🧠 MATHIR

⚠️ DISCLAIMER — MATHIR has NOT undergone formal security testing. Use at your own risk in production. License: MIT.

🧠 MATHIR

Memory-Augmented Tensor Hybrid with Intelligent Routing

The first cognitive memory layer for LLMs that actually thinks — promotes, forgets, consolidates, and links.

🆕 v8.9.1 — GUARDRAIL TIER — 6th tier, push-based always-active rules. Critical corrections auto-injected into every context response. God Mode · Client Bridge · CHANGELOG

⚡ Quick Start

git clone https://github.com/sil3d/MATHIR.git
cd MATHIR/mathir_mcp
pip install -e .
mathir-server &
# Add mathir to your MCP config — 27 tools available (including 2 for god-mode orchestration).

Full install: mathir_mcp/README.md · Cross-platform installer: python mathir_mcp/INSTALL_FOR_DEV/install_smart.py (see 🛠️ Install Scripts below)

🔱 Introducing: MATHIR GOD MODE

Multi-agent orchestration via shared memory. One orchestrator. N workers. Zero configuration.

Terminal 1:  mathir_god_agent()        → "I am MiMo. I'm fast at code generation..."
Terminal 2:  mathir_god_agent()        → "I am Codex. I excel at bulk testing..."
Terminal 3:  mathir_god_agent()        → "I am OpenCode. I'm good at docs..."
Terminal 4:  mathir_god_orchestre(directive="Refactor auth + tests + docs")
             → Sees all profiles. Assigns tasks by strength. Monitors. Verifies.

What it solves: You have 4 AI agents open. You copy-paste context between them. You decide who does what. You check each result manually. You are the bottleneck.

mindmap
  root((🔱 GOD MODE))
    🧠 Orchestrator
      Decomposes directive
      Reads worker profiles
      Assigns by strength
      Monitors & verifies
      Dispatches dependents
    👷 Workers
      Self-identify
        Name & capabilities
        Strengths & weaknesses
        Tool access
      Poll for tasks
      Execute & report
      Loop until shutdown
    📡 Shared Memory
      MATHIR Daemon :7338:
      Label Protocol
        god:reg — registration
        god:task — dispatch
        god:result — completion
        god:shutdown — stop
      No broker needed
      Cross-process
    ⚙️ Core Engine
      TaskGraph — DAG
        Dependency resolution
        Cycle detection DFS
      WorkerRegistry
        Capability matching
        Status tracking
      GodProtocol
        Label encode/decode
        Task ID generation
      WorktreeManager
        Git isolation
        Branch per task
    🛡️ Safety
      LIKE injection prevention
      Cycle detection
      No premature completion
      Honest self-assessment

How it works:

Workers call mathir_god_agent() with no arguments → they self-identify (name, strengths, weaknesses)
Orchestrator calls mathir_god_orchestre(directive="...") → sees all worker profiles → decomposes → assigns by strength → dispatches
Workers poll, execute, report. Orchestrator verifies and dispatches next tasks.
All communication goes through MATHIR shared memory. No new infrastructure.

Built-in intelligence:

Agents self-identify honestly — the orchestrator doesn't guess who's installed
Tasks matched to worker strengths — deep reasoning → Claude, bulk work → fast model
Dependency-aware — tasks dispatched only when prerequisites complete
Cycle detection — circular dependencies caught at creation time

Full guide: docs/GOD_MODE.md

🆕 Latest: v8.6.0 (2026-07-03)

26 MCP tools. 22 algorithms. INT8 quantization. Cross-encoder reranking. Multi-agent benchmark.

INT8 quantization — embedding storage reduced 4x (float32 → int8), zero recall loss. 410 DBs migrated: 1.9 GB → 825 MB. Cross-encoder reranking — ms-marco-MiniLM-L-6-v2 second-pass scoring: +20pp hit@10 on natural-language queries. Multi-agent benchmark — free-tier models (mimo, nemotron, north) score 0% without memory → 53% average with MATHIR. e5-small validated — e5-small + rerank (52.9%) beats e5-large alone (51.0%) at 47x less cost.

Full diff: mathir_mcp/CHANGELOG.md · Full report: benchmarks/06_results/current/README.md

🏗️ Universal Architecture — How MATHIR runs everywhere

MATHIR has 2 long-running processes + 1 cross-tool instruction file:

