Sidix AI Agent

<div align="center"> <img src="SIDIX_LANDING/sidix-logo.svg" alt="SIDIX Logo" width="120" height="120" /> <h1>SIDIX</h1> <p><strong>Free & Open Source AI Agent</strong></p> <p><em>Self-Hosted · Self-Learning · Self-Evolving · Own Stack · No Vendor API</em></p> <p> <img src="https://img.shields.io/badge/status-research%20build%20ended%20Aug%202026-lightgrey?style=flat-square" alt="Status: research build ended in August 2026" /> <img src="https://img.shields.io/badge/hosted%20app-discontinued-lightgrey?style=flat-square" alt="Hosted app: discontinued" /> <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-gold?style=flat-square" alt="MIT License" /></a> <a href="https://github.com/fahmiwol/migancore-research-method"><img src="https://img.shields.io/badge/continues%20in-MiganCore%20research-blue?style=flat-square" alt="Continues in MiganCore research" /></a> </p> <hr/> <h2>📖 Start here · Mulai dari sini</h2> <p> <strong><a href="https://github.com/fahmiwol/migancore-research-method">🔬 MiganCore research method</a></strong> · <strong><a href="https://github.com/fahmiwol/migancore-research-method/blob/main/README.id.md">Metode riset MiganCore</a></strong> · <strong><a href="providers/quran-lab/">📖 Quran Lab</a></strong> · <strong><a href="AUTHORSHIP.md">🧾 Authorship · Kepenemuan</a></strong> </p> <p> <a href="https://sidixlab.com">🌐 Website</a> · <a href="#-the-ihos-foundation">🧠 The Foundation</a> · <a href="#-architecture">🏗️ Architecture</a> · <a href="#-contribute">🤝 Contribute</a> </p> </div>

🧭 Status & what came next · Status & kelanjutannya

EN — SIDIX was an open research build from April to August 2026. The hosted app was discontinued on purpose and will not return. Anyone who wants to use SIDIX will download the code and run it on their own machine; the useful parts of the code are being repackaged in a readable, documented form. The source recovered from the private archive is now in this repository as an unmaintained research archive: apps/ (brain_qa agent loop, MCP server, gateways, image/audio/vision), extension/, api/ and a few synthesized docs under docs/. It is a cleaned snapshot, not the full history; datasets, model weights, deployment scripts and working notes are intentionally not included. To run it: cd apps/brain_qa && python -m venv .venv && pip install -r requirements.txt && pytest tests (some tests fail or hang without optional dependencies; as of the recovery 678 pass, 4 fail, 4 files time out). Also here: the license, authorship statement and the Quran Lab provider. Everything further down this page is a snapshot of that build.

The research continues in MiganCore — an own, Indonesian-first language model and the measurement discipline built around it. SIDIX's sanad (chain-of-transmission) idea lives on there: six of its trained models so far carry a provenance chain that is checked link by link.

ID — SIDIX adalah pembangunan riset terbuka dari April sampai Agustus 2026. Aplikasi yang di-hosting sengaja dihentikan dan tidak akan dihidupkan lagi. Yang ingin memakai SIDIX cukup mengunduh kodenya dan menjalankannya di mesin sendiri; bagian kode yang berguna sedang dikemas ulang agar mudah dibaca dan terdokumentasi. Kode yang dipulihkan dari arsip privat kini ada di repositori ini sebagai arsip riset tanpa perawatan: apps/ (loop agent brain_qa, server MCP, gateway, gambar/audio/visi), extension/, api/ dan beberapa dokumen sintesis di docs/. Ini potret yang sudah dibersihkan, bukan riwayat lengkap; dataset, bobot model, skrip penyebaran, dan catatan kerja sengaja tidak disertakan. Cara menjalankan: cd apps/brain_qa && python -m venv .venv && pip install -r requirements.txt && pytest tests (sebagian tes gagal atau menggantung tanpa dependensi opsional; saat pemulihan 678 lulus, 4 gagal, 4 berkas kehabisan waktu). Juga ada: lisensi, pernyataan kepenemuan, dan provider Quran Lab. Semua di bawah bagian ini adalah potret build tersebut.

