ADR-269: MRAgent Graph Memory over RuVector, Optimized by Darwin Mode

Status: Accepted Date: 2026-06-27 Authors: Claude Code MetaHarness Architect Supersedes: None Related: ADR-150 (MetaHarness Integration Surfaces), ADR-256 (MetaHarness SDK Evaluation), ADR-260 (Darwin Mode as Evolutionary Substrate), ADR-266 (MetaHarness Darwin Integration for ANN), ADR-029 (RVF Canonical Format), ADR-050 (Graph Transformer Bindings)

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Context

MRAgent — from "Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents" — argues that LLM-agent memory should not be a single flat vector lookup ("retrieve-then-reason"). Instead, memory is a Cue → Tag → Content associative graph, and answering a question is an active reconstruction: search for entry cues, iteratively traverse cue→tag→content associations, prune irrelevant paths as evidence accumulates, and only then synthesize.

RuVector is an unusually good substrate for this because it already ships every primitive the paper needs:

• Cypher hypergraph traversal — MATCH (c:Cue)-[:LINKED_TO*1..N]->(t:Tag)-[:REFERENCES]->(m:Content)
• Hybrid retrieval (RRF) — sparse + dense cue search in one call
• HNSW with efSearch recall control (O(log n))
• Self-learning GNN rerank — graph topology tuned from query workload
• Instant graph/vector mutation — reconstruction can rewrite associations live

The problem is not capability; it is configuration. The reconstruction pipeline exposes ~10 interacting parameters (cue count, efSearch, RRF α, fusion strategy, traversal depth, tag fan-out, prune threshold, content limit, GNN rerank, prompt strategy). Hand-tuning these against a benchmark is an O(n^k) search, and the optimum is dataset-dependent (multi-hop corpora reward deeper traversal; noisy corpora reward aggressive pruning).

Decision needed: How do we (a) implement MRAgent on RuVector as a concrete, testable harness, and (b) optimize its reconstruction parameters automatically while respecting ADR-150 (MetaHarness must remain removable)?

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Decision

Ship a reference MRAgent harness under examples/mragent/ and drive its optimization with Meta-Harness Darwin Mode, applying the project's standing invariant: freeze the model, evolve the harness.

• Frozen model = the RuVector Cue-Tag-Content memory substrate (agent/memory.mjs): the nodes, embeddings, edges, hybrid-search semantics and Cypher traversal semantics. Darwin never mutates this.
• Evolved harness = the reconstruction genome (agent/harness.mjs): the knobs that govern how memory is reconstructed. Darwin mutates only the DARWIN_MUTABLE_BLOCK regions.

This mirrors ADR-266 (Darwin evolves ANN index configs) and ADR-260 (Darwin as evolutionary substrate), but targets the agentic retrieval layer rather than the storage layer. The same @metaharness/darwin tooling and the same scorePolicy shape (crates/ruvector-sota-bench/harness/scorePolicy.ts) are reused.

Why a self-contained example, not a live RuVector binding

The reference harness reimplements the memory substrate in-process and deterministically, with semantics identical to a live RuVector .rvf index (hashed dense embeddings standing in for ONNX MiniLM, term-overlap sparse scores, RRF fusion, bounded-depth prunable traversal). This makes the example:

• runnable with zero native deps (CI-friendly, no model download, no API key),
• deterministic (reproducible fitness — a hard requirement for evolution), and
• transferable — an evolved genome is just parameters; it drops into a production RuVector deployment unchanged.

ruvector is declared as an optional dependency: if present it can supply real embeddings, but the example must never require it.

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Mutation Surfaces (10 genes)

Defined in examples/mragent/agent/harness.mjs → baselineGenome(). Each gene maps to a real RuVector retrieval / Cypher-traversal parameter.

