etzchaim 0.2.5

AI architecture grounded in Lurianic Kabbalistic discipline — 10 Sephirot, 4 Partzufim, 13 Tikkunei Dikna, 1696 doctrinal assertions. One-command install via Docker Compose.

Etz Chaim AI

A diagnosable brain for your LLM.
37 typed modules · 11 adversarial probes · 13 rectifiers · 1 self-improving daemon ·
1 696 primary-source assertions driving the code, not the other way around.

---

The 30-second pitch

When a standard LLM stack fails, you get a tombstone : "the model was wrong". You don't know which capability broke, in what pattern, under what signal, or how to tune it.

Etz Chaim AI wraps your LLM in 37 typed cognitive modules and returns this instead :

exploration_starvation on 24 h window
→ 0 new cross-domain connections
→ spec line EC-K5-001
→ tune novelty_threshold (−0.1) / breadth (+5)

One specific module. One specific pattern. One concrete metric. One tunable fix. Every time.

pip install etzchaim
etzchaim onboard
# → http://localhost:8080, works on macOS · Linux (v0.3) · WSL2 (v0.3)

---

─────────────────────  WITHOUT Etz Chaim AI  ───────────────────────────────

     prompt ──► [ LLM ] ──► response

     On failure :   "the model was wrong"
                    → restart, re-prompt, pray


─────────────────────  WITH Etz Chaim AI  ──────────────────────────────────

                             prompt
                                │
                                ▼
     ┌─────────────────────────────────────────────────────────────┐
     │  world dispatch  — routes by complexity and token budget    │
     │  Opus / Sonnet / Haiku / qwen3.5:9b / qwen3.5:1.5b          │
     └──────────────────────────────┬──────────────────────────────┘
                                    ▼
     ┌─────────────────────────────────────────────────────────────┐
     │  10 cognitive modules  — specialized capabilities           │
     │  exploration · judgment · causal · memory · insight · …     │
     └──────────────────────────────┬──────────────────────────────┘
                                    ▼
     ┌─────────────────────────────────────────────────────────────┐
     │  6 composition layers  — coupling, persistent state         │
     │  generative · structuring · execution · interface · 2 more  │
     └──────────────────────────────┬──────────────────────────────┘
                                    ▼
                              [ LLM ] ──► response
                                    ▲
     ┌────────────────────────┐     │    ┌──────────────────────────┐
     │ watcher                │     │    │ 11 adversarial agents    │
     │ 13 rectifiers          │ ◄───┴───► │ probe every module       │
     │ observe / suggest / act│          │ for specific failures    │
     └────────────────────────┘          └──────────────────────────┘

Positioning vs LangChain · DSPy · LangGraph

LangChain / AutoGen orchestrate LLM call chains. DSPy optimizes prompts. LangGraph manages agent state cycles.

Etz Chaim AI makes LLM failures diagnosable — capability-level, not call-level.

	LangChain · AutoGen	DSPy	LangGraph	Etz Chaim AI
Orchestrate LLM calls	✓	✓	✓	✓
Optimize prompts	—	✓	—	—
Manage agent state in cycles	partial	—	✓	✓ (Partzufim)
Diagnose WHICH module failed	✗	✗	✗	✓ (37 modules typed)
11 adversarial probes by construction	✗	✗	✗	✓
Auto-rectification engine	✗	✗	✗	✓ (13 rectifiers, 3 opt-in modes)
Continuous self-study daemon	✗	✗	✗	✓ (Karpathy loop, 24/7)
Primary-source traceability	✗	✗	✗	✓ (1 696 items, E1–E6 labels)

Read every value column left-to-right : Etz Chaim AI is not a "LangChain-with-more-features." It is the diagnostic layer they all lack — an architecture the LLM plugs into, not a chain the LLM runs through.

