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MABEAM

Documentation for MABEAM from the Ds ex repository.

Rethinking Variable Systems for Multi-Agent Orchestration on the BEAM

The traditional approach to ML optimization treats parameter tuning as a local, program-specific concern. However, the BEAM’s actor model and OTP’s supervision trees suggest a fundamentally different architecture: Variables as Universal Coordinators. Instead of variables being trapped within individual programs, they become the orchestration layer that manages the entire multi-agent ecosystem. A Variable doesn’t just control a program’s temperature; it controls which agents are active, how they communicate, what resources they consume, and how they adapt based on collective performance. This transforms the Variable system from a parameter tuner into a distributed cognitive control plane.

This approach leverages OTP’s natural strengths—fault tolerance, hot code swapping, and distributed coordination—to create a self-optimizing multi-agent framework. Variables become the mechanism for agents to negotiate resources, discover optimal team compositions, and adapt their behavior based on both local and global performance metrics. The Variable Registry becomes a distributed coordination service, enabling agents across different nodes to share optimization insights and constraints. This creates an emergent intelligence where the system continuously reorganizes itself for optimal performance, with SIMBA and other teleprompters operating not just on individual programs, but on the entire agent topology.

ElixirML Variable System API - Multi-Agent Orchestration

Core Variable Orchestration API

defmodule ElixirML.Variable.Orchestrator do
  @moduledoc """
  Universal Variable system for multi-agent coordination and optimization.
  Variables control not just program parameters, but agent lifecycle, 
  communication patterns, and resource allocation across the BEAM cluster.
  """
  
  @type agent_reference :: pid() | {atom(), node()} | atom()
  @type resource_allocation :: %{memory: pos_integer(), cpu_weight: float(), network_priority: atom()}
  @type coordination_pattern :: :broadcast | :ring | :star | :mesh | :custom
  @type adaptation_strategy :: :reactive | :predictive | :emergent | :consensus_based
  
  defstruct [
    :id,                        # Universal variable identifier
    :scope,                     # :local | :global | :cluster | :federation
    :type,                      # Variable type for orchestration
    :agents,                    # Agents this variable affects
    :coordination_fn,           # How agents coordinate on this variable
    :adaptation_fn,             # How the variable adapts based on performance
    :constraints,               # Multi-agent constraints
    :resource_requirements,     # Resource allocation rules
    :fault_tolerance,           # How to handle agent failures
    :distribution_strategy,     # FUTURE: For cluster deployment
    :telemetry_config          # Performance monitoring configuration
  ]
  
  @type t :: %__MODULE__{
    id: atom(),
    scope: variable_scope(),
    type: orchestration_type(),
    agents: [agent_reference()],
    coordination_fn: coordination_function(),
    adaptation_fn: adaptation_function(),
    constraints: [orchestration_constraint()],
    resource_requirements: resource_allocation(),
    fault_tolerance: fault_tolerance_config(),
    distribution_strategy: term(),  # FUTURE: cluster_strategy()
    telemetry_config: telemetry_config()
  }
  
  @type variable_scope :: :local | :global | :cluster | :federation
  @type orchestration_type :: 
    :agent_selection | :resource_allocation | :communication_topology | 
    :adaptation_rate | :performance_threshold | :coordination_pattern | :custom
  
  @type coordination_function :: (t(), [agent_reference()], term() -> coordination_result())
  @type adaptation_function :: (t(), performance_metrics(), term() -> adaptation_result())
  
  @type coordination_result :: {:ok, [agent_directive()]} | {:error, coordination_error()}
  @type adaptation_result :: {:ok, t()} | {:error, adaptation_error()}
  
  @type agent_directive :: %{
    agent: agent_reference(),
    action: agent_action(),
    parameters: map(),
    priority: pos_integer(),
    timeout: pos_integer()
  }
  
  @type agent_action :: 
    :start | :stop | :reconfigure | :migrate | :scale_up | :scale_down | 
    :change_role | :update_communication | :adjust_parameters
  
  @doc """
  Create an orchestration variable that coordinates multiple agents.
  