Riset berlanjut di MiganCore — model bahasa milik sendiri yang mengutamakan bahasa Indonesia, beserta disiplin pengukuran di sekelilingnya. Gagasan sanad SIDIX hidup terus di sana: sejauh ini enam model yang dilatih membawa rantai asal-usul yang diperiksa mata demi mata.

Read the continuation · Baca kelanjutannya	English	Bahasa Indonesia
MiganCore research method · Metode riset MiganCore	README	README
From SIDIX to MiganCore: the ecosystem · Ekosistem	01	01
The research method · Metode	02	02
Measurement integrity · Integritas pengukuran	03	03
Hallucination · Halusinasi	05	05
Case studies · Studi kasus	07	07

Contact · Kontak: Fahmi Ghani — fahmiwol@gmail.com · collaboration welcome · terbuka untuk kolaborasi

📦 Providers

EN: Quran Lab — engineering interpretations with citations and explicit unreviewed status.

ID: Quran Lab — interpretasi rekayasa dengan sitasi dan status belum ditinjau yang dinyatakan jelas.

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🌱 Autonomous AI Agent — Thinks, Learns & Creates

Not a chatbot. SIDIX is an AI Agent with initiative, opinions, and creativity. It brainstorms with you, builds for you, and grows from every conversation. Self-hosted. MIT licensed. Yours forever.

✨ What's New (Vol 14 → Vol 20, 2026-Q1/Q2)

Vol	Feature	Impact
20-fu3	Simple-tier fast-path (greetings/ack)	78s → 2s (37× speedup)
20	Semantic cache L2 + BGE-M3 embedding	<100ms warm, multilingual ID
20	Complexity router (simple/standard/deep)	Auto-route reasoning depth
20	Domain detector (fiqh/medis/coding/factual)	Per-domain cache threshold + sanad gating
20	Style anomaly filter (BadStyle defense)	Corpus poisoning prevention
19	Relevance + Quality Sprint (4 modules)	Better retrieval ranking
17	CodeAct enrich + MCP wrap	Code blocks auto-execute
16	Creative Agent Ecosystem (10 domain × 37 agent)	Multi-agent debate/iteration
15	LoRA SIDIX adapter on Qwen2.5-7B	Self-trained, 4-bit QLoRA

Production stack (historical, April–August 2026): VPS (FastAPI brain · BGE-M3 CPU · 2.287 corpus docs) + RunPod GPU serverless (vLLM v2.14.0 · Qwen2.5-7B + LoRA).

Karakter: GENIUS · KREATIF · INOVATIF

Direction: AI Agent yang BEBAS dan TUMBUH — bebas dari single-prompt loop, tumbuh compound dari setiap interaksi.

Definisi Inti (One-Sentence)

"SIDIX adalah entitas kecerdasan komprehensif yang tidak hanya mengeksekusi perintah multi-modal, tetapi secara PROAKTIF mengevaluasi, memori-optimasi, dan mengorkestrasi ekosistem tools untuk menciptakan nilai komersial dan inovasi TANPA PENGAWASAN TERUS-MENERUS."

3 Fondasi

• 🧠 The Mind — Self-Correction & Metacognition · Distributed Long-Term Memory (RAG) · Chain/Tree of Thoughts
• ✋ The Hands & Tools — Tool Orchestration & API Mastery · Aesthetic & Commercial Judgement · Resource Management
• 🚀 The Drive — Intrinsic Proactivity (no prompt needed) · Boundary & Context Awareness

4-Pilar Arsitektur

• 🧠 Memory — 5-layer immutable + LoRA + RAG (no catastrophic forgetting)
• 🎭 Multi-Agent — Innovator (Burst) + Critic + Tadabbur 3-persona convergence
• 🔄 Continuous Learning — auto_lora + rehearsal buffer + nightly retrain
• 🤖 Proactive — anomaly detect + self-prompt + daily digest (gerak sendiri)

5 Persona LOCKED (Cognitive Style Routing)

UTZ (creative/visual) · ABOO (engineer/technical) · OOMAR (strategist/business) · ALEY (academic/research) · AYMAN (general/hangat)

Multitasking — Semua Indera Aktif (Q3 2026 → Q1 2027)

👁 melihat · 👂 mendengar · 🗣 berbicara · ✋ merasakan · 🤲 1000 tangan paralel (design + code + riset + posting bersamaan)

"The measure of intelligence is not how much you know, but how precisely you know what you don't know — and how honestly you say so."

