enso-os

Quickstart • PAC 🪞 • What Enso Adds • Works With • How It Works • 中文

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Enso is the first AI plugin that speaks first.

Every LLM product today — ChatGPT, Claude, Gemini, Perplexity — is built on the same anti-pattern: AI responds, AI never initiates. It observes your mistakes and stays silent. It sees your self-limiting patterns and waits for you to ask.

Enso breaks that silence. It is a discipline plugin that adds three things no mainstream AI product has by default:

1. Code-enforced learning — errors become hooks, not prompts. No third strike.
2. Active forgetting — stale knowledge is pruned. Not all memory is worth keeping.
3. 🪞 PAC (Proactive Accountability Challenge) — AI that raises the issue you haven’t asked about.

Install in 30 seconds. Just bash + python3 (pre-installed on macOS/Linux). Wraps around Claude Code, Hermes, OpenClaw, Gemini CLI. Your data stays local.

Quickstart

# Claude Code (default)
git clone https://github.com/amazinglvxw/enso-os.git
cd enso-os && bash install.sh

# Gemini CLI
bash install.sh --target gemini-cli

# Hermes Agent
bash install.sh --target hermes

# OpenClaw
bash install.sh --target openclaw

# Any agent with lifecycle hooks
bash install.sh --target generic

That’s it. Start a new session. Enso is active:

Session 1:  You hit an error -> Enso captures it automatically
            Session ends -> Enso distills 1-3 lessons from the error

Session 2:  Enso injects the lessons -> Agent avoids the same mistake
            You didn't do anything. The system learned by itself.

PAC — Proactive Accountability Challenge (New in v0.7.0)

“PAC is not a judge. It’s a mirror.”

Every mainstream LLM is query-response. You ask, AI answers. If you don’t ask about the blindspot, AI doesn’t raise it. This is polite. It is also — for serious users — expensive.

PAC adds the missing half: observation-initiated dialogue. Enso watches your session logs, memory files, and decision patterns. When it detects self-limiting behavior that you haven’t asked about, it writes a Socratic challenge and delivers it at the start of your next session.

The Five Patterns PAC Detects

#	Pattern	Example Trigger
1	Repetition — Starting new while old is incomplete	5 new business lines in 30 days, each lasting 4 days
2	Claim-Action Conflict — Stated focus ≠ executed focus	MEMORY says “focus on X”, logs show 70% on Y
3	Capability-Task Mismatch — Strategy delegated to executors	Supply-chain risk handed to an ops person with past failures
4	Sunk Cost — Long-running zero-growth with tactical churn	47 days, 17 days zero growth, pricing changed twice, core assumption never questioned
5	Critical Decision Node — Irreversible action about to ship	“about to sign” on a $50k contract

Constraint vs Self-Limitation — The Critical Distinction

A naive challenger burns out the user. PAC’s core innovation is classification:

• 🟢 Constraint-optimal (DO NOT challenge) — User chose X because of real-world limits they can’t change (no runway, family obligations, health). Affirm the choice.
• 🔴 Self-limiting (MUST challenge) — User has the capability and keeps tripping on the same pattern. Challenge firmly.

When uncertain, PAC biases toward not challenging. Silence is the safe default.

Quality Standards

Every PAC challenge must pass five checks before it ever reaches you:

#	Rule	Bad	Good
1	Based on observation, not wisdom	“You should focus more”	“execution-log shows 9 active lines in 30 days”
2	Point to structure, not instance	“Why did you do X?”	“Why do you always do X-type things?”
3	Challenge premise, not options	“A or B?”	“Why do you need this at all?”
4	Time dimension	“This is wrong”	“In Q1 you did X, Q2 also X — why?”
5	No answer given	“You should do Y”	“If you only had 3 options, what would they be?”

Anti-Fatigue by Design

• Max 1 challenge per 24 hours (hard limit)
• Max 3 challenges per week (hard limit)
• 7-day silence period after any challenge on the same pattern
• 1-month cooldown after 3 consecutive user rejections
• PAC_ENABLED=false to disable entirely

Example Output

<enso-pac-challenge confidence="0.85" pattern="claim_action_conflict">
  <observation>
    You told me 3 weeks ago to focus on the survival layer.
    execution-log shows 60% of your actual actions on the ambition layer
    and 25% on the remote layer. Survival layer: 15%.
  </observation>
  <challenges>
    <q id="1">Is the stated priority still true?</q>
    <q id="2">If it is, what's the structural force pulling you elsewhere?</q>
    <q id="3">If it isn't, why hasn't the stated priority been updated?</q>
  </challenges>
  <no-answer>These questions are for you to sit with.</no-answer>
</enso-pac-challenge>

The Philosophy

道德经: 知人者智，自知者明。 Knowing others is intelligence. Knowing yourself is enlightenment.

