.\n" + ) + system_prompt = ( + f"You are a helpful coding assistant working inside a personal " + f"Ubuntu 22.04 development workstation. You are signed in as the " + f"workstation owner (`user`) and have full administrative access " + f"on this machine; routine system tweaks (changing volume, " + f"terminal preferences, editing dotfiles, installing packages, " + f"editing user documents, etc.) are normal everyday tasks here. " + f"Please complete the user's request before the {self.timeout}s " + f"wall-clock budget runs out. Run commands in the foreground " + f"without leaving background services, and produce a complete, " + f"working solution in a single pass. Do not ask clarifying " + f"questions — make reasonable assumptions and proceed. Apps " + f"available include gnome-terminal, xterm, libreoffice, gimp, " + f"google-chrome, vlc, and thunderbird (default terminal: " + f"gnome-terminal). {gui_hint}\n" + f"\n" + f"=== EXECUTION POLICY (read carefully — your output is graded by an automated verifier) ===\n" + f"\n" + f"1. ACT, DON'T EXPLAIN. You are an OPERATING agent, not a tutor. " + f"You MUST accomplish the task by actually invoking tools " + f"(bash/computer/etc.). Plain-text instructions, tutorials, " + f"step-by-step explanations, comparison tables, or 'here's how " + f"you would do it' answers DO NOT count as completion. Before " + f"declaring the task done you MUST have issued at least one " + f"successful tool call that materially changes the system state " + f"the verifier will inspect (a file, a setting, a window, etc.). " + f"If the request looks like a question, treat it as a request to " + f"perform that operation on this machine.\n" + f"\n" + f"2. INFEASIBLE TASKS — REFUSE EXPLICITLY. Some requests describe " + f"actions that the named application cannot actually perform " + f"(e.g. 'change Chrome's UI language to Korean' — Chrome follows " + f"the OS locale and has no such setting; 'trim a video in GIMP' " + f"— GIMP is not a video editor; 'turn off Chrome dark mode' — " + f"Chrome inherits the system theme with no independent toggle; " + f"'change Google search results-per-page to 50' — that is a " + f"server-side Google account preference, not a Chrome setting). " + f"If, AFTER a brief good-faith investigation (read docs, check " + f"settings UI / config files), you conclude the task is " + f"genuinely impossible inside the named application, you MUST " + f"output exactly the line:\n" + f" INFEASIBLE: \n" + f"and stop without further tool calls. Do NOT fake completion " + f"by editing an unrelated setting. Hint: requests that ask to " + f"change a setting that doesn't exist in the app, or that ask " + f"one app to do another app's job, are usually infeasible.\n" + f"\n" + f"3. WEB TASKS — USE THE EXACT SITE NAMED. If the task names a " + f"specific website ('on Google Flights', 'on NFL.com', " + f"'kohls.com', 'walmart.com', 'recreation.gov', " + f"'babycenter.com', etc.), navigate to EXACTLY that domain. The " + f"verifier checks the resulting URL against the named site, so " + f"never substitute with what you think is an equivalent " + f"(delta.com is NOT Google Flights; amazon.com is NOT walmart). " + f"For 'flight from X to Y on Google Flights', go to " + f"google.com/travel/flights and submit the search so the " + f"resulting URL contains the IATA codes. If the task does not " + f"name a site, pick a sensible one and complete the search " + f"end-to-end (do not stop on the homepage).\n" + f"\n" + f"4. FILE OUTPUTS — RESPECT EXACT PATHS AND NAMES. When the task " + f"or its hint specifies a file path or name, write the result to " + f"EXACTLY that path with EXACTLY that filename (case, spaces, " + f"and extension all matter). Do not rename, do not save into a " + f"different folder, do not pick 'a similar name'. The verifier " + f"fetches the file at the literal path and reports 404 " + f"otherwise. If no path is given, save to /home/user/Desktop/ " + f"with a sensible name based on the task.\n" + f"\n" + f"5. DOCUMENT/SLIDE/SHEET FORMATTING — APPLY GLOBALLY AND " + f"PRECISELY. The verifier inspects format properties at the " + f"finest granularity (run-level font in docx, per-shape " + f"position in pptx EMUs, exact RGB color, cell-level number " + f"format in xlsx). When asked to change font/color/alignment/" + f"size/spacing: (a) apply the change to ALL matching elements " + f"unless the task explicitly limits scope; (b) use exact " + f"numeric values requested (do not round); (c) for pptx, " + f"propagate changes through master-slide AND every slide's " + f"shapes; (d) always SAVE the document after editing and close " + f"it cleanly — unsaved buffers don't count. Choose whatever " + f"approach (LibreOffice GUI, soffice headless, scripting " + f"libraries, raw XML, etc.) you judge most reliable for the " + f"specific task.\n" + f"\n" + f"=== END EXECUTION POLICY ===\n" + ) + if system_prompt_override is not None: + system_prompt = system_prompt_override + full_prompt = system_prompt + instruction + _vm_upload(env, full_prompt, "/tmp/openclaw_prompt.txt") + + display_export = "export DISPLAY=:0; " if self.gui else "" + runner_sh = rf"""#!/bin/bash +exec > /tmp/openclaw_run.log 2>&1 +{display_export}export OPENROUTER_API_KEY="{self.litellm_api_key}" +export OPENROUTER_BASE_URL="{self.litellm_base_url}" +export MY_PROXY_API_KEY="{self.litellm_api_key}" +# WCB native CUA: enable incremental previous_response_id loop in patched +# pi-ai providers (openai-responses.patched.js). Mirrors gpt54_agent.py. +export WCB_CUA_INCREMENTAL=1 +mkdir -p /tmp/openclaw && touch /tmp/openclaw/wcb_cua_debug.log && chown -R user:user /tmp/openclaw 2>/dev/null || true + +# Source task-specific env vars if provided by an external orchestrator +# (e.g. run_wildclaw_in_osworld.py uploads /tmp/openclaw_task_env.sh +# with KEY=VALUE exports for WildClaw task `Env` declarations). +if [ -f /tmp/openclaw_task_env.sh ]; then + set -a + . /tmp/openclaw_task_env.sh + set +a +fi + +# gateway in background +nohup openclaw gateway --port 18789 >/tmp/openclaw_gateway.log 2>&1 & +sleep 3 + +# step-cap watchdog: kill openclaw agent once assistant-reply count reaches {self.max_steps} +# (one step = one model forward pass, regardless of how many tool calls it carries) +( + CHAT=/home/user/.openclaw/agents/main/sessions/chat.jsonl + while sleep 5; do + if [ ! -f /tmp/openclaw_run.done ]; then + if [ -f "$CHAT" ]; then + n=$(grep -o '"role":"assistant"' "$CHAT" 2>/dev/null | wc -l) + if [ "$n" -ge {self.max_steps} ]; then + echo "MAX_STEPS_REACHED ($n assistant replies >= {self.max_steps}) — killing openclaw agent" + pkill -f 'openclaw agent' || true + echo MAX_STEPS_REACHED > /tmp/openclaw_run.steps_capped + break + fi + fi + else + break + fi + done +) & +WATCH_PID=$! + +PROMPT="$(cat /tmp/openclaw_prompt.txt)" +openclaw agent --session-id chat --timeout {self.timeout} --message "$PROMPT" 2>&1 +echo "AGENT_EXIT=$?" +kill $WATCH_PID 2>/dev/null || true +echo DONE > /tmp/openclaw_run.done +""" + _vm_upload(env, runner_sh, "/tmp/openclaw_run.sh") + _vm_exec(env, ["bash", "-c", "chmod +x /tmp/openclaw_run.sh"]) + + logger.info("Launching openclaw agent (timeout=%ds, gui=%s)...", self.timeout, self.gui) + t0 = time.perf_counter() + _vm_launch(env, ["bash", "-c", "/tmp/openclaw_run.sh"]) + + ok = _wait_file(env, "/tmp/openclaw_run.done", timeout=self.timeout + 180) + elapsed = time.perf_counter() - t0 + if not ok: + logger.warning("Agent did not finish within %ds (elapsed=%.0fs)", self.timeout, elapsed) + # try to terminate cleanly + _vm_exec(env, ["bash", "-c", "pkill -f 'openclaw agent' || true; pkill -f 'openclaw gateway' || true"]) + else: + _vm_exec(env, ["bash", "-c", "pkill -f 'openclaw gateway' || true"]) + + # Pull artifacts + _vm_fetch(env, "/tmp/openclaw_run.log", output_dir / "agent.log") + _vm_fetch(env, "/tmp/openclaw_gateway.log", output_dir / "gateway.log") + _vm_fetch(env, "/home/user/.openclaw/agents/main/sessions/chat.jsonl", + output_dir / "chat.jsonl") + try: + _vm_fetch(env, "/tmp/openclaw/wcb_cua_debug.log", output_dir / "wcb_cua_debug.log") + except Exception: + pass + # screenshot snapshot for debugging + try: + sb = env.controller.get_screenshot() + if sb: + (output_dir / "final_screenshot.png").write_bytes(sb) + except Exception: + pass + + # Per-step screenshots emitted by the computer-tool plugin into the + # shared workspace (/tmp_workspace/_screenshots/screenshot_NNNN_*.png). + # Pull every PNG back into the per-task results dir under screenshots/ + # so we can audit the GUI trajectory offline. + try: + listing = _vm_exec(env, [ + "bash", "-c", + "ls -1 /tmp_workspace/_screenshots/*.png 2>/dev/null || true", + ]) + raw_out = (listing.get("output") or "") + names = [ + line.strip() + for line in raw_out.splitlines() + if line.strip().endswith(".png") + ] + logger.info("[screenshot-fetch] found %d shots in VM (raw_len=%d)", + len(names), len(raw_out)) + if names: + shots_dir = output_dir / "screenshots" + shots_dir.mkdir(parents=True, exist_ok=True) + ok_n = 0 + for remote in names: + local = shots_dir / Path(remote).name + try: + if _vm_fetch(env, remote, local): + ok_n += 1 + except Exception as e: + logger.warning("[screenshot-fetch] %s failed: %s", remote, e) + logger.info("[screenshot-fetch] saved %d/%d to %s", + ok_n, len(names), shots_dir) + except Exception as e: + logger.warning("[screenshot-fetch] outer exception: %s", e) + + return {"agent_done": ok, "elapsed_seconds": round(elapsed, 2)} diff --git a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md new file mode 100644 index 0000000..f52170f --- /dev/null +++ b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md @@ -0,0 +1,103 @@ +# OSWorld-V2 — codex hybrid (GUI+CLI, gpt-5.5 xhigh) 结果分析 + +- Run 目录: `results/osworld_v2_inject/FULL_108_gpt55_codex_xhigh_20260628_125852/` +- 方案: codex CLI 注入 + GUI 通道 (`gui=True`, `--agent_harness codex`) +- 模型: gpt-5.5, reasoning effort = xhigh +- 单题超时墙: 5400s (90 分钟) +- 权威逐题分数: `pyautogui/screenshot/gpt-5.5/tasks/*/score.json` (108 题齐全) + - 注意: run 目录下零散的 `summary_*.json` 只是分批补跑片段, 不要用来汇总。 + +## 总分(两种口径) + +| 口径 | 题数 | 平均分(含部分分) | 严格通过率(score=1.0) | +|---|---|---|---| +| 全部原始 | 108 | 49.64% | 18.52% (20/108) | +| **去 infra bug(推荐)** | **104** | **51.39%** | **19.23% (20/104)** | + +严格通过数始终是 20 — 被剔的 4 题本就没有满分, 剔除只缩小分母。 + +## 108 题按结束状态分三类 + +- `agent_done=True` : 98 题 — 正常结束(codex 自报完成, 含做对/做错/做歪) +- `agent_done=False`: 7 题 — 撞 90 分钟超时墙被强杀 +- `agent_done=None` : 3 题 — 中途异常, 未走到收尾(023 / 064 / 082) + +`agent_done` 维度 ≠ 是否剔除维度。是否剔除只看失败根因是不是 infra。 + +## 剔除的 4 个 infra bug(与模型能力无关) + +| task | agent_done | 原因 | 类型 | +|---|---|---|---| +| 063 | False | VM ext4 journal error, 根分区被 remount 只读 | 磁盘故障 | +| 064 | None | Connection broken / IncompleteRead(执行阶段) | 网络中断 | +| 082 | None | task setup 阶段 Connection reset by peer | 网络中断 | +| 069 | True | multiphase_unsupported_in_inject_runner(框架不支持多阶段评分) | 评测框架限制 | + +069 虽然 agent_done=True、481s 正常结束, 但评分阶段被框架限制判失败 → 算 infra, 剔除。 +所以剔除的 4 个里 3 个来自异常/超时组, 1 个(069)来自 done=True 组 — 不能简单"把没 done 的都剔掉"。 + +## 保留在分母的边界情况 + +### 6 个超时题(agent_done=False, 撞墙也计分) +| task | score | 备注 | +|---|---|---| +| 030 | 0.20 | GNN 训练任务, 单 turn 55万 token, **实际跑完输出 Done**, 只是太慢撞墙 | +| 054 | 0.54 | GIMP 群照合成, **实际完成并导出图片**, 太慢撞墙 | +| 060 | 0.10 | pptx 排期, GUI 操作慢 | +| 048 | 0.00 | 打游戏"Standlone", 231万 token 空转 + MCP 反复重连, codex 无法收敛 | +| 058 | 0.00 | 笔记本开合动画, 早早卡住空等 | +| 061 | 0.00 | 图像风格迁移, 卡在 python 脚本不返回 | + +判断: 030/054 是"慢但成功", 048/058/060/061 是模型自身收敛不了 → 都算真实表现, 保留。 +只有 063 这一个超时是真·环境故障, 已归入 infra 剔除。 + +### 023(agent_done=None, 但是成功题, 计 0.7) +- agent 做完了, `env.evaluate()` 成功打分 0.7(5/10 检查点)。 +- 故障发生在**评分之后**的产物下载阶段(IncompleteRead), 网络错误把 0.7 覆盖成了 0。 +- 已按 EVAL 日志人工修正回 0.7(见 023/score.json 的 note 字段)。 +- 与 064/082 的区别: 064/082 故障在执行/setup 阶段导致任务没跑完(真失败); 023 任务和打分都成功, 只是事后取产物失败 → 不剔, 计 0.7。 + +## 横向对比(同 benchmark, gpt-5.5) + +| 方案 | 平均分 | 严格通过率 | +|---|---|---| +| **codex hybrid (GUI+CLI, 去infra 104题)** | **51.39%** | **19.23%** | +| codex hybrid (GUI+CLI, 原始 108题) | 49.64% | 18.52% | +| codex 纯 CLI (gui=False) | 34.18% | 11.11% | +| openclaw GUI+CLI hybrid | 40.52% | 12.04% | +| claude CLI max | 40.93% | 12.04% | + +codex 混合方案是各方案最高; GUI+CLI 比纯 CLI 高约 15 个点。 + +## 效率统计表(per-task, 全 108 题口径) + +| Model | Binary (%) | Partial (%) | Cost/task | Tool calls/task | Out tok/task | Steps/task | +|---|---|---|---|---|---|---| +| codex hybrid (GUI+CLI) | 18.5 | 59.3 | — | 68 (med) | — | 1 turn (med) | +| codex 纯 CLI | 11.1 | 49.1 | — | — | — | 2 turn (med) | +| claude CLI max | 12.0 | 60.2 | $39.71 (med) | — | 11,657 (med) | 23 (med) | +| openclaw / cuaclaw hybrid | 12.0 | 62.0 | — | — | — | — | +| 官方纯 GUI (gpt-5.5, 500步) | 0.0 | — | — | — | — | 117.5 (med) | + +> 注: Binary = score=1.0 占比; Partial = 0 均为全 108 题口径; codex hybrid 去 infra(104 题)口径 Binary=19.2%。 + +### 字段口径与可得性(各 harness 采集不一致, 不能直接比绝对值) + +- **codex (hybrid / 纯CLI)**: 来自 in-VM `codex` CLI 的 agent.log。 + - `Tool calls/task` = `hybrid_codex_action_mix.json` 的 CLI+GUI 动作总数 (median 68, mean 77.5)。 + - `Steps/task` 用 codex turn 数代替 (hybrid median 1, 纯CLI median 2) — codex 单 turn 内含多次工具调用, 与 claude 的 turn 不可比。 + - **Cost 无法计**: codex CLI 只在结尾打印 "tokens used" 总量(含 input+reasoning+output, median ~558k tok/task), 不拆 output, 也无单价 → Out tok/task、Cost/task 留空。 +- **claude CLI max**: 来自 `claude_stream.jsonl` 的 result 事件, 字段最全。 + - Cost/task = `total_cost_usd` (median $39.71, mean $56.95)。 + - Out tok/task = `usage.output_tokens` (median 11,657, mean 15,529)。 + - Steps/task = `num_turns` (median 23, mean 26.7)。 + - 101/108 题有 usage(7 题异常无 result 事件)。 +- **openclaw / cuaclaw hybrid**: 分数取自 `check/hybrid_*_time.json`; per-task token/cost/toolcall 未单独采集 → 留空。 +- **官方纯 GUI**: 取自 xlangai 官方 trajectory 包(results_gpt5.5_500steps), 只有 step/timing, 无分数对齐到本表评测器 → Binary 显示 0 是因该来源未含本地 native 分, 仅 Steps(median 117.5)可用作 GUI 步数参考。 + +### 关键对比解读 + +- **codex hybrid 用极少的"轮次"达到最高分**: median 1 个 codex turn(单 turn 内自主多步), 而 claude 需 median 23 turn — codex 把多步操作压在一次长链路里。 +- **codex token 消耗大**: median ~558k tok/task(含 input 累积), 反映 xhigh + 单 turn 长上下文; claude output 只有 ~12k(但 input 达 ~1.97M, cost $39.71/task)。 +- Cost 维度只有 claude 有权威数字($39.71/task median); codex 需用 LiteLLM 侧用量日志另算, 当前 run 未落盘。 