forked from Rockachopa/Timmy-time-dashboard
d8d976aa60
This commit implements six major features: 1. Event Log System (src/swarm/event_log.py) - SQLite-based audit trail for all swarm events - Task lifecycle tracking (created, assigned, completed, failed) - Agent lifecycle tracking (joined, left, status changes) - Integrated with coordinator for automatic logging - Dashboard page at /swarm/events 2. Lightning Ledger (src/lightning/ledger.py) - Transaction tracking for Lightning Network payments - Balance calculations (incoming, outgoing, net, available) - Integrated with payment_handler for automatic logging - Dashboard page at /lightning/ledger 3. Semantic Memory / Vector Store (src/memory/vector_store.py) - Embedding-based similarity search for Echo agent - Fallback to keyword matching if sentence-transformers unavailable - Personal facts storage and retrieval - Dashboard page at /memory 4. Cascade Router Integration (src/timmy/cascade_adapter.py) - Automatic LLM failover between providers (Ollama → AirLLM → API) - Circuit breaker pattern for failing providers - Metrics tracking per provider (latency, error rates) - Dashboard status page at /router/status 5. Self-Upgrade Approval Queue (src/upgrades/) - State machine for self-modifications: proposed → approved/rejected → applied/failed - Human approval required before applying changes - Git integration for branch management - Dashboard queue at /self-modify/queue 6. Real-Time Activity Feed (src/events/broadcaster.py) - WebSocket-based live activity streaming - Bridges event_log to dashboard clients - Activity panel on /swarm/live Tests: - 101 unit tests passing - 4 new E2E test files for Selenium testing - Run with: SELENIUM_UI=1 pytest tests/functional/ -v --headed Documentation: - 6 ADRs (017-022) documenting architecture decisions - Implementation summary in docs/IMPLEMENTATION_SUMMARY.md - Architecture diagram in docs/architecture-v2.md
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ADR 017: Event Logging System
Status
Accepted
Context
The swarm system needed a way to audit all agent actions, task lifecycle events, and system events. Without centralized logging, debugging failures and understanding system behavior required grep-ing through application logs.
Decision
Implement a centralized event logging system in SQLite (event_log table) that captures all significant events with structured data.
Event Types
| Type | Description |
|---|---|
task.created |
New task posted |
task.bidding |
Task opened for bidding |
task.assigned |
Task assigned to agent |
task.started |
Agent started working |
task.completed |
Task finished successfully |
task.failed |
Task failed |
agent.joined |
New agent registered |
agent.left |
Agent deregistered |
bid.submitted |
Agent submitted bid |
tool.called |
Tool execution started |
tool.completed |
Tool execution finished |
system.error |
System error occurred |
Schema
CREATE TABLE event_log (
id TEXT PRIMARY KEY,
event_type TEXT NOT NULL,
source TEXT NOT NULL,
task_id TEXT,
agent_id TEXT,
data TEXT, -- JSON
timestamp TEXT NOT NULL
);
Usage
from swarm.event_log import log_event, EventType, get_task_events
# Log an event
log_event(
event_type=EventType.TASK_ASSIGNED,
source="coordinator",
task_id=task.id,
agent_id=winner.agent_id,
data={"bid_sats": winner.bid_sats},
)
# Query events
events = get_task_events(task_id)
summary = get_event_summary(minutes=60)
Integration
The coordinator automatically logs:
- Task creation, assignment, completion, failure
- Agent join/leave events
- System warnings and errors
Consequences
- Positive: Complete audit trail, easy debugging, analytics support
- Negative: Additional database writes, storage growth over time
Mitigations
prune_events()function removes events older than N days- Indexes on
task_id,agent_id, andtimestampfor fast queries