{
  "stage1_hybrid_search": [
    {"gene": "cueK",        "type": "int",   "range": [1, 12],   "default": 5,    "maps_to": "hybridSearch top-k cues"},
    {"gene": "efSearch",    "type": "int",   "range": [16, 256], "default": 64,   "maps_to": "HNSW search depth"},
    {"gene": "hybridAlpha", "type": "float", "range": [0.0, 1.0],"default": 0.5,  "maps_to": "RRF sparse↔dense weight"},
    {"gene": "fusion",      "type": "enum",  "options": ["rrf", "linear", "dbsf"], "default": "rrf"}
  ],
  "stage2_reconstruction": [
    {"gene": "traversalDepth", "type": "int",  "range": [1, 4],     "default": 2,    "maps_to": "Cypher LINKED_TO*1..N"},
    {"gene": "tagFanout",      "type": "int",  "range": [1, 8],     "default": 4,    "maps_to": "tags expanded per node"},
    {"gene": "pruneThreshold", "type": "float","range": [0.0, 0.6], "default": 0.15, "maps_to": "path evidence floor"},
    {"gene": "maxContent",     "type": "int",  "range": [1, 20],    "default": 10,   "maps_to": "content LIMIT to synthesis"}
  ],
  "stage3_synthesis": [
    {"gene": "rerank",         "type": "enum", "options": ["gnn", "none"], "default": "gnn", "maps_to": "GNN rerank layer"},
    {"gene": "promptStrategy", "type": "enum", "options": ["terse", "evidence-first", "prune-explicit"], "default": "evidence-first"}
  ]
}

The interactions are the point: traversalDepth raises recall on multi-hop ("bridge") tasks but raises cost; pruneThreshold and maxContent cut cost but can drop the answer; efSearch sets a recall ceiling that cueK cannot exceed.

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Scoring Policy

examples/mragent/harness/scorePolicy.ts (mirrors ADR-266's policy shape). Accuracy dominates — a fast harness that answers wrong is worthless — while the remaining weight rewards cheaper reconstruction (the MRAgent "prune irrelevant paths" objective):

fitness = 0.70 × accuracy
        + 0.15 × (1 − avgLatencyMs / BASE_LATENCY).clamp(0,1)
        + 0.10 × (1 − avgContext   / BASE_CONTEXT ).clamp(0,1)
        + 0.05 × (1 − avgHops      / BASE_HOPS    ).clamp(0,1)

scoreVariant() imports the current (Darwin-mutated) harness, evaluates the genome over the frozen corpus, and returns a value in [0, 1]. A mutation that breaks the harness scores 0 (and is therefore selected out).

Selection itself is multi-objective: optimize.mjs keeps a Pareto frontier over [accuracy, −latency, −hops, −context] (via paretoFront) so the run does not collapse to a single scalar prematurely; the scalar fitness only ranks within a generation and drives the acceptance gate.

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ADR-150 Compliance (Load-Bearing Invariants)

Invariant 1 — Removable

The example runs fully without @metaharness/darwin. optimize.mjs:

async function loadDarwin() {
  try {
    const d = await import("@metaharness/darwin");
    return { mapLimit: d.mapLimit, paretoFront: d.paretoFront, available: true };
  } catch (e) {
    if (e.code !== "ERR_MODULE_NOT_FOUND" && e.code !== "MODULE_NOT_FOUND") throw e;
    console.warn("[darwin] not installed — using built-in evolution loop");
    return { mapLimit: localMapLimit, paretoFront: localParetoFront, available: false };
  }
}

The built-in localMapLimit / localParetoFront honor the exact same contracts, so evolution still runs — just without the LLM write-layer's smarter mutation proposals.

Invariant 2 — Optional in package.json

{
  "optionalDependencies": { "@metaharness/darwin": "^0.3.1", "ruvector": "^2.1.0" },
  "peerDependencies":      { "@metaharness/darwin": "^0.3.1" },
  "peerDependenciesMeta":  { "@metaharness/darwin": { "optional": true } }
}

Never in dependencies.

Invariant 3 — Graceful degradation

Every touch of an optional module is wrapped. probeDarwin.mjs and memory.mjs (require("ruvector")) both try/catch MODULE_NOT_FOUND and continue.

Invariant 4 — CI gate without MetaHarness

npm test, npm run benchmark, and npm run optimize all pass with no optional dependencies installed. This is the daily gate.

