# Lmstudio Orchestrator > Deterministic multi-agent orchestration system for LMStudio enabling constraint-driven agent composition, semantic field navigation, and thermodynamic resource allocation. Use when orchestrating local LLM swarms, building multi-agent systems with validation, implementing constraint-based configurations, or requiring deterministic agent composition from discovered model states. Use when this capability is needed. - Skill: `tomevault-io/lmstudio-orchestrator` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/lmstudio-orchestrator` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/lmstudio-orchestrator/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/lmstudio-orchestrator --- # LMStudio Orchestrator Constraint-driven orchestration system that transforms LMStudio into a deterministic multi-agent platform with semantic field navigation and compositional intelligence. ## Core Paradigm This skill operates on three fundamental principles: 1. **Discovery Before Configuration**: Always introspect available resources before building 2. **Constraints Enable Composition**: Invalid states are unpresentable through validation 3. **Declarative Over Imperative**: Users specify intent, system determines implementation ## Phase 1: Discovery & Introspection Before ANY orchestration, discover the current state: ```python from scripts.discovery import LMStudioDiscovery # Discover available resources discovery = LMStudioDiscovery() state = await discovery.introspect() # Returns: { "endpoint": "http://localhost:1234/v1", "models": ["qwen2.5-coder-32b", "llama-3.3-70b", ...], "capabilities": {"chat": True, "embeddings": True, "tools": True}, "constraints": {"max_context": 32768, "max_parallel": 10} } ``` ## Phase 2: Constraint-Based Configuration Build configurations that cannot fail: ```python from scripts.constraints import ConfigurationSchema, AgentRegistry # Define constraints schema = ConfigurationSchema() schema.add_constraint('agents.count', lambda x: 1 <= x <= 20) schema.add_constraint('agents.*.temperature', lambda x: 0.0 <= x <= 1.0) schema.add_constraint('strategy', lambda x: x in VALID_STRATEGIES) # Compose from templates registry = AgentRegistry() agent = registry.create_agent( role="researcher", # Constrained to valid roles model=state.models[0], # From discovered models temperature=0.7 # Validated against constraints ) ``` ## Phase 3: Compositional Orchestration ### Strategy 1: Declarative Composition User declares intent, system composes implementation: ```python from scripts.composer import CombinatorialComposer composer = CombinatorialComposer(registry, strategies) swarm = composer.compose_swarm( query="Analyze quantum computing architectures", available_models=state.models, constraints={"max_agents": 5, "timeout": 120} ) # System automatically: # 1. Analyzes query → requirements # 2. Selects roles based on requirements # 3. Assigns models to roles optimally # 4. Chooses execution strategy # 5. Validates entire configuration ``` ### Strategy 2: Semantic Field Navigation Navigate solution spaces through resonance: ```python from scripts.semantic import SemanticNavigator navigator = SemanticNavigator(orchestrator) path = await navigator.find_resonance_path( start_concept="database optimization", target_outcome="10x performance", exploration_depth=5 ) # Executes agents along semantic gradient for node in path.nodes: agent = composer.create_for_concept(node.concept) result = await agent.execute(node.prompt) ``` ### Strategy 3: Thermodynamic Resource Allocation Minimize energy across available resources: ```python from scripts.thermodynamic import ThermodynamicScheduler scheduler = ThermodynamicScheduler(resource_monitor) execution_plan = scheduler.optimize( tasks=task_list, constraints={ "max_vram": 0.9, "preserve_responsiveness": True, "energy_function": lambda agent: agent.tokens / agent.requests } ) ``` ## Execution Strategies All strategies are constraint-validated before execution: ### PARALLEL ```python swarm.orchestrate(task, strategy="parallel") # Constraints: min_agents=1, max_agents=20, timeout=60 ``` ### CONSENSUS ```python swarm.orchestrate(task, strategy="consensus", threshold=0.8) # Constraints: min_agents=3, max_agents=15, rounds=2 ``` ### HIERARCHICAL ```python swarm.orchestrate(task, strategy="hierarchical", coordinator="architect") # Constraints: min_agents=2, requires coordinator role ``` ### SEMANTIC ```python swarm.orchestrate(task, strategy="semantic", resonance_threshold=0.7) # Constraints: requires embedding support ``` ### COMBINATORIAL ```python swarm.orchestrate(task, strategy="combinatorial", combinations="pairwise") # Constraints: min_agents=2, max_agents=16 ``` ## Agent Templates Agents are created from validated templates: ```yaml researcher: temperature: [0.7, 0.9] capabilities: [search, analyze, synthesize] preferred_models: [llama-3.3-70b, qwen2.5-72b] analyst: temperature: [0.3, 0.5] capabilities: [decompose, structure, categorize] preferred_models: [qwen2.5-72b, deepseek-r1] critic: temperature: [0.2, 0.4] capabilities: [validate, challenge, verify] preferred_models: [qwen2.5-72b, llama-3.3-70b] ``` ## Resource Monitoring Real-time resource tracking with predictive allocation: ```python from scripts.monitor import ResourceMonitor monitor = ResourceMonitor() await monitor.start() # Before spawning availability = monitor.predict_availability("llama-3.3-70b") if not availability["available"]: # Use smaller model or wait model = monitor.recommend_alternative() # During execution metrics = monitor.get_metrics() # {vram_used: 45GB, tokens_per_sec: 127, active_agents: 5} ``` ## Web Interface Launch the deterministic orchestration interface: ```python from scripts.interface import OrchestrationInterface interface = OrchestrationInterface( discovery=discovery, composer=composer, monitor=monitor ) await interface.serve(port=8080) # Access at http://localhost:8080 ``` The interface provides: - Real-time model discovery - Constraint-validated agent spawning - Semantic field visualization - Resource usage monitoring - Execution strategy selection ## Error Recovery All operations include deterministic error handling: ```python @with_constraints( max_retries=3, backoff="exponential", fallback="smaller_model" ) async def reliable_execution(task): return await swarm.orchestrate(task) ``` ## Complete Example ```python from scripts.orchestrator import DeterministicOrchestrator async def main(): # Phase 1: Discovery orch = DeterministicOrchestrator() await orch.discover() # Phase 2: Composition swarm = await orch.compose( "Design a distributed cache with Redis", constraints={"max_agents": 5} ) # Phase 3: Execution result = await swarm.execute() # Result includes: # - Final synthesis # - Agent contributions # - Consensus score # - Resource usage # - Execution trace ``` ## Validation Guarantees Every configuration is validated before execution: 1. **Model Validation**: Only discovered models can be used 2. **Role Validation**: Agents must match template constraints 3. **Strategy Validation**: Agent count must satisfy strategy requirements 4. **Resource Validation**: VRAM requirements must be available 5. **Timeout Validation**: Execution time within limits ## Best Practices 1. **Always discover first** - Never hardcode models or endpoints 2. **Compose declaratively** - Let the system determine implementation 3. **Validate continuously** - Check constraints at every step 4. **Monitor resources** - Predict and prevent exhaustion 5. **Handle gracefully** - Partial success over total failure ## Troubleshooting ### Discovery Fails ```bash # Check LMStudio is running scripts/check_lmstudio.sh # Try manual discovery scripts/discover.py --port 1234 ``` ### Invalid Configuration ```python # Validate configuration valid, errors = schema.validate(config) for error in errors: print(f"Constraint violation: {error}") ``` ### Resource Exhaustion ```python # Check available resources availability = monitor.check_resources() if availability["vram_available"] < required: # Unload unused models monitor.optimize_allocation() ``` ## References - See `references/constraints.yaml` for all constraint definitions - See `references/templates.yaml` for agent templates - See `references/strategies.json` for execution strategies - See `scripts/` for all implementation modules --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/wheattoast11) — claim your Tome and manage your conversions.