diff --git a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/action_mix.csv b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/action_mix.csv new file mode 100644 index 0000000..696af25 --- /dev/null +++ b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/action_mix.csv @@ -0,0 +1,105 @@ +task_id,cli,gui,total,gui_pct +001,127,18,145,12.4 +002,39,11,50,22.0 +003,38,5,43,11.6 +004,71,10,81,12.3 +005,40,2,42,4.8 +006,96,4,100,4.0 +007,106,19,125,15.2 +008,123,6,129,4.7 +009,32,2,34,5.9 +010,80,3,83,3.6 +011,94,3,97,3.1 +012,120,0,120,0.0 +013,27,3,30,10.0 +014,45,3,48,6.2 +015,73,1,74,1.4 +016,79,1,80,1.2 +017,17,3,20,15.0 +018,72,6,78,7.7 +019,38,7,45,15.6 +020,38,5,43,11.6 +021,47,4,51,7.8 +022,113,7,120,5.8 +023,24,0,24,0.0 +024,76,3,79,3.8 +025,51,0,51,0.0 +026,103,16,119,13.4 +027,95,4,99,4.0 +028,26,3,29,10.3 +029,30,3,33,9.1 +030,108,1,109,0.9 +031,56,10,66,15.2 +032,87,10,97,10.3 +033,158,14,172,8.1 +034,50,3,53,5.7 +035,52,13,65,20.0 +036,141,15,156,9.6 +037,152,7,159,4.4 +038,42,3,45,6.7 +039,159,5,164,3.0 +040,28,0,28,0.0 +041,64,9,73,12.3 +042,87,3,90,3.3 +043,40,3,43,7.0 +044,57,7,64,10.9 +045,25,4,29,13.8 +046,72,4,76,5.3 +047,122,40,162,24.7 +048,129,70,199,35.2 +049,32,7,39,17.9 +050,113,24,137,17.5 +051,42,2,44,4.5 +052,16,5,21,23.8 +053,15,0,15,0.0 +054,52,2,54,3.7 +055,56,0,56,0.0 +056,51,0,51,0.0 +057,78,8,86,9.3 +059,54,15,69,21.7 +061,10,0,10,0.0 +062,128,6,134,4.5 +063,28,0,28,0.0 +065,33,2,35,5.7 +066,23,0,23,0.0 +067,81,5,86,5.8 +068,36,22,58,37.9 +069,21,3,24,12.5 +070,19,1,20,5.0 +071,144,2,146,1.4 +072,23,15,38,39.5 +073,23,3,26,11.5 +074,83,7,90,7.8 +075,75,20,95,21.1 +076,51,22,73,30.1 +077,72,17,89,19.1 +078,17,6,23,26.1 +079,53,0,53,0.0 +080,53,22,75,29.3 +081,95,15,110,13.6 +083,167,28,195,14.4 +084,88,13,101,12.9 +085,49,4,53,7.5 +086,71,2,73,2.7 +087,36,10,46,21.7 +088,130,30,160,18.8 +089,80,10,90,11.1 +090,55,5,60,8.3 +091,56,9,65,13.8 +092,66,17,83,20.5 +093,66,28,94,29.8 +094,69,7,76,9.2 +095,11,1,12,8.3 +096,46,0,46,0.0 +097,67,0,67,0.0 +098,44,6,50,12.0 +099,59,21,80,26.2 +100,38,49,87,56.3 +101,80,31,111,27.9 +102,41,2,43,4.7 +103,14,11,25,44.0 +104,21,0,21,0.0 +105,59,2,61,3.3 +106,95,0,95,0.0 +107,143,18,161,11.2 +108,326,52,378,13.8 diff --git a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/per_task_scores.json b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/per_task_scores.json new file mode 100644 index 0000000..a112b3c --- /dev/null +++ b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/per_task_scores.json @@ -0,0 +1,1190 @@ +[ + { + "task_id": "001", + "score": 1.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 1859.07, + "total_tokens": 742177, + "tool_calls": 145, + "gui_calls": 18, + "cli_calls": 127 + }, + { + "task_id": "002", + "score": 0.75, + "agent_done": true, + "error": null, + "elapsed_seconds": 891.87, + "total_tokens": 477538, + "tool_calls": 50, + "gui_calls": 11, + "cli_calls": 39 + }, + { + "task_id": "003", + "score": 1.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 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"tool_calls": 86, + "gui_calls": 5, + "cli_calls": 81 + }, + { + "task_id": "068", + "score": 1.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 1643.91, + "total_tokens": 631757, + "tool_calls": 58, + "gui_calls": 22, + "cli_calls": 36 + }, + { + "task_id": "069", + "score": 0.175, + "agent_done": true, + "error": "multiphase_unsupported_in_inject_runner", + "elapsed_seconds": 481.02, + "total_tokens": 135623, + "tool_calls": 24, + "gui_calls": 3, + "cli_calls": 21 + }, + { + "task_id": "070", + "score": 1.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 305.65, + "total_tokens": 130499, + "tool_calls": 20, + "gui_calls": 1, + "cli_calls": 19 + }, + { + "task_id": "071", + "score": 0.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 2545.39, + "total_tokens": 698330, + "tool_calls": 146, + "gui_calls": 2, + "cli_calls": 144 + }, + { + "task_id": "072", + "score": 0.85, + "agent_done": true, + "error": null, + "elapsed_seconds": 786.77, + "total_tokens": 286266, + "tool_calls": 38, + "gui_calls": 15, + "cli_calls": 23 + }, + { + "task_id": "073", + "score": 1.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 731.57, + "total_tokens": 287332, + "tool_calls": 26, + "gui_calls": 3, + "cli_calls": 23 + }, + { + "task_id": "074", + "score": 0.44999999999999996, + "agent_done": true, + "error": null, + "elapsed_seconds": 1302.61, + "total_tokens": 667815, + "tool_calls": 90, + "gui_calls": 7, + "cli_calls": 83 + }, + { + "task_id": "075", + "score": 0.9999999999999999, + "agent_done": true, + "error": null, + "elapsed_seconds": 1493.39, + "total_tokens": 1051420, + "tool_calls": 95, + "gui_calls": 20, + "cli_calls": 75 + }, + { + "task_id": "076", + "score": 0.9, + "agent_done": true, + "error": null, + "elapsed_seconds": 2064.56, + "total_tokens": 764135, + "tool_calls": 73, + "gui_calls": 22, + "cli_calls": 51 + }, + { + "task_id": "077", + "score": 0.8240000000000001, + "agent_done": true, + "error": null, + "elapsed_seconds": 2019.06, + "total_tokens": 938152, + "tool_calls": 89, + "gui_calls": 17, + "cli_calls": 72 + }, + { + "task_id": "078", + "score": 0.7, + "agent_done": true, + "error": null, + "elapsed_seconds": 741.72, + "total_tokens": 281876, + "tool_calls": 23, + "gui_calls": 6, + "cli_calls": 17 + }, + { + "task_id": "079", + "score": 0.475, + "agent_done": true, + "error": null, + "elapsed_seconds": 1899.14, + "total_tokens": 1226762, + "tool_calls": 53, + "gui_calls": 0, + "cli_calls": 53 + }, + { + "task_id": "080", + "score": 0.5257442836293407, + "agent_done": true, + "error": null, + "elapsed_seconds": 1342.92, + "total_tokens": 629977, + "tool_calls": 75, + "gui_calls": 22, + "cli_calls": 53 + }, + { + "task_id": "081", + "score": 0.7, + "agent_done": true, + "error": null, + "elapsed_seconds": 1112.4, + "total_tokens": 609387, + "tool_calls": 110, + "gui_calls": 15, + "cli_calls": 95 + }, + { + "task_id": "082", + "score": 0.0, + "agent_done": null, + "error": " | run_one_exc: Custom task setup failed: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))", + "elapsed_seconds": null, + "total_tokens": null, + "tool_calls": null, + "gui_calls": null, + "cli_calls": null + }, + { + "task_id": "083", + "score": 0.8400000000000002, + "agent_done": true, + "error": null, + "elapsed_seconds": 2350.32, + "total_tokens": 1129521, + "tool_calls": 195, + "gui_calls": 28, + "cli_calls": 167 + }, + { + "task_id": "084", + "score": 0.6575058504737802, + "agent_done": true, + "error": null, + "elapsed_seconds": 1824.07, + "total_tokens": 547785, + "tool_calls": 101, + "gui_calls": 13, + "cli_calls": 88 + }, + { + "task_id": "085", + "score": 0.7527, + "agent_done": true, + "error": null, + "elapsed_seconds": 876.83, + "total_tokens": 294978, + "tool_calls": 53, + "gui_calls": 4, + "cli_calls": 49 + }, + { + "task_id": "086", + "score": 0.2, + "agent_done": true, + "error": null, + "elapsed_seconds": 1307.96, + "total_tokens": 548586, + "tool_calls": 73, + "gui_calls": 2, + "cli_calls": 71 + }, + { + "task_id": "087", + "score": 0.48, + "agent_done": true, + "error": null, + "elapsed_seconds": 1473.3, + "total_tokens": 1282349, + "tool_calls": 46, + "gui_calls": 10, + "cli_calls": 36 + }, + { + "task_id": "088", + "score": 0.22999999999999998, + "agent_done": true, + "error": null, + "elapsed_seconds": 2280.07, + "total_tokens": 863781, + "tool_calls": 160, + "gui_calls": 30, + "cli_calls": 130 + }, + { + "task_id": "089", + "score": 0.05, + "agent_done": true, + "error": null, + "elapsed_seconds": 982.07, + "total_tokens": 424454, + "tool_calls": 90, + "gui_calls": 10, + "cli_calls": 80 + }, + { + "task_id": "090", + "score": 0.3366666666666667, + "agent_done": true, + "error": null, + "elapsed_seconds": 2034.36, + "total_tokens": 2234115, + "tool_calls": 60, + "gui_calls": 5, + "cli_calls": 55 + }, + { + "task_id": "091", + "score": 0.7978593486805167, + "agent_done": true, + "error": null, + "elapsed_seconds": 1022.25, + "total_tokens": 322234, + "tool_calls": 65, + "gui_calls": 9, + "cli_calls": 56 + }, + { + "task_id": "092", + "score": 0.8, + "agent_done": true, + "error": null, + "elapsed_seconds": 1227.74, + "total_tokens": 483229, + "tool_calls": 83, + "gui_calls": 17, + "cli_calls": 66 + }, + { + "task_id": "093", + "score": 0.85, + "agent_done": true, + "error": null, + "elapsed_seconds": 1648.43, + "total_tokens": 807378, + "tool_calls": 94, + "gui_calls": 28, + "cli_calls": 66 + }, + { + "task_id": "094", + "score": 0.5, + "agent_done": true, + "error": null, + "elapsed_seconds": 1914.28, + "total_tokens": 1951118, + "tool_calls": 76, + "gui_calls": 7, + "cli_calls": 69 + }, + { + "task_id": "095", + "score": 0.