Enhance EAT Framework with AlphaEvolve Principles for Advanced Self-Improvement and Algorithmic Discovery

[matiasmolinas]

The Evolving Agents Toolkit (EAT) currently supports component creation and evolution. To push its capabilities further towards genuine self-improvement and the discovery of novel, high-performing agents/tools, we can integrate principles from advanced research in AI-driven algorithmic discovery, specifically inspired by Google DeepMind's AlphaEvolve.

Solution
This issue proposes to enhance the EAT framework by incorporating core concepts from the AlphaEvolve paper and blog post. The goal is to enable EAT to:

1. Perform more precise, performance-driven code-level evolution of its components.
2. Evolve not just individual components, but also the "search algorithms" or orchestration strategies used by agents like SystemAgent and ArchitectZero.
3. Utilize richer context and automated evaluation feedback to guide the evolution process more effectively.

Key Inspirations from AlphaEvolve:

• Paper: AlphaEvolve: A coding agent for scientific and algorithmic discovery
• Blog Post: AlphaEvolve, a Gemini-powered coding agent for designing advanced algorithms

Proposed Enhancements & Areas of Implementation:

The following enhancements, derived from the AlphaEvolve methodology, are proposed for integration into the EAT framework:

1. Direct Code-Level Evolution with Automated Evaluation:

   • Component: EvolveComponentTool, CreateComponentTool, SmartLibrary.
   • Enhancement:
     • Modify tools to support LLM generation of code changes in diff format for targeted updates or full rewrites.
     • Introduce a standardized evaluate(component_code, test_inputs) -> performance_metrics function definition associated with AGENT and TOOL records in SmartLibrary.
     • EvolveComponentTool will trigger this evaluation; evolution is accepted based on correctness and performance improvement.
     • SmartLibrary to store evaluation scores, correctness proofs, and performance history for each component version.

2. Evolution of "Search Algorithms" / Orchestration Strategies:

   • Component: SystemAgent, ArchitectZero.
   • Enhancement:
     • Develop mechanisms to evolve the internal reasoning logic or guiding prompts of SystemAgent and ArchitectZero.
     • Focus on evolving high-level strategies for tackling specific problem classes (e.g., "best strategy for invoice processing workflow generation").
     • Evaluation would be based on the performance of the solutions/workflows these evolved strategies produce.

3. Richer Context and Feedback in Prompts for Evolution:

   • Component: SmartContext, SmartLibrary, EvolveComponentTool, CreateComponentTool, IntentReviewSystem.
   • Enhancement:
     • When evolving/creating components, provide the LLM with:
       • Parent component's code and historical performance data.
       • Details of successful and failed past evolution attempts.
       • Feedback from IntentReviewSystem if applicable.
       • User-provided "literature" (e.g., API docs, algorithm descriptions).

4. Meta-Prompt Evolution (Advanced):

   • Component: Core system prompts within SystemAgent, ArchitectZero, CreateComponentTool, EvolveComponentTool.
   • Enhancement:
     • Investigate a meta-evolution loop where key system prompts are varied and evaluated based on the quality of the outputs they help generate.

5. Ensemble of LLMs for Code Generation/Modification:

   • Component: LLMService, EvolveComponentTool, CreateComponentTool.
   • Enhancement:
     • LLMService to manage an ensemble of LLMs (fast/cheap for exploration, powerful/expensive for refinement).
     • Evolution tools to leverage this ensemble for a multi-stage candidate generation and refinement process.

6. Granular API for Code Modification:

   • Component: SmartLibrary (component code structure), EvolveComponentTool.
   • Enhancement:
     • Consider adopting a convention (e.g., # EVOLVE-BLOCK-START/END) to allow EvolveComponentTool to focus LLM changes on specific, annotated code sections.

