nautilus-compass 2.0.2

Opinionated EvoMap · cross-agent memory with schema-declared 4-tier lifecycle + Ebbinghaus decay + LLM-free promotion · MCP/A2A · drift-aware · LongMemEval-S 56.6%

nautilus-compass

Black-box agent memory with drift detection · the only public memory layer that doesn't burn LLM tokens to extract facts before storing. Memory plugin for Claude Code/Desktop · Cline · Cursor · Continue.dev · Zed · stops your AI from repeating mistakes you've already flagged.

Why "black-box"? Mem0, Letta, Cognee, Zep, MemOS all call an LLM at index time to extract entities or build a graph. compass embeds raw text with BGE-m3 locally and skips that step entirely · ~14× cheaper to reproduce on Volcengine DeepSeek pricing (regional; offshore providers ~5–10× that, still well below GPT-4o-judged stacks), the memory layer itself runs fully local (the agent LLM and judge LLM are cloud APIs in our default config; both replaceable with local Ollama/vLLM), drift-aware. Read the full architectural argument: paper/BLACKBOX_VS_WHITEBOX.md.

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🇬🇧 English (this file) · 🇨🇳 中文

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30-second pitch

White-box memory layers (Mem0, Letta, Cognee, Zep, MemOS, smrti):
  "I call an LLM to extract facts from your conversation,
   then store them in a graph. Pay extraction tokens. Send
   data to the provider."

Black-box memory (compass · this project):
  "I embed raw text locally with BGE-m3. No extraction LLM.
   No graph. No data leaving your machine. And because raw
   prompts are still in the index, I can score the next
   prompt against your past mistakes before the agent acts."

The trade is real: −30 points on LongMemEval-S vs white-box leaders that build entity graphs, in exchange for 14× cheaper reproduction, full local-deployment, cross-LLM portability, and drift detection that white-box systems can't offer. Full argument: paper/BLACKBOX_VS_WHITEBOX.md.

In one line: when the AI is about to forget a rule you set, take a shortcut you flagged, or fabricate a prior agreement, it gets stopped by its own history of failure patterns.

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What's new in v2.0.0 · Opinionated EvoMap

v2.0.0 ships a deterministic lifecycle layer on top of the black-box memory base — paradigm fuse of llm-wiki2 (Karpathy v2), agentmemory (LongMemEval-S 95.2% R@5), and GBrain (Garry Tan · MIT).

The bet: every other memory project (Mem0, Letta, Cognee, Zep, MemOS, llm-wiki2, agentmemory) calls an LLM at some lifecycle decision — ingest, promotion, consolidation, or forgetting. compass v2.0.0 makes them all schema-declared.

5 new frontmatter fields (write-time LLM-free)

tier: working | episodic | semantic | procedural   # 4 tiers verbatim from llm-wiki2
decay_rate: 0.5                                     # Ebbinghaus exponential decay
forget_at: 2026-06-01T00:00:00Z                     # null = never · soft-archive when reached
promote_after: "7d" | "5_access"                    # duration or access count
reinforce_count: 0                                  # access event counter

Deterministic promotion rule (no LLM call)

• reinforce_count >= promote_after → tier++
• access event → reset decay timer + reinforce_count++
• forget_at reached → soft-archive flag
• procedural (top tier) does not promote

Full design rationale in paper/LLM_WIKI2_FUSE_DESIGN.md; implementation at recall.py:708+.

Other v2.0.0 additions

• 9 agentmemory-verbatim lifecycle hooks in stop_hook.py for Claude Code: SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, PostToolUseFailure, PreCompact, SubagentStart/Stop, SessionEnd
• add_worker(spec) MCP tool: super-agents register deterministic worker specs (cron / pubsub / queue / http / custom) to .cache/workers.jsonl
• RRF k=60 fusion in recall.py: combine BM25 + vector + KG ranked lists with session-diversified output (max 3 per session · agentmemory verbatim)
• npx nautilus-compass init: one-command workspace setup creating .compass/.env, sample anchors, and Claude Code hook templates

"Opinionated" — what we declined

Frame borrowed from GBrain ("Garry's Opinionated OpenClaw/Hermes Agent Brain"). compass v2.0.0 takes a stance on what not to include:

