[ISSUE] Genetic AI principles with memmory & cognetive layer

[DarshanKumar89]

GitHub issue plan for evolving MultiMindSDK into a Genetic AI framework, using insights from both:

• 🧬 Genetic AI principles (modular evolution, reflexion, self-optimization)

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🔧 Core Modules to Add (New Issues)

🧠 Memory & Cognitive Layer

1. [Feature] GraphMemoryAgent
   → Implement Mem0-style symbolic memory as (subject, predicate, object) triples
   → Use networkx or optional Neo4j backend

2. [Feature] MemoryUpdateAgent
   → Automatically decides when to add, update, merge, or delete memory
   → Uses LLM scoring + vector + graph similarity

3. [Feature] TimelineMemoryAgent
   → Stores conversation/task memory as a chronological sequence of events
   → Useful for episodic tasks and summarization

4. [Feature] ContextScorerAgent
   → Injects only the most relevant memory chunks into the prompt
   → Optimizes for token count, recency, and relevance

5. [Feature] UnifiedMemoryStore Abstraction
   → Merge vector, graph, key-value memory under one interface
   → Supports modular backend switching

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🧬 Genetic AI Evolution Modules

6. [Feature] MetaControllerAgent
   → Mutates agent DAG mid-task (e.g., swaps Rewriter → Planner order)
   → Uses performance feedback to reroute agent flow

7. [Feature] AgentArena
   → Competing agent flows solve the same task
   → Fitness-based selection (JudgeAgent, metrics-based)

8. [Feature] AgentMutator
   → Introduces randomness or policy-based mutation into agent chains
   → Useful for exploration or auto-curriculum learning

9. [Feature] EvolutionMemory
   → Stores agent-chain performance history per task type
   → Helps guide future selection + planning

10. [Feature] MultiObjectiveJudgeAgent
    → Scores based on multiple metrics (accuracy, cost, speed, novelty)
    → Enables genetic-style fitness evaluation

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⚙️ Agent Pipeline Infrastructure

11. [Feature] AgentWorkflowRunner (DAG-style)
    → Load agents as YAML/JSON graph and execute DAG
    → Enables reflexive loops (e.g., Judge ➝ Rewriter ➝ Planner ➝ Judge)

12. [Enhancement] MemoryManagerAgent++
    → Route between vector, graph, timeline, and summary memory
    → Decision-making can be rule-based or LLM-powered

13. [Enhancement] SelfReflectAgent
    → Reuses past memory to critique and rewrite task solutions
    → Similar to Mem0’s Reflexion loop

14. [Feature] FactExtractorAgent
    → Parses LLM output into structured facts for GraphMemoryAgent
    → Bridges text and symbolic knowledge

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📊 Developer / Visualization / API

15. [Feature] AgentTraceLogger
    → Logs all agent actions, inputs, and outputs for debugging/explanation

16. [Feature] MemoryInspector API (for Chrome + Streamlit)
    → Read + display memory content: graph, vector, summary
    → Useful for ContextHop and future agent dashboards

17. [Feature] Plugin Loader for Agents
    → Dynamically load agents or memories via YAML or entrypoints
    → Enables plug-and-play architecture

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🔬 Roadmap Tags

Tag	Meaning
genetic-ai	Evolutionary agent pipelines, mutation, selection
reflexive-loop	Agents that reprocess their own outputs
hybrid-memory	Combines symbolic, vector, and timeline memory
multi-objective	Fitness scoring based on multiple criteria
adaptive-agent	Self-improving or goal-adapting agent workflows