Aelvoxim

Biomimetic Autonomous Cognitive AI Brain

A self-learning, hallucination-resistant AI cognitive entity that never forgets and can control your desktop — fully self-hosted.

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Architecture

┌─────────────────────────────────────────────────────┐
│  Application Layer                                 │
│  Desktop control, file ops, browser automation     │
├─────────────────────────────────────────────────────┤
│  Tool Layer                                         │
│  Code execution, API calls, data analysis, MCP     │
├─────────────────────────────────────────────────────┤
│  Cognitive Layer                                    │
│  Reasoning, planning, decision-making, learning     │
├─────────────────────────────────────────────────────┤
│  Memory Layer                                       │
│  Working → Episodic → Semantic → Procedural        │
│  Knowledge graph, entity extraction                │
└─────────────────────────────────────────────────────┘

What you see, in order:

1. Aelvoxim — the project
2. Biomimetic Autonomous Cognitive AI Brain — what it is
3. A self-learning, hallucination-resistant AI that never forgets and can control your desktop — what it does
4. Four-layer architecture — how it's built

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How Aelvoxim Compares

A realistic comparison of Aelvoxim against major AI platforms — written from current capability, not roadmap.

Dimension	Aelvoxim	DeepSeek	ChatGPT	Claude	Llama
Nature	Cognitive engine framework (plugs into any LLM)	LLM	LLM + platform	LLM	Open-source LLM
Persistent memory	✅ 4-tier memory, cross-session knowledge graph	❌ No built-in memory	⚠️ Limited (ChatGPT Memory)	❌ No built-in memory	❌ No built-in memory
Metacognition	✅ MetaCogMonitor + 6 ethics gates (L1-L6)	❌	❌	⚠️ Constitutional AI (different approach)	❌
Self-learning	✅ Learner loop + background knowledge extraction	❌	❌	❌	❌
Expert orchestration	✅ 8 expert modules + orchestrator voting	❌	❌	❌	❌
Code generation	⚠️ Depends on backend LLM (can use DeepSeek, etc.)	✅ Excellent	✅ Strong	✅ Strong	⚠️ Fine-tuned variants
Reasoning depth	⚠️ Depends on backend LLM	✅ Strong chain-of-thought	✅ Strong	✅ Strong, safety-aligned	⚠️ Varies by size
Local deployment	✅ CPU-only, Python 3.11+, optional PostgreSQL	✅ Needs GPU	❌ API-only	❌ API-only	✅ Needs GPU
Open source	✅ MIT	✅ Weights open	❌ Closed	❌ Closed	✅ Weights open
Multimodal	⚠️ Via tool integration	❌ Text-only	✅ GPT-4o multimodal	✅ Multimodal	⚠️ Partial
Tool calling	✅ Unified orchestrator	⚠️ Function Call	⚠️ Function Call	⚠️ Tool Use	⚠️ Self-wrapped
Security	✅ 6 ethics gates (L1-L6) + community edition gating	⚠️ Basic content filter	⚠️ Policy filter	✅ Constitutional AI	❌ None built-in
Business model	Open-source + self-hosted	Open weights + API	Closed API	Closed API	Open weights + ecosystem

Key takeaways:

1. Memory & continuous learning — Aelvoxim's core moat. Every competitor is stateless per session. ChatGPT Memory exists but is a simple snippet store — no forgetting curve, no confidence scoring, no 4-tier architecture.
2. Metacognition & self-learning — No competitor has runtime self-monitoring, degradation detection, or hypothesis generation. Aelvoxim's MetaCogMonitor + 6 ethics gates are unique. Other models' "reflection" is prompt-induced text generation, not system-level self-check.
3. Code & reasoning — Aelvoxim's advantage is flexibility: it doesn't lock you into one model. Plug in DeepSeek for code, Claude for safety, or run multiple models and let the orchestrator vote.
4. Deployment — Aelvoxim runs on CPU, no GPU required. DeepSeek and Llama need GPU for local inference.

Bottom line: Aelvoxim is not competing with LLMs — it's the operating system for LLMs: managing memory, monitoring health, orchestrating tools, and learning continuously. You choose the brain (model), Aelvoxim gives it a body that remembers and improves.

Product	One-liner
Aelvoxim	Gives any LLM persistent memory, metacognition, and self-learning
DeepSeek	Open-source code king, cost-effective reasoning
ChatGPT	Closed-source all-rounder, multimodal + plugin ecosystem
Claude	Safest closed-source model, long-document reasoning
Llama	Open-source LLM standard — powerful but needs engineering to productize

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Features

1. Cross-Session Memory

Every conversation updates an evolving memory system. Start a new session — the AI picks up exactly where you left off. No lost context, no repeating yourself.

• Concepts, relationships, and user preferences are structured into a persistent knowledge graph
• Memory is queryable, exportable, and resettable
• Four-tier retention: working (session) → episodic (7 days) → semantic (90 days) → procedural (permanent)

2. Self-Learning & Evolution

The system doesn't just answer questions — it learns from them.

• Proactively initiates learning plans ("learn Rust", "study PostgreSQL indexing")
• Curiosity engine detects unfamiliar topics during conversation and schedules background learning
• Learning progress is trackable; acquired knowledge can be recalled and explained back to you

3. Reasoning & Planning

• Multi-step logical reasoning, causal analysis, and contradiction detection
• Complex tasks are decomposed into plans and executed step by step
• Tool-calling for code execution, API integration, and data analysis
• Metacognition layer checks output quality (factual consistency, topic drift, safety, clarity)

4. Desktop Control (via Windows-MCP)

Control your Windows desktop through the AI — mouse, keyboard, file system, browser.

