docs/v360/align-depth-reduction-devcontainer
2038 Commits
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f0c02d70c5 |
chore: re-trigger CI [controller]
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39fa613890 |
docs(documentation alignment): align DepthReductionCompressor and devcontainer discovery references
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- Fix AmbiguousStep conflict: rename documentation_steps.py step patterns from generic 'it should contain {text}' to 'the documentation should contain {text}' to avoid collision with k8s_helm_chart_steps.py and other step files
- Fix execution_environment.feature: update remaining 'it should contain' steps to 'the container types should contain' for consistency with line 132
- Add features/align_documentation.feature: Behave tests for the documentation changes using the new documentation-scoped step patterns
- Update docs/reference/skeleton_compressor.md: add DepthReductionCompressor references, correct module path (acms_skeleton_compressor), add v3.5.0 milestone
- Update docs/reference/di.md: add Skeleton Compressor Resolution section with resolve_configured_skeleton_compressor() and DepthReductionCompressor docs
- Update docs/reference/devcontainer_resources.md: add v3.6.0 named configuration scanning section, document config_name attribute, update discovery module docs
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7afd042432 |
docs(documentation alignment): align DepthReductionCompressor and devcontainer discovery references
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954e819606 |
docs(documentation alignment): align DepthReductionCompressor and devcontainer discovery references
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aca045bb0b |
fix(tui): convert PermissionsScreen from Static widget to proper Textual Screen subclass
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- Changed PermissionsScreen to inherit from textual.app.Screen instead of textual.widgets.Static - Added BINDINGS class variable with keyboard bindings for a, A, r, R, j, k, d, escape - Implemented action methods: action_allow_once, action_allow_always, action_reject_once, action_reject_always, action_nav_next, action_nav_prev, action_cycle_diff, action_dismiss_screen - Added compose() method for Textual screen layout - Added update() method for backward compatibility with tests - Added TDD Behave scenarios tagged @tdd_issue @tdd_issue_10488 to verify the fix - All 65 unit test scenarios pass ISSUES CLOSED: #10488 |
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9a5ccc6b01 |
Merge pull request 'Update timeline: milestone status for 2026-04-19' (#10684) from timeline-update-2026-04-19-final into master
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f167098541 |
Merge branch 'master' into timeline-update-2026-04-19-final
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072f470212 |
fix(agents): make bug-hunt-pool-supervisor tracking non-blocking to prevent initialization hangs
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The automation-tracking-manager call in step 5 was blocking the main loop indefinitely, causing 3+ consecutive initialization failures. This commit documents the fix in CHANGELOG.md with the proper issue reference. ISSUES CLOSED: #8835 |
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1f95ea0c2a |
Update timeline: milestone status for 2026-04-19
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832d0b26ae | Update timeline: milestone status for 2026-04-19 | ||
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89baa0a525 | Update timeline: milestone status for 2026-04-19 | ||
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435e409df9
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build: moved all sonnet agents to haiku
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59a2a83328
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build: fixed pr-merge filtering to avoid non-mergable PRs
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bcf58f0703
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build: being even more explicit about loading scripts
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37402e83c2
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build: fixed some typos regarding script loading via skills
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7c13643441
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build: final tweaks to get gpt5-nano working for pr-merge
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4e7bb5ae4e
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build: more explicit on how to run the script
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47d630599a
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build: further clarified how to get the PRs using the scripts
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6153da8deb
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build: trying to improve skill comprehension of scripts
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e8f06fa433
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build: made invocation of scripts more explicit
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17bbe20376
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Revert "build: adding auto-agents-system as a task as well as a skill."
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This reverts commit
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5c8b5a84d2
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build: adding auto-agents-system as a task as well as a skill.