Impact on EAT Components:

• SmartLibrary: Will need to store more comprehensive data: performance metrics (T_eval), correctness proofs, evolution history/rationale, and potentially evaluation scripts. Retrieval will become performance-aware.
• EvolveComponentTool / CreateComponentTool: Become central engines for AlphaEvolve-style component improvement, integrating LLM diffing, automated evaluation, and rich context.
• SystemAgent: Orchestrates evolution when needed, defines evaluation criteria, and its own strategies become evolvable.
• SmartAgentBus: Performance logs from agent interactions can trigger component evolution.
• IntentReviewSystem: Feedback from reviews can guide evolution; can also be used to review significant proposed evolutions.

New Potential EAT Components:

• AutomatedEvaluatorService: A dedicated service/tool for executing component evaluation scripts and returning metrics.
• EvolutionOrchestratorAgent: A specialized agent managing the iterative evolutionary loop for a specific component, tasked by SystemAgent.

Implementation Steps (High-Level Checklist):

• Design and implement diff-based code modification capabilities in LLM interaction logic.
• Develop a standardized automated evaluation framework (evaluate function, test case management).
• Enhance SmartLibrary to store and retrieve performance metrics, evaluation results, and evolution history.
• Refactor EvolveComponentTool to incorporate automated evaluation and iterative improvement loops.
• Integrate richer feedback loops (evaluation scores, IntentReview feedback) into EvolveComponentTool prompts.
• Investigate and prototype mechanisms for evolving SystemAgent orchestration strategies.
• Explore adapting LLMService to support and utilize an ensemble of models for generation/evolution.
• (Optional) Define conventions for granular code evolution blocks within component code.
• (Optional) Design and prototype an AutomatedEvaluatorService.
• (Optional) Design and prototype an EvolutionOrchestratorAgent.

Expected Outcomes:

• More robust and autonomous self-improvement capabilities for EAT components.
• Potential for EAT to discover novel or significantly more performant agents and tools.
• Improved overall efficiency and effectiveness of the EAT framework.
• Closer alignment with state-of-the-art AI-driven discovery methodologies.

Additional context
This enhancement is based on the analysis of the AlphaEvolve paper and its potential application to the EAT framework. The full analysis can be found in the follwing comment:

[matiasmolinas]

The AlphaEvolve paper offers several fascinating concepts that could significantly enhance your Adaptive-Agents-Framework (EAT). Your framework already has many of the foundational pieces (evolution, component library, orchestration), making it a good candidate for incorporating AlphaEvolve's more specialized techniques.

Here's a breakdown of how AlphaEvolve's ideas can be mapped to and improve your EAT project:

Core AlphaEvolve Principles to Integrate:

1. Direct Code-Level Evolution with Automated Evaluation:

   • AlphaEvolve: Directly modifies code of algorithms (Python, C++, etc.) using LLMs, and these changes are rigorously tested against automated evaluators.
   • EAT Enhancement:
     • EvolveComponentTool & CreateComponentTool: Currently, these likely use LLMs to generate/modify components based on natural language requirements or high-level changes. You can enhance this by:
       • Adopting Diff-Based Modifications: Instruct the LLM (within these tools) to output changes in a diff format (like AlphaEvolve's <<<<<<< SEARCH ... ======= ... >>>>>>> REPLACE example for targeted updates, or full rewrites for smaller components). This makes evolution more precise.
       • Introducing Standardized Automated Evaluation: For each AGENT or TOOL in SmartLibrary, allow developers to define an evaluate(component_code, test_inputs) -> performance_metrics function.
       • When EvolveComponentTool runs, it would not just generate a new version but also trigger its evaluation. The evolution is only "accepted" if it passes correctness checks and improves on (or meets) performance metrics.
       • SmartLibrary: Store these evaluation scores and correctness proofs alongside component versions. This data becomes crucial for selection.