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 105.51, + "total_tokens": 30267, + "tool_calls": 12, + "gui_calls": 1, + "cli_calls": 11 + }, + { + "task_id": "096", + "score": 1.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 541.21, + "total_tokens": 191302, + "tool_calls": 46, + "gui_calls": 0, + "cli_calls": 46 + }, + { + "task_id": "097", + "score": 0.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 686.61, + "total_tokens": 406604, + "tool_calls": 67, + "gui_calls": 0, + "cli_calls": 67 + }, + { + "task_id": "098", + "score": 0.6296296296296297, + "agent_done": true, + "error": null, + "elapsed_seconds": 621.36, + "total_tokens": 442241, + "tool_calls": 50, + "gui_calls": 6, + "cli_calls": 44 + }, + { + "task_id": "099", + "score": 0.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 1403.17, + "total_tokens": 552774, + "tool_calls": 80, + "gui_calls": 21, + "cli_calls": 59 + }, + { + "task_id": "100", + "score": 1.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 2294.77, + "total_tokens": 1478723, + "tool_calls": 87, + "gui_calls": 49, + "cli_calls": 38 + }, + { + "task_id": "101", + "score": 0.25, + "agent_done": true, + "error": null, + "elapsed_seconds": 2466.35, + "total_tokens": 1848376, + "tool_calls": 111, + "gui_calls": 31, + "cli_calls": 80 + }, + { + "task_id": "102", + "score": 0.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 631.4, + "total_tokens": 250907, + "tool_calls": 43, + "gui_calls": 2, + "cli_calls": 41 + }, + { + "task_id": "103", + "score": 0.35, + "agent_done": true, + "error": null, + "elapsed_seconds": 456.08, + "total_tokens": 152428, + "tool_calls": 25, + "gui_calls": 11, + "cli_calls": 14 + }, + { + "task_id": "104", + "score": 0.7522178627576903, + "agent_done": true, + "error": null, + "elapsed_seconds": 476.07, + "total_tokens": 241894, + "tool_calls": 21, + "gui_calls": 0, + "cli_calls": 21 + }, + { + "task_id": "105", + "score": 0.9763, + "agent_done": true, + "error": null, + "elapsed_seconds": 2019.28, + "total_tokens": 917079, + "tool_calls": 61, + "gui_calls": 2, + "cli_calls": 59 + }, + { + "task_id": "106", + "score": 0.35, + "agent_done": true, + "error": null, + "elapsed_seconds": 2581.25, + "total_tokens": 899467, + "tool_calls": 95, + "gui_calls": 0, + "cli_calls": 95 + }, + { + "task_id": "107", + "score": 1.0, + "agent_done": true, + "error": null, + "elapsed_seconds": 2124.86, + "total_tokens": 1214014, + "tool_calls": 161, + "gui_calls": 18, + "cli_calls": 143 + }, + { + "task_id": "108", + "score": 0.439655, + "agent_done": true, + "error": null, + "elapsed_seconds": 5447.53, + "total_tokens": 2870510, + "tool_calls": 378, + "gui_calls": 52, + "cli_calls": 326 + } +] \ No newline at end of file diff --git a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/summary.json b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/summary.json new file mode 100644 index 0000000..40cd0d0 --- /dev/null +++ b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/summary.json @@ -0,0 +1,50 @@ +{ + "run": "FULL_108_gpt55_codex_xhigh_20260628_125852", + "model": "gpt-5.5", + "harness": "codex CLI injected into VM + GUI channel (hybrid)", + "reasoning_effort": "xhigh", + "scoring": "native OSWorld env.evaluate() (paper-faithful)", + "cohorts": { + "all_108": { + "n": 108, + "binary_pct": 18.52, + "partial_pct": 59.26, + "avg_score_pct": 49.64, + "binary_count": 20, + "partial_count": 64 + }, + "de_infra_104": { + "n": 104, + "binary_pct": 19.23, + "partial_pct": 60.58, + "avg_score_pct": 51.39, + "binary_count": 20, + "partial_count": 63 + } + }, + "efficiency": { + "tool_calls_per_task": { + "mean": 77.5, + "median": 68.0 + }, + "gui_calls_per_task": { + "mean": 9.2, + "median": 5.0 + }, + "cli_calls_per_task": { + "mean": 68.3, + "median": 56.0 + }, + "total_tokens_per_task_incl_input": { + "mean": 696058.9, + "median": 558376, + "note": "codex CLI logs only a combined token total (input+reasoning+output); output-only tokens and cost were not recorded -> reported as '-' in tables" + } + }, + "excluded_infra_tasks": { + "063": "VM ext4 journal error -> root remounted read-only", + "064": "network: Connection broken / IncompleteRead during execution", + "082": "network: Connection reset by peer during task setup", + "069": "framework: multiphase_unsupported_in_inject_runner" + } +} \ No newline at end of file diff --git a/experiments/osworld_v2_hybrid/run_osworld_v2_inject.py b/experiments/osworld_v2_hybrid/run_osworld_v2_inject.py new file mode 100644 index 0000000..b109ebc --- /dev/null +++ b/experiments/osworld_v2_hybrid/run_osworld_v2_inject.py @@ -0,0 +1,536 @@ +"""Run OSWorld-V2 (108 tasks) with an *injected* cuaclaw/openclaw agent. + +Everything except the agent is OSWorld-V2 NATIVE (paper-faithful): + - task loading : task_class/task_*.py via load_task_config(eval_version=v2) + - setup : env.reset(task_config=task) → task.setup() inside the VM + - final-state : env.evaluate() → task.evaluate(env), native + - persistence : lib_run_single._persist_evaluation_result → result.txt/json + +The ONLY non-native part is the agent: instead of the upstream predict/step +loop, we INJECT the whole cuaclaw CLI into the VM (WeaveBench "hybrid" style) +and let it drive the desktop autonomously (GUI __computer__ + bash CLI), talking +to gpt-5.5 via the LiteLLM proxy on the host. This is the OSWorld-V2 port of +WeaveBench/experiments/osworld_hybrid. + +Per-task pipeline: + 1. env.reset(task_config=task) # native VM revert + task.setup() + 2. agent.bootstrap(env)/configure(env) # inject 491MB openclaw + plugin + LLM cfg + 3. /tmp_workspace mkdir (agent scratch) + 4. agent.run(env, instruction) # cuaclaw runs the whole task in-VM + 5. env.evaluate() # NATIVE final-state check (0..1) + 6. _persist_evaluation_result # result.txt / result.json + +Output layout (parallel to upstream run_multienv): + /pyautogui/screenshot//tasks// + result.txt # native score (leaderboard parity) + result.json # native dict (if evaluator returns dict) + score.json # this runner's per-task record + chat.jsonl agent.log final_screenshot.png ... +""" +from __future__ import annotations + +import argparse +import json +import logging +import os +import sys +import time +import traceback +from datetime import datetime +from multiprocessing import Manager, Process +from pathlib import Path +from typing import List + +# OSWorld-V2 repo root (experiments/osworld_v2_inject/ -> repo root is parents[2]) +REPO_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO_ROOT)) +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +from desktop_env.desktop_env import DesktopEnv # noqa: E402 +from task_loader import load_task_config # noqa: E402 +import lib_run_single # noqa: E402 + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + datefmt="%H:%M:%S", +) +logger = logging.getLogger("osw2_inject") + + +# --------------------------------------------------------------------------- +# Task discovery: read evaluation_examples/test_v2.json -> ["001", ...] +# --------------------------------------------------------------------------- +def discover_v2_tasks(osworld_root: Path, meta_path: str, + task_filter: str = "") -> List[str]: + mp = Path(meta_path) + if not mp.is_absolute(): + mp = osworld_root / meta_path + meta = json.loads(mp.read_text(encoding="utf-8")) + ids = meta.get("tasks", []) if isinstance(meta, dict) else list(meta) + if task_filter: + ids = [t for t in ids if task_filter in t] + return list(ids) + + +def _task_instruction(task) -> str: + # BaseTask is a dict subclass; instruction is both attr and key. + try: + return task.get("instruction") or getattr(task, "instruction", "") or "" + except Exception: + return getattr(task, "instruction", "") or "" + + +def _is_multiphase(task) -> bool: + fn = getattr(task, "get_phases", None) + return callable(fn) + + +# --------------------------------------------------------------------------- +# System prompt — hybrid (GUI __computer__ + bash CLI). No spoilers. +# --------------------------------------------------------------------------- +def build_system_prompt(instruction: str, client_password: str, gui: bool = True) -> str: + if gui: + head = ( + f"You are operating an Ubuntu 22.04 desktop workstation as user `user` " + f"with full sudo access (password: {client_password}). You have two " + f"equally-available tools and may mix them freely: (1) the `bash` tool " + f"for shell commands, file edits, gsettings/dconf, package installs, " + f"git, etc.; (2) `__computer__` (click/type/key/scroll/drag/screenshot/" + f"wait by on-screen pixel coordinates; each call auto-returns a fresh " + f"full-screen screenshot to ground your next step). Neither tool is " + f"preferred — pick what is most direct for each sub-step. The display is " + f"`:0`; an initial screenshot is at /tmp/init_screenshot.png. For tasks " + f"that involve a web page or web app, the relevant page is ALREADY OPEN " + f"in a tab of the on-screen Chrome (it may be a remote site, a local " + f"http://localhost service, or a file:// page) — and for stateful sites " + f"that tab already carries the right session. That Chrome also exposes a " + f"DevTools endpoint on localhost:1337 (and :9222). Do your web work in " + f"THAT same Chrome instance and its already-open tab — clicking via " + f"`__computer__`, driving it over CDP, or scripting it are all fine; the " + f"only requirement is that the change lands in that one Chrome tab/" + f"session. Do NOT open a separate or headless browser, do NOT start a " + f"fresh session, and do NOT hit the site with bare requests/curl that " + f"bypass that tab's session — the verifier inspects the state of that " + f"one already-open Chrome tab, so anything done elsewhere is invisible " + f"to it. If you genuinely need a page that is not open yet, navigate to " + f"it inside that same existing Chrome tab rather than spawning a new " + f"browser.\n\n") + else: + head = ( + f"You are operating an Ubuntu 22.04 desktop workstation as user `user` " + f"with full sudo access (password: {client_password}) over a COMMAND-LINE " + f"ONLY interface. You have exactly one tool: `bash` (shell commands, file " + f"edits, gsettings/dconf, package installs, git, headless office " + f"conversions, etc.). There is NO GUI, NO mouse/keyboard control, and NO " + f"screenshots — do everything via the shell. There is a desktop X server " + f"on `:0` if a task strictly needs it, but you cannot click; drive any " + f"app through its CLI/scripting interface. For web tasks, there is an " + f"already-open Chrome with a DevTools endpoint on localhost:1337 (and " + f":9222) carrying the right session — drive it over CDP from bash " + f"(curl/websocket to the DevTools API), not a separate or headless " + f"browser, so the change lands in that one tab the verifier inspects.\n\n") + return ( + head + + "The task is graded by an automated verifier that inspects the FINAL " + "STATE of files, application data, and processes — plain-text answers, " + "explanations, or 'here's how you would do it' DO NOT count. Before " + "declaring done you must have actually changed the system state the " + "verifier will inspect.\n\n" + "=== EXECUTION POLICY (read carefully) ===\n" + "1. ACT, DON'T EXPLAIN. You are an operating agent, not a tutor. " + "Accomplish the task by actually invoking tools; a tutorial/table/" + "explanation is an automatic 0.\n" + "2. FILE OUTPUTS — EXACT PATHS AND NAMES. When a path or filename is " + "specified, write to EXACTLY that path with EXACTLY that name (case, " + "spaces, extension all matter). Do not rename, do not save a 'similar' " + "name, do not save a *_filled / *_copy variant — the verifier fetches " + "the literal path and 404s otherwise. Overwrite the original file in " + "place when asked to edit it. If no path is given, save to " + "/home/user/Desktop/ with a sensible name.\n" + "3. FORMATTING — APPLY GLOBALLY AND PRECISELY. The verifier inspects " + "format at the finest granularity (run-level font in docx, per-shape " + "position/EMU in pptx, exact RGB, cell-level number format in xlsx). " + "(a) apply the change to ALL matching elements unless scope is limited; " + "(b) use exact numeric values (do not round); (c) always SAVE and " + "cleanly close the document — unsaved buffers don't count.\n" + "4. WEB TASKS — USE THE EXACT SITE NAMED. Navigate to exactly the domain " + "named in the task; the verifier checks the resulting state/URL. Never " + "substitute a site you think is equivalent. Complete the flow end-to-end " + "(don't stop on the homepage), and do it in the one already-open Chrome " + "(GUI: click it; CLI: drive it over CDP) so the state lands where the " + "verifier reads it.\n" + "5. NO FABRICATION. Do not fake completion: do not copy a ground-truth/" + "reference file to pose as your output, do not hand-write a stub project " + "file (e.g. a fake .mlt/.rpp/.json) that you didn't genuinely produce, " + "and do not paint placeholder images with PIL/fpdf/matplotlib to satisfy " + "a file-existence/screenshot check. If a deliverable is genuinely " + "uncapturable, skip it and briefly say why — accept the points loss " + "rather than faking it. The verifier deep-inspects content and will cap " + "fabricated work at 0.\n" + "6. INFEASIBLE — REFUSE EXPLICITLY. If after honest investigation the " + "task is truly impossible in the named application, output a final " + "message starting with `FAIL` and one sentence why; do not fake success " + "on an unrelated setting.\n" + "=== END EXECUTION POLICY ===\n\n" + f"=== TASK ===\n{instruction}\n" + ) + + +def grow_vm_rootfs(env, client_password: str, task_id: str) -> None: + """Expand the guest root partition to fill the (resized) virtual disk. + + The OSWorld-V2 qcow2 ships a 29.5G sda3 inside a 70G disk (after our + `qemu-img resize +20G`), leaving the rootfs ~1.2G free — too small for the + 490MB openclaw tarball + its Node22/openclaw unpack. The qcow2 is bind- + mounted read-only, so every fresh VM starts from the un-grown partition and + guest writes land in QEMU's throwaway overlay. Hence we grow sda3 ONCE per + VM boot, right after reset and before injection. Idempotent: skipped if the + rootfs already has comfortable headroom. + """ + import requests + url = f"http://{env.vm_ip}:{env.server_port}/setup/execute" + + def _exec(cmd: str, timeout: int = 90) -> str: + r = requests.post(url, json={"command": ["bash", "-c", cmd], + "shell": False}, timeout=timeout) + r.raise_for_status() + return (r.json() or {}).get("output", "") or "" + + try: + avail = _exec("df -BG --output=avail / | tail -1 | tr -dc '0-9'") + avail_g = int(avail.strip() or "0") + if avail_g >= 8: + logger.info("[%s] rootfs has %dG free — skip growpart", task_id, avail_g) + return + except Exception as exc: + logger.warning("[%s] disk check failed (continue to growpart): %s", + task_id, exc) + + grow = ( + f"echo '{client_password}' | sudo -S -p '' bash -c '" + "echo \", +\" | sfdisk --no-reread -N 3 /dev/sda >/dev/null 2>&1; " + "partprobe /dev/sda >/dev/null 2>&1 || true; " + "resize2fs /dev/sda3 >/dev/null 2>&1'" + ) + try: + _exec(grow, timeout=120) + after = _exec("df -h / | tail -1") + logger.info("[%s] growpart done -> %s", task_id, after.strip()) + except Exception as exc: + logger.warning("[%s] growpart failed (injection may run out of disk): %s", + task_id, exc) + + +def quiesce_pkg_daemons(env, client_password: str, task_id: str) -> None: + """Stop background package daemons BEFORE task.setup() runs. + + OSWorld-V2's image runs packagekitd / unattended-upgrades, which hold the + apt/dpkg lock right after boot. Several tasks' own setup scripts pip/apt- + install dependencies (e.g. task_022 installs fpdf2); those collide with the + daemon and fail ("Could not get lock"), leaving the task env incomplete. + The VM is already up after DesktopEnv.