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🌱 Why SIDIX Exists

Most AI tools are black boxes controlled by corporations. You pay per token. Your data trains their model. You have no idea what they do with it.

SIDIX is built on a different premise:

• Free — run it without paying anyone per-query
• Open source — every line of code is auditable
• Self-hosted — your server, your data, your model
• Self-learning — improves from real usage, structured, every quarter

Inspired by a 1,400-year-old knowledge system, SIDIX asks: what if the architecture of knowledge matters more than its volume?

What if an AI that knows why it knows, how it knows, and the limits of what it knows — is more trustworthy than one that simply knows a lot?

That question is the origin of IHOS — and the reason SIDIX exists.

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🧠 The IHOS Foundation

IHOS (Islamic Holistic Ontological System) is not a religious constraint. It is an epistemological architecture — a framework for how knowledge should be structured, validated, and used.

It was derived from observing one of the oldest self-expanding knowledge systems ever recorded.

The Blueprint: A 1,400-Year-Old Cognitive System

Consider this: a single text of ~77,000 words has generated 14 centuries of derivative scholarship — yielding jurisprudence, medicine, cosmology, mathematics, linguistics, ethics, and governance. Different readers, in different times and places, derive entirely different — yet internally consistent — bodies of knowledge from the same source.

This is not a coincidence of literary richness. It is a designed architecture.

The classical Islamic scholars identified it precisely:

Qur'anic Layer	Technical Analog	SIDIX Implementation
Zahir — the explicit text	Frozen Foundation Model	Qwen2.5-7B + LoRA — immutable base weights
Batin — the latent meaning	Latent space / embeddings	BM25 + vector corpus — contextual retrieval
Asbabun Nuzul — grounded context	Grounded Generation	Every output grounded in brand_brief + user_state + platform_context
Sanad — the chain of transmission	Provenance tracking	[FACT] / [OPINION] / [UNKNOWN] labels + citation chain
Maqashid — the higher objectives	Objective function	5-axis filter: life · intellect · faith · lineage · wealth
Ijtihad — reasoned interpretation	Agentic reasoning	agent_react.py ReAct loop
Naskh — abrogation/update	Knowledge conflict resolution	naskh_handler.py — sanad-tier based supersession
Tafakkur — deliberate reflection	Meta-cognition	muhasabah_loop.py — Niyah→Amal→Muhasabah self-refinement
Tadrij — progressive revelation	Curriculum learning	curriculum_engine.py L0→L4 knowledge ladder

The key insight: Al-Qur'an's power doesn't come from size — it comes from architecture. A frozen core. Context-sensitive derivation. Verified transmission chains. Purpose-aligned interpretation. This is what SIDIX translates into code.

The 5 Principles That Follow

1. FROZEN CORE, LIVING EDGES
   The base model doesn't retrain arbitrarily — it grows through structured LoRA adapters.
   Like a text that never changes, but whose understanding deepens with the reader.

2. SOURCE CHAIN IS NON-NEGOTIABLE
   Every claim is labeled. Every output has a traceable path.
   Not because we're cautious — because honesty is a design constraint.

3. CONTEXT IS FIRST-CLASS INPUT
   User state, brand context, time, platform — these are not metadata.
   They are part of the inference function.

4. PURPOSE FILTERS KNOWLEDGE
   Not all technically correct answers are appropriate answers.
   Maqashid as objective function: output is evaluated against human flourishing, not just accuracy.

5. GROWTH IS STRUCTURAL, NOT INCIDENTAL
   Self-learning isn't a feature. It's the architecture.
   Daily corpus ingestion → curation → LoRA retrain → deploy. Every quarter, SIDIX improves.