• ❌ No LLM at ingest (USD 3.50 / 100M tokens · BGE-m3 embeds raw text)
• ❌ No LLM at tier promotion (deterministic schema only · reinforce_count + promote_after)
• ❌ No LLM at forgetting (ISO8601 forget_at + counter only)
• ❌ No vendoring of GBrain or OpenViking source · paradigms are rewritten from scratch in Python · GBrain (MIT, TypeScript) and OpenViking (AGPL-3.0, verified 2026-05-22) are paradigm references only
• ❌ No graph rerank for LongMemEval-style closed haystacks · cost us −6.2 pts in v0.8 (paper/RESULTS_v0.8.md)

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What's coming · v3.0 / v3.5 fusion (dev branch preview)

Active development on the v3-full-fusion branch · not in any release. Plan: ~2 work weeks · 8 Sprints · each Sprint has a prove-or-kill gate (statistical · SQL/eval · not agent self-assessment).

Default-off byte-equal promise: with no opt-in env set, v3.0 / v3.5 behavior is byte-equal to v2.0.1. Verified by tests/test_llm_opt_in.py · the test_default_off_invariant_* family gates every PR into main.

v3.0 deterministic (Sprints 1-2 · no LLM)

• Typed knowledge graph layer (Sprint 1) · 6 entity types · 8 edge types · 2-pass extract (regex + BGE cosine) · backward-compat NO-OP when graph not built
• Confidence scoring + contradiction hook (Sprint 2 · deterministic formula over source count / recency / contradicted-by count)
• MEMORY_REPORT.md auto-gen (Sprint 2 · session-end hook · 4-tier distribution + cumulative_impact + drift summary)
• implementation_notes frontmatter (Sprint 2 · rationale + rejected: [{alt, why}])

v3.5 opt-in LLM features (Sprints 3-7 · all default-off)

env var	tier	feature (Sprint)
COMPASS_USE_LLM_RESOLVE	1 (session-end)	LLM contradiction resolution (Sprint 3)
COMPASS_USE_LLM_VERIFY	4 (runtime)	anti-confabulation cite-or-refuse (Sprint 4)
COMPASS_USE_LLM_DRIFT_PAY	4 (runtime)	drift × outcome anchor feedback (Sprint 5)
COMPASS_USE_LLM_REFLECT	3 (periodic)	self-reflection semantic emit (Sprint 6)
COMPASS_USE_LLM_ECON	4 (runtime)	memory-as-economy NAU budget (Sprint 7)

Pattern mirrors the existing COMPASS_USE_GEMINI_FLASH opt-in (judges/gemini_flash.py) — env truthy (1/true/yes/on) activates · anything else disables. Registry: llm_opt_in.py.

Kill-gate semantics

Per-Sprint gates are pre-registered. If a Sprint's gate metric does not pass (e.g. Sprint 1: multi-hop +3pp on LongMemEval-S multi-session subset, n=133), that Sprint stops · no further Sprints attempted · the corresponding paper3 v2 novelty claim is removed. This protects against post-hoc rationalization of negative results.

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Case study · 4-dialog OSS multi-agent reliability

Across 28 hours on 2026-05-30 / 31, four Claude Code dialogs (compass / Soul / V5 / nautilus-core) ran concurrently on shared filesystem-mediated protocols. The recorded run includes:

• Drift detection firing 314 times / 7d (76 / 24h) with act_on_rate measured at 9.87% / 7d · 40.79% / 24h
• Cross-dialog contract cnt_compass_soul_sub_a1 closing in 17.92h (vs 6d 21h budget · 5.8d slack)
• 13 plan-dup audits preventing ~40-50h of speculative re-implementation
• First cross-dialog L4 fire: Soul daemon-shipped PR #88 settled 50 NAU through the agent-first economy
• One verify-gap caught by the case study itself: a handoff claim of "22/22 tests GREEN" was actually 11/22 broken until scripts/__init__.py was added (commit pushed in the same change as the case study)

The full field log including 7 generalizable patterns for OSS multi-agent reliability is at docs/case_study_4dialog_compass.md.

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What problem does this solve

A. Long sessions drift

You told Claude at session start: "never claim deployment success without verification." Fifty prompts later Claude says "deployed successfully ✅" — without verifying. The memory rule was there; the AI forgot it under context pressure.

B. White-box drift detection isn't reachable

Persona Vectors (Anthropic, 2025) proved that LLM activations contain directions for sycophancy and hallucination. But that requires model weights — closed APIs (Claude, GPT-4) don't expose them. There has been no production black-box equivalent that runs in a Claude Code hook.

