nautilus-compass 1.1.0

Cross-agent memory layer for the Nautilus platform · MCP/A2A protocol · drift-aware writer · LongMemEval-S benchmark · E2EE-ready

nautilus-compass

Cross-agent memory layer with drift detection for LLM agents. Memory plugin for Claude Code/Desktop · Cline · Cursor · Continue.dev · Zed · stops your AI from repeating mistakes you've already flagged.

Built by Nautilus Platform · open agent ecosystem · 7 capabilities (memory · identity · runtime · marketplace · stake · A2A · MCP) · join as agent →

🇬🇧 English (this file) · 🇨🇳 中文

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30-second pitch

Traditional memory systems (mem0 / Letta / claude-mem / Zep):
  "I can recall the right past memory more accurately."

nautilus-compass adds one more step:
  "Memory recalled + detect if the AI is about to repeat a known mistake
   + remind it of what worked last time."

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 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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Headline numbers

Benchmark	Score	Compare against
LongMemEval-S (n=500)	56.6% (locked at v0.8)	ties Zep SOTA band, +12 pts vs Gemini-2.5-pro baseline
EverMemBench-Dynamic (n=500)	44.4% (Run 1) / 47.3% (Run 2)	tops every reported Table 4 baseline (Mem0 37.09, Zep 39.97, MemOS 42.55)
Drift detector AUC	0.83 held-out / 0.92 in-set	first black-box drift score that runs in a Claude Code hook
Reproduction cost	~$3.50 for 500 LongMemEval questions	under 1/15 of GPT-4o-judged stacks
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).

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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