Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Agent Orchestrator
Multi-agent orchestration with 5 proven patterns - Work Crew, Supervisor, Pipeline, Council, and Auto-Routing
skill openclawclawhub Free
Multi-agent orchestration with 5 proven patterns - Work Crew, Supervisor, Pipeline, Council, and Auto-Routing
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Package facts
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- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- CHANGELOG.md, CONVENTION.md, PUBLISHING.md, README.md, SECURITY.md, SKILL.md
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Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.5
Provenance
- Publisher
- variable190
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Documentation
agent-orchestrator
Multi-agent orchestration for OpenClaw. Implements 5 proven patterns for coordinating multiple AI agents: Work Crew, Supervisor, Pipeline, Expert Council, and Auto-Routing. USE WHEN: A task can be parallelized for speed or redundancy (Work Crew) Complex tasks need dynamic planning and delegation (Supervisor) Work follows a predictable sequence of stages (Pipeline) Cross-domain input is needed from multiple specialists (Expert Council) Mixed task types need automatic routing to appropriate specialists (Auto-Routing) Research tasks require breadth-first exploration of multiple angles High-stakes decisions need confidence through multiple perspectives DON'T USE WHEN: Simple tasks that fit in one agent's context window (use main session instead) Sequential tasks with no parallelization opportunity (use regular tool calls) One-shot deterministic tasks (use single agent) Tasks requiring real-time inter-agent conversation (this uses async spawning) Tasks where 15x token cost cannot be justified Quick/simple tasks where coordination overhead exceeds benefit Outputs: Aggregated results from multiple parallel agents Synthesized consensus recommendations Routing decisions to appropriate specialists Structured output from staged processing
Decision Matrix
PatternUse WhenAvoid WhencrewSame task from multiple angles, verification, research breadthResults cannot be easily compared/mergedsuperviseDynamic decomposition needed, complex planningFixed workflow, simple delegationpipelineWell-defined sequential stages, content creationPath needs runtime adaptationcouncilCross-domain expertise, risk assessment, policy reviewSingle-domain task, need fast consensusrouteMixed workload types, automatic classificationTask type is already known
Auto-Routing Pattern
The route command analyzes tasks and automatically classifies them by type, then routes to the appropriate specialist: # Basic routing claw agent-orchestrator route --task "Write Python parser" # With custom specialist pool claw agent-orchestrator route \ --task "Analyze data and create report" \ --specialists "analyst,data,writer" # Force specific specialist claw agent-orchestrator route \ --task "Something complex" \ --force coder
Confidence Thresholds
High confidence (>0.85): Auto-route immediately Good confidence (0.7-0.85): Propose with confirmation option Moderate confidence (0.5-0.7): Show top alternatives Low confidence (<0.5): Request clarification Available specialists: coder, researcher, writer, analyst, planner, reviewer, creative, data, devops, support
Common Workflows
# Parallel research with consensus claw agent-orchestrator crew \ --task "Research Bitcoin Lightning 2026 adoption" \ --agents 4 \ --perspectives technical,business,security,competitors \ --converge consensus # Best-of redundancy for critical analysis claw agent-orchestrator crew \ --task "Audit this smart contract for vulnerabilities" \ --agents 3 \ --converge best-of # Supervisor-managed code review claw agent-orchestrator supervise \ --task "Refactor authentication module" \ --workers coder,reviewer,tester \ --strategy adaptive # Staged content pipeline claw agent-orchestrator pipeline \ --stages research,draft,review,finalize \ --input "topic: AI agent adoption trends" # Expert council for decision claw agent-orchestrator council \ --question "Should we publish this blog post about unreleased features?" \ --experts skeptic,ethicist,strategist \ --converge consensus \ --rounds 2 # Auto-route mixed tasks claw agent-orchestrator route \ --task "Write Python function to analyze CSV data" \ --specialists coder,researcher,writer,analyst # Force route to specific specialist claw agent-orchestrator route \ --task "Debug authentication error" \ --force coder \ --confidence-threshold 0.9 # Route and output as JSON for scripting claw agent-orchestrator route \ --task $TASK \ --format json \ --specialists "coder,data,analyst"
Negative Examples
DON'T: Use crew for simple single-answer questions # WRONG: Wasteful for simple facts claw agent-orchestrator crew --task "What is 2+2?" --agents 3 # RIGHT: Use main session directly What is 2+2? DON'T: Use supervise when pipeline suffices # WRONG: Over-engineering fixed workflows claw agent-orchestrator supervise --task "Draft, edit, publish" # RIGHT: Use pipeline for fixed sequences claw agent-orchestrator pipeline --stages draft,edit,publish DON'T: Route when task type is obvious # WRONG: Unnecessary classification overhead claw agent-orchestrator route --task "Write Python code" # RIGHT: Direct to appropriate specialist claw agent-orchestrator crew --pattern code --task "Write Python code" DON'T: Use multi-agent for very small context tasks # WRONG: Coordination overhead exceeds value claw agent-orchestrator crew --task "Fix typo" --agents 2 # RIGHT: Single agent or direct edit edit file.py "typo" "correct"
Token Cost Warning
Multi-agent patterns use approximately 15x more tokens than single-agent interactions. Use only for high-value tasks where quality improvement justifies the cost. See Anthropic research: token usage explains 80% of performance variance in complex tasks.
Dependencies
Python 3.8+ OpenClaw sessions_spawn capability OpenClaw sessions_list capability OpenClaw sessions_history capability
Files
__main__.py - CLI entry point crew.py - Work Crew pattern implementation supervise.py - Supervisor pattern (Phase 2) council.py - Expert Council pattern (Phase 2) pipeline.py - Pipeline pattern (Phase 2) route.py - Auto-Routing pattern (Phase 2) utils.py - Shared utilities for session management
Status
MVP: Work Crew pattern implemented Phase 2: 100% Complete Supervisor pattern implemented - dynamic task decomposition and worker delegation Pipeline pattern implemented - sequential staged processing with validation gates Council pattern implemented - multi-expert deliberation with convergence methods Route pattern implemented - intelligent task classification and specialist routing
References
Anthropic Multi-Agent Research System LangGraph Supervisor Pattern CrewAI Framework AutoGen Conversational Agents
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- CHANGELOG.md
- CONVENTION.md
- PUBLISHING.md
- README.md
- SECURITY.md