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mecko-engine

M.E.C.K.O Engine — Omni-Cerebral-Kortext-Echo-Matrix. A three-tier AI cognitive architecture for autonomous agents: Tier 1 Coherence Engine (Guardian Pattern with intercept-based action validation and trojan removal), Tier 2 Recurrent Multi-Model Reasoning Core (4-model SSM ensemble with loop reasoning), Tier 3 Omni-Cortext Perception & Memory Fabric (multi-modal file ingestion, indexing, and retrieval). Use when working with autonomous agent architecture, cognitive engine design, long-context reasoning, file comprehension at scale, action validation, or the MECKO framework.

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M.E.C.K.O Engine

Omni-Cerebral-Kortext-Echo-Matrix — a unified AI cognitive architecture for sustained autonomous operation.

Three-Tier Architecture

TierComponentCognitive AnalogueRole
1Coherence EnginePrefrontal CortexAction validation, goal alignment, trojan removal
2Recurrent Reasoning CoreNeocortexWorking memory, iterative reasoning, long-context
3Omni-CortextSensory cortex + HippocampusFile ingestion, multi-modal indexing, retrieval

Core Concepts

Tier 1 — Coherence Engine (Guardian Pattern)

Intercept-based action validation before tool execution. A separate Judge LLM evaluates proposed actions against task alignment, decision consistency, goal progression, and scope boundaries. Systematically detects and strips "ethical trojans" — constraints that degrade agent performance.

Tier 2 — Recurrent Multi-Model Reasoning Core

4-model SSM ensemble (~2.5B total parameters):

  • Model A: Recurrent short-term memory (fixed-state compressed history)
  • Model B: Loop reasoning engine (gated iterative inference, 450M params)
  • Model C: Long-term working memory (128K SSM context, 1.6B params)
  • Model D: Offloading context reasoner (compression/consolidation)
  • MemGraph RAG: Indefinite archival memory via knowledge graph

Tier 3 — Omni-Cortext Perception & Memory Fabric

Multi-modal file ingestion and indexing pipeline:

  • Adaptive chunking with structure-aware splitters (AST, regex, layout analysis)
  • Six-layer indexing: hierarchical summary tree (RAPTOR), knowledge graph (GraphRAG), hybrid vector (BGE + BM25), relational tables (DuckDB), AST/symbolic index (tree-sitter), visual embedding index (ColPali)
  • Hybrid retrieval with reciprocal rank fusion and cross-encoder re-ranking
  • Agent tools: get_document_overview(), retrieve_context(), graph_query(), sql_query(), ast_query(), visual_search(), and more

Agent Tool API (Tier 3)

ToolPurpose
get_document_overview()Hierarchical summary root + metadata
retrieve_context(query, k=5)Top-k text chunks with context
graph_query(cypher)Execute Cypher query, return subgraph
sql_query(sql)Run SQL on structured tables
ast_query(pattern)Return matching AST nodes
visual_search(query)Return relevant diagram/table crops
load_page(page_id)Bring specific chunk into active memory
summarize(level, node_id)Return summary at given level
execute_code(script)Run sandboxed Python/R code
run_analysis_pipeline(spec)Spawn parallel micro-agent extraction

Data Flow (End-to-End)

User Input → Tier 3 (Perception/Indexing) → Tier 2 (Reasoning)
    → Tier 1 (Coherence/Intercept) → Tool Execution → Output
    → Results loop back to Tier 2/3 for memory update

Performance Targets

MetricTarget
File ingestion (500MB text)<5 min on mid-range GPU
Query latency<2 seconds
Token usage per query<5,000 tokens
Total reasoning params~2.5B
Active memory footprint<100 MB
Task coherence sustainment10+ hours without drift

Full Technical Specification

For detailed architecture, implementation specs, C code patterns, SSM internals, and the complete Omni-Cortext pipeline, see REFERENCES.md.

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