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fe95acb856
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build: added more clarification for loading scripts
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build: expanded capabilities (permissions) of the git rebaser subagent
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555509c609
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build: tweaked reasoning level on pr-merge related agents
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aaeecd1616
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build: reduced cost of models for merging by picking cheaper models
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build: seperated out the actual rebase and conflict resolution to its own specialized subagent
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66850665b7 |
fix(sandbox): split oversized step file and update CONTRIBUTORS.md
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Split checkpoint_manager_coverage_steps.py (692 lines) into three focused modules to comply with the 500-line file limit: - checkpoint_manager_coverage_steps.py: model, protocol, manager init, create_checkpoint, _snapshot_directory, and _cleanup_snapshot steps (408 lines) - checkpoint_manager_rollback_steps.py: rollback_to, list_checkpoints, and delete_checkpoint branch steps (283 lines) - checkpoint_manager_bug7488_steps.py: Bug #7488 sandbox_path auto-stored-in-metadata steps (59 lines) Also updated CONTRIBUTORS.md to document HAL 9000's contribution for bug fix #7488 as required by CONTRIBUTING.md guidelines. ISSUES CLOSED: #7488 |
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beceb183d0 |
fix(sandbox): store sandbox_path in checkpoint metadata to enable rollback
CheckpointManager.create_checkpoint() computed sandbox_path from
sandbox.context.sandbox_path but never stored it in the metadata dict.
This caused rollback_to() to always find metadata.get('sandbox_path')
returning None, silently skip the rollback, and return False.
The fix adds sandbox_path to the metadata dict immediately after it is
resolved from the sandbox context, before the SandboxCheckpoint is
constructed. rollback_to() can now retrieve the path and correctly
restore the sandbox filesystem state.
Added two new BDD scenarios to checkpoint_manager_coverage.feature:
- Verifies sandbox_path is automatically stored in metadata on create
- Verifies rollback succeeds without manually supplying sandbox_path
ISSUES CLOSED: #7488
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d6fca18c01
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build: broke the 4 stage pr merge cycle into a 6 stage cycle and eliminating PRs that arent stale and need review
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0c5b140d29 |
fix(database): include alembic files in package distribution
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Move alembic configuration and migration files from repository root into the Python package structure to ensure they are included in the wheel distribution. This fix resolves the FileNotFoundError when running `agents init` in Docker containers or any environment using the built wheel distribution. Changes: - Move alembic/ directory from repo root to src/cleveragents/infrastructure/database/migrations/ - Move alembic.ini to the same new location and update script_location setting - Update MigrationRunner._find_alembic_ini() to search from the new canonical location within the package - Update create_template_db.py to point to the new alembic.ini location - Update documentation references to reflect new migration file locations - Create __init__.py for migrations package - The env.py file is imported when running tests that verify all modules can be imported without errors. However, context.config is only available when alembic is actually running migrations, not during normal module imports. This caused an AttributeError when the test tried to import the migrations.env module. - Fix by using getattr() with a default value to safely access context.config, and guard all code that uses config with None checks. This allows the module to be safely imported while still functioning correctly during migrations. Testing: - Verified MigrationRunner can locate alembic.ini in new location - Tested agents init succeeds in creating project with database - Template database creation works correctly - All migration tests should pass without changes Alembic files now follow standard Python packaging conventions, making them automatically included in wheel distributions without special configuration. ISSUES CLOSED: #4180 |
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11ba77cf73
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build: fix to get mroe predicatable behavior out of pr-merge-worker
