- Inject wrap-up nudges/escalations after token/turn thresholds are met
- Update compaction logic to include key accumulated facts in summaries
- Refine knowledge extraction and accumulation tests
- Update main entry point for revised AI configuration
- Introduce KnowledgeAccumulator to persist facts across turns
- Enhance AgenticLoop to support knowledge injection and final text summaries
- Update chat service to wire up knowledge components
- Frontend updates to support enhanced chat capabilities
- Discovery: classify transient errors (429, timeout, connection refused, etc.)
and return IsError:true so models stop retrying rate-limited calls
- Agentic loop: detect identical tool calls repeated >3 times and block with
LOOP_DETECTED error, forcing the model to try a different approach
- OpenAI provider: skip tool_choice for DeepSeek Reasoner which doesn't support it
- Read-only classifier: fix curl -I case sensitivity (uppercase flags lowered),
add iostat/vmstat/mpstat/sar/lxc-ls/lxc-info/nc -z to allowlist,
fix 2>&1 false positive in input redirect detection
When pruning older messages to fit context limits, we may cut off
a user message that preceded an assistant message with tool calls.
This leaves an orphaned tool call sequence at the start.
Extend pruneMessagesForModel to:
- Skip leading assistant messages with tool calls
- Also skip their following tool results
- Ensures clean message sequence for all providers
- Add ExecutePatrolStream method to chat.Service for patrol-specific execution
- Create chat_service_adapter.go to bridge chat.Service to ai.ChatServiceProvider
- Remove standalone patrol.go and patrol_test.go from chat package
- Add PatrolRequest/PatrolResponse types to chat service
- Add context injection for recent message context
This allows patrol to use an isolated agentic loop with its own system prompt
while leveraging the common chat infrastructure.
Major new AI capabilities for infrastructure monitoring:
Investigation System:
- Autonomous finding investigation with configurable autonomy levels
- Investigation orchestrator with rate limiting and guardrails
- Safety checks for read-only mode enforcement
- Chat-based investigation with approval workflows
Forecasting & Remediation:
- Trend forecasting for resource capacity planning
- Remediation engine for generating fix proposals
- Circuit breaker for AI operation protection
Unified Findings:
- Unified store bridging alerts and AI findings
- Correlation and root cause analysis
- Incident coordinator with metrics recording
New Frontend:
- AI Intelligence page with patrol controls
- Investigation drawer for finding details
- Unified findings panel with actions
Supporting Infrastructure:
- Learning store for user preference tracking
- Proxmox event ingestion and correlation
- Enhanced patrol with investigation triggers
- Add comprehensive tests for internal/api/config_handlers.go (Phases 1-3)
- Improve test coverage for AI tools, chat service, and session management
- Enhance alert and notification tests (ResolvedAlert, Webhook)
- Add frontend unit tests for utils (searchHistory, tagColors, temperature, url)
- Add proximity client API tests
Replace the OpenCode sidecar with a native chat service that handles:
- Real-time streaming responses from AI providers
- Multi-turn conversation sessions with history
- Tool execution with automatic function calling
- Agentic workflows for autonomous task completion
- Patrol integration for automated health analysis
The chat service directly communicates with AI providers using the
new StreamingProvider interface, eliminating the need for an external
sidecar process. Sessions are managed in-memory with configurable
history limits.
Key components:
- service.go: Main chat service with provider integration
- session.go: Session management and message history
- agentic.go: Agentic loop for autonomous tool execution
- patrol.go: Patrol-specific chat context and analysis
- tools.go: Tool execution bridge to tools package
- types.go: Chat message and event type definitions
