SmallClaw v1.0.2 adds background task system for local LLMs
SmallClaw v1.0.2 is now available, bringing a background task system that lets you run multi-step workflows without babysitting the chat interface. This update targets the core issue with small local LLMs: making them usable for real tasks.
Background Task System
The new task engine enables autonomous execution with these capabilities:
- Plan multi-step tasks
- Run tasks detached from active chat requests
- Pause and resume when blocked
- Stream live status updates to the UI
- Notify both originating chat and Telegram on completion
Architecture
The system has three layers:
- Data layer: Tasks persist as JSON under
.localclaw/tasks/with status, plan steps, journal, and resume context - Execution layer: BackgroundTaskRunner executes tasks in autonomous rounds via the same core chat loop, journals tool calls/results, verifies step completion before advancing, retries on transport errors, and pauses cleanly when it needs help
- Gateway and UI layer: REST + event stream to the web UI, where tasks render in a kanban board with detail panel showing plan, journal, summary, and pause/resume controls
Task Lifecycle
- Create task, status starts as queued with a plan and step index
- Runner starts, assigns deterministic session key
task_<taskId> - Runs in rounds: step-aware prompt, tool calls are logged and broadcast, completion is verified before moving forward
- On complete, writes final summary and delivers results back to original chat and Telegram if configured
- If it stalls or loops, pauses with
needs_assistanceand sends notification instead of burning cycles forever
Key Improvements
Step completion verification: Background task steps now get verified before the runner advances. This directly targets the classic small model issue where it claims something is done but did not actually do it. The system asks for a YES or NO completion check, only advances on YES, retries with feedback on NO, and pauses if it still cannot complete after the retry cap.
Completion delivery: Task results now reliably land back into the originating session, and delivery can be pushed to Telegram when configured.
Multi-agent flows: Multi-agent is optional and doesn't change SmallClaw's core objective. The small local model can stay your primary executor. Multi-agent makes the system better at planning, recovery, and tool-heavy workflows when small models start struggling.
Loop detection: Now in the main tool loop, tracking repeated toolName and args patterns and injecting guidance to break stalls instead of letting it spiral.
Tested configuration: This update has been tested on 4B class models, including qwen3:4b on an 8GB machine.
📖 Read the full source: r/openclaw
👀 See Also
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