- Add mosaicstack-telemetry>=0.1.0 to pyproject.toml dependencies
- Configure Gitea PyPI registry via pip.conf (extra-index-url)
- Integrate TelemetryClient in FastAPI lifespan (start_async/stop_async)
- Store client on app.state.mosaic_telemetry for downstream access
- Create mosaic_telemetry.py helper module with:
- get_telemetry_client(): retrieve client from app state
- build_task_event(): construct TaskCompletionEvent with coordinator defaults
- create_telemetry_config(): create config from MOSAIC_TELEMETRY_* env vars
- Add 28 unit tests covering config, helpers, disabled mode, and lifespan
- New module has 100% test coverage
Refs #370
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three fixes for the coordinator pipeline:
1. Use bandit.yaml config file (-c bandit.yaml) so global skips
and exclude_dirs are respected in CI.
2. Upgrade pip to >=25.3 in the install step so pip-audit doesn't
fail on the stale pip 24.0 bundled with python:3.11-slim.
3. Clean up nosec inline comments to bare "# nosec BXXX" format,
moving explanations to a separate comment line above. This
prevents bandit from misinterpreting trailing text as test IDs.
Fixes#365
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Coordinator: install all dependencies from pyproject.toml instead of
hardcoded subset (missing slowapi, anthropic, opentelemetry-*).
API: FederationAgentService now gracefully disables when orchestrator
URL is not configured instead of throwing and crashing the app.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds directory-specific agent context templates for AI-assisted
development across all apps and packages.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add sanitize_for_prompt() function to security module
- Remove suspicious control characters (except whitespace)
- Detect and log common prompt injection patterns
- Escape dangerous XML-like tags used for prompt manipulation
- Truncate user content to max length (default 50000 chars)
- Integrate sanitization in parser before building LLM prompts
- Add comprehensive test suite (12 new tests)
Refs #338
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Log ERROR when queue corruption detected with error details
- Create timestamped backup before discarding corrupted data
- Add comprehensive tests for corruption handling
Refs #338
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement circuit breaker pattern to prevent infinite retry loops on
repeated failures (SEC-ORCH-7). The circuit breaker tracks consecutive
failures and opens after a threshold is reached, blocking further
requests until a cooldown period elapses.
Circuit breaker states:
- CLOSED: Normal operation, requests pass through
- OPEN: After N consecutive failures, all requests blocked
- HALF_OPEN: After cooldown, allow one test request
Changes:
- Add circuit_breaker.py with CircuitBreaker class
- Integrate circuit breaker into Coordinator.start() loop
- Integrate circuit breaker into OrchestrationLoop.start() loop
- Integrate per-agent circuit breakers into ContextMonitor
- Add comprehensive tests for circuit breaker behavior
- Log state transitions and circuit breaker stats on shutdown
Configuration (defaults):
- failure_threshold: 5 consecutive failures
- cooldown_seconds: 30 seconds
Refs #338
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implements comprehensive LLM usage tracking with analytics endpoints.
Implementation:
- Added LlmUsageLog model to Prisma schema
- Created llm-usage module with service, controller, and DTOs
- Added tracking for token usage, costs, and durations
- Implemented analytics aggregation by provider, model, and task type
- Added filtering by workspace, provider, model, user, and date range
Testing:
- 20 unit tests with 90.8% coverage (exceeds 85% requirement)
- Tests for service and controller with full error handling
- Tests use Vitest following project conventions
API Endpoints:
- GET /api/llm-usage/analytics - Aggregated usage analytics
- GET /api/llm-usage/by-workspace/:workspaceId - Workspace usage logs
- GET /api/llm-usage/by-workspace/:workspaceId/provider/:provider - Provider logs
- GET /api/llm-usage/by-workspace/:workspaceId/model/:model - Model logs
Database:
- LlmUsageLog table with indexes for efficient queries
- Relations to User, Workspace, and LlmProviderInstance
- Ready for migration with: pnpm prisma migrate dev
Refs #309
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Updated pnpm version from 10.19.0 to 10.27.0 to fix HIGH severity
vulnerabilities (CVE-2025-69262, CVE-2025-69263, CVE-2025-6926).
Changes:
- apps/api/Dockerfile: line 8
- apps/web/Dockerfile: lines 8 and 81
Fixes#180
Implement session rotation that spawns fresh agents when context reaches
95% threshold.
TDD Process:
1. RED: Write comprehensive tests (all initially fail)
2. GREEN: Implement trigger_rotation method (all tests pass)
Changes:
- Add SessionRotation dataclass to track rotation metrics
- Implement trigger_rotation method in ContextMonitor
- Add 6 new unit tests covering all acceptance criteria
Rotation process:
1. Get current context usage metrics
2. Close current agent session
3. Spawn new agent with same type
4. Transfer next issue to new agent
5. Log rotation event with metrics
Test Results:
- All 47 tests pass (34 context_monitor + 13 context_compaction)
- 97% coverage on context_monitor.py (exceeds 85% requirement)
- 97% coverage on context_compaction.py (exceeds 85% requirement)
Prevents context exhaustion by starting fresh when compaction is insufficient.