2. Evolution of "Search Algorithms" to Find Solutions:

   • AlphaEvolve: Found it highly effective to evolve search algorithms that then find the desired mathematical objects or solutions, rather than evolving the objects/solutions directly.
   • EAT Enhancement:
     • SystemAgent / ArchitectZero as Evolvable Searchers: Your SystemAgent and ArchitectZero already perform a type of "search" by orchestrating tools and designing solutions. The logic they use to do this (their internal reasoning, or the prompts that guide them) could become a target for evolution.
     • Evolving Orchestration Strategies: Instead of just evolving individual agents/tools, SystemAgent could have a mechanism to evolve its own high-level strategy for tackling a class of problems. This could involve an LLM suggesting alternative sequences of tool usage or decision-making logic, which are then tested on a benchmark set of tasks.
     • Example: If SystemAgent frequently processes invoices, you could evolve its internal "invoice processing workflow generation strategy" to make it more efficient or accurate over time, guided by the performance of the workflows it produces.

3. Richer Context and Feedback in Prompts for Evolution:

   • AlphaEvolve: Emphasizes rich context for the LLM proposing changes: problem descriptions, previous solutions, evaluation results, even relevant literature (PDFs).
   • EAT Enhancement:
     • SmartContext & SmartLibrary: When EvolveComponentTool (or CreateComponentTool) is invoked:
       • Pass not just the parent component's code but also its past performance metrics from SmartLibrary.
       • Include successful and failed evolution attempts for that component or similar ones, along with reasons/evaluation feedback.
       • If human review (via IntentReviewAgent) provided feedback on a component, include that feedback in the prompt for its next evolution.
       • Allow users or SystemAgent to provide "literature" (e.g., relevant API docs, algorithm descriptions, best practice guides) to the LLM generating/evolving the code.

4. Meta-Prompt Evolution:

   • AlphaEvolve: The prompts themselves can be evolved.
   • EAT Enhancement:
     • The core prompts used by SystemAgent, ArchitectZero, or even the prompts within CreateComponentTool / EvolveComponentTool that guide the LLM's code generation could be subject to evolution.
     • This is advanced, but you could set up a meta-loop where variations of key system prompts are generated and evaluated based on the quality/performance of the components or plans they produce.

5. Ensemble of LLMs for Code Generation/Modification:

   • AlphaEvolve: Uses a combination of faster/cheaper LLMs for high-throughput candidate generation and more powerful/expensive LLMs for occasional high-quality suggestions.
   • EAT Enhancement:
     • LLMService: Could be enhanced to manage an ensemble of models.
     • EvolveComponentTool / CreateComponentTool: Could be configured to use this ensemble. For example, generate 10 candidate evolutions with a fast model, quickly evaluate them, and then ask a more powerful model to refine the top 2-3 candidates or generate a completely new one based on the initial batch's performance.

6. Granular API for Code Modification:

   • AlphaEvolve: Uses # EVOLVE-BLOCK-START and # EVOLVE-BLOCK-END to mark specific sections of code for evolution.
   • EAT Enhancement:
     • Your SmartLibrary stores code_snippet for components. You could adopt a similar convention if you want EvolveComponentTool to focus its changes on specific parts of a larger agent/tool's codebase, rather than always rewriting the whole snippet.

Mapping to EAT Components:

• SmartLibrary:

  • Store Performance & Evaluation Data: Alongside T_orig (code) and T_raz (applicability), store T_eval (evaluation results, correctness proofs, performance metrics for different test cases).
  • Store Evolution Lineage & Rationale: Track not just parent_id but also the reasoning or LLM's intent behind an evolution. This becomes context for future evolutions.
  • Performance-Aware Retrieval: SearchComponentTool could use T_eval to rank or filter components. "Find me the fastest invoice parser that is at least 95% accurate."

• EvolveComponentTool (and potentially CreateComponentTool):

  • Core Engine: This becomes the heart of your "AlphaEvolve for Components."
  • Input: Parent component ID, desired changes (natural language), target performance metrics, evaluation function/test cases.
  • Process:
    1. Retrieve parent component code and its historical data (performance, past evolution attempts) from SmartLibrary.
    2. Construct a rich prompt for an LLM (or ensemble).
    3. LLM proposes code modifications (diff or full).
    4. Apply changes to create candidate component.
    5. Crucial: Execute the candidate against the provided evaluation function and test cases.
    6. If successful (correct and improved/meets targets), register the new version in SmartLibrary with its evaluation results.
    7. Optionally, run this in a loop for a certain number of iterations or until a performance target is met.
  • Output: ID of the new, improved component version, or report of failure.