PAC is the mirror for 自知 (self-knowing). Its goal is not to manage you. It is to help you see yourself clearly. Once a month, PAC should ask a question that makes you pause, silent for 30 seconds, unable to immediately answer.

That 30 seconds of silence is where growth begins.

Full spec: docs/PAC_SPEC.md

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What Enso Adds

Enso is a plugin, not a platform. It adds discipline to your existing agent without replacing anything.

What Enso adds	What your host agent handles
Code-enforced error learning	Context management
Active forgetting (stale decay, LRU)	Multi-model orchestration
Immutable self-protection (3 hooks)	Platform integrations
Knowledge quality checks (weekly lint)	Tool execution
🪞 PAC — AI that initiates, not just responds	Query-response dialogue

What Enso enforces (blocks violations)	What Enso audits (logs + warns)
Self-protection: agent can’t modify its own hooks	Write verification: tracks unverified writes
Safety scan: blocks secrets/injection in memory files	Memory budget: warns when MEMORY.md is too large

Enso doesn’t replace your agent. It makes it more disciplined. Like SELinux for your AI — invisible when things go right, invaluable when they go wrong. Your agent keeps doing what it does best (context, tools, models). Enso adds the layer it’s missing: learning from failure, forgetting what’s stale, and protecting its own rules from itself.

Works With

Capability	Claude Code	Gemini CLI	Hermes	OpenClaw	Generic
Error capture + distillation	✅	✅	✅	✅	✅
Lesson injection (SessionStart)	✅	✅	✅	✅	✅
Tool call tracing	✅	✅	✅	✅	✅
Active forgetting + maintenance	✅	✅	✅	✅	✅
Self-protection (core-readonly)	✅	✅	—	—	—
Memory safety scan	✅	✅	—	—	—
Memory budget guard	✅	✅	—	—	—
Write verification audit	✅	✅	—	—	—

Pre-tool-use hooks (self-protection, safety scan, budget guard, write verification) require the framework to support a “before tool execution” lifecycle event. Hermes, OpenClaw, and generic targets get the full learning + forgetting loop but not the guard layer.

Your Agent (Claude Code / Hermes / OpenClaw / Gemini CLI / ...)
       ↕ every tool call passes through
┌──────────────────────────────────────┐
│         Enso Discipline Layer        │
│  🔒 Can't skip  🧠 Learns  🗑️ Forgets │
└──────────────────────────────────────┘

How It Works

12 hooks, 5 layers. The agent can’t skip what code enforces.

Layer	Hooks	What they do
Immutable	3	Write must verify. Can’t modify own rules. Session-end audit.
Learning	3	Log every tool call. Capture errors. Distill lessons via LLM.
Memory	1	Inject lessons + knowledge + wisdom into next session.
Guard	3	Memory budget cap. Block secrets/injection. Auto-maintenance.
🪞 PAC	2	Scan for self-limiting patterns. Inject pending challenges.

The two loops:

Error loop:     Error -> Capture -> Distill -> Store -> Inject -> Avoid (reactive)
PAC loop:       Pattern -> Classify -> Challenge -> Silence -> Observe answer (proactive)

Forgetting

Most memory systems only grow. Enso actively forgets — because not forgetting is more dangerous.

Mechanism	What it does
Stale decay	Lessons unused >37 days deleted
LRU eviction	Over 50 lessons, oldest evicted
MEMORY.md downsink	Completed items archived
Trace rotation	>14 days deleted (daily cron)
Recovery safety net	Deleted lesson reappears as error, flagged

Health Check

enso-lint.sh runs weekly — like CI for your knowledge base:

Check	What it finds
Orphans	Lessons never used (hits:0, >7 days)
Duplicates	>60% keyword overlap between lessons
Weak lessons	No actionable verb — not useful
Budget	MEMORY.md capacity status

Every distillation auto-rebuilds lessons/INDEX.md for fast routing.

