Forced tool_choice ("any"/"required") makes both Anthropic and OpenAI
skip any text/thinking block before the tool call, which silently made
rationale and thinking always None despite the planner-reflection-history
change's capture code being correct. Switch the primary call to
tool_choice="auto" (Anthropic: type=auto, disable_parallel_tool_use=true;
OpenAI: "auto") so the model can emit its reflection text, and add a
one-time forced retry (Anthropic "any", OpenAI "required", thinking
disabled) if the model responds without a tool call, guaranteeing a step
never stalls. Also add OpenAI text_output capture from message.content,
which was never extracted before (Anthropic-only gap).
Update planner-reflection-history design.md/tasks.md to document the bug
found during the pending manual smoke test (task 8.5) and the fix (new
section 9).
paddlepaddle has no Python 3.14 (cp314) wheel on PyPI, so host-agent
deployments on 3.14 can never install it, causing OCR to fail at
runtime with RuntimeError. Pin the workspace to Python 3.13 across
all pyproject.toml files, the Docker base image, and the Jenkins CI
image; regenerate uv.lock against 3.13.
Also fixes a pre-existing Python-2-style `except X, Y:` syntax error
(invalid in all Python 3.x) in runtime/task.py and
packages/cloud-platform/cloud/{sql_repository,internal_api/api}.py,
introduced in 22d37ca9 and unrelated to this change's scope, which
blocked the full test suite from collecting on any interpreter
version.
openspec change: downgrade-python-3-13-paddleocr
Host Agent now persists step-level execution detail locally (via a real
TaskMetadataStore/Timeline wired into TaskRunner) and reports a bounded
in-progress snapshot piggybacked on lease renewal. Cloud persists that
snapshot per active assignment and exposes it through the existing task
list/detail query path; Cloud Console renders it as a live badge. Host
Agent's local console gains authenticated, read-only task list and
detail/timeline pages (same-origin, server-rendered) with inlined
screenshots.
Also fixes a pre-existing gap in the shared Timeline: the actual
per-step LLM prompt is now recorded instead of the task goal, benefiting
both Runtime and Host Agent consoles. When a host uses the cloud planner
transport, each decide call's prompt and resulting tool decision are
durably logged in a new planner_decision_log table (with bounded
retention) and browsable from Cloud Console; direct-transport hosts
explicitly surface a "not reported" state.
Includes Alembic migrations 0008 (progress columns on scheduled_tasks)
and 0009 (planner_decision_log), bounded Host-Agent-local retention,
dual-backend repository parity, and Vitest + pytest coverage. Task 6.5
(manual end-to-end device verification) remains.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replaces the stub Planner's fixed describe_screen/[] behavior with a real
decision-maker: AIPlanner uses native tool/function calling (Anthropic or
OpenAI, pluggable via AI_PLANNER_PROVIDER) to select exactly one grounded
action per turn, with an explicit finish_task(success, reason) tool for
completion/failure instead of an ambiguous "no tool call" signal. Default
disabled (AI_PLANNER_ENABLED=false) and additive; TaskRunner falls back to
the existing stub Planner unchanged when disabled.
Amends CONSTITUTION.md's Perception Boundary with one narrow exception:
only the AI Planner may receive the current step's raw screenshot bytes
alongside Scene, for vision-grounded coordinate grounding. Also fixes a
latent gap in TaskRunner.run(): observe/plan exceptions are now caught per
iteration and turned into a failed task with a failure_reason, instead of
propagating uncaught.
openspec change: ai-planner-runtime.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
tools/ is a Hexagonal inner layer that must never depend on LLM
concerns (ADR 0002), but describe_screen_semantic.py imported
semantic.enricher, which pulls in the Anthropic client by default.
Relocated the wrapper to runtime/, which is where LLM-dependent code
is allowed to live; updated the tool registry and all test imports
accordingly. No behavior change.
openspec: semantic-scene capability, archived change semantic-scene-runtime
WorldModel routed all state through a single mutable _current_task_id,
so concurrent tasks sharing one instance could corrupt each other's
state. start_task() now returns a TaskWorldView handle scoped to that
task; TaskRunner.run() threads it through as a local variable instead
of reading self.world_model implicitly. Also extracts _start_world_view/
_record_step_result as reusable TaskRunner methods for composed runners.
openspec: world-model capability, archived change world-model-runtime
q792602257