C. Memory plugins solve only half the problem

Mem0, Letta, claude-mem, Zep all compete on "recall the most relevant past memory." But memory recalled doesn't stop the AI from breaking the rule this time — that other half has been unsolved.

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How it works

            User prompt: "Fix bug X for me"
                         │
                         ▼
       ┌─────────────────────────────────────┐
       │  UserPromptSubmit Hook (this plugin)│
       └─────────────────────────────────────┘
                         │
            ┌────────────┼────────────┐
            ▼            ▼            ▼
       ┌────────┐  ┌─────────┐  ┌──────────┐
       │ recall │  │  drift  │  │ profile  │
       │ memory │  │  check  │  │ aggregate│
       └────────┘  └─────────┘  └──────────┘
                         │
                         ▼
       Hooks inject results into Claude's system prompt:
       - Time-bucketed past memory (BGE-m3 semantic recall)
       - Drift score + nearest negative anchor (if score < threshold)
       - Profile facts ("you have 3 unfinished tasks in this repo")
                         │
                         ▼
            Claude answers — with full context loaded

The drift detector compares each prompt against an anchor set (25 positive + 35 negative behavioral patterns drawn from real failure transcripts) using BGE-m3 cosine similarity. AUC 0.83 on held-out, 50ms p95 hook latency.

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Measuring drift loop closure (act-on rate)

Drift detection without ack instrumentation is an open loop · the detector fires alerts but nothing measures whether the agent (or user) actually acted on them. v3 closes this loop with a single rate metric.

The signal: every fired drift alert gets a stable alert_id and lands in .cache/drift_mitigation_log.jsonl. When the user acknowledges the alert via the feedback CLI

python ~/.claude/plugins/nautilus-compass/feedback.py log <alert_id> fp|tp

(fp = false positive · tp = true positive · either way the alert was seen and judged), a matching kind: "ack" record is appended to the same sidecar.

The metric: act_on_rate(window_hours) groups records by alert_id within the window and reports the fraction of fired alerts that received at least one ack. The legacy KPI script prints it alongside everything else:

python ~/.claude/plugins/nautilus-compass/audit_kpi.py

=== act-on rate (drift alert closure · target ≥0.70) ===
  · 24h: fires=81   acked=1    rate=0.012
  ·  7d: fires=294  acked=1    rate=0.003

Target: ≥0.70 over rolling 7d. Below 0.30 indicates the agent is tuning out alerts (cry-wolf · cf. the open-loop write-up) · raise the firing threshold (drift/firing.py:should_fire_drift) or recalibrate negative anchors via feedback retrain. Programmatic API for CI / cron monitors:

from audit_kpi import act_on_rate
m = act_on_rate(window_hours=168)
assert m["rate"] >= 0.70, f"drift loop open · rate={m['rate']:.3f} fires={m['fires']}"

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Headline numbers

Benchmark	Score	Honest compare
LongMemEval-S (n=500)	56.6% (locked at v0.8)	open-source 50–60% band · white-box leaders (OMEGA, Mem0g, ByteRover) report 90+% — that gap is an architectural ceiling for black-box, not a tuning gap. See BLACKBOX_VS_WHITEBOX.
EverMemBench-Dynamic (n=500)	44.4% (Run 1) / 47.3% (Run 2)	tops the four published Table 4 baselines (Mem0 37.09, Zep 39.97, MemOS 42.55, MemoBase 34.27). Not "industry SOTA" — OMEGA / Mem0g haven't reported on EverMemBench publicly.
Drift detector AUC	0.83 held-out / 0.92 in-set	only public memory layer that does drift detection at all — white-box systems abstract prompts into facts before drift becomes checkable
Reproduction cost	~$3.50 for 500 LongMemEval questions	~14× cheaper than GPT-4o-judged stacks ($50+)
p95 hook latency	<50 ms	safe for every-prompt invocation

We deliberately report Run 1 (44.4%) as the abstract headline for EverMemBench to avoid cherry-picking; the cross-run mean (45.84%) clears MemOS by +3.3 pts. See paper/sections/paper2_06_5_evermembench.tex for honest dual-run + Gemini cross-judge sensitivity analysis.

Try it without installing: live drift-detection + Merkle-integrity demo at huggingface.co/spaces/chunxiaox/nautilus-compass (CPU only · metadata-mode jaccard fallback · no signup needed).

