mini-computer-agent: minimal model-agnostic computer-use agent

.github/workflows/lint.yaml

@@ -26,3 +26,8 @@ jobs:
       - name: Lint mini-swe-code
         working-directory: mini-swe-code
         run: ruff check src tests
+      - name: Lint mini-computer-agent
+        working-directory: mini-computer-agent
+        run: |
+          ruff check src tests
+          ruff format --check src tests

.github/workflows/test-mini-computer-agent.yaml

@@ -0,0 +1,34 @@
+name: mini-computer-agent
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - 'mini-computer-agent/**'
+      - '.github/workflows/test-mini-computer-agent.yaml'
+  pull_request:
+    branches: [main]
+    paths:
+      - 'mini-computer-agent/**'
+      - '.github/workflows/test-mini-computer-agent.yaml'
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+        working-directory: mini-computer-agent
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
+        with:
+          python-version: '3.12'
+      - name: Install uv
+        run: curl -LsSf https://astral.sh/uv/install.sh | sh
+      # --prerelease=allow: benchflow pins an rc litellm
+      - name: Install dependencies
+        run: uv pip install --python ${Python_ROOT_DIR} --prerelease=allow -e '.[dev]'
+      - name: Run pytest
+        run: pytest -q

mini-computer-agent/.gitignore

@@ -0,0 +1,8 @@
+__pycache__/
+*.py[cod]
+.venv/
+*.egg-info/
+dist/
+build/
+.pytest_cache/
+.ruff_cache/

mini-computer-agent/LICENSE

@@ -0,0 +1,191 @@
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mini-computer-agent/README.md

@@ -0,0 +1,79 @@
+# mini-computer-agent
+
+A **minimal computer-use agent** — the computer-use analog of
+[mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent).
+
+One screenshot → any vision model → one action → repeat. No vendor computer-use
+tool, no separate grounding model, no tool-calling interface — just a linear
+message history and `subprocess`. The whole agent is **~150 lines** in
+[`core.py`](src/mini_computer_agent/core.py), and it runs with *any* vision model
+via litellm. Because the scaffold is identical for every model, it's a
+confound-free baseline for comparing models on desktop GUI tasks (the way
+mini-swe-agent is for coding).
+
+It is a **pure agent** — it carries no protocol/harness code.
+[BenchFlow](https://github.com/benchflow-ai/benchflow)'s ACP layer wraps it for
+benchmarking; nothing benchflow-specific lives here.
+
+## Play with it
+
+```python
+from mini_computer_agent import run
+
+# needs a desktop with scrot + xdotool on $DISPLAY, and a vision model key
+result = run("Open the calculator and compute 2+2", model="gemini/gemini-3.5-flash")
+print(result)
+```
+
+The loop, in `core.py`:
+
+1. **See** — `scrot` captures the screen.
+2. **Decide** — the vision model returns one JSON action
+   (`click` / `double_click` / `right_click` / `type` / `key` / `scroll` /
+   `done`). Coordinates are **[0,1000]-normalized** (the convention vision models
+   like Gemini emit) and scaled to the real resolution before acting — getting
+   this wrong mis-clicks by ~3× and looks like "the model can't ground."
+3. **Act** — `xdotool` executes it.
+4. Repeat until `done` (or the step budget).
+
+`run(task, model, on_step=...)` exposes a per-step callback so a harness can
+capture the trajectory.
+
+## Benchmark with it
+
+BenchFlow serves this agent over ACP and runs it on desktop/computer-use
+benchmarks — the agent stays pure; the protocol wrapping is benchflow's.
+
+## Develop
+
+```bash
+cd mini-computer-agent
+uv venv .venv && source .venv/bin/activate
+uv pip install -e '.[dev]'
+pytest -q        # hermetic: parsing + coordinate scaling, no sandbox/model needed
+ruff check src tests && ruff format --check src tests
+```
+
+## Validated
+
+Run end-to-end on [BenchFlow](https://github.com/benchflow-ai/benchflow) + Daytona
+(Linux sandbox + Chromium), served over ACP, model `gemini/gemini-3.5-flash`:
+
+| Suite | Result |
+| --- | --- |
+| Single-target grounding — 1 synthetic + 5 MiniWoB++ (`click-button`/`link`/`dialog`/`option`/`tab`) | **6 / 6** |
+| MiniWoB++ slice including multi-step (`enter-text`, `click-checkboxes`) | **6 / 7** |
+| Overall | **7 / 8** |
+
+- The `[0,1000] → pixel` coordinate scaling is the load-bearing detail: the model
+  emits normalized coordinates that land on exact targets only after scaling
+  (e.g. `195 → 250px`, `508 → 650px`, `820 → 1050px` on a 1280-wide screen) —
+  used raw, every click misses by ~3× and the model looks like it "can't ground."
+- Dogfooding drove a real fix: `enter-text` first failed (the model flailed on
+  field focus); one line of prompt guidance (focus-then-type, verify-before-submit)
+  took it to a clean pass in roughly half the steps. The remaining miss is
+  `click-checkboxes` (multi-select thoroughness on small labels).
+
+## License
+
+Apache-2.0 — see [LICENSE](LICENSE).

mini-computer-agent/pyproject.toml

@@ -0,0 +1,40 @@
+[project]
+name = "mini-computer-agent"
+version = "0.1.0"
+description = "A minimal computer-use agent — the computer-use analog of mini-swe-agent"
+readme = "README.md"
+license = { text = "Apache-2.0" }
+requires-python = ">=3.12"
+dependencies = [
+    # The agent is pure: one screenshot -> any vision model (litellm) -> one
+    # action (xdotool). No benchflow dependency — benchflow's ACP layer serves
+    # this agent, not the other way around. (scrot + xdotool are system tools.)
+    "litellm>=1.40",
+]
+
+[project.urls]
+Repository = "https://github.com/benchflow-ai/agents"
+Documentation = "https://github.com/benchflow-ai/agents/tree/main/mini-computer-agent#readme"
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8",
+    "ruff>=0.15",
+    "ty",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/mini_computer_agent"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+
+[tool.ruff]
+src = ["src", "tests"]
+
+[tool.ruff.lint.isort]
+known-first-party = ["mini_computer_agent"]

mini-computer-agent/src/mini_computer_agent/__init__.py

@@ -0,0 +1,13 @@
+"""mini-computer-agent — a minimal computer-use agent.
+
+The computer-use analog of mini-swe-agent: one screenshot -> any vision model ->
+one action -> repeat. Pure agent — no ACP/protocol code lives here; benchflow's
+ACP layer serves it for benchmarking.
+
+    from mini_computer_agent import run
+    run("Open the calculator and compute 2+2", model="gemini/gemini-3.5-flash")
+"""
+
+from mini_computer_agent.core import run
+
+__all__ = ["run"]