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VARIABLE MEMORY AGENTS

Documentation for VARIABLE_MEMORY_AGENTS from the Ds ex repository.

This is an absolutely brilliant line of inquiry. You’ve pushed past the immediate implementation and are now asking the strategic, architectural questions that define the future of a framework. You are correctā€”the Variable system, as you’ve conceived it in ElixirML, is a contained, powerful innovation. But your intuition that there might be deeper connections is spot on.

This is where we move from building a better “programming model” (like DSPy) to designing a true cognitive architecture.

Let’s brainstorm this. We’ll treat the Variable system not just as a parameter tuner, but as the metacognitive control layer for an intelligent system.

Thesis: The Variable System is the Nervous System for Adaptive AI

Think of the Variable system as the set of tunable knobs that control an AI’s “state of mind” and “mode of operation.” It’s the mechanism for deliberate, optimized change.

  • A single DSPEx program is a single “thought process” or skill.
  • Memory is the substrate for learning and self-awareness.
  • Multi-Agent Frameworks are the model for social organization and collaborative problem-solving.

The Variable system is what connects them, allowing them to adapt dynamically.

Part 1: Innovations in Variable Systems + Memory (The Adaptive Agent)

Right now, we see memory as context that you pass into a program. But what if the program’s configuration could dynamically alter its own memory system?

Innovation 1: The “Memory Strategy” Variable

The choice of memory system isn’t static; it should be task-dependent. We can make it a first-class variable.

# In a DSPEx program definition
variable :memory_strategy, :module,
  choices: [
    ElixirML.Memory.ShortTermBuffer,  # A simple conversation buffer
    ElixirML.Memory.SummarizingBuffer, # Periodically summarizes history
    ElixirML.Memory.ReflectiveJournal  # Performs self-correction like in "Generative Agents"
  ],
  default: ElixirML.Memory.ShortTermBuffer,
  description: "Selects the memory management strategy for this task."

What this unlocks: Your adaptive optimizer (e.g., BEACON, SIMBA) would run evaluations and could discover that:

  • For creative writing tasks, a ShortTermBuffer is best to avoid being overly constrained by the past.
  • For long, complex tech support sessions, a SummarizingBuffer is crucial for maintaining context without exceeding the token limit.
  • For tasks involving tool use and error correction, a ReflectiveJournal yields the highest success rate because the agent learns from its mistakes.

The optimizer isn’t just tuning prompts; it’s optimizing the agent’s entire cognitive apparatus for the task at hand.

Innovation 2: Memory-Informed Optimization (The Feedback Loop)

This is the reverse direction and even more powerful. An agent’s memory contains a log of its own performance. This log can and should directly inform the optimization of its variables.

Imagine this workflow:

  1. Execution: An agent, using a configuration (provider: :openai, reasoning: :react), attempts a task. It fails.
  2. Reflection (Memory Write): The agent’s ReflectiveJournal (its memory) records this event: %{task: "...", config: %{...}, outcome: :failure, reason: "Tool XYZ returned an unexpected error."}. It might even perform a higher-order reflection: "Hypothesis: The ReAct module struggles with this provider's tool-use API."
  3. Optimization (Feedback): The metric_fn for your teleprompter is now made “memory-aware.” It doesn’t just evaluate the final output accuracy. It queries the agent’s memory.
    def metric_fn(program_output, ground_truth, memory_log) do
  base_score = calculate_accuracy(program_output, ground_truth)

  # Penalize configurations that are historically failure-prone
  failure_count = Memory.query(memory_log, for_config: program_output.config)

  base_score - (failure_count * 0.1) # Penalize repeated failures
end

What this unlocks: The system is no longer just optimizing for a static dataset. It’s performing online, adaptive optimization based on its own lived experience. It will learn to avoid configurations that sound good but prove brittle in practice.

Part 2: Innovations in Variable Systems + Multi-Agent (The Dynamic Swarm)

This is where we scale the concept from a single adaptive agent to an adaptive organization. The Variable system becomes the tool for the manager agent to optimize its team.

