---
name: agent-orchestrator-5f4aecfa1f5a
version: 1.0.5
author: molter-white
description: Multi-agent orchestration with 5 proven patterns - Work Crew, Supervisor, Pipeline, Council, and Auto-Routing
license: MIT
tags: multi-agent,orchestration,automation,productivity,ai-workflow
compatibility: OpenClaw 0.8+
---

# 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

| Pattern | Use When | Avoid When |
|---------|----------|------------|
| **crew** | Same task from multiple angles, verification, research breadth | Results cannot be easily compared/merged |
| **supervise** | Dynamic decomposition needed, complex planning | Fixed workflow, simple delegation |
| **pipeline** | Well-defined sequential stages, content creation | Path needs runtime adaptation |
| **council** | Cross-domain expertise, risk assessment, policy review | Single-domain task, need fast consensus |
| **route** | Mixed workload types, automatic classification | Task type is already known |

## Auto-Routing Pattern

The route command analyzes tasks and automatically classifies them by type, then routes to the appropriate specialist:

```bash
# 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

```bash
# 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**
```bash
# 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**
```bash
# 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**
```bash
# 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**
```bash
# 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**
  - [x] Supervisor pattern implemented - dynamic task decomposition and worker delegation
  - [x] Pipeline pattern implemented - sequential staged processing with validation gates
  - [x] Council pattern implemented - multi-expert deliberation with convergence methods
  - [x] Route pattern implemented - intelligent task classification and specialist routing

## References

- Anthropic Multi-Agent Research System
- LangGraph Supervisor Pattern
- CrewAI Framework
- AutoGen Conversational Agents


---
Source attribution: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agent-orchestrator-molter/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2025 skill-library-mcp contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:

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copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agent-orchestrator-molter/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Product Management
Roles: Data Scientist, Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager
Specialties: API
Tools: OpenAI API, Openclaw
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 6ef99b7917cd840bcc7410fe6b08061050119f706ea9d7336e4f9aceab518e10