Graphonomous gives AI agents real memory that persists, learns, and knows when its own knowledge has circular dependencies. It’s an open-source MCP server — plug it into Claude, ChatGPT, Cursor, or any model.
v0.2.0 · 22 MCP Tools · 6 Node Types · κ-Routing · Elixir/OTP · MCP Server · Apache 2.0The system analyzed a 4-node business cycle, routed reasoning depth automatically, and now supports both topology-aware deliberation and proactive attention cycles with model-tier adaptation.
routing: fast max_kappa: 0 action: Single-pass retrieval. No deliberation needed.
routing: deliberate max_kappa: 1 scc_count: 1 fault_line: Product Quality → Market Share budget: max_iterations: 2, agents: 1, confidence: 0.75
{
"routing": "deliberate",
"max_kappa": 1,
"scc_count": 1,
"sccs": [{
"id": "scc-0",
"nodes": ["market-share", "revenue", "r-and-d", "product-quality"],
"kappa": 1,
"approximate": false,
"fault_line_edges": [{
"source": "product-quality",
"target": "market-share"
}],
"routing": "deliberate",
"deliberation_budget": {
"max_iterations": 2,
"agent_count": 1,
"timeout_multiplier": 1.5,
"confidence_threshold": 0.75
}
}],
"dag_nodes": []
}
Live result from Graphonomous MCP server. The system detected a circular dependency between market share, revenue, R&D, and product quality — and identified the exact edge (Product Quality → Market Share) where the feedback loop is weakest. No other agent memory system does this.
Agents store episodic, semantic, and procedural knowledge as typed graph nodes with confidence scores and provenance. Edges capture causal, temporal, and associative relationships.
On every retrieval, Graphonomous computes the topological structure of the relevant subgraph. Tarjan's SCC algorithm detects circular dependencies. The κ invariant measures entanglement depth.
κ = 0 → fast retrieval. On constrained tiers, low-κ regions can be enriched without full deliberation. Higher-friction regions route to deliberate (decompose fault lines, reconcile, write conclusions back). Attention then prioritizes what to do next under autonomy and budget controls.
| Tool | Description |
|---|---|
| store_node | Persist knowledge nodes with type, confidence, metadata |
| store_edge | Create directed relationships between nodes (16 edge types, default weight 0.3) |
| delete_node | Remove a node and its connected edges |
| manage_edge | Edge lifecycle — list, update weight/decay, delete |
| retrieve_context | Semantic search + neighborhood expansion + topology annotations + κ-aware routing |
| query_graph | List, filter, similarity search across the graph |
| topology_analyze | Compute SCCs, κ values, routing decision, fault-line edges |
| graph_traverse | BFS walk with depth and relationship filters |
| graph_stats | Aggregate counts, type distributions, confidence stats, orphan detection |
| retrieve_episodic | Time-range filtered episodic node retrieval |
| retrieve_procedural | Semantic search scoped to procedural how-to nodes |
| coverage_query | Standalone epistemic coverage — act/learn/escalate decision |
| learn_from_outcome | Update confidence across causal chains from grounded outcomes |
| learn_from_feedback | Positive/negative/correction feedback on nodes |
| learn_detect_novelty | Similarity-based novelty scoring for new concepts |
| learn_from_interaction | Full pipeline: novelty → store → extract claims → link |
| deliberate | κ-driven focused reasoning over cyclic regions with optional crystallization |
| manage_goal | Goal lifecycle — create, transition, link nodes, set progress |
| review_goal | Coverage-driven decision gate for goals |
| run_consolidation | 7-stage pipeline: decay, prune, strengthen, merge, promote, abstract |
| attention_survey | Ranked attention map across goals, coverage, and topology signals |
| attention_run_cycle | Trigger one survey/triage/dispatch attention cycle with autonomy override |
Every agent memory system retrieves context. Graphonomous is the only one that tells you the shape of what you retrieved.
Graphonomous computes κ — a proved graph-theoretic invariant — on every retrieval. When your knowledge has feedback loops, the system tells you exactly where they are and how to reason through them.
The κ invariant is proved on 1,926,351 finite systems with zero counterexamples. The proof is browser-runnable at opensentience.org.
The theoretical foundations, deliberation protocol, attention engine, and governance model are published as open research protocols OS-001 through OS-008.
The first empirical evaluation (OS-E001) benchmarks the full engine on 18,165 files across 14 projects: 12,880 edges, 22 SCCs, κ=27, graph beats flat retrieval (+0.103 recall), 100% test pass rate across all 22 MCP tools. Raw data and reproduction scripts included.
OpenSentience · OS-E001 Benchmark · Ampersand Box Design · [&] Protocol Spec