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Multi-LLM orchestration for OpenClaw with fan-out, pipeline, and consensus patterns. Dispatches tasks across 40+ models using AAHP v3 inspired handoffs.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install homeofe/openclaw-model-orchestratorExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Dispatch tasks across multiple LLMs from chat. Uses AAHP v3 structured handoffs for minimal token overhead.
Split a task into parallel subtasks, each executed by a different model. A planner model decomposes the task, workers execute in parallel, a reviewer merges results.
/orchestrate --mode fan-out --task "Build a REST API with auth" --planner copilot-opus --workers copilot52c,grokfast --reviewer copilot-sonnet46
Chain models sequentially. Each model refines the previous model's output. Ideal for plan -> implement -> review -> polish workflows.
/orchestrate --mode pipeline --task "Design and implement a caching layer" --planner copilot-opus --workers copilot52c,copilot-sonnet46 --reviewer copilot-opus
Send the same question to multiple models, then synthesize the best answer. Identifies agreement, disagreement, and unique insights across models.
/orchestrate --mode consensus --task "What are the security risks of this API design?" --workers copilot-opus,gemini25,sonnet --reviewer copilot-opus
The orchestrator auto-classifies tasks and recommends optimal model combinations:
/orchestrate recommend "Build a REST API with JWT auth"
Returns: task classification, recommended planner/workers/reviewer, reasoning, and a ready-to-run command.
Use help as any flag value for context-aware recommendations:
/orchestrate --task "Audit security" --planner help
Pre-configured model combinations optimized for common task types:
| Profile | Planner | Workers | Reviewer | Use Case |
|---|---|---|---|---|
| coding | copilot-opus | copilot52c, grokfast | copilot-sonnet46 | Feature development |
| research | gemini25 | gemini-flash, copilot-flash | copilot-opus | Analysis, investigation |
| security | copilot-opus | copilot-sonnet46, gemini25 | sonnet | Security audi... |
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
Multi-LLM orchestration for OpenClaw. Fan-out, pipeline, and consensus patterns with AAHP v3 handoffs.
clawhub install openclaw-model-orchestrator
# Get help
/orchestrate help
# See available models
/orchestrate models
# Get recommendations for a task
/orchestrate recommend "Build a REST API with JWT auth"
# Fan-out: parallel subtasks across models
/orchestrate --mode fan-out --task "Build REST API" --planner copilot-opus --workers copilot52c,grokfast --reviewer copilot-sonnet46
# Pipeline: sequential refinement
/orchestrate --mode pipeline --task "Design caching layer"
# Consensus: compare model answers
/orchestrate --mode consensus --task "Security risks of this design?" --workers copilot-opus,gemini25,sonnet
All inter-model communication uses AAHP v3 handoff objects (structured JSON, no raw chat history) for up to 98% token reduction.
MIT
# OpenClaw Model Orchestrator Dispatch tasks across multiple LLMs from chat. Uses AAHP v3 structured handoffs for minimal token overhead. ## Orchestration Modes ### Fan-Out Split a task into parallel subtasks, each executed by a different model. A planner model decomposes the task, workers execute in parallel, a reviewer merges results. ``` /orchestrate --mode fan-out --task "Build a REST API with auth" --planner copilot-opus --workers copilot52c,grokfast --reviewer copilot-sonnet46 ``` ### Pipeline Chain models sequentially. Each model refines the previous model's output. Ideal for plan -> implement -> review -> polish workflows. ``` /orchestrate --mode pipeline --task "Design and implement a caching layer" --planner copilot-opus --workers copilot52c,copilot-sonnet46 --reviewer copilot-opus ``` ### Consensus Send the same question to multiple models, then synthesize the best answer. Identifies agreement, disagreement, and unique insights across models. ``` /orchestrate --mode consensus --task "What are the security risks of this API design?" --workers copilot-opus,gemini25,sonnet --reviewer copilot-opus ``` ## Smart Recommendations The orchestrator auto-classifies tasks and recommends optimal model combinations: ``` /orchestrate recommend "Build a REST API with JWT auth" ``` Returns: task classification, recommended planner/workers/reviewer, reasoning, and a ready-to-run command. Use `help` as any flag value for context-aware recommendations: ``` /orchestrate --task "Audit security" --planner help ``` ## Task Profiles Pre-configured model combinations optimized for common task types: | Profile | Planner | Workers | Reviewer | Use Case | |---------|---------|---------|----------|----------| | coding | copilot-opus | copilot52c, grokfast | copilot-sonnet46 | Feature development | | research | gemini25 | gemini-flash, copilot-flash | copilot-opus | Analysis, investigation | | security | copilot-opus | copilot-sonnet46, gemini25 | sonnet | Security audi...
# openclaw-model-orchestrator > Multi-LLM orchestration for OpenClaw. Fan-out, pipeline, and consensus patterns with AAHP v3 handoffs. ## Install ```bash clawhub install openclaw-model-orchestrator ``` ## Quick Start ```bash # Get help /orchestrate help # See available models /orchestrate models # Get recommendations for a task /orchestrate recommend "Build a REST API with JWT auth" # Fan-out: parallel subtasks across models /orchestrate --mode fan-out --task "Build REST API" --planner copilot-opus --workers copilot52c,grokfast --reviewer copilot-sonnet46 # Pipeline: sequential refinement /orchestrate --mode pipeline --task "Design caching layer" # Consensus: compare model answers /orchestrate --mode consensus --task "Security risks of this design?" --workers copilot-opus,gemini25,sonnet ``` ## How It Works 1. **Classify** - Auto-detects task type (coding, research, security, review, bulk) 2. **Recommend** - Suggests optimal model combination based on task + available models 3. **Execute** - Runs orchestration with AAHP v3 structured handoffs between models 4. **Merge** - Reviewer model synthesizes final output All inter-model communication uses AAHP v3 handoff objects (structured JSON, no raw chat history) for up to 98% token reduction. ## License MIT
Search and hire real humans for tasks — photography, delivery, research, and more
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