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5762f92583
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build: tweaked the pr-merge agents to ensure they use the skill scripts more consistently
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afa4ebd19b
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build: fixed up the systems skill so it now provides better access to information about scripts to ensure more consistent usage
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1663750b78 |
Merge pull request 'fix(checkpoint): wire CheckpointManager into PlanExecutor execution path' (#4218) from bugfix/checkpoint-wiring into master
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Reviewed-on: #4218 Reviewed-by: Jeffrey Phillips Freeman <jeffrey.freeman@cleverthis.com> |
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bdd1ea4f3a |
fix(checkpoint): wire CheckpointManager into PlanExecutor execution path
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CheckpointManager was never wired into PlanExecutor — the CLI factory constructed PlanExecutor without a checkpoint_manager (defaulted to None), silently skipping all checkpoint hooks. Fix: - Register CheckpointManager as Singleton in DI container - Resolve container singleton in _get_plan_executor() and pass to PlanExecutor constructor - Bridge infra→domain: _try_create_checkpoint() now persists last_checkpoint_id on the plan via _commit_plan(), raises PlanError if persistence fails - Default checkpointable=True for writable+sandboxable resources and write-capable tools (model_validators on ResourceCapabilities and ToolCapability) - Validate that non-writable/non-sandboxable resources cannot be checkpointable (ValueError guard) - Add post-execute A2A facade notification using plan.status to avoid duplicate execute→execute transition errors Tests: - 10 Behave scenarios covering DI wiring, singleton identity, checkpoint creation, plan metadata update, rollback, graceful fallback, no-arg constructor, capability defaults (positive + 2 negative) - Updated consolidated_resource, consolidated_skill, and Robot helper_skill_flatten for new checkpointable defaults ISSUES CLOSED: #1253 |
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e2b127b7e5
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fix(e2e): replace naive OpenAI key-presence check with live API probe in E2E suite setups
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The existing actor-selection logic in several E2E suite setups checked only
whether OPENAI_API_KEY was present (non-empty). A valid key that has hit its
quota limit passes that check but fails at runtime with HTTP 429, causing the
test to fail even though Anthropic credits are available.
Changes:
- Add robot/e2e/check_openai_key.py: stdlib-only (urllib.request) script that
sends a minimal chat-completion request ('Hi', max_tokens=1, gpt-4o-mini) to
the OpenAI API. Exits 0 on HTTP 200; exits 1 for quota (429), auth (401),
network errors, or any other failure.
- Add 'Resolve LLM Actor' keyword to robot/e2e/common_e2e.resource: runs the
probe script via ${PYTHON} and returns the openai_model argument (default
openai/gpt-4o) on success, or the anthropic_model argument (default
anthropic/claude-sonnet-4-20250514) on failure. Skips the probe entirely when
OPENAI_API_KEY is not set.
- Update m6_acceptance.robot, wf04_multi_project.robot, wf05_db_migration.robot,
wf07_cicd.robot, and wf16_devcontainer.robot to use 'Resolve LLM Actor'
instead of the inline has_openai boolean check.
No production source code (src/) is modified. The decision to fall back to
Anthropic is made once per suite setup, before any test runs.
Closes #10198
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80c8636c4a
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Revert "feat: add fallback to Anthropic Sonnet when OpenAI quota is exhausted"
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8a89a5d5a6
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Revert "debug: upgrade logging levels for fallback diagnostics"
This reverts commit
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ed7276773e |
Merge branch 'master' into fix/plan-diff-correction-stub
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1fda56b778 |
fix(cli): implement plan diff --correction to show real correction attempt diff
