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Agent Orchestrator

Meta-agent skill for orchestrating complex tasks.

Rating
4.2 (226 reviews)
Downloads
3,134 downloads
Version
1.0.0

Overview

Meta-agent skill for orchestrating complex tasks.

Complete Documentation

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Agent Orchestrator

Orchestrate complex tasks by decomposing them into subtasks, spawning autonomous sub-agents, and consolidating their work.

Core Workflow

Phase 1: Task Decomposition

Analyze the macro task and break it into independent, parallelizable subtasks:

text
1. Identify the end goal and success criteria
2. List all major components/deliverables required
3. Determine dependencies between components
4. Group independent work into parallel subtasks
5. Create a dependency graph for sequential work

Decomposition Principles:

  • Each subtask should be completable in isolation
  • Minimize inter-agent dependencies
  • Prefer broader, autonomous tasks over narrow, interdependent ones
  • Include clear success criteria for each subtask

Phase 2: Agent Generation

For each subtask, create a sub-agent workspace:

bash
python3 scripts/create_agent.py <agent-name> --workspace <path>

This creates:

text
<workspace>/<agent-name>/
├── SKILL.md          # Generated skill file for the agent
├── inbox/            # Receives input files and instructions
├── outbox/           # Delivers completed work
├── workspace/        # Agent's working area
└── status.json       # Agent state tracking

Generate SKILL.md dynamically with:

  • Agent's specific role and objective
  • Tools and capabilities needed
  • Input/output specifications
  • Success criteria
  • Communication protocol
See references/sub-agent-templates.md for pre-built templates.

Phase 3: Agent Dispatch

Initialize each agent by:

  • Writing task instructions to inbox/instructions.md
  • Copying required input files to inbox/
  • Setting status.json to {"state": "pending", "started": null}
  • Spawning the agent using the Task tool:
python
# Spawn agent with its generated skill
Task(
    description=f"{agent_name}: {brief_description}",
    prompt=f"""
    Read the skill at {agent_path}/SKILL.md and follow its instructions.
    Your workspace is {agent_path}/workspace/
    Read your task from {agent_path}/inbox/instructions.md
    Write all outputs to {agent_path}/outbox/
    Update {agent_path}/status.json when complete.
    """,
    subagent_type="general-purpose"
)

Phase 4: Monitoring (Checkpoint-based)

For fully autonomous agents, minimal monitoring is needed:

python
# Check agent completion
def check_agent_status(agent_path):
    status = read_json(f"{agent_path}/status.json")
    return status.get("state") == "completed"

Periodically check status.json for each agent. Agents update this file upon completion.

Phase 5: Consolidation

Once all agents complete:

  • Collect outputs from each agent's outbox/
  • Validate deliverables against success criteria
  • Merge/integrate outputs as needed
  • Resolve conflicts if multiple agents touched shared concerns
  • Generate summary of all work completed
python
# Consolidation pattern
for agent in agents:
    outputs = glob(f"{agent.path}/outbox/*")
    validate_outputs(outputs, agent.success_criteria)
    consolidated_results.extend(outputs)

Phase 6: Dissolution & Summary

After consolidation:

  • Archive agent workspaces (optional)
  • Clean up temporary files
  • Generate final summary:
  • What was accomplished per agent
  • Any issues encountered
  • Final deliverables location
  • Time/resource metrics
python
python3 scripts/dissolve_agents.py --workspace <path> --archive

File-Based Communication Protocol

See references/communication-protocol.md for detailed specs.

Quick Reference:

  • inbox/ - Read-only for agent, written by orchestrator
  • outbox/ - Write-only for agent, read by orchestrator
  • status.json - Agent updates state: pending → running → completed | failed

Example: Research Report Task

text
Macro Task: "Create a comprehensive market analysis report"

Decomposition:
├── Agent: data-collector
│   └── Gather market data, competitor info, trends
├── Agent: analyst
│   └── Analyze collected data, identify patterns
├── Agent: writer
│   └── Draft report sections from analysis
└── Agent: reviewer
    └── Review, edit, and finalize report

Dependency: data-collector → analyst → writer → reviewer

Sub-Agent Templates

Pre-built templates for common agent types in references/sub-agent-templates.md:

  • Research Agent - Web search, data gathering
  • Code Agent - Implementation, testing
  • Analysis Agent - Data processing, pattern finding
  • Writer Agent - Content creation, documentation
  • Review Agent - Quality assurance, editing
  • Integration Agent - Merging outputs, conflict resolution

Best Practices

  • Start small - Begin with 2-3 agents, scale as patterns emerge
  • Clear boundaries - Each agent owns specific deliverables
  • Explicit handoffs - Use structured files for agent communication
  • Fail gracefully - Agents report failures; orchestrator handles recovery
  • Log everything - Status files track progress for debugging

Installation

Terminal bash 

openclaw install agent-orchestrator
Copied!