  ## Examples
      
      # Variable that selects which coder agent to use based on task type
      coder_selector = Orchestrator.agent_selection(:coder_agent,
        agents: [:python_coder, :elixir_coder, :javascript_coder],
        coordination: &task_based_selection/3,
        adaptation: &performance_based_adaptation/3,
        constraints: [max_concurrent: 2, resource_limit: :memory_bound]
      )
      
      # Variable that manages communication topology between reasoning agents  
      reasoning_topology = Orchestrator.communication_topology(:reasoning_net,
        agents: [:planner, :executor, :reviewer],
        coordination: &adaptive_topology/3,
        patterns: [:star, :ring, :mesh],
        fault_tolerance: :graceful_degradation
      )
  """
  @spec agent_selection(atom(), keyword()) :: t()
  def agent_selection(id, opts)
  
  @spec resource_allocation(atom(), keyword()) :: t()
  def resource_allocation(id, opts)
  
  @spec communication_topology(atom(), keyword()) :: t()
  def communication_topology(id, opts)
  
  @spec adaptation_rate(atom(), keyword()) :: t()
  def adaptation_rate(id, opts)
  
  @spec performance_threshold(atom(), keyword()) :: t()
  def performance_threshold(id, opts)
  
  @spec coordination_pattern(atom(), keyword()) :: t()
  def coordination_pattern(id, opts)
  
  @doc """
  Coordinate agents based on the variable's current value and state.
  """
  @spec coordinate(t(), term(), coordination_context()) :: coordination_result()
  def coordinate(variable, value, context)
  
  @doc """
  Adapt the variable based on collective agent performance.
  """
  @spec adapt(t(), performance_metrics()) :: adaptation_result()
  def adapt(variable, metrics)
  
  @doc """
  FUTURE: Distribute variable coordination across cluster nodes.
  """
  @spec distribute(t(), [node()]) :: {:ok, t()} | {:error, term()}
  def distribute(variable, nodes)
end

Multi-Agent Variable Space

defmodule ElixirML.Variable.MultiAgentSpace do
  @moduledoc """
  Variable space that manages the entire multi-agent ecosystem.
  Handles agent lifecycle, resource negotiation, and emergent optimization.
  """
  
  defstruct [
    :id,
    :name,
    :agents,                    # Registry of all agents
    :orchestration_variables,   # Variables that coordinate agents
    :local_variables,          # Agent-specific variables
    :resource_pool,            # Available system resources
    :coordination_graph,       # Agent dependency and communication graph
    :performance_metrics,      # Collective performance tracking
    :adaptation_history,       # History of adaptations and their outcomes
    :fault_recovery,           # Fault recovery strategies
    :scaling_policies,         # Auto-scaling policies for agents
    :distribution_config      # FUTURE: Multi-node coordination
  ]
  
  @type t :: %__MODULE__{
    id: atom(),
    name: String.t(),
    agents: %{atom() => agent_config()},
    orchestration_variables: %{atom() => ElixirML.Variable.Orchestrator.t()},
    local_variables: %{atom() => %{atom() => ElixirML.Variable.t()}},
    resource_pool: resource_pool(),
    coordination_graph: coordination_graph(),
    performance_metrics: performance_metrics(),
    adaptation_history: [adaptation_event()],
    fault_recovery: fault_recovery_config(),
    scaling_policies: [scaling_policy()],
    distribution_config: term()  # FUTURE: distribution_config()
  }
  
  @type agent_config :: %{
    module: module(),
    supervision_strategy: atom(),
    resource_requirements: resource_allocation(),
    communication_interfaces: [atom()],
    local_variable_space: ElixirML.Variable.Space.t(),
    role: agent_role(),
    status: agent_status()
  }
  
  @type agent_role :: :coordinator | :executor | :evaluator | :optimizer | :monitor | :custom
  @type agent_status :: :active | :inactive | :starting | :stopping | :migrating | :failed
  