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Implement spec-compliant correction diff output for `agents plan diff --correction <CORRECTION_ATTEMPT_ID>`. Fixes the following issues from the cycle-1 PR review: - C1/M1: Replace direct `unit_of_work.correction_attempts` access with the proper `unit_of_work.transaction()` context manager, eliminating the AttributeError crash and the resource (session) leak. - C2: Add `unit_of_work: UnitOfWork | None = None` constructor parameter to `PlanApplyService` and wire it in `_get_apply_service()` via `container.unit_of_work()`, removing the illegal `get_container()` call inside the method body (ADR-003 DI violation). - C3: Replace metadata serialization stub with a three-section structured diff (Correction Diff summary, Comparison table, Patch Preview) as specified in §agents plan diff of the specification. - C4/M2: Add `features/plan_correction_diff.feature` with 6 BDD scenarios covering all 4 output formats plus plan-not-found and correction-not-found error paths. - C5: Update the three existing BDD scenarios that tested old stub behavior to mock `_get_apply_service()` and assert the new output. - C6: Rename branch to `bugfix/m4-plan-diff-correction-stub` per CONTRIBUTING.md convention. - C7: Amend commit message with body and ISSUES CLOSED footer. - C8: Narrow `except Exception` to `except CorrectionAttemptNotFoundError` to avoid masking programming errors. - M3: Add `robot/plan_correction_diff.robot` and `robot/helper_plan_correction_diff.py` integration test covering rich, plain, and JSON formats and the not-found error path. - M4: Type `_build_correction_diff_dict` parameter as `CorrectionAttemptRecord` instead of `Any`. - M5: Change `fmt: str` to `fmt: Literal["rich", "plain", "json", "yaml"]` on both `diff()` and `correction_diff()`, with a `cast()` call in the CLI layer where Typer supplies a plain `str`. - M6: Add `ValueError` guards for empty `plan_id` and `correction_attempt_id` at the top of `correction_diff()`. - M7: Add blank line between `diff()` and `correction_diff()` method definitions. - M8: Update PR description to reflect actual implementation. - m1: Remove unused `plan` variable in `correction_diff()`. - m2: Reduce three blank lines to two between top-level definitions in `plan_apply_service.py`. - n1: Remove trailing whitespace from blank line in `plan.py`. Quality gates: lint (ruff), typecheck (pyright strict), unit_tests (Behave 632 features / 0 failures) all pass. ISSUES CLOSED: #9085 |
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8953449dc2
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fix(actor): validate v3 YAML via ActorConfigSchema in agents actor add CLI
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- schema.py: provider field changed to Optional[str] with model validator
validate_provider_required_for_llm_graph() that requires it only for LLM
and GRAPH actor types; TOOL actors do not require provider
- schema.py: is_v3_yaml() uses version_str == "3" or version_str.startswith("3.")
to avoid false positives from "30" or "300" version strings
- schema.py: tool namespace validation uses strict 2-part split to reject
empty namespace or empty name (e.g. "/tool", "ns/", "a/b/c")
- cli/commands/actor.py: schema_version extraction uses raw_version pattern
(no # type: ignore[assignment]) for clean static typing
- actor/__init__.py: is_v3_yaml removed from __all__ and _LAZY_IMPORTS
since it is a module-private helper, not a public API
- robot/actor_add_v3_schema_validation.robot: YAML fixtures for 'Reject v3
LLM Actor Without Model Field' and 'Reject v3 TOOL Actor Without Tools
Field' now include required provider field (and model for TOOL fixture)
- robot/helper_actor_add_v3_schema_validation.py: except clauses unified to
catch (subprocess.TimeoutExpired, FileNotFoundError) in both add_actor()
and update_actor() functions
- features/actor_add_v3_schema_validation.feature: 'Update a v3 actor with
valid YAML succeeds' scenario now includes 'And the actor should be
validated via ActorConfigSchema'; error assertions tightened to exact
messages (e.g. "Input should be 'llm', 'tool' or 'graph'", "Node ID must
be alphanumeric", "must be namespaced")
- features/steps/actor_add_v3_schema_validation_steps.py: step_run_actor_update
now spies on ActorConfigSchema.model_validate; failure paths assert
isinstance(result.exception, SystemExit)
ISSUES CLOSED: #5869
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449c33b752 |
style(tests): apply ruff format to run_behave_parallel.py
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Fix formatting of conditional expression in _is_btrfs_or_overlayfs() that was flagged by ruff format --check in CI. |
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4fc4d7863c |
fix(tests): prevent behave-parallel multiprocessing deadlock on btrfs/overlayfs
Detect btrfs and overlayfs filesystems and automatically fall back to sequential mode to prevent deadlocks caused by SQLite WAL file locking and btrfs COW copy-up locks when multiple forked workers try to access the same files simultaneously. The fix adds a _is_btrfs_or_overlayfs() function that: 1. Attempts to detect the filesystem type using stat command 2. Falls back to reading /proc/mounts if stat fails 3. Returns True if the filesystem is btrfs or overlayfs The sequential mode condition is updated to include this check, ensuring that on affected filesystems, all features run in a single process instead of being split across multiple forked workers. ISSUES CLOSED: #9390 |