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🏗️ Architecture

SIDIX is not a chatbot with a nice UI. It's a three-layer cognitive agent running 100% on your own stack:

┌─────────────────────────────────────────────────────────────┐
│                    LAYER 1 — BRAIN (LLM)                    │
│  Local LLM + optional adapter                               │
│  Generative inference — token by token, own stack           │
│  No vendor API required for default mode                    │
└─────────────────────────┬───────────────────────────────────┘
                          │ ReAct loop (agent_react.py)
┌─────────────────────────▼───────────────────────────────────┐
│              LAYER 2 — HANDS (Tools + RAG)                  │
│  35 active tools:                                           │
│  ├── Knowledge: search_corpus · read_chunk · concept_graph  │
│  ├── Web:       web_fetch · web_search · pdf_extract        │
│  ├── Code:      code_sandbox · code_analyze · code_validate │
│  ├── Creative:  generate_copy · brand_kit · plan_campaign   │
│  ├── Image:     text_to_image (SDXL self-hosted)            │
│  ├── Multi-Agent: Raudah Protocol (parallel specialists)    │
│  └── Growth:    roadmap_* · workspace_* · muhasabah_refine  │
└─────────────────────────┬───────────────────────────────────┘
                          │ daily cycle
┌─────────────────────────▼───────────────────────────────────┐
│              LAYER 3 — MEMORY (Growth Loop)                 │
│  50+ open sources → corpus queue → curation → JSONL        │
│  → adapter retrain (offline pipeline) → adapter deploy     │
│  SIDIX gets smarter every quarter. Structurally.            │
└─────────────────────────────────────────────────────────────┘

Raudah Protocol — Multi-Agent Parallel Orchestration

New in v0.6: Raudah (روضة المعرفة — Garden of Knowledge) — a multi-agent parallel system where specialists work concurrently and the Orchestrator synthesizes a consensus answer.

Task → RaudahOrchestrator.urai_task()
     → IHOS Guardrail (Maqashid check)
     → asyncio.gather([Researcher, Analyst, Writer, Engineer, Verifier])
     → RaudahOrchestrator.agregasi()   ← Ijma' (consensus synthesis)
     → RaudahResult.jawaban_final

Unlike "swarm" architectures: no vendor API, IHOS guardrail before spawn, Sanad Validator per output.

import asyncio
from brain.raudah.core import run_raudah

result = asyncio.run(run_raudah("Research 5 productive waqf models in Southeast Asia"))
print(result.jawaban_final)   # synthesized consensus
print(result.durasi_s)        # e.g. 45.2s on RTX 3060

🔌 Optional plugin ecosystem

SIDIX can optionally expose a local plugin server for compatible clients (e.g., Claude Desktop, Cursor, GPT Actions, Codex). This is not required for the default standing-alone mode.

Compatible client
        │  MCP (stdio)
        ▼
apps/sidix-mcp/src/index.js        ← 13 tools total
        │
        ├── SIDIX brain backend
        ├── Extension bridge (optional)
        └── Messaging bridge (optional)

Install & run (optional): See integration docs under docs/ for client-specific setup. This is an adapter path, not a dependency.

Social tools (example):

Tool	What it does
scan_instagram_profile	ER + sentiment + tier dari profil publik IG
scan_threads_profile	Analisis profil Threads
scan_youtube_channel	Engagement rate YouTube channel
scan_twitter_profile	Analisis X/Twitter profil
analyze_social	Analisis mendalam dari URL apapun
compare_social_accounts	Banding 2+ akun lintas platform
social_post_threads	Auto-post ke Threads (butuh token)
wa_send	Kirim pesan via messaging bridge (opsional)
wa_receive	Baca inbox via messaging bridge (opsional)

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Capabilities at v0.8.0 (snapshot of 2026-04-23 — the hosted app is offline)

Domain	Agent / Tool	Status
Coding	code_sandbox · code_analyze · code_validate · project_map	✅ Live
Self-awareness	self_inspect — SIDIX reads its own tool registry	✅ Live
Copywriting	generate_copy (AIDA/PAS/FAB, 3 variants)	✅ Live
Content Strategy	generate_content_plan (7/14/30-day calendar)	✅ Live
Brand Building	generate_brand_kit (name + archetype + palette + voice)	✅ Live
Visual Content	generate_thumbnail + text_to_image (SDXL)	✅ Live
Campaign	plan_campaign (AARRR funnel + KPI)	✅ Live
Ads	generate_ads (FB/Google/TikTok copy)	✅ Live
Quality Gate	muhasabah_refine (CQF ≥ 7.0 loop)	✅ Live
Multi-Agent	Raudah Protocol v0.1 (parallel specialists, local backbone)	✅ Live
Knowledge Conflict	Naskh Handler (sanad-tier based resolution)	✅ Live
Maqashid Filter	v2 mode-based: CREATIVE/ACADEMIC/IJTIHAD/GENERAL	✅ Live
Self-Evolution	prompt_optimizer — L1 flywheel, weekly improvement	✅ Live
Knowledge	BM25 corpus · Wikipedia · web_search · web_fetch	✅ Live
Image	Image generation (local-first)	✅ Live
Social Intelligence	scan_instagram_profile · scan_threads · scan_youtube · scan_twitter · analyze_social · compare_social_accounts	✅ Live
Messaging Automation	wa_send · wa_receive (optional bridge)	✅ Live
Plugin Server	Optional plugin server (13 tools via stdio MCP)	✅ Live
Chrome Extension	Social Radar MV3 — DOM scrape + background service worker	✅ Live
Voice / Video	Whisper + TTS + FFmpeg	🗓 Sprint 8
3D / Gaming	Hunyuan3D + Blender API	🗓 Sprint 9
Raudah v0.2	TaskGraph DAG + POST /raudah/run endpoint	🗓 Next sprint