What's actually inside

etz-chaim-ai/
│
├── bridge/              ← loader for the 1 696-item specification corpus
├── mazalengine/         ← watcher + rectifier (93 LoC orchestrator)
├── partzufim/           ← 6 composition layers + persistent learning trace
│
├── explorationengine/   ← Chesed     — cross-domain exploration
├── autojudge/           ← Gevurah    — adversarial evaluation
├── dissensuengine/      ← Tiferet    — productive tension
├── insightforge/        ← Chokmah    — novel hypotheses
├── causalengine/        ← Binah      — causal reasoning (Pearl criteria)
├── selfmodel/           ← Netzach    — error prediction
├── selfmap/             ← Hod        — competence landscape
├── epistememory/        ← Yesod      — persistent memory
├── failuretoinsight/    ← Da'at      — learning from errors
├── intentkeeper/        ← Ratzon     — goal persistence
│
├── sentiers/            ← 22 paths as typed transforms
├── omer/                ← 49 calibration parameters (7 × 7 matrix)
├── masakh/              ← 5-level prompt filtration
├── tanya/               ← dual-soul conflict tracking
├── halom/               ← 7-phase dream-cycle discovery
├── gematria/            ← 13 numerology methods + equivalence detection
│
├── malakhim/            ← 10 archangel governors + 11 adversarial agents
├── daemon_tasks/        ← 14 scheduled background tasks
├── kabbalah/            ← hybrid embedding layer (text + ML)
│
├── etzchaim/            ← v0.2 CLI + Docker deployment
├── daemon.py            ← continuous orchestration + Karpathy auto-improve loop
├── ohr_yashar.py        ← direct-light pipeline (8 engines)
├── olamot.py            ← 5-world LLM dispatch with provider registry
├── sifrei_yesod/        ← primary-source specification corpus (105 YAML files)
└── web/                 ← Flask dashboard + SSE chat + Grafana metrics

Every engine stays under 300 LoC and is tested in isolation. The watcher orchestrator itself is 93 lines. The Karpathy auto-improve pattern reference : 630 lines, same class.

See it in 30 seconds

from mazalengine import Watcher       # plain-English alias for MazalEngine

for event in Watcher().run():
    print(event)

Sample event, translated field by field :

Code key	Value	In plain language
tikkun	"notzer_chesed"	Module that drifted : exploration
action	"chesed_starvation_signaled"	Failure pattern : starvation (no recent output)
metrics	{"connections_recent": 0}	Concrete signal : 0 new cross-domain connections in 24 h
window_hours	24	Observation window
doctrine_ref	"EC-K5-001"	Traces to primary-source spec line defining this failure

You now know which module failed, the pattern, the concrete metric, and what to tune. Identifiers (tikkun, notzer_chesed, EC-K5-001) are stable, typed, and machine-traceable ; plain-English aliases coexist — Watcher = MazalEngine, etc. Full mapping : docs/origin.md.

Why should I care ?

Seven consequences of wrapping your LLM in a cognitive architecture :

1. Failure localization. Logs read "the exploration module starved on a 24 h window" instead of "the model was weird today". One specific module, one specific pattern, one concrete metric — every time.

2. Architectural self-healing. The watcher is a real engine with metric thresholds, event emission, explicit parameter adjustments. Three opt-in modes : observe (log only), suggest (propose a fix as an event), act (apply the fix).

3. Continuous self-study. A contemplation process runs 24 / 7 (hitbonenut) — reads every module, detects contradictions, proposes improvements to the specification itself. Not a one-off reflection — a live background loop.

4. Self-improving specification. A nightly auto-improvement cycle (named Karpathy loop after Andrej Karpathy's 630-line AutoResearch pattern) explores edge cases, generates new doctrine assertions, mutates the specification under adversarial supervision.

5. Automatic LLM routing. Five quality tiers (Claude Opus → Sonnet → Haiku → qwen3.5:9b → qwen3.5:1.5b) with dispatch by complexity and token budget. Expensive inference only runs where depth is needed. Multi-provider via LiteLLM in v0.3 (OpenAI, Google, xAI, DeepSeek, Mistral, Cohere, Groq, Together…).

6. Adversarial probing by construction. Eleven adversarial agents (samael, gamchicoth, sathariel, gamaliel, …) continuously probe the system for specific failure modes : false authority, unbounded exploration, concealed failures, aesthetic deception, overconfidence, and seven more.