  @type resource_pool :: %{
    total_memory: pos_integer(),
    available_memory: pos_integer(),
    total_cpu: float(),
    available_cpu: float(),
    network_bandwidth: pos_integer(),
    storage: pos_integer()
  }
  
  @type coordination_graph :: %{
    nodes: [atom()],
    edges: [{atom(), atom(), edge_type()}],
    topology: coordination_pattern()
  }
  
  @type edge_type :: :command | :data | :feedback | :coordination | :supervision
  
  @type performance_metrics :: %{
    aggregate_performance: float(),
    individual_performance: %{atom() => float()},
    resource_utilization: %{atom() => float()},
    communication_efficiency: float(),
    adaptation_success_rate: float(),
    fault_recovery_time: pos_integer()
  }
  
  @type adaptation_event :: %{
    timestamp: DateTime.t(),
    trigger: adaptation_trigger(),
    changes: [variable_change()],
    outcome: adaptation_outcome(),
    performance_impact: float()
  }
  
  @type adaptation_trigger :: :performance_degradation | :resource_constraint | 
    :fault_detected | :load_increase | :optimization_opportunity | :external_signal
  
  @type variable_change :: %{
    variable_id: atom(),
    old_value: term(),
    new_value: term(),
    affected_agents: [atom()]
  }
  
  @type adaptation_outcome :: :success | :partial_success | :failure | :rolled_back
  
  @doc """
  Create a multi-agent variable space for DSPEx orchestration.
  """
  @spec new(keyword()) :: t()
  def new(opts \\ [])
  
  @doc """
  Register an agent in the multi-agent space.
  """
  @spec register_agent(t(), atom(), agent_config()) :: t()
  def register_agent(space, agent_id, config)
  
  @doc """
  Add an orchestration variable that coordinates multiple agents.
  """
  @spec add_orchestration_variable(t(), ElixirML.Variable.Orchestrator.t()) :: t()
  def add_orchestration_variable(space, variable)
  
  @doc """
  Start all agents in the space with fault tolerance.
  """
  @spec start_agents(t()) :: {:ok, t()} | {:error, [agent_start_error()]}
  def start_agents(space)
  
  @doc """
  Coordinate all agents based on current variable values.
  """
  @spec coordinate_agents(t()) :: {:ok, t()} | {:error, coordination_error()}
  def coordinate_agents(space)
  
  @doc """
  Adapt the entire system based on collective performance.
  """
  @spec adapt_system(t(), performance_metrics()) :: {:ok, t()} | {:error, adaptation_error()}
  def adapt_system(space, metrics)
  
  @doc """
  Handle agent failure and recovery.
  """
  @spec handle_agent_failure(t(), atom(), term()) :: {:ok, t()} | {:error, term()}
  def handle_agent_failure(space, agent_id, failure_reason)
  
  @doc """
  Scale agents based on load and performance.
  """
  @spec auto_scale(t()) :: {:ok, t()} | {:error, scaling_error()}
  def auto_scale(space)
  
  @doc """
  FUTURE: Distribute agents across cluster nodes.
  """
  @spec distribute_agents(t(), [node()]) :: {:ok, t()} | {:error, term()}
  def distribute_agents(space, nodes)
end

Agent Coordination Protocols

defmodule ElixirML.Variable.Coordination do
  @moduledoc """
  Coordination protocols for multi-agent variable optimization.
  Handles negotiation, consensus, and conflict resolution between agents.
  """
  
  @type negotiation_strategy :: :auction | :consensus | :hierarchical | :market_based | :genetic
  @type conflict_resolution :: :priority_based | :voting | :leader_election | :compromise
  @type consensus_algorithm :: :raft | :pbft | :gossip | :blockchain | :simple_majority
  
  @doc """
  Negotiate variable values between multiple agents.
  