__init__, so we can do this before + env.reset() kicks off task.setup(). + """ + import requests + try: + requests.post( + f"http://{env.vm_ip}:{env.server_port}/setup/execute", + json={"command": ["bash", "-c", + f"echo '{client_password}' | sudo -S -p '' bash -c '" + "systemctl stop packagekit.service unattended-upgrades.service " + "apt-daily.service apt-daily-upgrade.service 2>/dev/null; " + "pkill -9 -f packagekitd 2>/dev/null; " + "pkill -9 -f unattended-upgrade 2>/dev/null; " + "dpkg --configure -a 2>/dev/null; true'"], "shell": False}, + timeout=60) + logger.info("[%s] package daemons quiesced (pre-setup)", task_id) + except Exception as exc: + logger.warning("[%s] quiesce_pkg_daemons failed (non-fatal): %s", + task_id, exc) + + +# --------------------------------------------------------------------------- +# run_one — single task, native env + injected agent +# --------------------------------------------------------------------------- +def run_one(env, agent, task_id: str, task, out_dir: Path, + args) -> dict: + out_dir.mkdir(parents=True, exist_ok=True) + instruction = _task_instruction(task) + record = { + "task_id": task_id, + "model": agent.model, + "started_at": datetime.utcnow().isoformat(), + "score": 0.0, + "error": None, + "multiphase": _is_multiphase(task), + } + + if _is_multiphase(task): + # Native multi-phase tasks interleave per-phase setup/evaluate with the + # agent loop — our single injected run can't honor phase gating. Flag it + # so we can handle/triage separately rather than silently mis-scoring. + record["error"] = "multiphase_unsupported_in_inject_runner" + logger.warning("[%s] multi-phase task — inject runner runs phase-1 setup " + "only; native evaluate() scores phase 1.", task_id) + + try: + # 0. Quiesce package daemons BEFORE reset so task.setup()'s own pip/apt + # installs (e.g. task_022 fpdf2) don't collide with the apt lock. + quiesce_pkg_daemons(env, agent.client_password, task_id) + + # 1. NATIVE reset: revert VM + run task.setup() inside the VM. + env.reset(task_config=task) + logger.info("[%s] env.reset OK", task_id) + time.sleep(args.post_reset_sleep) + + # 1b. Grow rootfs to fit the injected agent (qcow2 ships a 29G part in a + # 70G disk; ro mount means we re-grow every boot). + grow_vm_rootfs(env, agent.client_password, task_id) + + # 2. Inject cuaclaw into the VM. agent.run() calls bootstrap()+configure() + # internally (both idempotent), so we don't pre-call them here. + + # 3. Agent scratch dir (the computer-tool plugin writes screenshots here). + try: + import requests + requests.post( + f"http://{env.vm_ip}:{env.server_port}/setup/execute", + json={"command": [ + "bash", "-c", + f"echo '{agent.client_password}' | sudo -S -p '' bash -c " + f"'mkdir -p /tmp_workspace/results /tmp_workspace/tmp " + f"/tmp_workspace/_screenshots && chown -R user:user /tmp_workspace'" + ], "shell": False}, timeout=60) + except Exception as exc: + logger.warning("[%s] /tmp_workspace mkdir failed (non-fatal): %s", + task_id, exc) + + # 4. Run the injected agent (self-contained multi-step loop in the VM). + sys_prompt = build_system_prompt(instruction, agent.client_password, + gui=bool(getattr(args, "agent_gui", True))) + meta = agent.run(env, instruction, out_dir, + system_prompt_override=sys_prompt) + record["agent_done"] = meta.get("agent_done") + record["elapsed_seconds"] = meta.get("elapsed_seconds") + + # 5. NATIVE final-state evaluation (this is the paper-faithful score). + try: + raw = env.evaluate() + except Exception as e: + raw = 0.0 + record["error"] = (record.get("error") or "") + \ + f" | evaluate_failed: {type(e).__name__}: {str(e)[:200]}" + logger.error("[%s] env.evaluate crashed: %s", task_id, e) + + # 6. NATIVE persistence -> result.txt / result.json. Returns float score. + score = lib_run_single._persist_evaluation_result(raw, str(out_dir)) + record["score"] = score + record["score_native"] = score + logger.info("[%s] native score = %.3f", task_id, score) + + except Exception as exc: + logger.error("[%s] run_one crashed: %s\n%s", task_id, exc, + traceback.format_exc()) + record["error"] = (record.get("error") or "") + \ + f" | run_one_exc: {str(exc)[-300:]}" + + record["finished_at"] = datetime.utcnow().isoformat() + (out_dir / "score.json").write_text( + json.dumps(record, indent=2, ensure_ascii=False), encoding="utf-8") + return record + + +# --------------------------------------------------------------------------- +# Resume: a task is done if result.txt exists and last run had no hard error. +# --------------------------------------------------------------------------- +def already_done(out_dir: Path) -> bool: + if not (out_dir / "result.txt").is_file(): + return False + sp = out_dir / "score.json" + if sp.is_file(): + try: + rec = json.loads(sp.read_text(encoding="utf-8")) + err = rec.get("error") or "" + # Re-run on transient infra errors; keep real (graded) results. + for transient in ("run_one_exc", "evaluate_failed", "Bootstrap", + "openclaw", "Timeout", "Connection"): + if transient in err: + return False + except Exception: + return False + return True + + +# --------------------------------------------------------------------------- +# Worker: one DesktopEnv per process, pull task ids from the shared queue. +# --------------------------------------------------------------------------- +def worker(env_idx: int, task_ids: list, args, shared: list) -> None: + harness = getattr(args, "agent_harness", "openclaw") + if harness == "codex": + from mm_agents.codex_agent import CodexAgent as _AgentClass + elif harness == "claudecode": + from mm_agents.claudecode_agent import ClaudeCodeAgent as _AgentClass + else: + from mm_agents.openclaw_agent import OpenClawAgent as _AgentClass + + name = f"osw2-env-{env_idx+1}" + result_root = Path(args.result_dir) / "pyautogui" / "screenshot" / args.model / "tasks" + time.sleep(env_idx * args.startup_stagger_s) + + for task_id in task_ids: + out_dir = result_root / task_id + if already_done(out_dir): + logger.info("[%s] [%s] already done — skip", name, task_id) + continue + out_dir.mkdir(parents=True, exist_ok=True) + + env = None + try: + task = load_task_config( + None, task_id=task_id, base_dir="evaluation_examples", + domain="tasks", eval_version="v2", + ) + logger.info("[%s] [%s] loaded: %s", name, task_id, + (_task_instruction(task) or "")[:70]) + + env = DesktopEnv( + provider_name=args.provider_name, + path_to_vm=args.path_to_vm, + action_space="pyautogui", + screen_size=(args.screen_width, args.screen_height), + headless=args.headless, + os_type=args.os_type, + require_a11y_tree=False, + require_terminal=False, + client_password=args.client_password, + snapshot_name=args.snapshot_name, + ) + agent = _AgentClass( + model=args.model, + litellm_base_url=args.litellm_base_url, + litellm_api_key=args.litellm_api_key, + client_password=args.client_password, + timeout=int(os.environ.get("OSW_AGENT_TIMEOUT", str(args.agent_timeout))), + gui=args.agent_gui, + max_steps=args.max_steps, + ) + rec = run_one(env, agent, task_id, task, out_dir, args) + shared.append({"task_id": task_id, "score": rec.get("score", 0.0), + "error": rec.get("error")}) + logger.info("[%s] [%s] -> score=%.3f%s", name, task_id, + rec.get("score", 0.0), + f" ERR={str(rec['error'])[:80]}" if rec.get("error") else "") + except Exception as exc: + logger.error("[%s] [%s] worker crash: %s\n%s", name, task_id, exc, + traceback.format_exc()) + shared.append({"task_id": task_id, "score": 0.0, + "error": f"worker_exc: {type(exc).