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🎭 5 Personas

SIDIX adapts its voice, depth, and framing based on who it's talking to. Each persona maps to a Maqashid mode — a different lens for evaluating and presenting knowledge.

Persona	Character	Specialization	Maqashid Mode
AYMAN	Strategic Sage	Research synthesis, long-form, Islamic epistemology, vision	IJTIHAD
ABOO	The Analyst	Data, logic, structured argument, code review, decisions	ACADEMIC
OOMAR	The Craftsman	Technical deep-dives, system design, build & implementation	IJTIHAD
ALEY	The Learner	Teaching, curriculum, beginner-friendly, patient explanation	GENERAL
UTZ	The Generalist	Daily tasks, creative work, conversational, quick answers	CREATIVE

# Auto-routing — SIDIX picks the right persona from your question
from brain_qa.persona import route_persona

result = route_persona("help me design a logo for a tech startup")
# → PersonaDecision(persona='AYMAN', confidence=0.63, reason='signal=creative/design')

# Or specify explicitly
from brain_qa.agent_react import run_react
session = run_react(question="audit this Python function", persona="ABOO")

Backward compatible: legacy persona aliases are accepted internally, but never surfaced in public UI/content.

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⚡ Quick Start

EN — The hosted app is discontinued and the source code is not on this branch. A clean, documented package for running SIDIX on your own machine is being prepared; this section will point to it once it is published.

ID — Aplikasi yang di-hosting sudah dihentikan dan kode sumbernya tidak ada di cabang ini. Paket yang rapi dan terdokumentasi untuk menjalankan SIDIX di mesin sendiri sedang disiapkan; bagian ini akan menautkannya begitu terbit.

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🆕 What's New in v0.8.0

Jiwa 7-Pillar Architecture (Live)

SIDIX now has a soul — 7-pillar self-awareness system:

• Nafs (Pilar 1): 7-topic routing with persona character injection
• Aql (Pilar 2): Self-learning — every good interaction becomes training data (CQF ≥7.0)
• Qalb (Pilar 3): Health monitoring — auto-heals on degradation
• Hayat (Pilar 5): Self-iteration — refines answers when quality is low

Optional plugin server (13 tools)

Optional local plugin server for compatible clients (disabled by default).

Jiwa Standalone Architecture (brain/)

Complete 7-pillar standalone modules in brain/ directory:

• brain/nafs/ — 3-layer knowledge fusion (60% parametric + 30% KG + 10% static)
• brain/aql/ — Jariyah v2: capture→CQF→validate→store
• brain/qalb/ — SyifaHealer: 4-level health monitoring
• brain/hayat/ — generate→evaluate→refine loop
• brain/ruh/ — weekly evaluation + improvement planning
• brain/ilm/ — knowledge gap detection + auto-crawl
• brain/hikmah/ — QLoRA retrain trigger

Typo Resilient Framework (brain/typo/)

SIDIX understands Indonesian slang, abbreviations, and typos gracefully. 4-layer stack: Normalizer → Semantic Matcher → Confidence Scorer → Context Responder

Host integration bridge (optional)

Host integration bridge (optional).