  ## Examples
      
      # Agents negotiate temperature based on their task requirements
      negotiation_result = Coordination.negotiate_value(
        :temperature,
        [
          {coder_agent, 0.1, weight: 0.4},      # Wants low temperature for precision
          {creative_agent, 1.2, weight: 0.3},   # Wants high temperature for creativity  
          {reviewer_agent, 0.3, weight: 0.3}    # Wants moderate temperature for balance
        ],
        strategy: :weighted_consensus,
        constraints: [{:min, 0.0}, {:max, 2.0}]
      )
  """
  @spec negotiate_value(atom(), [{agent_reference(), term(), keyword()}], keyword()) :: 
    {:ok, term()} | {:error, negotiation_error()}
  def negotiate_value(variable_id, agent_preferences, opts \\ [])
  
  @doc """
  Establish consensus on system-wide configuration changes.
  """
  @spec establish_consensus([agent_reference()], map(), keyword()) :: 
    {:ok, map()} | {:error, consensus_error()}
  def establish_consensus(agents, proposed_changes, opts \\ [])
  
  @doc """
  Resolve conflicts when agents have incompatible variable requirements.
  """
  @spec resolve_conflicts([variable_conflict()], conflict_resolution()) :: 
    {:ok, [variable_resolution()]} | {:error, resolution_error()}
  def resolve_conflicts(conflicts, strategy)
  
  @doc """
  Coordinate resource allocation between competing agents.
  """
  @spec coordinate_resources([resource_request()], resource_allocation()) :: 
    {:ok, [resource_grant()]} | {:error, allocation_error()}
  def coordinate_resources(requests, available_resources)
  
  @doc """
  Implement hierarchical coordination with coordinator agents.
  """
  @spec hierarchical_coordination(agent_reference(), [agent_reference()], coordination_plan()) :: 
    {:ok, [agent_directive()]} | {:error, coordination_error()}
  def hierarchical_coordination(coordinator, subordinates, plan)
  
  @type variable_conflict :: %{
    variable_id: atom(),
    conflicting_agents: [agent_reference()],
    requested_values: [term()],
    conflict_type: conflict_type()
  }
  
  @type conflict_type :: :value_incompatible | :resource_competition | :temporal_constraint
  
  @type variable_resolution :: %{
    variable_id: atom(),
    resolved_value: term(),
    compromise_type: compromise_type(),
    affected_agents: [agent_reference()]
  }
  
  @type compromise_type :: :weighted_average | :time_sharing | :priority_override | :alternative_solution
  
  @type resource_request :: %{
    agent: agent_reference(),
    resource_type: atom(),
    amount: pos_integer(),
    priority: pos_integer(),
    duration: pos_integer() | :indefinite
  }
  
  @type resource_grant :: %{
    agent: agent_reference(),
    resource_type: atom(),
    granted_amount: pos_integer(),
    grant_duration: pos_integer(),
    conditions: [resource_condition()]
  }
  
  @type resource_condition :: 
    {:release_on_idle, pos_integer()} | 
    {:share_with, agent_reference()} | 
    {:priority_preemption, pos_integer()}
end

Teleprompter Integration for Multi-Agent Optimization

defmodule ElixirML.Teleprompter.MultiAgent do
  @moduledoc """
  Multi-agent teleprompter that optimizes entire agent ecosystems.
  Extends SIMBA and other algorithms to work across agent boundaries.
  """
  
  @doc """
  Multi-agent SIMBA that optimizes agent coordination and individual performance.
  
  ## Examples
      
      # Optimize a coder-reviewer-tester agent team
      {:ok, optimized_space} = MultiAgent.simba(
        multi_agent_space,
        training_tasks,
        &team_performance_metric/2,
        generations: 20,
        mutation_strategies: [
          :agent_selection,      # Change which agents are active
          :topology_mutation,    # Change communication patterns
          :parameter_mutation,   # Optimize individual agent parameters
          :resource_reallocation # Redistribute computational resources
        ]
      )
  """
  @spec simba(ElixirML.Variable.MultiAgentSpace.t(), [training_example()], metric_function(), keyword()) :: 
    {:ok, ElixirML.Variable.MultiAgentSpace.t()} | {:error, term()}
  def simba(space, training_data, metric_fn, opts \\ [])
  