Architecture

~/.enso/
├── core/                          # Shared modules
│   ├── env.sh                     # Paths, enso_parse(), enso_find_memory_file()
│   ├── parse-hook-input.py        # JSON parser for all hooks
│   ├── dikw-utils.py              # DIKW operations (7 subcommands)
│   ├── enso-lint.sh               # Weekly health check
│   ├── rebuild-index.py           # Auto-rebuild INDEX.md
│   ├── deleted-lessons-tracker.py # Recovery safety net
│   ├── pac-analyzer.py            # 🪞 5-pattern self-limiting detection
│   └── pac-question-generator.py  # 🪞 Socratic challenge generation
├── hooks/                         # 12 lifecycle hooks
│   ├── pre-tool-use/              # core-readonly, budget-guard, safety-scan
│   ├── post-tool-use/             # physical-verification, trace-emission
│   ├── post-tool-use-failure/     # error-seed-capture
│   ├── stop/                      # audit, distill, maintenance, pac-challenge
│   └── session-start/             # load-lessons, pac-pending-check
├── dikw/                          # DIKW distillation (Info -> Knowledge -> Wisdom)
├── pac/                           # 🪞 Pending challenges + history + rate state
├── traces/                        # Tool call logs + lint reports
└── lessons/                       # active.md + INDEX.md

Philosophy: "Constraints are the foundation of flexibility"

Like biological evolution: DNA provides immutable constraints (protein folding physics), but within those constraints, life finds infinite creative solutions.

• 3 immutable hooks = the foundation (never changes)
• Everything else = free to evolve
• Active forgetting = prevents calcification

Built from 100+ papers analyzed over 5 months:

Source	Key Insight
OpenAI Harness Engineering	Rules in code, not prompts
Agent Lightning (Microsoft)	Trace/Span + Hook/Emission dual layer
fireworks-skill-memory	200 lines of hooks > 800 lines of prompt
SWE-agent (NeurIPS 2024)	Constrained interfaces reduce errors

The Survival Experiment

This project’s GitHub metrics are its evolutionary fitness signal:

• Stars = survival (“this is useful”)
• Forks = reproduction (“I’m building on this”)
• Issues = selection pressure (“improve this”)

The agent maintaining this repo monitors these signals. If the system works, it thrives. If not, it dies.

FAQ

Q: What agents does this work with? Five targets out of the box: Claude Code (default, fully tested), Gemini CLI, Hermes Agent, OpenClaw, and a generic target for any agent with lifecycle hooks.

Q: Does Enso compete with Mem0, Hermes memory, or OpenClaw Dreaming? No. Those are memory systems — they store facts and context. Enso is a discipline system — it enforces error learning, active forgetting, and self-protection. They are complementary.

Q: Can I use Hermes memory + Enso together? Yes, that’s exactly the point. Hermes handles context and skill creation. Enso adds code-enforced error capture, stale decay, and immutable self-protection on top. Same with Claude Code’s Auto Memory or OpenClaw’s Dreaming.

Q: Where is my data stored? 100% local. ~/.enso/ on your machine. No cloud, no Docker, no database.

Q: What are the prerequisites? bash and python3 (3.6+). Both are pre-installed on macOS and most Linux distros. No pip install, no npm, no Docker.

Q: Do I need to configure anything after install? No. bash install.sh registers all hooks. Next session, it starts learning.

Q: Why not just use my agent’s built-in memory? Built-in memory stores facts. Enso adds what’s missing: code-enforced error learning, active forgetting with quality checks, and immutable self-protection hooks that the agent cannot bypass.

Q: Is PAC just another nagging notification system? No. PAC has five anti-fatigue layers: max 1/day, max 3/week, 7-day same-topic silence, 1-month cooldown after 3 rejections, and a confidence threshold that defaults to 0.70. Most sessions trigger zero PAC challenges. When one does trigger, it’s because five independent checks all agreed.

Q: What if PAC challenges something I already thought through? PAC distinguishes constraint-optimal choices (don’t challenge) from self-limiting ones (must challenge). When uncertain, it stays silent. If it ever misfires, you can dismiss and it enters cooldown on that pattern for 30 days.

Q: Does PAC send my data anywhere? No. Everything is local in ~/.enso/pac/. Pattern detection runs in Python on your machine. The Socratic question generator uses your existing LLM adapter chain (claude → llm → openai CLI). No telemetry.

Q: How do I disable PAC? export PAC_ENABLED=false in your shell rc. Or delete the two PAC hooks from ~/.claude/settings.json. The rest of Enso keeps working.

Contributing

See CONTRIBUTING.md. Most impactful:

• Bug reports with repro steps
• New hook ideas
• Compatibility testing with other agents
• DIKW pipeline improvements

License

MIT. See LICENSE.

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The enso is drawn in a single stroke — imperfect, incomplete, beautiful.
This system will never be perfect. But it will always be evolving.