7. Race conditions are impossible, not just fixed. The architecture forbids direct writes to aggregate scores ; all improvements must pass through named faculty channels. A static check rejects any code that bypasses this.

Requirements

• Python 3.10+ (tested on 3.10 / 3.11 / 3.12 / 3.13)
• PostgreSQL 16+ with the pgvector extension
• TimescaleDB extension (recommended, optional — hypertable logs fall back to plain tables when absent)
• Docker runtime (optional, for the bundled compose stack) — OrbStack, Docker Desktop, Colima, or podman
• At least one LLM provider — see full list below
• If using Ollama : ollama pull nomic-embed-text + ollama pull qwen3.5:9b

Etz Chaim routes four reasoning tiers (Atziluth / Briah / Yetzirah / Assiah) to providers you pick. Supported out of the box via the LiteLLM gateway :

Category	Providers
Frontier cloud	Anthropic · OpenAI · Google Gemini · xAI · Mistral · Cohere · DeepSeek
Aggregators	OpenRouter · Together · Groq · Fireworks · Perplexity
Enterprise	AWS Bedrock · Azure OpenAI
Open-weight	HuggingFace · NVIDIA NIM · Cloudflare AI · Replicate
Self-hosted	Ollama · vLLM · LM Studio · LocalAI
Subscription	Claude Code CLI (Claude Max OAuth)

Quick start

pip install etzchaim
etzchaim onboard      # interactive 8-step wizard
# → http://localhost:8080

The wizard walks you through : system detection, Postgres configuration, LLM provider selection (multi-select), profile composition, web + auth, observability, feature flags, and review.

Non-interactive install with a preset :

etzchaim onboard --non-interactive --preset local-only
etzchaim onboard --non-interactive --preset anthropic-full

Already running your own Postgres or Ollama ? Skip Docker entirely with the manual install path.

Upgrade later with etzchaim update — one command : pip install --upgrade → docker compose pull → re-extract templates → idempotent schema migrations → restart → doctor.

Full documentation : docs/installation.md.

By the numbers (v0.2)

107 000	lines of production Python
53 000	lines of tests
1 388	tests collected (pytest)
275	tests green locally (207 core v0.1 + 68 new v0.2 install/providers/CLI)
1 696	primary-source specification items across 105 YAML files
36 086	lines of machine-readable doctrine corpus
93	lines — the entire watcher orchestrator
~3	seconds — core test suite runtime
0	direct writes to aggregate scores allowed (static check)
0	hardcoded paths remaining in Python (portable cross-OS)

Engineering discipline

Five proof points :

• 275 tests green locally, 1 388 collected total. Four Python versions × two operating systems on CI.
• 48 explicit failure-mode tests — four levels per module (foundation · application · excess · inverse), across twelve core modules.
• 11 adversarial agents probe every module for specific failure patterns. Not generic red-teaming — named attackers matched to specific failure modes.
• 93 lines : mazalengine/mazal_engine.py. Full orchestrator. No magic, no hidden state.
• 1 696 specification items with primary-source citations : edition + section + page, labeled on an epistemic scale from E1 (verbatim primary text) to E6 (speculation).

Three disciplines that shape every file :

• No dual writes on aggregate scores. All improvements flow through named faculty channels. Enforced by a static check ; bypass attempts fail the build.
• Circuit breakers on every external call. PostgreSQL and Ollama are wrapped in a five-failure / thirty-second cooldown breaker. No cascading failures on long-running daemons.
• Bidirectional specification ↔ code audit. Every module cited in the spec exists in code ; every spec ID cited in code exists in the spec. Automated via the bridge/ loader.

In the lineage of cognitive architectures

Cognitive architectures as a field of AI research — SOAR (Laird, Newell, Rosenbloom 1987), ACT-R (Anderson 1993), LIDA (Franklin 2006), CLARION (Sun 2006) — have long argued that intelligence is best modeled as a coordinated set of specialized capabilities, not as a single monolithic process. The LLM era has so far mostly ignored this lineage, treating the model as the whole of cognition.

