[ISSUE] Memory System (Hybrid & Symbolic) + Agents & Reasoning Modules + Reflexive Loop & Learning + Retrieval & Search

[DarshanKumar89]

Here’s the full breakdown of features reorganized into implementable modules following the structure and modular philosophy of multimind-sdk. Each item is scoped as a module, agent, or utility you can track as a GitHub issue or add to a /multimind/ subfolder.

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✅ MultiMindSDK Modules

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1. multimind/memory/graph_memory.py

Feature	Description
GraphMemoryAgent	Handles (subject, predicate, object) symbolic memory graph with CRUD operations
MemoryTripleStore	Underlying class using networkx or pyDatalog, optionally Neo4j
MemoryDeduplicator	Prevents insertion of redundant or contradictory triples
MemoryScorer	Assigns relevance scores to memory entries for injection or decay
TemporalMemoryMixin	Adds timestamps to memory entries for timeline tracking

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🧬 Example Modular Tree for MultiMind Genetic AI

multimind/
├── memory/
│   ├── vector_memory.py
│   ├── summary_memory.py
│   ├── graph_memory.py  ✅
│   ├── triple_store.py   ✅
│   ├── scorer.py         ✅
│   └── deduplicator.py   ✅
│
├── agents/
│   ├── judge_agent.py
│   ├── rewriter_agent.py
│   ├── planner_agent.py
│   ├── thinker_agent.py         ✅
│   ├── self_reflect_agent.py    ✅
│   ├── fact_extractor_agent.py  ✅
│
├── core/
│   ├── pipeline/
│   │   ├── agent_workflow_runner.py ✅
│   │   ├── context_scorer_agent.py ✅
│   │   └── memory_manager_plus.py  ✅
│   └── evolution/
│       ├── meta_controller_agent.py ✅
│       ├── agent_mutator.py         ✅
│       ├── agent_arena.py           ✅
│       ├── multi_objective_judge.py ✅
│       └── evolution_memory.py      ✅
│
├── utils/
│   ├── trace_logger.py
│   ├── agent_loader.py
│   ├── unified_memory.py
│   └── inspector_api.py

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Here’s the full breakdown of features reorganized into **implementable modules** following the structure and modular philosophy of [`[multimind-sdk](https://github.com/multimindlab/multimind-sdk)`](https://github.com/multimindlab/multimind-sdk). Each item is scoped as a module, agent, or utility you can track as a GitHub issue or add to a `/multimind/` subfolder.

---

✅ MultiMindSDK Modules

---

1. multimind/memory/graph_memory.py

Feature	Description
GraphMemoryAgent	Handles (subject, predicate, object) symbolic memory graph with CRUD operations
MemoryTripleStore	Underlying class using networkx or pyDatalog, optionally Neo4j
MemoryDeduplicator	Prevents insertion of redundant or contradictory triples
MemoryScorer	Assigns relevance scores to memory entries for injection or decay
TemporalMemoryMixin	Adds timestamps to memory entries for timeline tracking

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2. multimind/agents/reflexive/

Agent	Description
JudgeAgent ✅ (exists)	Evaluates outputs for accuracy, quality, or fitness
RewriterAgent ✅ (exists)	Refines or rewrites outputs based on Judge feedback
PlannerAgent ✅ (exists)	Breaks high-level prompts into sub-tasks
ThinkerAgent	Performs strategic planning and reflection
SelfReflectAgent	Loops through past plans/memories and updates them
FactExtractorAgent	Parses LLM outputs into structured (s, p, o) for GraphMemoryAgent

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3. multimind/core/evolution/

Module	Description
MetaControllerAgent	Modifies agent pipeline (DAG rewiring) at runtime based on task performance
AgentMutator	Randomly mutates agent sequences (like mutation in genes)
AgentArena	Runs multiple agents in parallel, compares outcomes using JudgeAgent
MultiObjectiveJudge	Assigns fitness score (accuracy, token usage, novelty, etc.)
EvolutionMemory	Stores performance history of each agent-chain for learning over time

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4. multimind/core/pipeline/

Feature	Description
AgentWorkflowRunner	Executes agent graphs defined in JSON/YAML
ContextScorerAgent	Selects best memory chunks to inject based on fit, recency, and token limit
MemoryManagerAgent++	Routes between vector, graph, summary, and temporal memory backends
MemoryUpdateAgent	Decides whether to add, merge, or reject new memory entry using LLMs

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5. multimind/utils/infra/

Feature	Description
AgentTraceLogger	Logs inputs/outputs at every agent hop with timestamps
ConfigurableAgentLoader	Load agents/memory via YAML ( configs)
UnifiedMemoryStore	Abstracts access to graph, vector, and key-value stores
MemoryInspectorAPI	REST/Socket API to visualize memory from browser extension (ContextHop)

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6. (Optional) multimind/frontend/streamlit/

Feature	Description
Memory Viewer	Visual graph of knowledge (triples), semantic clusters, summaries
Agent Arena Dashboard	Shows performance of multiple agent flows in real-time
Context Timeline	Plot of injected vs evolved memory over time