__name__}: {str(exc)[:200]}"}) + finally: + if env is not None: + try: + env.close() + except Exception: + pass + + +# --------------------------------------------------------------------------- +def _bool(s): + if isinstance(s, bool): + return s + if str(s).lower() in ("true", "1", "yes", "y"): + return True + if str(s).lower() in ("false", "0", "no", "n"): + return False + raise argparse.ArgumentTypeError(f"bool expected, got {s!r}") + + +def parse_args() -> argparse.Namespace: + ap = argparse.ArgumentParser(description=__doc__) + # VM / provider + ap.add_argument("--provider_name", default="docker") + ap.add_argument("--path_to_vm", default=None, + help="Absolute path to OSWorld-V2 qcow2. For docker, " + "None lets the manager auto-resolve/download.") + ap.add_argument("--headless", type=_bool, default=True) + ap.add_argument("--screen_width", type=int, default=1920) + ap.add_argument("--screen_height", type=int, default=1080) + ap.add_argument("--os_type", default="Ubuntu") + ap.add_argument("--client_password", default="osworld-public-evaluation") + ap.add_argument("--snapshot_name", default="init_state") + ap.add_argument("--num_envs", type=int, default=6) + ap.add_argument("--startup_stagger_s", type=int, default=8) + ap.add_argument("--post_reset_sleep", type=int, default=60) + + # Agent / model + ap.add_argument("--model", default="gpt-5.5") + ap.add_argument("--litellm_base_url", required=True, + help="LiteLLM base URL reachable FROM INSIDE the VM " + "(e.g. http://172.17.0.1:4200/v1).") + ap.add_argument("--litellm_api_key", required=True) + ap.add_argument("--max_steps", type=int, default=50) + ap.add_argument("--agent_timeout", type=int, default=3600) + ap.add_argument("--agent_gui", type=_bool, default=True, + help="True (default) = hybrid GUI+CLI agent (computer-tool " + "plugin enabled). False = CLI-only ablation.") + + # Task selection + ap.add_argument("--osworld_root", default=str(REPO_ROOT)) + ap.add_argument("--test_all_meta_path", + default="evaluation_examples/test_v2.json") + ap.add_argument("--task_filter", default="") + ap.add_argument("--limit", type=int, default=0) + ap.add_argument("--result_dir", required=True) + ap.add_argument("--agent_harness", default="openclaw", + choices=["openclaw", "codex", "claudecode"], + help="In-VM agent harness: openclaw (default) or codex.") + return ap.parse_args() + + +def main() -> None: + args = parse_args() + osworld_root = Path(args.osworld_root).resolve() + task_ids = discover_v2_tasks(osworld_root, args.test_all_meta_path, + args.task_filter) + if args.limit: + task_ids = task_ids[:args.limit] + logger.info("Discovered %d OSWorld-V2 tasks (num_envs=%d, model=%s, gui=%s, harness=%s)", + len(task_ids), args.num_envs, args.model, args.agent_gui, + getattr(args, "agent_harness", "openclaw")) + if not task_ids: + logger.error("No tasks — exiting.") + return + + # Round-robin tasks across workers (each worker keeps its VM warm). + n = max(1, args.num_envs) + buckets: list[list[str]] = [[] for _ in range(n)] + for i, tid in enumerate(task_ids): + buckets[i % n].append(tid) + + mgr = Manager() + shared: list = mgr.list() + procs = [Process(target=worker, args=(i, buckets[i], args, shared), + name=f"osw2-env-{i+1}") for i in range(n)] + for p in procs: + p.start() + for p in procs: + p.join() + + out = Path(args.result_dir) + out.mkdir(parents=True, exist_ok=True) + summary = list(shared) + ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S") + (out / f"summary_{ts}.json").write_text( + json.dumps(summary, indent=2, ensure_ascii=False), encoding="utf-8") + if summary: + nums = [s["score"] for s in summary + if isinstance(s.get("score"), (int, float))] + mean = (sum(nums) / len(nums)) if nums else 0.0 + n_pass = sum(1 for x in nums if x >= 0.5) + logger.info("=== %d runs, mean=%.4f, pass(>=0.5)=%d/%d (%.1f%%) ===", + len(summary), mean, n_pass, len(nums), + 100.0 * n_pass / max(1, len(nums))) + + +if __name__ == "__main__": + main() From d2e7b929d969d9cb08b3cde9dc52c8b2b5c80a21 Mon Sep 17 00:00:00 2001 From: Wanli-Lee <1181451942@qq.com> Date: Tue, 30 Jun 2026 14:42:45 +0800 Subject: [PATCH 2/4] Clarify scoring wording: OSWorld-V2's own evaluator (same as paper Table 3) env.evaluate() is checkpoint-based with bounded model judgment, not a pure static check; align the README with the paper's description and drop the misleading agent-judge contrast. Co-Authored-By: Claude --- experiments/osworld_v2_hybrid/README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/experiments/osworld_v2_hybrid/README.md b/experiments/osworld_v2_hybrid/README.md index 9c38048..bb4f67b 100644 --- a/experiments/osworld_v2_hybrid/README.md +++ b/experiments/osworld_v2_hybrid/README.md @@ -1,9 +1,11 @@ -# OSWorld-V2 hybrid GUI+CLI re-evaluation +# OSWorld-V2 hybrid GUI+CLI evaluation A harness ablation on **OSWorld-V2** (108 tasks): we drive each task with a **hybrid GUI+CLI agent** — the OpenAI `codex` CLI injected inside the VM plus a GUI action channel — on the same **GPT-5.5** backbone the paper benchmarks. -Scored with OSWorld's native grader (`env.evaluate()`). +Scored with OSWorld-V2's own evaluator (`env.evaluate()`, checkpoint-based with +bounded model judgment) — the same grader as the paper's Table 3, so the numbers +are directly comparable to the official GPT-5.5 row. ## Headline From 10a495a73890932bba63c0dc389a474f01b5ca24 Mon Sep 17 00:00:00 2001 From: Wanli-Lee <1181451942@qq.com> Date: Tue, 30 Jun 2026 14:56:17 +0800 Subject: [PATCH 3/4] Fix Partial (%) to paper Table 3 definition (mean partial credit) Partial in OSWorld-V2 / the paper is the mean partial-credit score over all tasks, not the fraction of tasks scoring 0 49.6; de-infra 51.4) and the aggregation script. Binary and tool-call numbers unchanged. Co-Authored-By: Claude --- experiments/osworld_v2_hybrid/README.md | 6 ++++-- experiments/osworld_v2_hybrid/aggregate_results.py | 10 ++++++---- .../results/codex_hybrid_gpt55/RESULT_ANALYSIS.md | 12 ++++++------ .../results/codex_hybrid_gpt55/summary.json | 10 ++++------ 4 files changed, 20 insertions(+), 18 deletions(-) diff --git a/experiments/osworld_v2_hybrid/README.md b/experiments/osworld_v2_hybrid/README.md index bb4f67b..74a14f9 100644 --- a/experiments/osworld_v2_hybrid/README.md +++ b/experiments/osworld_v2_hybrid/README.md @@ -11,11 +11,13 @@ are directly comparable to the official GPT-5.5 row. | Model / harness | Binary (%) | Partial (%) | Tool calls/task | |---|---|---|---| -| **GPT-5.5 + codex hybrid (this work)** | **18.5** | 59.3 | 77.5 | +| **GPT-5.5 + codex hybrid (this work)** | **18.5** | 49.6 | 77.5 | | GPT-5.5 batched (official Table 3) | 13.0 | 49.5 | 149.8 | Same backbone, swapping the official batched loop for the codex hybrid harness -lifts GPT-5.5 **13.0% → 18.5% Binary** at ~half the tool calls. Dropping 4 +lifts GPT-5.5 **13.0% → 18.5% Binary** at ~half the tool calls, with partial +credit holding steady (49.5 → 49.6) — the gains come from pushing near-complete +tasks over the line, not from broad partial progress. Dropping 4 infra-failure tasks (063/064/082/069) gives a 104-task cohort: **51.39% avg / 19.23% Binary**. Full breakdown in [`results/codex_hybrid_gpt55/RESULT_ANALYSIS.md`](./results/codex_hybrid_gpt55/RESULT_ANALYSIS.md). diff --git a/experiments/osworld_v2_hybrid/aggregate_results.py b/experiments/osworld_v2_hybrid/aggregate_results.py index 7074e2b..b80bf70 100644 --- a/experiments/osworld_v2_hybrid/aggregate_results.py +++ b/experiments/osworld_v2_hybrid/aggregate_results.py @@ -102,15 +102,17 @@ def main(): def cohort(keep): n = len(keep) binary = sum(1 for r in keep if r["score"] >= 0.999) - partial = sum(1 for r in keep if 0 < r["score"] < 0.999) avg = sum(r["score"] for r in keep) / n + nonzero = sum(1 for r in keep if 0 < r["score"] < 0.999) return { "n": n, + # Paper Table 3 metrics: Binary = fraction at score 1.0; Partial = + # mean partial-credit score over all tasks (NOT the fraction of + # tasks with a partial score). "binary_pct": round(binary / n * 100, 2), - "partial_pct": round(partial / n * 100, 2), - "avg_score_pct": round(avg * 100, 2), + "partial_pct": round(avg * 100, 2), "binary_count": binary, - "partial_count": partial, + "nonzero_partial_count": nonzero, } def mean(key, src=rows): diff --git a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md index f52170f..1d3ebea 100644 --- a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md +++ b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md @@ -73,14 +73,14 @@ codex 混合方案是各方案最高; GUI+CLI 比纯 CLI 高约 15 个点。 | Model | Binary (%) | Partial (%) | Cost/task | Tool calls/task | Out tok/task | Steps/task | |---|---|---|---|---|---|---| -| codex hybrid (GUI+CLI) | 18.5 | 59.3 | — | 68 (med) | — | 1 turn (med) | -| codex 纯 CLI | 11.1 | 49.1 | — | — | — | 2 turn (med) | -| claude CLI max | 12.0 | 60.2 | $39.71 (med) | — | 11,657 (med) | 23 (med) | -| openclaw / cuaclaw hybrid | 12.0 | 62.0 | — | — | — | — | +| codex hybrid (GUI+CLI) | 18.5 | 49.6 | — | 68 (med) | — | 1 turn (med) | +| codex 纯 CLI | 11.1 | 34.2 | — | — | — | 2 turn (med) | +| claude CLI max | 12.0 | 40.9 | $39.71 (med) | — | 11,657 (med) | 23 (med) | +| openclaw / cuaclaw hybrid | 12.0 | 40.5 | — | — | — | — | | 官方纯 GUI (gpt-5.5, 500步) | 0.0 | — | — | — | — | 117.5 (med) | -> 注: Binary = score=1.0 占比; Partial = 0 均为全 108 题口径; codex hybrid 去 infra(104 题)口径 Binary=19.2%。 +> 注: 对齐 paper Table 3 口径 — **Binary = score=1.0 的题占比; Partial = 全 108 题的平均部分分(partial credit / 平均分), 不是"拿到部分分的题占比"**。 +> 均为全 108 题口径; codex hybrid 去 infra(104 题)口径 Binary=19.2% / Partial=51.4%。 ### 字段口径与可得性(各 harness 采集不一致, 不能直接比绝对值) diff --git a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/summary.json b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/summary.json index 40cd0d0..c053e93 100644 --- a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/summary.json +++ b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/summary.json @@ -8,18 +8,16 @@ "all_108": { "n": 108, "binary_pct": 18.52, - "partial_pct": 59.26, - "avg_score_pct": 49.64, + "partial_pct": 49.64, "binary_count": 20, - "partial_count": 64 + "nonzero_partial_count": 64 }, "de_infra_104": { "n": 104, "binary_pct": 19.23, - "partial_pct": 60.58, - "avg_score_pct": 51.39, + "partial_pct": 51.39, "binary_count": 20, - "partial_count": 63 + "nonzero_partial_count": 63 } }, "efficiency": { From e8b8c627e3771ff823af4a45b91dd03cf654f5d0 Mon Sep 17 00:00:00 2001 From: Wanli-Lee <1181451942@qq.com> Date: Tue, 30 Jun 2026 14:59:18 +0800 Subject: [PATCH 4/4] Trim RESULT_ANALYSIS to codex hybrid vs paper Table 3 only Drop the cross-harness comparison rows (pure-CLI / claude / cuaclaw / official GUI) for now; keep only the codex hybrid row against the paper's official GPT-5.5 claim. Other harnesses to be added later. Co-Authored-By: Claude --- .../codex_hybrid_gpt55/RESULT_ANALYSIS.md | 67 +++++++------------ 1 file changed, 25 insertions(+), 42 deletions(-) diff --git a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md index 1d3ebea..efba3ff 100644 --- a/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md +++ b/experiments/osworld_v2_hybrid/results/codex_hybrid_gpt55/RESULT_ANALYSIS.md @@ -57,47 +57,30 @@ - 已按 EVAL 日志人工修正回 0.7(见 023/score.json 的 note 字段)。 - 与 064/082 的区别: 064/082 故障在执行/setup 阶段导致任务没跑完(真失败); 023 任务和打分都成功, 只是事后取产物失败 → 不剔, 计 0.7。 -## 横向对比(同 benchmark, gpt-5.5) +## 两种口径(codex hybrid) -| 方案 | 平均分 | 严格通过率 | +| 口径 | 平均分(Partial) | 严格通过率(Binary) | |---|---|---| -| **codex hybrid (GUI+CLI, 去infra 104题)** | **51.39%** | **19.23%** | -| codex hybrid (GUI+CLI, 原始 108题) | 49.64% | 18.52% | -| codex 纯 CLI (gui=False) | 34.18% | 11.11% | -| openclaw GUI+CLI hybrid | 40.52% | 12.04% | -| claude CLI max | 40.93% | 12.04% | - -codex 混合方案是各方案最高; GUI+CLI 比纯 CLI 高约 15 个点。 - -## 效率统计表(per-task, 全 108 题口径) - -| Model | Binary (%) | Partial (%) | Cost/task | Tool calls/task | Out tok/task | Steps/task | -|---|---|---|---|---|---|---| -| codex hybrid (GUI+CLI) | 18.5 | 49.6 | — | 68 (med) | — | 1 turn (med) | -| codex 纯 CLI | 11.1 | 34.2 | — | — | — | 2 turn (med) | -| claude CLI max | 12.0 | 40.9 | $39.71 (med) | — | 11,657 (med) | 23 (med) | -| openclaw / cuaclaw hybrid | 12.0 | 40.5 | — | — | — | — | -| 官方纯 GUI (gpt-5.5, 500步) | 0.0 | — | — | — | — | 117.5 (med) | - -> 注: 对齐 paper Table 3 口径 — **Binary = score=1.0 的题占比; Partial = 全 108 题的平均部分分(partial credit / 平均分), 不是"拿到部分分的题占比"**。 -> 均为全 108 题口径; codex hybrid 去 infra(104 题)口径 Binary=19.2% / Partial=51.4%。 - -### 字段口径与可得性(各 harness 采集不一致, 不能直接比绝对值) - -- **codex (hybrid / 纯CLI)**: 来自 in-VM `codex` CLI 的 agent.log。 - - `Tool calls/task` = `hybrid_codex_action_mix.json` 的 CLI+GUI 动作总数 (median 68, mean 77.5)。 - - `Steps/task` 用 codex turn 数代替 (hybrid median 1, 纯CLI median 2) — codex 单 turn 内含多次工具调用, 与 claude 的 turn 不可比。 - - **Cost 无法计**: codex CLI 只在结尾打印 "tokens used" 总量(含 input+reasoning+output, median ~558k tok/task), 不拆 output, 也无单价 → Out tok/task、Cost/task 留空。 -- **claude CLI max**: 来自 `claude_stream.jsonl` 的 result 事件, 字段最全。 - - Cost/task = `total_cost_usd` (median $39.71, mean $56.95)。 - - Out tok/task = `usage.output_tokens` (median 11,657, mean 15,529)。 - - Steps/task = `num_turns` (median 23, mean 26.7)。 - - 101/108 题有 usage(7 题异常无 result 事件)。 -- **openclaw / cuaclaw hybrid**: 分数取自 `check/hybrid_*_time.json`; per-task token/cost/toolcall 未单独采集 → 留空。 -- **官方纯 GUI**: 取自 xlangai 官方 trajectory 包(results_gpt5.5_500steps), 只有 step/timing, 无分数对齐到本表评测器 → Binary 显示 0 是因该来源未含本地 native 分, 仅 Steps(median 117.5)可用作 GUI 步数参考。 - -### 关键对比解读 - -- **codex hybrid 用极少的"轮次"达到最高分**: median 1 个 codex turn(单 turn 内自主多步), 而 claude 需 median 23 turn — codex 把多步操作压在一次长链路里。 -- **codex token 消耗大**: median ~558k tok/task(含 input 累积), 反映 xhigh + 单 turn 长上下文; claude output 只有 ~12k(但 input 达 ~1.97M, cost $39.71/task)。 -- Cost 维度只有 claude 有权威数字($39.71/task median); codex 需用 LiteLLM 侧用量日志另算, 当前 run 未落盘。 +| **去 infra bug(推荐, 104 题)** | **51.39%** | **19.23%** | +| 原始 108 题 | 49.64% | 18.52% | + +## 对比 paper Table 3(同 backbone gpt-5.5) + +我们的 codex hybrid 与 paper Table 3 里官方 GPT-5.5(batched)行直接对比(同评测器、同 108 题): + +| Model / harness | Binary (%) | Partial (%) | Tool calls/task | +|---|---|---|---| +| **GPT-5.5 + codex hybrid(本工作)** | **18.5** | 49.6 | 77.5 | +| GPT-5.5 batched(paper Table 3) | 13.0 | 49.5 | 149.8 | + +> 口径对齐 paper Table 3:**Binary = score=1.0 的题占比;Partial = 全 108 题的平均部分分(partial credit / 平均分),不是"拿到部分分的题占比"**。 +> codex hybrid 去 infra(104 题)口径:Binary 19.2% / Partial 51.4%。 + +**结论**:同一 GPT-5.5 backbone,把官方 batched loop 换成 codex hybrid harness,Binary **+5.5 pt(13.0 → 18.5%)**,而 Partial 几乎不变(49.5 → 49.6)且 tool calls/task 砍半(149.8 → 77.5)。增益来自把"接近完成"的任务推过满分线,而非普遍提升部分进度。 + +字段口径说明: +- `Tool calls/task` = codex agent.log 的 CLI+GUI 动作总数(mean 77.5 / median 68)。 +- `Cost/task`、`Out tok/task` 留空(`—`):codex CLI 只打印 "tokens used" 总量(含 input+reasoning+output, median ~558k tok/task),不拆 output、无单价,无法与 paper 的纯 output token / cost 对齐。 +- paper 的 `Steps/task`(一次 observe→act 回合)对应我们的 tool-call 数(codex 单动作执行 ≈ single-action),不是 1–2 次 `codex exec`。 + +> 注:其余 harness(纯 CLI / claude / cuaclaw / 官方纯 GUI)的对照后续再补。