• SystemAgent:

  • Orchestrator of Evolution: When SystemAgent decides a component needs improvement (e.g., based on IntentReview feedback, poor performance reported by AgentBus, or explicit user request), it tasks the EvolveComponentTool.
  • Defines Evaluation Criteria: For a given task, SystemAgent could dynamically define the evaluation criteria and even generate simple test cases for the evolution process.
  • Evolvable Strategies: Its own internal decision-making prompts/logic for choosing tools or designing workflows could be targets for evolution, making the SystemAgent itself self-improving in its orchestration capabilities.

• SmartAgentBus:

  • Could log performance metrics from request_capability calls. This data could feed back into SmartLibrary and trigger evolution if a component consistently underperforms or fails.

• IntentReviewSystem:

  • Can be a source of "feedback" that triggers evolution. If a human reviewer rejects a plan because a component is flawed, that feedback (reason for rejection) becomes input for EvolveComponentTool.
  • Could also be used to review proposed evolutions before they are committed, especially if automated evaluation is complex or risky.

New Potential Components Inspired by AlphaEvolve:

• AutomatedEvaluatorService: A dedicated service/tool that EvolveComponentTool uses. It would take component code, test inputs (potentially from SmartContext or user), and an evaluation script, then return metrics.
• EvolutionOrchestratorAgent: A specialized agent, perhaps used by SystemAgent, that manages the detailed evolutionary loop for a specific component. SystemAgent says "Improve Tool X for speed," and EvolutionOrchestratorAgent runs many iterations of LLM suggestions + evaluations.

Workflow Example with AlphaEvolve Ideas:

1. User Goal: "Process this batch of 1000 complex invoices with 99.9% accuracy and under 50ms per invoice."
2. SystemAgent Analysis:
   • Searches SmartLibrary for an "InvoiceProcessingAgent".
   • Finds InvoiceProcessor_v2 with 98% accuracy, 70ms/invoice (from stored T_eval).
   • Determines InvoiceProcessor_v2 doesn't meet requirements.
3. SystemAgent Triggers Evolution:
   • Invokes EvolveComponentTool (or tasks EvolutionOrchestratorAgent).
   • Target: InvoiceProcessor_v2.
   • Goal: Achieve 99.9% accuracy, <50ms/invoice.
   • Evaluation: Provides a (possibly SystemAgent-generated) set of 100 test invoices and an evaluation script that measures accuracy and speed.
   • Context for LLM: Code of v2, its performance, reasons for past failures (if any), user goal, potentially snippets of best practices for high-speed OCR/parsing.
4. Evolution Loop (within EvolveComponentTool or EvolutionOrchestratorAgent):
   • LLM (ensemble) proposes code change (diff) for InvoiceProcessor_v2.
   • Candidate InvoiceProcessor_v2.1_candidate1 is created.
   • AutomatedEvaluatorService tests it: Fails correctness (90% accuracy).
   • LLM proposes another change, incorporating feedback from _candidate1's failure.
   • _candidate2 created. Tested: 99.9% accuracy, 48ms/invoice. SUCCESS.
5. SystemAgent Receives InvoiceProcessor_v2.1 (now InvoiceProcessor_v3):
   • Registers InvoiceProcessor_v3 in SmartLibrary with its new performance data.
   • Uses InvoiceProcessor_v3 to fulfill the user's original goal.
6. IntentReviewSystem (Optional): Could review the final proposed InvoiceProcessor_v3 before it's used, especially if the changes were significant or involved sensitive logic.

By integrating these AlphaEvolve-inspired mechanisms, your EAT framework can move towards more autonomous, performance-driven self-improvement of its components, truly embodying the "evolving" aspect of its name. The key shift is from primarily human-described evolution goals to machine-evaluated performance driving the evolution.