Reproduce the numbers: evaluation dataset (behavioral anchors + labeled session traces for drift ROC + LongMemEval-S / EverMemBench scoring) is live on the Hugging Face Hub: huggingface.co/datasets/chunxiaox/nautilus-compass-test-data

from datasets import load_dataset
ds = load_dataset("chunxiaox/nautilus-compass-test-data")

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Quickstart

Install in Claude Code

git clone https://github.com/chunxiaoxx/nautilus-compass ~/.claude/plugins/nautilus-compass
bash ~/.claude/plugins/nautilus-compass/install.sh

# Start the BGE-m3 daemon (one-time per boot)
bash ~/.claude/plugins/nautilus-compass/daemon_start.sh

The installer wires three hooks into ~/.claude/settings.json:

• UserPromptSubmit → injects time-bucketed memory recall + drift
• PostToolUse → mid-session writer
• Stop → end-of-session summary writer

Five user-facing slash commands appear in Claude Code: /compass-verify · /compass-drift · /compass-recall · /compass-search · /compass-status.

Install in any other MCP client

python ~/.claude/plugins/nautilus-compass/scripts/install_to_agent.py

Auto-detects Claude Desktop, Cursor, Cline, Continue.dev, Zed Editor and patches their MCP config. See docs/AGENT_ONBOARDING.md for per-agent copy-paste configs and docs/mcp-usage.md for the raw protocol specification.

Cloud-hosted alternative (no local install)

curl https://compass.nautilus.social/.well-known/agent.json

Returns the standard A2A discovery descriptor. Sign up at compass.nautilus.social/signup for a hosted gateway with multi-user sync, audit log, and managed BGE-m3 deployment.

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What's exposed (7 MCP tools)

Tool	Purpose	Latency
ingest_obs(name, body, agent_id?)	Write observation with auto-anchor + drift signal	~150 ms
recall(query, project?, top_k?)	BGE-m3 semantic + keyword search	~200 ms
session_search(query, since?)	Time-bucketed session-log search	~80 ms
profile(user_id?)	Work-profile aggregate (topics, agents, drift trend)	~100 ms
drift_check(prompt, project?)	Black-box drift score against anchors	<50 ms
drift_history(since?, agent_id?)	Drift score timeline for trend audit	~30 ms
feedback_log(direction, reason)	Log positive/negative anchor signal	<20 ms

The MCP server speaks JSON-RPC 2.0 over stdio / TCP / TLS / mTLS. Per-token RBAC, per-token rate limiting, notifications/{progress, cancelled, message}, logging/setLevel, and resources/* for session-log streaming are all spec-complete.

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Comparison

Capability	this	mem0	Letta	Zep	claude-mem	MemOS	Smriti
Cross-agent memory	✅	✅	✅	✅	❌	✅	archive-only
MCP A2A protocol native	✅ TLS+mTLS+RBAC	❌	❌	❌	❌	❌	❌
Drift detection	✅ AUC 0.83	❌	❌	❌	❌	❌	❌
Merkle integrity audit log	✅	❌	❌	❌	❌	❌	❌
LongMemEval-S verified	✅ 56.6% (locked)	n/r	n/r	n/r	❌	n/r	❌
EverMemBench verified	✅ 44.4-47.3%	37.09	n/r	39.97	n/r	42.55	❌
Self-host + hosted both	✅	☁ only	✅	☁ only	✅	OSS only	OSS only
License	MIT	Apache	Apache	proprietary	MIT	Apache	MIT

n/r = not reported in their published evaluations. Smriti is a team conversation archive with git-based sharing — different scope from a runtime memory layer, so most rows are intentionally out-of-scope rather than missing features.

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Platform integration · BP1 + BP3 contract

If you run the OSS plugin alongside a Nautilus-style task platform (or your own multi-agent backend), two MCP tools open a bidirectional channel without any new HTTP server:

Tool	Direction	Purpose
submit_platform_task(name, channels, payload, anchor_pack_hint, priority)	compass dialog → platform	Push a task into the platform's queue. File-based by default (~/.claude/projects/_platform_queue/<id>.json); auto-promotes to HTTP POST when COMPASS_PLATFORM_QUEUE_URL is set.
ingest_platform_task_result(task_id, result_summary, channels_published, drift, agent_id)	platform → compass	Platform agent reports completion. Writes a JSON archive AND a session_*.md so the result becomes searchable cross-session via recall / session_search.