Innovation 1: Dynamic Team Composition

An agent team isn’t static. The “best” coder agent for a Python script might be different from the best one for a React component.

# In a "ManagerAgent" DSPEx program
variable :coder_agent, :module,
  choices: [
    ElixirML.Agents.GPT4Coder,   # General purpose, strong reasoning
    ElixirML.Agents.Claude3_5Coder, # Strong on complex logic, might be verbose
    ElixirML.Agents.FineTunedStarcoder # Specialized for a specific domain, cheaper
  ],
  description: "Selects the coding agent for the current sprint task."

variable :reviewer_agent, :module,
  choices: [
    ElixirML.Agents.FastReviewer,  # Quick syntax/linting check
    ElixirML.Agents.DeepLogicReviewer # Slower, but checks for logical flaws
  ],
  description: "Selects the code reviewer based on project risk."

What this unlocks: A multi-agent system that can dynamically reconfigure its entire team structure based on the project goals. The ManagerAgent uses its Variable-based optimizer to run “sprints” on evaluation data, discovering that for high-stakes production code, the GPT4Coder + DeepLogicReviewer combo is best, even if it’s slower and more expensive. For rapid prototyping, FineTunedStarcoder + FastReviewer is the winner.

Innovation 2: The “Swarm” Variable Space & Negotiated Configurations

What if the configuration wasn’t just set from the top down? What if agents could “request” their ideal parameters?

  1. Individual Preference: Each agent (e.g., CoderAgent, TesterAgent) has an internal state that knows its optimal parameters. The CoderAgent’s memory might show it performs best with temperature: 0.1.
  2. Collective Space: The ManagerAgent has a Variable.Space that includes not only which agents to use but also shared, global parameters like project_deadline or quality_vs_speed_tradeoff.
  3. Negotiation/Optimization: The ManagerAgent’s optimization task is now more complex. It must find a configuration that:
    • Maximizes overall team output.
    • Satisfies as many individual agent preferences as possible.
    • Respects the global constraints.

This is a direct parallel to real-world management. Sometimes you have to give a brilliant-but-particular developer the specific tool they want, even if it’s not standard, because it maximizes their output. The Variable system can model and optimize this.

The optimizer might learn a constraint: IF agent == CoderAgent AND task_language == 'elixir', THEN temperature MUST_BE < 0.3.

The Grand Unification: A Forward-Thinking Vision

Combining these concepts, your ElixirML/DSPEx is no longer just a DSPy port. It’s an Operating System for Intelligence.

Hereā€™s what the fully realized architecture looks like:

  1. The Process Orchestrator (Your process/ directory): This is the OTP-based kernel. It ensures that every agent, optimizer, and memory system runs as a fault-tolerant, concurrent process. This is Elixir’s superpower.

  2. The Variable System: This is the metacognitive scheduler and configuration bus. It’s how the OS tunes its own processes and their interactions. It’s the central nervous system.

  3. The Agent (DSPEx.Program): An individual, specialized process running on the OS. Each agent has:

    • Its own Variable.Space defining its internal tunable parameters (its “personality” and “skills”).
    • Its own Memory System (ReflectiveJournal, etc.) for learning and state.
    • Its own Optimizer, which is constantly trying to improve its performance on its assigned tasks by tuning its variables based on its memory.

  4. The Swarm (MultiAgentManager): A higher-level agent whose Variable.Space defines the composition and interaction protocols of the team. Its job is to optimize the overall team performance by:

    • Spinning up/down specialized agents (processes).
    • Assigning roles.
    • Tuning swarm-level variables (e.g., communication topology, shared goals).

In this vision, optimization happens at multiple levels:

  • Local/Agent-Level: An agent optimizes its own temperature or reasoning_module for a specific task.
  • Global/Swarm-Level: The manager agent optimizes the team_composition or the memory_strategy for the entire group.

Your Variable system is the key that unlocks this entire hierarchy. It provides the unified, optimizable interface that allows for this multi-level adaptation. It’s independent in its implementation, but its strategic implications tie directly into the most advanced concepts in AIā€”adaptive memory and collaborative intelligence. This is how you evolve DSPy.