🧬 Genetic AI Features

[DarshanKumar89]

🧬 Genetic AI Features

1. MetaControllerAgent

• Description: Agent that mutates the agent DAG (pipeline) at runtime based on feedback/performance.
• Tasks:
  • Design agent pipeline as a mutable DAG.
  • Implement runtime mutation (add/remove/swap agents, change config).
  • Integrate feedback loop for performance-driven mutation.
• Labels: genetic-ai, reflexive, pipeline

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2. AgentMutator

• Description: Module/class that randomly or heuristically mutates agent order, config, or structure.
• Tasks:
  • Define mutation operators (swap, add, remove, parameter tweak).
  • Support both random and heuristic-driven mutation.
  • Expose API for pipeline mutation.
• Labels: genetic-ai, pipeline

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3. AgentArena

• Description: Run multiple agents in parallel for the same task and select the best output.
• Tasks:
  • Implement parallel agent execution for a given input.
  • Score outputs using pluggable metrics.
  • Return best output and log results.
• Labels: genetic-ai, pipeline, evaluation

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4. MultiObjectiveJudgeAgent

• Description: Agent that scores outputs by multiple dimensions (accuracy, cost, speed, creativity).
• Tasks:
  • Define scoring schema and metrics.
  • Implement multi-objective evaluation logic.
  • Integrate with AgentArena and pipeline.
• Labels: genetic-ai, evaluation

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5. EvolutionMemory

• Description: Store which agent pipelines performed best across different tasks.
• Tasks:
  • Design memory schema for pipeline/task/outcome.
  • Implement retrieval and update logic.
  • Integrate with MetaControllerAgent.
• Labels: genetic-ai, memory

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6. AutoCurriculumEngine

• Description: Agents evolve task-solving strategies based on past performance.
• Tasks:
  • Track agent performance over time.
  • Adjust task difficulty or agent strategy based on history.
  • Integrate with pipeline and memory.
• Labels: genetic-ai, learning

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7. SpeciationHandler

• Description: Group agent types by task specialty and manage competition/crossover.
• Tasks:
  • Implement agent grouping and specialty detection.
  • Design crossover/competition logic.
  • Integrate with mutation and arena.
• Labels: genetic-ai, evolution

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8. FitnessScoreRecorder

• Description: Track how well each agent config performs over time (leaderboard).
• Tasks:
  • Implement fitness score calculation and storage.
  • Expose leaderboard API.
  • Integrate with pipeline and memory.
• Labels: genetic-ai, evaluation, memory

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🧠 Inspired Features

1. GraphMemoryAgent

• Description: Agent that stores structured triples (subject, predicate, object) using networkx or Neo4j.
• Tasks:
  • Implement agent interface for graph memory.
  • Integrate with knowledge graph backend.
  • Expose add/query/traverse API.
• Labels: symbolic, memory, agent

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2. FactExtractorAgent

• Description: Parses LLM outputs into structured facts to populate the knowledge graph.
• Tasks:
  • Implement fact extraction logic (regex, LLM, etc.).
  • Integrate with GraphMemoryAgent.
  • Expose API for fact extraction from text.
• Labels: symbolic, agent, extraction

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3. MemoryDeduplicator

• Description: Detects and prevents redundant or contradictory facts.
• Tasks:
  • Implement deduplication and contradiction detection logic.
  • Integrate with memory modules.
  • Expose deduplication API.
• Labels: symbolic, memory, consistency

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4. MemoryMergeEngine

• Description: Uses LLM to merge, reject, or update existing memory entries.
• Tasks:
  • Implement merge/update logic using LLM.
  • Integrate with memory backends.
  • Expose merge API.
• Labels: symbolic, memory, llm

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5. ThinkerAgent

• Description: General-purpose reasoning agent (multi-domain, chain-of-thought capable).
• Tasks:
  • Implement agent interface for symbolic reasoning.
  • Support chain-of-thought and multi-step reasoning.
  • Integrate with memory and pipeline.
• Labels: symbolic, agent, reasoning

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6. SelfReflectAgent

• Description: Triggers loops like Planner ➝ Judge ➝ Rewriter to self-improve results.
• Tasks:
  • Implement reflexive loop logic.
  • Integrate with pipeline and memory.
  • Expose self-reflection API.
• Labels: reflexive, agent, pipeline

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7. TaskFeedbackLoop

• Description: Stores task outcomes and judgments for later reuse.
• Tasks:
  • Implement feedback storage and retrieval.
  • Integrate with agent pipeline.
  • Expose feedback API.
• Labels: reflexive, memory, pipeline

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8. MemoryScorer

• Description: Ranks memory entries for usefulness and relevance.
• Tasks:
  • Implement scoring logic (LLM, heuristics, etc.).
  • Integrate with memory modules.
  • Expose scoring API.
• Labels: symbolic, memory, ranking

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9. TimelineMemoryAgent

• Description: Tracks memory/events in chronological order.
• Tasks:
  • Implement agent interface for timeline memory.
  • Integrate with event storage.
  • Expose timeline query API.
• Labels: symbolic, memory, agent

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10. RetrieverAgent (hybrid)

• Description: Retrieves relevant facts from both symbolic and semantic memory.
• Tasks:
  • Implement hybrid retrieval logic.
  • Integrate with vector and graph memory.
  • Expose retrieval API.
• Labels: hybrid-memory, agent, retrieval

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11. ContextScorerAgent

• Description: Selects and filters memory before LLM injection based on token budget and relevance.
• Tasks:
  • Implement context scoring and filtering logic.
  • Integrate with memory and pipeline.
  • Expose context selection API.
• Labels: symbolic, agent, context

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📦 Integration & Shared Utilities

1. MemoryManagerAgent++

• Description: Routes queries between vector, graph, summary, and timeline memory.
• Tasks:
  • Implement unified memory manager interface.
  • Support routing and fallback logic.
  • Expose unified memory API.
• Labels: hybrid-memory, memory, manager

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2. AgentWorkflowRunner

• Description: Runs agents from a YAML/JSON-defined DAG (supports reflexion + mutation).
• Tasks:
  • Implement DAG parser and runner.
  • Support agent mutation and reflexion hooks.
  • Expose workflow API.
• Labels: pipeline, agent, workflow

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3. AgentTraceLogger

• Description: Logs all agent actions, inputs, outputs, and memory updates.
• Tasks: (Already implemented, consider extending for new agents)
• Labels: logging, trace, agent

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4. UnifiedMemoryStore

• Description: Abstracts memory backend switching across modalities (vector, graph, etc.).
• Tasks:
  • Implement unified memory interface.
  • Support backend switching and migration.
  • Expose unified store API.
• Labels: hybrid-memory, memory, abstraction

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5. ModelRouter

• Description: Dynamically routes to Claude, GPT, Mistral, RWKV, etc.
• Tasks: (Already implemented, consider extending for new models)
• Labels: routing, model, dynamic

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🏷️ Suggested Labels

• genetic-ai
• symbolic
• reflexive
• hybrid-memory
• pipeline
• agent
• memory
• evaluation
• workflow
• logging

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🧑‍💻 How This Benefits Developers

• Clear modular roadmap: Developers can pick up and implement features independently.
• Plug-and-play: New agents, memory types, and orchestration logic can be added without breaking existing code.
• Hybrid intelligence: Enables both evolutionary (Genetic AI) and symbolic memory workflows.
• Traceability and feedback: Logging and feedback loops make debugging and improvement easier.
• Future extensibility: The architecture supports further research and advanced AI workflows.

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