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🗺️ Roadmap (as planned in April 2026)

✅ Sprint 7b (April 2026) — SIDIX Socio Bot MCP

• 13 tools: 4 core (query, capture, learn, status) + 9 social intelligence
• Chrome Extension + WA Bridge + Extension Bridge
• OpenAPI spec (tooling integration)

✅ Sprint 7c (April 2026) — Jiwa 7-Pillar

• 7-topic routing (ngobrol/umum/kreatif/koding/sidix_internal/agama/etika)
• Persona character injection per response
• Self-learning training pairs (Aql)
• Health monitoring (Qalb)

🚧 Sprint 8a (Mei 2026) — Foundation Hardening

• Nafs 3-layer wire to main agent
• Jariyah v3 real-time capture (thumbs feedback)
• Branch system (multi-client)
• PostgreSQL schema

📅 Sprint 8b — Generative Core

• FLUX.1 image generation
• TTS (Piper)
• Code validator

📅 Sprint 8c — Agency OS UI

• Sidebar UI Framework
• Image Editor v1
• Branch selector

📅 Sprint 8d — Intelligence Layer

• Raudah v2 multi-agent
• Brand Guidelines Maker
• Auto-retrain trigger

v0.9 beta target: Q3 2026 — Agency Kit + Raudah v2 + multimodal parity

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📐 The Technical Translation of IHOS

For those who want to go deeper:

# Sanad (chain of transmission) → citation chain in every output
{
  "answer": "...",
  "label": "[FACT]",          # Zahir — what is explicitly stated
  "citations": [              # Sanad — who said it, where
    {"source": "...", "sanad_tier": "primer", "chunk_id": "..."}
  ],
  "maqashid_filter": "passed" # Maqashid — does this serve human flourishing?
}

# Naskh (abrogation) → knowledge conflict resolution
from brain_qa.naskh_handler import NaskhHandler
handler = NaskhHandler()
winner, status, reason = handler.resolve(old_item, new_item)
# → ("superseded", "New source has higher sanad tier (primer > aggregator)")

# Tafakkur (deliberate reflection) → muhasabah loop
def muhasabah_loop(output, brief):
    niyah  = validate_intent(brief)       # Was the intention clear?
    amal   = score_cqf(output)            # Was the action good? (CQF ≥ 7.0)
    review = reflect_on_gaps(output)      # What can be improved?
    return refine(output) if amal < 7.0 else output

# Maqashid v2 — mode-based, not keyword blacklist
from brain_qa.maqashid_profiles import evaluate_maqashid, MaqashidMode
result = evaluate_maqashid(
    user_query="Write copy for our coffee brand",
    generated_output="Bold flavor, honest origin...",
    mode=MaqashidMode.CREATIVE       # → BOOST intellect & wealth, no block
)
# → {"status": "pass", "tagged_output": "...\n\n[Intellect-Optimized | Value-Creation Mode]"}

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🤝 Contribute

EN — The most useful contributions now are scholarly review of the Quran Lab studies and discussion of the MiganCore research method. Open an issue in either repository.

ID — Kontribusi yang paling berguna sekarang adalah tinjauan ilmiah atas kajian Quran Lab dan diskusi metode riset MiganCore. Buka issue di salah satu repositori.

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🔒 Security & Privacy

• ✅ No vendor API in inference pipeline — zero data leaves your server
• ✅ G1 Safety Policy — anti-injection, anti-PII, anti-toxic
• ✅ Maqashid v2 — intent-based filter, not keyword blacklist (creative-safe)
• ✅ Audit log (append-only, hash-chained) for every tool call
• ✅ Identity masking for public-facing endpoints
• ✅ 4-label epistemic tagging — hallucinations are labeled, not hidden

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Related tools

Building agents that keep their own state? These are the pieces I extracted from doing it:

• Agent Memory Starter — free, open source. Four memory types and three prompts.
• Agent Memory OS — $3. The full twelve-prompt method.
• Second Brain Kit — $7. One memory for every agent, served over MCP.
• MCP Server Starter — $5. Build an MCP server, then prove it behaves.

All of them: fahmiwolf.gumroad.com

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📜 License

MIT License — see LICENSE. The list of original methods and the authorship statement are in AUTHORSHIP.md.

Lisensi MIT — lihat LICENSE. Daftar metode orisinal dan pernyataan kepenemuan ada di AUTHORSHIP.md.

Use it. Fork it. Teach it. Build on it.

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<div align="center">

Project website: sidixlab.com

"We don't build AI that replaces human judgment. We build AI that makes human judgment more informed."

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