← Back to graphonomous.com All payloads captured 2026-04-02 from Graphonomous v0.2.0

Live MCP Demo Suite

Eleven interactive A/B demos showing Graphonomous v0.2.0 in action, covering all 22 MCP tools. Every node ID, confidence score, similarity value, and κ routing decision below was captured from a real MCP session. Click any card to explore the full chat replay with payload inspector.

Codebase Skill Learning

Baseline retrieval (top similarity 0.062) vs learned procedural skills (top similarity 0.521). Store skills, learn from outcomes, link to goals, then re-query.

retrieve_context store_node learn_from_outcome manage_goal

8.4× similarity lift

+0.093 confidence Δ

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κ-Routing & Deliberation

DAG region (κ=0, fast routing) vs 4-node business cycle (κ=1, deliberate routing). Topology analysis detects circular dependencies and assigns deliberation budgets.

topology_analyze deliberate

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Novelty Detection

Known concept (novelty 0.14, Graphonomous itself) vs completely novel concept (novelty 0.92, quantum error correction). The system knows what it knows — and what it doesn’t.

learn_detect_novelty

0.855 max similarity

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Attention Engine

Proactive goal prioritization combining urgency, coverage gap, and κ-routing. 7 real goals ranked by attention score with topology-aware reasoning depth per goal.

attention_survey manage_goal

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Consolidation Pipeline

Before & after the 7-stage consolidation pipeline: decay, pruning, co-activation strengthening, similarity merging, timescale promotion, and abstraction generation.

run_consolidation graph_stats

7 pipeline stages

0.02 decay rate

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Coverage & Decision Gate

Epistemic coverage assessment with act/learn/escalate routing. Low coverage (0.486) triggers “learn” — the system knows it doesn’t know enough to act.

coverage_query review_goal

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Full Learning Pipeline

Automated interaction ingestion (novelty 0.844, 5 claims extracted, 8 edges created) vs explicit feedback correction that replaces content and adjusts confidence.

learn_from_interaction learn_from_feedback

6 nodes learned

+0.090 feedback Δ

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Graph Inspection Suite

Graph health overview (29 nodes, 22 edges, 9 orphans) vs deep BFS traversal from a node exploring 12-node neighborhood across 2 hops of causal chains.

query_graph graph_stats graph_traverse

12 traversal reach

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Typed Retrieval

Episodic memory (5 time-filtered session events) vs procedural skill search (only 1 match at 0.034 similarity). Shows rich event history but sparse skill coverage.

retrieve_episodic retrieve_procedural

5 episodes today

0.034 skill relevance

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Building relationships (store_edge, weight 0.7) vs pruning stale knowledge (delete_node, manage_edge). Full graph lifecycle from creation to cleanup.

store_edge delete_node manage_edge

122 total edges

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Attention Dispatch

Passive survey vs full attention cycle with autonomy override. Survey → triage → dispatch pipeline in advise mode: 3 goals surveyed, 0 dispatched, all deferred.

attention_run_cycle attention_survey

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