💻Code Examples

5. Create a dependency graph for sequential work

5-create-a-dependency-graph-for-sequential-work.txt
**Decomposition Principles:**
- Each subtask should be completable in isolation
- Minimize inter-agent dependencies
- Prefer broader, autonomous tasks over narrow, interdependent ones
- Include clear success criteria for each subtask

### Phase 2: Agent Generation

For each subtask, create a sub-agent workspace:

└── status.json # Agent state tracking

-statusjson--agent-state-tracking.txt
**Generate SKILL.md dynamically** with:
- Agent's specific role and objective
- Tools and capabilities needed
- Input/output specifications
- Success criteria
- Communication protocol

See [references/sub-agent-templates.md](references/sub-agent-templates.md) for pre-built templates.

### Phase 3: Agent Dispatch

Initialize each agent by:

1. Writing task instructions to `inbox/instructions.md`
2. Copying required input files to `inbox/`
3. Setting `status.json` to `{"state": "pending", "started": null}`
4. Spawning the agent using the Task tool:

)

.txt
### Phase 4: Monitoring (Checkpoint-based)

For fully autonomous agents, minimal monitoring is needed:

return status.get("state") == "completed"

-return-statusgetstate--completed.txt
Periodically check `status.json` for each agent. Agents update this file upon completion.

### Phase 5: Consolidation

Once all agents complete:

1. **Collect outputs** from each agent's `outbox/`
2. **Validate deliverables** against success criteria
3. **Merge/integrate** outputs as needed
4. **Resolve conflicts** if multiple agents touched shared concerns
5. **Generate summary** of all work completed

consolidated_results.extend(outputs)

-consolidatedresultsextendoutputs.txt
### Phase 6: Dissolution & Summary

After consolidation:

1. **Archive agent workspaces** (optional)
2. **Clean up temporary files**
3. **Generate final summary**:
   - What was accomplished per agent
   - Any issues encountered
   - Final deliverables location
   - Time/resource metrics

python3 scripts/dissolve_agents.py --workspace <path> --archive

python3-scriptsdissolveagentspy---workspace-path---archive.txt
## File-Based Communication Protocol

See [references/communication-protocol.md](references/communication-protocol.md) for detailed specs.

**Quick Reference:**
- `inbox/` - Read-only for agent, written by orchestrator
- `outbox/` - Write-only for agent, read by orchestrator
- `status.json` - Agent updates state: `pending` → `running` → `completed` | `failed`

## Example: Research Report Task
example.txt
1. Identify the end goal and success criteria
2. List all major components/deliverables required
3. Determine dependencies between components
4. Group independent work into parallel subtasks
5. Create a dependency graph for sequential work
example.txt
<workspace>/<agent-name>/
├── SKILL.md          # Generated skill file for the agent
├── inbox/            # Receives input files and instructions
├── outbox/           # Delivers completed work
├── workspace/        # Agent's working area
└── status.json       # Agent state tracking
example.py
# Spawn agent with its generated skill
Task(
    description=f"{agent_name}: {brief_description}",
    prompt=f"""
    Read the skill at {agent_path}/SKILL.md and follow its instructions.
    Your workspace is {agent_path}/workspace/
    Read your task from {agent_path}/inbox/instructions.md
    Write all outputs to {agent_path}/outbox/
    Update {agent_path}/status.json when complete.
    """,
    subagent_type="general-purpose"
)
example.py
# Check agent completion
def check_agent_status(agent_path):
    status = read_json(f"{agent_path}/status.json")
    return status.get("state") == "completed"

Tags

#ai_and-llms

Quick Info

Category Development
Model Claude 3.5
Complexity Multi-Agent
Author aatmaan1
Last Updated 3/10/2026
🚀
Optimized for
Claude 3.5
🧠

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openclaw install agent-orchestrator

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