  @doc """
  Multi-agent BEACON for rapid team composition optimization.
  """
  @spec beacon(ElixirML.Variable.MultiAgentSpace.t(), [training_example()], metric_function(), keyword()) :: 
    {:ok, ElixirML.Variable.MultiAgentSpace.t()} | {:error, term()}
  def beacon(space, training_data, metric_fn, opts \\ [])
  
  @doc """
  Bootstrap few-shot optimization across multiple specialized agents.
  """
  @spec bootstrap_fewshot(ElixirML.Variable.MultiAgentSpace.t(), [training_example()], keyword()) :: 
    {:ok, ElixirML.Variable.MultiAgentSpace.t()} | {:error, term()}
  def bootstrap_fewshot(space, training_data, opts \\ [])
  
  @doc """
  Evaluate the performance of a multi-agent configuration.
  """
  @spec evaluate_team_performance(ElixirML.Variable.MultiAgentSpace.t(), [training_example()], metric_function()) :: 
    {:ok, team_performance_result()} | {:error, term()}
  def evaluate_team_performance(space, examples, metric_fn)
  
  @type training_example :: %{
    input: map(),
    expected_output: map(),
    task_type: atom(),
    complexity: atom(),
    resource_requirements: map()
  }
  
  @type metric_function :: (team_performance_result(), training_example() -> float())
  
  @type team_performance_result :: %{
    overall_score: float(),
    individual_scores: %{atom() => float()},
    coordination_efficiency: float(),
    resource_utilization: float(),
    communication_overhead: float(),
    fault_tolerance_score: float(),
    adaptation_speed: float(),
    execution_time: pos_integer(),
    memory_usage: pos_integer(),
    agent_activations: %{atom() => pos_integer()}
  }
end

Performance Monitoring and Telemetry

defmodule ElixirML.Variable.Telemetry do
  @moduledoc """
  Comprehensive telemetry system for multi-agent variable optimization.
  Tracks performance, resource usage, and coordination efficiency.
  """
  
  @doc """
  Start telemetry monitoring for a multi-agent space.
  """
  @spec start_monitoring(ElixirML.Variable.MultiAgentSpace.t(), keyword()) :: {:ok, telemetry_config()}
  def start_monitoring(space, opts \\ [])
  
  @doc """
  Collect performance metrics from all agents.
  """
  @spec collect_metrics(telemetry_config()) :: performance_metrics()
  def collect_metrics(config)
  
  @doc """
  Generate optimization insights from telemetry data.
  """
  @spec generate_insights(performance_metrics(), keyword()) :: [optimization_insight()]
  def generate_insights(metrics, opts \\ [])
  
  @doc """
  Create real-time dashboard for monitoring agent coordination.
  """
  @spec create_dashboard(telemetry_config(), keyword()) :: {:ok, dashboard_config()}
  def create_dashboard(config, opts \\ [])
  
  @type telemetry_config :: %{
    collection_interval: pos_integer(),
    metrics_to_track: [atom()],
    aggregation_strategies: %{atom() => aggregation_strategy()},
    alert_thresholds: %{atom() => threshold_config()},
    storage_backend: storage_config()
  }
  
  @type aggregation_strategy :: :sum | :average | :max | :min | :percentile | :custom
  
  @type threshold_config :: %{
    warning: number(),
    critical: number(),
    action: alert_action()
  }
  
  @type alert_action :: :log | :notify | :auto_scale | :restart_agent | :rebalance_resources
  
  @type optimization_insight :: %{
    type: insight_type(),
    description: String.t(),
    suggested_action: suggested_action(),
    confidence: float(),
    impact_estimate: float()
  }
  
  @type insight_type :: 
    :bottleneck_detected | :resource_inefficiency | :coordination_overhead | 
    :underutilized_agent | :optimization_opportunity | :fault_pattern
  