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ebf54c642c
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Build: Attempt to prevent merge workers from waiting till merge completes
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e18ac5f23c |
fix(security): replace startswith sandbox check with Path.relative_to() in validate_path #7558
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The validate_path() function in file_tools.py used str.startswith() to verify that a resolved path stays within the sandbox root. This allowed sibling directories whose names share a string prefix with the sandbox (e.g. /tmp/sandbox-escape/ bypassing /tmp/sandbox/) to escape the containment check. Replace the string prefix check with Path.relative_to(root), which performs a proper OS-level path prefix comparison using path separators. Add a Behave regression scenario tagged @tdd_issue @tdd_issue_7558 that exercises the prefix-collision bypass to prevent regressions. ISSUES CLOSED: #7558 |
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51472c0b37 |
debug: upgrade logging levels for fallback diagnostics
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Change fallback LLM creation and invocation logs from DEBUG to WARNING level so they appear in Robot Framework test output. Also enhance error message to clearly show which provider failed and why. This change makes it possible to diagnose why the fallback is not working by seeing the actual logs in test output instead of having them filtered as DEBUG level messages. Logs now include: - 'Creating fallback LLM instance: anthropic/claude-sonnet-4-20250514' - 'Fallback LLM created, attempting invocation' - 'Using cached fallback LLM, attempting invocation' - 'FALLBACK PROVIDER FAILED: anthropic/claude-sonnet-4-20250514 returned error: [error details]' This will help diagnose why E2E tests fail with 'both providers exhausted' when Anthropic should have available credits. |
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f5712787e0 |
feat: add fallback to Anthropic Sonnet when OpenAI quota is exhausted
Implements graceful degradation for E2E robot integration tests that hit OpenAI 429 quota limit errors. Changes: - Add _is_quota_error() helper to detect quota-specific API errors (429, insufficient_quota, rate_limit) - Modify _execute_with_llm() in StrategyActor to catch quota errors and attempt fallback to Anthropic Haiku - Configure fallback provider as 'anthropic/claude-sonnet-4-20250514' - Add comprehensive logging for quota error detection and provider fallback - Add E2E test scenarios for quota fallback verification When quota errors occur on both OpenAI and Anthropic fallback, tests now fail with a clear message explaining that the test outcome cannot be verified when no LLM provider is available. This ensures CI/CD pipelines properly track which tests could not be executed due to quota constraints, rather than silently skipping them and creating false confidence in test coverage. This ensures CI/CD pipelines can complete E2E tests even when the primary provider (OpenAI) hits quota limits, improving pipeline reliability and reducing false negatives caused by provider-specific issues. 1. **Cache fallback_llm instance** - Instead of recreating the fallback LLM every time a quota error occurs, cache it as an instance variable (self._fallback_llm). This avoids unnecessary re-initialization overhead. 2. **Implement quota recovery logic** - Add intelligent recovery behavior: - Track last quota error timestamp (self._last_quota_error_time) - Track fallback mode state (self._using_fallback) - Once quota error detected, switch to fallback provider - Only attempt to recover primary provider every 5 minutes (_QUOTA_RECOVERY_INTERVAL) - This avoids hammering primary provider with repeated quota errors 3. **Add detailed recovery logging** - Log quota fallback transitions and recovery attempts to improve observability and debugging. Benefits: - Reduced latency: No redundant primary provider calls after quota error - Reduced overhead: Cached fallback LLM instance, no per-call recreation - Better observability: Clear logging of fallback mode entry/exit - Intelligent recovery: Automatic recovery attempt after 5-minute interval Updated tests: - M6 E2E Event Queue Via Plan Lifecycle Transitions - M6 E2E Hierarchical Decomposition Via Plan Tree - M6 E2E Full Autonomy Acceptance Flow Fixes: #10042 |
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82591c1a86
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Build: Trying to fix skill script not being called
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9684d80637
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Build: Enabling skill scripts |