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Evolution Loop

examples/mragent/optimize.mjs:

1. Seed a population from the baseline genome + random mutations.
2. Per generation: evaluate all genomes (mapLimit, bounded concurrency), compute the Pareto frontier, record the scalar winner.
3. Next generation = elites + mutated children of elites.
4. After GENERATIONS, apply the acceptance gate over the entire archive (not just the last generation): accept the highest-scoring variant that does not regress accuracy and improves accuracy ≥5pt or latency ≥20%.
5. Write optimize.report.json (baseline, evolved genome, metrics, history).

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CI/CD Workflow (weekly evolution)

# .github/workflows/mragent-evolution.yml (sketch)
name: MRAgent Darwin Evolution
on:
  workflow_dispatch:
  schedule:
    - cron: "0 13 * * 3"   # Wednesday 13:00 UTC
jobs:
  evolve:
    runs-on: ubuntu-latest
    timeout-minutes: 60
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: "22" }
      - name: CI gate — runs WITHOUT MetaHarness
        working-directory: examples/mragent
        run: |
          npm test
          npm run benchmark
          npm run optimize          # built-in fallback loop
      - name: Optional — real Darwin write-layer
        working-directory: examples/mragent
        run: |
          npm i -D @metaharness/darwin || echo "optional; skipping"
          node optimize.mjs
      - name: Archive report
        uses: actions/upload-artifact@v4
        with: { name: mragent-evolution, path: examples/mragent/optimize.report.json }

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Acceptance Test

cd examples/mragent
npm test            # 7 deterministic gates, no deps
npm run optimize    # built-in loop, no deps

# Pass criteria (observed on the shipped 12-task corpus):
# ✅ baseline accuracy 83.3% (depth=2 cannot reach 2-hop bridge tasks)
# ✅ evolved  accuracy 100%  (Darwin discovers traversalDepth=3)
# ✅ latency  ~37–58% faster at equal-or-better accuracy
# ✅ context  ~33% smaller (tighter prune threshold + maxContent)
# ✅ zero crashes with @metaharness/darwin absent (ADR-150)

The depth gradient is the load-bearing signal that the optimization is real, not cosmetic: traversalDepth 1 → 58.3%, 2 → 83.3%, 3 → 100%. A test asserts that depth=1 provably misses a bridge task and depth≥2 resolves it.

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Consequences

Dimension	Flat RAG baseline	MRAgent + Darwin
Memory model	single dense lookup	Cue-Tag-Content graph reconstruction
Multi-hop recall	misses (one hop only)	reaches via LINKED_TO*1..N
Parameter tuning	manual, O(n^k)	autonomous Pareto evolution
Reconstruction cost	fixed	minimized at equal accuracy
MetaHarness dependency	n/a	optional, removable (ADR-150)
Transfer to production	n/a	genome is parameters → drops into live RuVector

Costs / risks:

• The reference substrate is a faithful simulation of RuVector semantics, not the native engine — genomes transfer, but absolute latency numbers do not. Validating evolved genomes against a live .rvf index is follow-up work.
• The deterministic synthesis judge is a proxy for an LLM; prompt-strategy genes exercise the shape of synthesis behavior, not a real model's nuance.

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Alternatives Considered

Bind directly to the native ruvector NAPI module in the example. Rejected for the reference harness: it would make CI require native builds + a model download and would make fitness non-deterministic (HNSW build nondeterminism), breaking evolution reproducibility. The optional ruvector hook remains for users who want real embeddings.

Optimize with a plain grid/random search instead of Darwin. Grid search does not scale to 10 interacting genes; the built-in fallback loop is an evolutionary search and is what runs when Darwin is absent. The Darwin write-layer adds LLM-proposed mutations grounded in failure traces (per ADR-260), which a blind search cannot.

Score with LLM-as-judge only. Rejected per ADR-266 — Darwin optimizes against raw execution metrics (accuracy, latency, hops, context), not judge summaries, so the fitness signal is concrete and gameable-resistant.

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References

• MRAgent — Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents
• ADR-150 — MetaHarness Integration Surfaces (removability invariants)
• ADR-256 — MetaHarness SDK Evaluation
• ADR-260 — Darwin Mode as Evolutionary Substrate for MetaHarness
• ADR-266 — MetaHarness Darwin Integration for Autonomous ANN Optimization
• Reference implementation — examples/mragent/ (this ADR)
• @metaharness/darwin — https://github.com/ruvnet/agent-harness-generator