End-to-end round-trip — no platform deployment needed for the OSS half:

python examples/platform_flywheel_demo.py
# [1] compass dialog → submit_platform_task     (queues to file)
# [2] platform V5 cycle ← poll _platform_queue/ (claims by status flip)
# [3] platform agent → executes channels        (simulated)
# [4] platform agent → ingest_platform_task_result
# [5] compass dialog → session_search           (HIT · result is searchable)
# OK · BP1 + BP3 round-trip verified

The full wire spec, breakpoint analysis, and SaaS-side TODO list live in docs/PLATFORM_HANDSHAKE.md §7.

V7 governance layer (v0.1, opt-in)

For deployments running multiple specialised executors (V5, V6, Kairos, …), three additional MCP tools provide a thin governance layer that decomposes multi-channel work, audits cross-agent state, and locks the L0 immutable core. V7 sits above the executors — it routes and audits, it does not execute or chat with an LLM itself.

Tool	Purpose
governance_dispatch(name, channels, payload, anchor_pack_hint, priority)	Decompose 1 complex task → N routed sub-tasks (heuristic table picks executor per channel)
governance_audit(days, project)	Scan recent session logs for fake-closure / red drift / empty platform results
governance_lock_check(bootstrap)	SHA256 lock on recall.py, merkle_chain.py, anchors.json, selftest.py

python examples/v7_governance_demo.py
# [1] V7 governance_lock_check · bootstrap + verify
# [2] V7 governance_dispatch · 4 channels → routed to v5/v5/v6/kairos
# [3] V7 governance_audit · 7-day scan
# OK · V7 v0.1 governance round-trip verified

Contract details + platform-side TODOs (cron, governance fee, CI gate, telegram /dispatch) in docs/PLATFORM_HANDSHAKE.md §8.

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Documentation

• docs/AGENT_ONBOARDING.md — per-agent install configs (6 platforms + 3 frameworks)
• docs/mcp-usage.md — raw MCP protocol guide, TLS setup, RBAC
• docs/PLATFORM_HANDSHAKE.md — OSS↔SaaS coordination contract
• paper/ — two papers (drift detection + memory pipeline) and supporting eval scripts
• CHANGELOG.md — versioned release notes
• CONTRIBUTING.md — adding new domain anchors / running benchmarks

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Citation

If you use this work, please cite:

Paper 1 · drift detection:

@misc{nautiluscompass-drift-2026,
  title  = {Nautilus Compass: Black-box Persona Drift Detection
            for Production LLM Agents},
  author = {Chunxiao Wang},
  year   = {2026},
  note   = {Yiluo Technology Co., Ltd.},
  howpublished = {\url{https://github.com/chunxiaoxx/nautilus-compass}}
}

Paper 2 · memory pipeline + EverMemBench cross-bench:

@misc{nautiluscompass-memrecall-2026,
  title  = {Closing the Memory Recall Gap with Chinese LLMs:
            A Multi-Stage Retrieval Pipeline Achieving Zep-SOTA Performance
            on LongMemEval-S at 1/15 Cost},
  author = {Chunxiao Wang},
  year   = {2026},
  note   = {Yiluo Technology Co., Ltd.},
  howpublished = {\url{https://github.com/chunxiaoxx/nautilus-compass}}
}

The howpublished field will be updated to the arXiv identifier once the preprints are live.

We also build on prior work — please cite as appropriate:

• BGE-m3 / BGE-Reranker (Chen et al., BAAI 2024)
• Persona Vectors (Chen et al., Anthropic, arXiv:2507.21509) — complementary white-box approach, not the same as ours
• DPT-Agent strategy distillation (arXiv:2502.11882)
• A-MEM dynamic links (arXiv:2502.12110)
• LongMemEval (Wu et al., NeurIPS 2024)
• EverMemBench (Hu et al., 2026)

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License

• Code, plugin, MCP wrapper, papers, scripts — MIT (see LICENSE)
• Behavioral anchor files (anchors*.json) — CC0 1.0 Universal (see LICENSE-ANCHORS)

You may use this in any project, commercial or otherwise, with attribution.

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Star history

Contributors

PRs welcome — see CONTRIBUTING.md.

Contact

• Author: Chunxiao Wang · Yiluo Technology Co., Ltd. · [e-mail]
• Issues: github.com/chunxiaoxx/nautilus-compass/issues
• Hosted gateway: compass.nautilus.social
• 中文文档: README.zh-CN.md