Acceptance Criteria (All Met):
✓ Rotation triggered at 95% context threshold
✓ Current session closed cleanly
✓ New agent spawned with same type
✓ Next issue transferred to new agent
✓ Rotation logged with session IDs and context metrics
✓ Unit tests with 85%+ coverage
Fixes#152
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add comprehensive tests for context compaction functionality:
- Request summary from agent of completed work
- Replace conversation history with summary
- Measure context reduction achieved
- Integration with ContextMonitor
Tests cover:
- Summary generation and prompt validation
- Conversation history replacement
- Context reduction metrics (target: 40-50%)
- Error handling and failure cases
- Integration with context monitoring
Coverage: 100% for context_compaction module
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement the main orchestration loop that coordinates all components:
- Queue processing with priority sorting (issues by number)
- Integration with ContextMonitor for tracking agent context usage
- Integration with QualityOrchestrator for running quality gates
- Integration with ForcedContinuationService for rejection prompts
- Metrics tracking (processed_count, success_count, rejection_count)
- Graceful start/stop with proper lifecycle management
- Error handling at all levels (spawn, context, quality, continuation)
The OrchestrationLoop flow:
1. Read issue queue (priority sorted by issue number)
2. Mark issue as in progress
3. Spawn agent (stub implementation for Phase 0)
4. Check context usage via ContextMonitor
5. Run quality gates via QualityOrchestrator
6. On approval: mark complete, increment success count
7. On rejection: generate continuation prompt, increment rejection count
99% test coverage for coordinator.py (183 statements, 2 missed).
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add comprehensive test suite for OrchestrationLoop class that integrates:
- Queue processing with priority sorting
- Agent assignment (50% rule)
- Quality gate verification on completion claims
- Rejection handling with forced continuation prompts
- Context monitoring during agent execution
- Lifecycle management (start/stop)
- Error handling for all edge cases
- Metrics tracking (processed, success, rejection counts)
33 new tests covering all acceptance criteria.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Fixed code review findings:
- Removed unused imports (AsyncMock, MagicMock)
- Fixed line length violation in test_forced_continuation.py
All 15 tests still passing after fixes.
Implement comprehensive test suite for four core quality gates:
- BuildGate: Tests mypy type checking enforcement
- LintGate: Tests ruff linting with warnings as failures
- TestGate: Tests pytest execution requiring 100% pass rate
- CoverageGate: Tests coverage enforcement with 85% minimum
All tests follow TDD methodology - written before implementation.
Total: 36 tests covering success, failure, and edge cases.
Related to #147
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add comprehensive cost optimization test scenarios and validation report.
Test Scenarios Added (10 new tests):
- Low difficulty assigns to MiniMax/GLM (free agents)
- Medium difficulty assigns to GLM when within capacity
- High difficulty assigns to Opus (only capable agent)
- Oversized issues rejected with actionable error
- Boundary conditions at capacity limits
- Aggregate cost optimization across all scenarios
Results:
- All 33 tests passing (23 existing + 10 new)
- 100% coverage of agent_assignment.py (36/36 statements)
- Cost savings validation: 50%+ in aggregate scenarios
- Real-world projection: 70%+ savings with typical workload
Documentation:
- Created cost-optimization-validation.md with detailed analysis
- Documents cost savings for each scenario
- Validates all acceptance criteria from COORD-006
Completes Phase 2 (M4.1-Coordinator) testing requirements.
Fixes#146
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implements the Coordinator class with main orchestration loop:
- Async loop architecture with configurable poll interval
- process_queue() method gets next ready issue and spawns agent (stub)
- Graceful shutdown handling with stop() method
- Error handling that allows loop to continue after failures
- Logging for all actions (start, stop, processing, errors)
- Integration with QueueManager from #159
- Active agent tracking for future agent management
Configuration settings added:
- COORDINATOR_POLL_INTERVAL (default: 5.0s)
- COORDINATOR_MAX_CONCURRENT_AGENTS (default: 10)
- COORDINATOR_ENABLED (default: true)
Tests: 27 new tests covering all acceptance criteria
Coverage: 92% overall (100% for coordinator.py)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add Capability enum (HIGH, MEDIUM, LOW) for agent difficulty levels
- Add AgentName enum for all 5 agents (opus, sonnet, haiku, glm, minimax)
- Implement AgentProfile data structure with validation
- context_limit: max tokens for context window
- cost_per_mtok: cost per million tokens (0 for self-hosted)
- capabilities: list of difficulty levels the agent handles
- best_for: description of optimal use cases
- Define profiles for all 5 agents with specifications:
- Anthropic models (opus, sonnet, haiku): 200K context, various costs
- Self-hosted models (glm, minimax): 128K context, free
- Implement get_agent_profile() function for profile lookup
- Add comprehensive test suite (37 tests, 100% coverage)
- Profile data structure validation
- All 5 predefined profiles exist and are correct
- Capability enum and AgentName enum tests
- Best_for validation and capability matching
- Consistency checks across profiles
Fixes#144
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implements ContextMonitor class with real-time token usage tracking:
- COMPACT_THRESHOLD at 0.80 (80% triggers compaction)
- ROTATE_THRESHOLD at 0.95 (95% triggers rotation)
- Poll Claude API for context usage
- Return appropriate ContextAction based on thresholds
- Background monitoring loop (10-second polling)
- Log usage over time
- Error handling and recovery
Added ContextUsage model for tracking agent token consumption.
Tests:
- 25 test cases covering all functionality
- 100% coverage for context_monitor.py and models.py
- Mocked API responses for different usage levels
- Background monitoring and threshold detection
- Error handling verification
Quality gates:
- Type checking: PASS (mypy)
- Linting: PASS (ruff)
- Tests: PASS (25/25)
- Coverage: 100% for new files, 95.43% overall
Fixes#155
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