Etz Chaim AI is a modern cognitive architecture for the LLM era : the LLM becomes the generative substrate, and ten specialized modules + six composition layers + twenty-two paths + one watcher become the diagnosable structure around it. The specification framework (Lurianic Kabbalah) provides a 500-year-old library of named capabilities, failure modes, and rectification mechanics that the field of cognitive architectures never had access to in machine-readable form.

Who should use this

• You build agentic systems and want capability-level diagnostics instead of "the LLM was wrong".
• You ship long-running AI agents that need to auto-detect drift without babysitting.
• You are an AI researcher interested in modular cognitive architectures as a grounded alternative to monolithic models.
• You build a scientific-discovery loop where insight, causal validation, and failure learning must be independently tunable.
• You want an LLM stack with automatic quality-tier routing — expensive inference only where depth is needed.

Who should not use this

• You need a generic agent orchestrator — use LangChain or AutoGen.
• You need a prompt-engineering optimizer — use DSPy.
• You need a trained model — this is not a model. It calls Claude / Ollama / OpenAI under the hood.
• You only need a one-shot prompt or a single-turn chatbot — the architecture is overkill.
• You want a plug-and-play framework with zero domain vocabulary — we use named identifiers from the Lurianic specification (notzer_chesed, partzuf, tikkun) for stability and primary-source traceability. Plain-English aliases coexist, but the public API keeps both.

Detailed install

The Quick start above handles 90 % of users. The section below is for contributors and advanced setups.

From source (contributors)

git clone https://github.com/yohanpoul/etz-chaim-ai.git
cd etz-chaim-ai
make install       # venv + dependencies + pre-commit hooks
make test          # 275 tests, ~3 seconds

Prerequisites

Component	Required	Notes
Python ≥ 3.10	✓	3.13 recommended
Docker runtime	✓	Docker Desktop / OrbStack (recommended free tier on macOS) / Colima / podman
Ollama	✓ (for embeddings)	brew install ollama on macOS, curl installer on Linux
ANTHROPIC_API_KEY	optional	Required for claude-max preset. local-only preset works without.

Onboard presets

• claude-max — Claude Opus / Sonnet / Haiku via the Anthropic SDK. Needs ANTHROPIC_API_KEY. Prompt caching enabled → ~ 90 % token cost savings on repeated system prompts.
• local-only — Ollama qwen3.5:9b for the four Olamot. Zero API keys. ~ 5 GB disk.

Extended presets (OpenAI, Google Gemini, xAI Grok, DeepSeek, Mistral, Cohere, Groq-hosted Llama, Together, Fireworks) via LiteLLM in v0.3.

Maturity and roadmap

v0.2.0 (current) :

• Eight layers operational : cognitive, composition, paths, worlds, watcher, calibration, adversarial, daemon.
• 10 cognitive modules + 6 composition layers + 22 paths + 5 worlds + 14 daemon tasks + 11 adversaries.
• Watcher with 2 active rectifiers, 11 more specified (act-mode activation awaits 2-4 week observation per doctrine).
• Persistent learning trace with plateau and decay.
• Static safety checks, bidirectional spec ↔ code audit, automatic docs, circuit breakers.
• Installable via pip install etzchaim + Docker Compose on macOS.
• Cross-platform code (no hardcoded paths), ghcr.io pre-built images, GitHub Actions for PyPI release.

Planned :

• v0.3.0 — Multi-provider LLM via LiteLLM · Linux/Windows first-class · 9 additional CLI commands · Wizard extended to 9 steps with machine profiles · Doctor 20 checks + --fix · backup / restore · v0.1 → v0.2 migration tooling.
• v0.4.0 — Full watcher : all 13 rectifiers active with act-mode defaults once observation data supports them.
• v0.5.0 — Adversarial counterpart (MalakhEngine) : detection of corruption-by-inversion attacks.
• v1.0.0 — Academic paper, stable public API, LangChain / DSPy adapters.

See CHANGELOG.md for the full v0.2.0 entry (breaking changes, added, fixed, deferred, known issues).