• Requires Windows-MCP running on the Windows host (see Windows-MCP/install_and_run.bat)
• PowerShell execution, screenshots, app launching, file operations
• Suitable for test automation, data harvesting, daily office tasks

5. Security

• All requests filtered through SentriKit (optional security gate)
• Tool permissions are tiered — sensitive operations require confirmation
• No prompt injection, no unauthorized system modification

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Security

See SECURITY.md for the full security policy.

Quick security checklist for users:

Concern	Status
Prompt injection guard	✅ Built-in, enabled via AELVOXIM_CONTENT_FILTER=1
API Key authentication	✅ Required for all endpoints
Rate limiting	✅ Built into MetaCogMonitor (L5)
Data encryption at rest	⚠️ JSON file storage — encrypt at filesystem level
PostgreSQL connection	✅ Uses password auth, localhost-only by default

CI & Code Quality

Check	Service	When
Lint (Ruff)	GitHub Actions	Every push/PR
Tests (3 Python versions)	GitHub Actions	Every push/PR
Security scan	GitHub Actions + CodeQL	Every push/PR + weekly
Dependency updates	Dependabot	Weekly (security only)

All CI workflows are in .github/workflows/.
PR template is at .github/PULL_REQUEST_TEMPLATE.md.

Contributing

See CONTRIBUTING.md for detailed guidelines.

Quick rules:

• One feature per PR
• All code, comments, and commit messages in English only
• Stdlib-first — minimize external dependencies
• Type hints required for public APIs
• Run pytest tests/ -v before submitting
• Update README if API or config changes

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Port Map

Port	Service	Description
9701	API Server (FastAPI)	Core brain — chat, auth, admin, knowledge, learning
9702	Frontend (ChatAEL-v2)	Web chat interface (compiled SPA)
5432	PostgreSQL	Sessions, messages, knowledge base, users

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Quickstart

Prerequisites

• Python 3.11+
• PostgreSQL 15+ (optional — falls back to JSON file storage)
• An LLM API key (OpenAI, DeepSeek, Anthropic, or any OpenAI-compatible provider)

Installation

git clone https://github.com/macor24/aelvoxim.git
cd aelvoxim

# Python dependencies
pip install -e .

# Configure PostgreSQL (optional, skip if using JSON storage)
psql -U postgres -c "CREATE DATABASE aelvoxim;"
psql -U postgres -c "CREATE USER aelvoxim WITH PASSWORD 'your_password';"
psql -U postgres -c "GRANT ALL PRIVILEGES ON DATABASE aelvoxim TO aelvoxim;"
export AELVOXIM_DATABASE_URL="host=localhost port=5432 dbname=aelvoxim user=aelvoxim password=your_password"

Configure LLM

Set one of these environment variables (see docs for full list):

# DeepSeek
export DEEPSEEK_API_KEY="sk-..."
export LLM_PROVIDER="deepseek"

# OpenAI
export OPENAI_API_KEY="sk-..."
export LLM_PROVIDER="openai"

Running

# Start the brain
PYTHONPATH=src python3 src/run_server.py 9701

# (separate terminal) Start the frontend
python3 serve_chatael.py --port 9702

# (on Windows host) Start desktop control — see Windows-MCP/install_and_run.bat

Open http://localhost:9702 in your browser. Register an account and start chatting.

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API Endpoints

All endpoints on port 9701:

Path	Description
POST /v1/auth/register	Create a new user account
POST /v1/auth/login	Authenticate — returns API key
POST /v1/llm/chat/stream	Streaming chat (SSE)
GET /v1/admin/panel	Admin management panel
GET /v1/health	Service health check

A full OpenAPI spec is available at http://localhost:9701/docs.

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Configuration

Variable	Default	Description
LLM_PROVIDER	deepseek	LLM provider name
DEEPSEEK_API_KEY	—	API key for DeepSeek
OPENAI_API_KEY	—	API key for OpenAI
AELVOXIM_DATABASE_URL	(none)	PostgreSQL DSN — leave unset for JSON file storage
AELVOXIM_CONTENT_FILTER	0	Enable prompt injection guard
AELVOXIM_LLM_CHECK	0	Enable LLM-based fact contradiction check

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Project Structure

aelvoxim/
├── src/
│   └── aelvoxim/
│       ├── server/        # API routes, auth, chat, tool execution
│       ├── cortex/        # Intent routing, expert orchestration
│       ├── chimera/       # Emotion engine, intent classification
│       ├── control/       # Metacognition, generation quality checks
│       ├── learn/         # Autonomous learning, knowledge acquisition
│       ├── memory/        # Cross-session memory, entity extraction
│       ├── proactive/     # Background proactive engine
│       ├── storage/       # Database layer (PostgreSQL + JSON fallback)
│       ├── utils/         # Utility functions
│       └── planner/       # Long-term task planning
├── frontend/              # ChatAEL-v2 SPA
├── scripts/               # CI, lint, migration helper scripts
├── tests/                 # Test suite
├── serve_chatael.py       # Frontend static server entry point
└── requirements.txt       # Locked dependencies

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License

MIT License — see LICENSE for details.

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Links

• Version: v1.2.0
• GitHub: https://github.com/macor24/aelvoxim
• Pages: https://macor24.github.io/aelvoxim
• Issues: https://github.com/macor24/aelvoxim/issues
• Docs: https://github.com/macor24/aelvoxim/tree/master/docs

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Built with no GPU required.