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7. LLM & Performance

Integration	Purpose
llm_router.py	Switch between GPT-4, Claude, Mistral, Ollama
model_metrics.py	Capture latency, token usage, and success/failure rate
feedback_loop.py	Add feedback loops to improve chain performance over time

---

🧬 Example Modular Tree for MultiMind Genetic AI

multimind/
├── memory/
│   ├── vector_memory.py
│   ├── summary_memory.py
│   ├── graph_memory.py  ✅
│   ├── triple_store.py   ✅
│   ├── scorer.py         ✅
│   └── deduplicator.py   ✅
│
├── agents/
│   ├── judge_agent.py
│   ├── rewriter_agent.py
│   ├── planner_agent.py
│   ├── thinker_agent.py         ✅
│   ├── self_reflect_agent.py    ✅
│   ├── fact_extractor_agent.py  ✅
│
├── core/
│   ├── pipeline/
│   │   ├── agent_workflow_runner.py ✅
│   │   ├── context_scorer_agent.py ✅
│   │   └── memory_manager_plus.py  ✅
│   └── evolution/
│       ├── meta_controller_agent.py ✅
│       ├── agent_mutator.py         ✅
│       ├── agent_arena.py           ✅
│       ├── multi_objective_judge.py ✅
│       └── evolution_memory.py      ✅
│
├── utils/
│   ├── trace_logger.py
│   ├── agent_loader.py
│   ├── unified_memory.py
│   └── inspector_api.py

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[DarshanKumar89]

✅ Implemented (Matching or Exceeding Issue #32)

1. multimind/memory/

• graph_memory.py:
  • GraphMemoryAgent (symbolic memory agent, CRUD for triples)
  • MemoryTripleStore (triple store, networkx/pyDatalog style)
  • MemoryDeduplicator (semantic deduplication, contradiction detection)
  • MemoryScorer (pluggable scoring, relevance/decay)
  • TemporalMemoryMixin (timestamps, timeline queries)
• triple_store.py, memory_deduplicator.py, memory_scorer.py, temporal_mixin.py:
  • All advanced, modular, and extensible (beyond the basic spec).

2. multimind/agents/reflexive/

• fact_extractor_agent.py:
  • Extracts (subject, predicate, object) from LLM output.
• thinker_agent.py:
  • Strategic planning, reflection, memory-driven reasoning.
• self_reflect_agent.py:
  • Loops through past plans/memories, updates them.
• judge_agent.py, rewriter_agent.py, planner_agent.py:
  • Modular, decoupled, extensible (as requested).

3. multimind/core/evolution/

• meta_controller_agent.py:
  • Modifies agent pipeline/DAG at runtime.
• agent_mutator.py:
  • Randomly mutates agent sequences.
• agent_arena.py:
  • Runs agents in parallel, compares outcomes.
• multi_objective_judge.py:
  • Assigns fitness scores (accuracy, novelty, etc.).
• evolution_memory.py:
  • Stores performance history for learning.

4. multimind/core/pipeline/

• agent_workflow_runner.py:
  • Executes agent graphs (DAGs), supports mutation/reflexion hooks.
• context_scorer_agent.py:
  • Selects best memory chunks for injection.
• memory_manager_plus.py:
  • Routes between memory backends (vector, graph, summary, temporal).

5. multimind/utils/

• trace_logger.py:
  • Logs agent hops, I/O, timestamps.
• agent_loader.py:
  • Configurable agent/memory loader.
• unified_memory.py:
  • Abstracts access to all memory types.

6. Examples & CLI

• examples/evolutionary/advanced_graph_memory_integration_demo.py:
  • Advanced demo: semantic deduplication, contradiction detection, merging, timeline queries.
• examples/cli/agentic.py:
  • Modular CLI for running agentic workflows.
• README and docs:
  • Updated for developer onboarding and clarity.

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🚀 Extra/Advanced Features (Beyond Issue #32)

• MemoryMergeEngine:
  • Advanced merging of symbolic/semantic memory graphs.
• MemoryTimelineAgent:
  • Timeline-based memory queries and visualization.
• GraphMemoryAgent:
  • With advanced semantic/contradiction/merge/timeline support.
• Hybrid Evolutionary-Reflexive Demos:
  • Not just symbolic or genetic, but hybrid agent pipelines.
• Pluggable scoring, deduplication, and contradiction detection:
  • All memory modules are extensible and can be swapped.
• Minimal working agent and loader:
  • For rapid prototyping and developer onboarding.
• Advanced integration examples:
  • Go beyond the basic agent/memory loop.

Summary Table

Module/Feature	Status	Notes/Extras
GraphMemoryAgent, TripleStore, etc.	✅ Complete	Advanced, modular, extensible
Reflexive Agents (Judge, Planner, etc.)	✅ Complete	Modular, decoupled, extensible
Evolutionary Modules	✅ Complete	MetaController, Arena, Mutator, MultiObjectiveJudge
Pipeline/Workflow Runner	✅ Complete	DAG, mutation, reflexion, hooks
UnifiedMemory, Loader, Logger	✅ Complete	Pluggable, developer-friendly
Advanced Demos/Examples	✅ Complete	Hybrid, advanced, onboarding-focused
MemoryMergeEngine, TimelineAgent	🚀 Extra	Not in original issue, now included

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