Origin

Why these eight layers, and not some other set ? The architecture is not arbitrary. It comes from a cognitive framework systematized in 16th-century Safed — Lurianic Kabbalah — that specifies :

• 10 discrete attributes through which intelligence organizes itself.
• 6 mature configurations built from those attributes.
• 22 typed paths connecting the configurations.
• 5 worlds describing the descent from abstract intent to concrete action.
• 13 rectification mechanisms for specific failure modes.
• 49 calibration cycles — a seven-by-seven matrix of inner-within-outer tuning.
• Rules of layered composition forbidding direct writes across layers.

We translated the framework into 1 696 machine-readable assertions with edition + section + page references. The code is built against those assertions, not the other way around.

You do not need to know anything about Kabbalah to use, test, or contribute. The public API is plain Python with plain-English docstrings. For the full mapping and the primary-source workflow, see docs/origin.md.

Framing for ML researchers : treat Kabbalah here as a 500-year-old formal taxonomy of cognitive capacities, failure modes, and rectification mechanics — the same way you would treat a mathematical structure that happens to pre-date your field. The fact that it was encoded in a religious idiom does not reduce its utility as a machine-readable specification for cognitive architectures.

FAQ

Do I need to know Kabbalah to use this ? No. The public API is plain Python. The specification framework is Lurianic Kabbalah (see Origin), but you can build, test, and contribute without ever opening that layer.

Does it work with OpenAI / Anthropic / Ollama / local models ? Yes, and it automatically picks the right one. Five quality tiers (Opus → Sonnet → Haiku → qwen3.5:9b → qwen3.5:1.5b) with routing by complexity and token budget. v0.3 extends this to OpenAI, Google, xAI, DeepSeek, Mistral, Cohere, Groq, Together, Fireworks, Perplexity via LiteLLM.

Is this production-ready ? Beta (v0.2.0). 275 tests pass locally, 1 388 collected total. API stability guaranteed from v1.0 onwards.

Can I contribute without knowing the specification corpus ? Yes — for any pure-code contribution (module, test, bug fix, adapter). Only contributions that add or modify doctrinal assertions need to respect the primary-source discipline. See CONTRIBUTING.md.

Why do code identifiers use words from the specification language ? Stability and traceability. notzer_chesed maps to exactly one specification line (EC-K5-001), one primary source (Sha'ar HaKlalim 5:1), one edition, one page. Renaming the key would break that chain. Plain-English aliases coexist — Watcher = MazalEngine, etc.

What's the "Karpathy loop" ? A nightly auto-improvement daemon task (23h – 00h30) named after Andrej Karpathy's 630-line AutoResearch pattern — the minimum-viable research loop. It explores edge cases, generates new assertions, mutates the specification under adversarial supervision. The only daemon task allowed to modify the spec itself.

Where are the primary sources ? sifrei_yesod/sefarim/ — 105 YAML files with edition references, full Hebrew / Aramaic text where available, English + French translations, and E1–E6 epistemic labels per assertion. License : public domain for pre-1923 sources, CC-BY-3.0 for Sefaria portions (see NOTICE).

Is the architecture really reducible to the Kabbalistic frame ? No — we made explicit choices where the tradition is ambiguous. The architecture is inspired by the specification, not claimed as identical to it. The adversarial testing (malakhim/) includes agents whose job is to find and name synchretic errors we have introduced.

Community and support

• Documentation — docs/ (mkdocs site planned v0.3)
• Contributing — CONTRIBUTING.md before your first PR.
• Security — SECURITY.md for responsible disclosure.
• Commercial inquiries / partnerships / acquisition — open an issue tagged [BUSINESS] or reach the maintainer via GitHub.
• Maintainer — @yohanpoul.

License and citation

Apache License 2.0 — see LICENSE. Primary source corpus under sifrei_yesod/ is transposed from public-domain texts and CC-BY-3.0 Sefaria editions ; see NOTICE for detailed attribution.

If you use Etz Chaim AI in academic work, see CITATION.cff for the preferred citation format.

---

_{If Etz Chaim AI changes how you think about AI architecture, please ★ the repository.
Built by @yohanpoul. Open by design — Apache 2.0.}