  @type suggested_action :: %{
    action_type: action_type(),
    parameters: map(),
    expected_improvement: float(),
    risk_level: risk_level()
  }
  
  @type action_type :: 
    :scale_agent | :redistribute_resources | :change_topology | 
    :adjust_parameters | :add_agent | :remove_agent | :migrate_agent
  
  @type risk_level :: :low | :medium | :high
end

Integration with DSPEx Programs

defmodule ElixirML.Variable.DSPExIntegration do
  @moduledoc """
  Integration layer that enables DSPEx programs to participate in 
  multi-agent variable optimization while maintaining their signatures.
  """
  
  @doc """
  Convert a DSPEx program into a multi-agent participant.
  
  ## Examples
      
      # Convert a DSPEx coder program into an agent
      coder_agent = DSPExIntegration.agentize(
        CoderProgram,
        agent_id: :python_coder,
        role: :executor,
        coordination_variables: [
          :coder_selection,
          :resource_allocation,
          :communication_topology
        ],
        local_variables: [
          :temperature,
          :max_tokens,
          :reasoning_strategy
        ]
      )
  """
  @spec agentize(module(), keyword()) :: agent_config()
  def agentize(program_module, opts)
  
  @doc """
  Create a multi-agent workflow from multiple DSPEx programs.
  """
  @spec create_workflow([{module(), keyword()}], keyword()) :: ElixirML.Variable.MultiAgentSpace.t()
  def create_workflow(programs, opts \\ [])
  
  @doc """
  Enable a DSPEx program to respond to orchestration variables.
  """
  @spec enable_orchestration(module(), [atom()]) :: {:ok, module()} | {:error, term()}
  def enable_orchestration(program_module, orchestration_variables)
  
  @doc """
  Bridge DSPEx signatures with multi-agent variable spaces.
  """
  @spec bridge_signature(module(), ElixirML.Variable.MultiAgentSpace.t()) :: 
    {:ok, bridged_program()} | {:error, term()}
  def bridge_signature(signature_module, multi_agent_space)
  
  @type bridged_program :: %{
    original_program: module(),
    agent_wrapper: pid(),
    coordination_interface: coordination_interface(),
    local_optimization: local_optimization_config()
  }
  
  @type coordination_interface :: %{
    variable_subscriptions: [atom()],
    coordination_callbacks: %{atom() => function()},
    resource_requirements: resource_allocation(),
    communication_protocols: [atom()]
  }
  
  @type local_optimization_config :: %{
    enabled: boolean(),
    teleprompter: module(),
    optimization_frequency: pos_integer(),
    metric_function: function()
  }
end

FUTURE: Distributed Cluster Support

defmodule ElixirML.Variable.Cluster do
  @moduledoc """
  FUTURE: Distributed variable coordination across BEAM cluster nodes.
  Enables scaling multi-agent systems across multiple machines.
  """
  
  # FUTURE: Placeholder implementations for cluster distribution
  @spec distribute_space(ElixirML.Variable.MultiAgentSpace.t(), [node()]) :: {:ok, distributed_space()}
  def distribute_space(_space, _nodes), do: {:ok, :not_implemented}
  
  @spec cluster_consensus([node()], map()) :: {:ok, map()}
  def cluster_consensus(_nodes, _proposal), do: {:ok, :not_implemented}
  
  @spec migrate_agent(agent_reference(), node()) :: {:ok, agent_reference()}
  def migrate_agent(_agent, _target_node), do: {:ok, :not_implemented}
  
  @spec cluster_telemetry([node()]) :: cluster_metrics()
  def cluster_telemetry(_nodes), do: %{nodes: [], metrics: :not_implemented}
  
  @type distributed_space :: term()  # FUTURE: Proper distributed space type
  @type cluster_metrics :: term()    # FUTURE: Proper cluster metrics type
end

This API fundamentally rethinks variables as universal coordinators for multi-agent systems on the BEAM, providing the foundation for emergent intelligence through distributed optimization and fault-tolerant coordination.