| """Tiny Computer Agent - an agentic harness that drives a real computer with a |
| tiny, fully local model. |
| |
| The harness is deliberately boring: it owns the loop, the closed action vocabulary, |
| perception and the safety gates, so the model only ever answers one question - |
| "which item from this short menu, and which verb?" Nothing here needs a network |
| call at run time, and the core imports nothing outside the standard library. |
| |
| from tiny_computer_agent import AgentLoop, GrabScreen, IndexedScreen, OcrDetector |
| from tiny_computer_agent.models import TextPlannerBrain |
| |
| screen = IndexedScreen(GrabScreen(), OcrDetector()) # pixels -> labels |
| brain = TextPlannerBrain() # 0.5B, CPU-friendly |
| loop = AgentLoop(screen=screen, brain=brain) # dry run by default |
| episode = loop.run("save the document") |
| print(episode.render()) |
| |
| An episode can be saved as a portable *trace*, replayed offline, and scored, so |
| "the model looked plausible on that screenshot" becomes a number: |
| |
| from tiny_computer_agent import Trace, benchmark, run_scenario |
| |
| trace = run_scenario(Scenario("click 'Save'", frames=[...], expected="Save"), brain) |
| trace.save("traces/save.json") # diffable, no pixels |
| print(benchmark(scenarios, {"planner": lambda s: PlannerBrain()}).render()) |
| """ |
|
|
| from tiny_computer_agent.act import ( |
| ActionResult, |
| DryRunExecutor, |
| Executor, |
| InputUnavailable, |
| PyAutoGuiExecutor, |
| resolve_point, |
| ) |
| from tiny_computer_agent.actions import ( |
| ACTION_KINDS, |
| ACTION_MENU, |
| Action, |
| ActionError, |
| parse_action, |
| ) |
| from tiny_computer_agent.brain import ( |
| Brain, |
| BrainError, |
| DecisionMenu, |
| GoalUnreachable, |
| MenuItem, |
| MockBrain, |
| ModelBrain, |
| PlannerBrain, |
| extract_json_object, |
| extract_json_objects, |
| parse_decision, |
| ) |
| from tiny_computer_agent.loop import AgentLoop, Episode, Step |
| from tiny_computer_agent.models import ( |
| DEFAULT_TEXT_MODEL, |
| DEFAULT_VISION_MODEL, |
| MODEL_BRAINS, |
| TextPlannerBrain, |
| VLMBrain, |
| make_model_brain, |
| model_cached, |
| model_dependencies, |
| pick_dtype, |
| resolve_device, |
| ) |
| from tiny_computer_agent.demo import fake_desktop, write_fake_desktop |
| from tiny_computer_agent.observe import ( |
| Element, |
| FakeScreen, |
| GrabScreen, |
| ImageScreen, |
| Observation, |
| ScreenSource, |
| ScreenUnavailable, |
| normalise_label, |
| ) |
| from tiny_computer_agent.desktop import ( |
| BRAIN_NAMES, |
| DEFAULT_KILL_SWITCH, |
| Check, |
| build_agent, |
| display_available, |
| doctor, |
| run_checks, |
| ) |
| from tiny_computer_agent.perception import ( |
| AutoDetector, |
| Detector, |
| DetectorUnavailable, |
| GridDetector, |
| IndexedScreen, |
| OcrDetector, |
| detector_available, |
| make_detector, |
| ) |
| from tiny_computer_agent.safety import Policy, PolicyViolation |
| from tiny_computer_agent.trace import ( |
| Scenario, |
| Scoreboard, |
| Trace, |
| TraceStep, |
| Verdict, |
| benchmark, |
| benchmark_traces, |
| load_traces, |
| run_scenario, |
| score, |
| score_replay, |
| score_trace, |
| ) |
|
|
| __version__ = "0.3.0" |
|
|
| __all__ = [ |
| "ACTION_KINDS", "ACTION_MENU", "Action", "ActionError", |
| "ActionResult", "AgentLoop", "AutoDetector", "BRAIN_NAMES", |
| "Brain", "BrainError", "Check", "DEFAULT_KILL_SWITCH", |
| "DEFAULT_TEXT_MODEL", "DEFAULT_VISION_MODEL", "DecisionMenu", "Detector", |
| "DetectorUnavailable", "DryRunExecutor", "Element", "Episode", |
| "Executor", "FakeScreen", "GoalUnreachable", "GrabScreen", |
| "GridDetector", "ImageScreen", "IndexedScreen", "InputUnavailable", |
| "MODEL_BRAINS", "MenuItem", "MockBrain", "ModelBrain", |
| "Observation", "OcrDetector", "PlannerBrain", "Policy", |
| "PolicyViolation", "PyAutoGuiExecutor", "Scenario", "Scoreboard", |
| "ScreenSource", "ScreenUnavailable", "Step", "TextPlannerBrain", |
| "Trace", "TraceStep", "VLMBrain", "Verdict", |
| "__version__", "benchmark", "benchmark_traces", "build_agent", |
| "detector_available", "display_available", "doctor", "extract_json_object", |
| "extract_json_objects", "fake_desktop", "load_traces", "make_detector", |
| "make_model_brain", "model_cached", "model_dependencies", "normalise_label", |
| "parse_action", "parse_decision", "pick_dtype", "resolve_device", |
| "resolve_point", "run_checks", "run_scenario", "score", |
| "score_replay", "score_trace", "write_fake_desktop", |
| ] |
|
|