┌─────────────────────────────────────────────┐
│ DAEMON (port 7338) — mathir-server          │
│ Flask + Waitress · sqlite-vec · embedder    │
│ Holds the model in RAM/VRAM, exposes HTTP   │
└─────────────────────────────────────────────┘
                    ▲ HTTP
                    │
   ┌────────────────┼────────────────┐
   │                │                │
┌─────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ MCP BRIDGE      │ │ MCP BRIDGE        │ │ MCP BRIDGE        │
│ opencode        │ │ mimocode          │ │ any OpenAI-comp.  │
│ (stdio → HTTP)  │ │ (stdio → HTTP)    │ │ Claude / Cline /  │
└─────────────────┘ └──────────────────┘ │ Cursor / etc.     │
                                          └──────────────────┘
                                                    ▲
                                                    │ baseUrl
                                          ┌──────────────────┐
                                          │ PROXY (port 7339) │
                                          │ mathir-proxy      │
                                          │ Universal OpenAI- │
                                          │ compatible injec- │
                                          │ tion (works for   │
                                          │ ANY agent that    │
                                          │ redirects baseUrl)│
                                          └──────────────────┘

3 coverage tiers (honest disclosure):

Tier	Mechanism	Agents	Coverage
A — Plugin auto-inject	`mathir-auto-inject.ts` hooks `session.started` + `experimental.chat.system.transform` — no agent cooperation needed	opencode, mimocode	TRUE auto-inject
B — Instructions + MCP	MCP server registered + `GLOBAL_INSTRUCTIONS.md` injected. Agent must follow the advisory instruction to call `memory_session_start`	claude-code, cursor, cline, zcode, codex, etc. (14 agents)	SOFT — agent must comply
C — MCP only	MCP server registered, no behavioral prompt	windsurf, gemini-cli, kilo, qwen, kiro-ide, warp, trae, crush, etc. (34 agents)	NONE — set `OPENAI_BASE_URL=http://127.0.0.1:7339/v1`

Universal escape hatches (escape from tier C → true auto-inject):

mathir-proxy on port 7339 — OpenAI-compatible LLM proxy. Intercepts every /v1/chat/completions, queries daemon /api/context, prepends <mathir-auto-injection> block to system prompt. Works for ANY agent that redirects its baseUrl.
```
export OPENAI_BASE_URL=http://127.0.0.1:7339/v1
```
AGENTS.md at repo root — read automatically by 26+ agents (Aider, Amp, Claude Code, Codex, Cursor, Devin, Factory, Goose, JetBrains Junie, Jules, OpenCode, VS Code Copilot, Warp, Zed, etc. — see agents.md). Instructs the agent to call memory_session_start on first turn + memory_context before each task.
```
cp mathir_mcp/opencode_templates/AGENTS.md /path/to/your/project/AGENTS.md
```

Per-project DB routing — each project gets its own .mathir/mathir.db:

Mycerise_V2_Taur → Mycerise_V2_Taur/.mathir/mathir.db
mathir_mcp (installer) → ~/.config/MATHIR/mathir_mcp/.mathir/mathir.db
Future projects → <project>/.mathir/mathir.db (auto-created on first save)

Routing is fixed in v8.5.1: mathir_mcp_server.py injects project + cwd into every request; mathir_server.py uses them to pick the right DB.

🧭 Project Origin — 2 years, 1 question

This is the story behind MATHIR. It's also my end-of-study project.

Can modern cars navigate an unknown environment?

Not a highway with lane markings. Not a pre-mapped city. A place they've never seen, where the rules change every meter.

A car following pre-programmed rules in a perfect simulation isn't intelligent — it's scripted. True autonomy requires the ability to learn, remember, and adapt across situations it's never seen before.

That's where MATHIR started. An AI can't be intelligent if it can't remember — every session starts from zero, that's amnesia, not intelligence.

Next step: MATHIR has been validated in software (26 MCP tools, 6-tier architecture, plug-and-play MCP). The next step is to build a 3D-printed RC car and test MATHIR as its actual memory layer in a real autonomous-driving scenario.

🔥 5 real-world problems MATHIR solves

	Problem	MATHIR solution
1	Medical AI — "We've never seen this disease before"	Rare case stored as episodic memory → next patient gets instant recall. The model learns from experience.
2	Chat sessions — "Sorry, who are you?"	Context persists across sessions, tools, time. Switch Claude → Gemini → Llama — memory stays.
3	Autonomous driving — "The sensor just died"	Car doesn't just see — it remembers. "Last time I was here, speed bump at this GPS." Memory fills sensor gaps.
4	Fine-tuning — "My data is a mess"	MATHIR auto-classifies, dedupes, links. Data ready for fine-tuning as you add it.
5	Knowledge drift — "Is this still accurate?"	Memories decay when unused. Old memory fades when API changes. Self-maintaining.

🧠 What is MATHIR?

A plug-and-play 6-tier cognitive memory layer for any LLM:

Tier	Role	Example
🩷 Working	Scratchpad (current session)	"Right now"
🩵 Episodic	Events	"Last time you asked, the API was at /v2"
🟩 Semantic	Stable facts	"Water boils at 100°C"
🟨 Procedural	How-to / recipes	"How to deploy: pytest → docker build → aws ecs"
🟥 Immunological	Anomaly detection	"Prompt injection detected"
🛡️ Guardrail	Always-active rules (immune to decay)	"NEVER call _get_project_db() from agent code"

Memories decay when unused (Ebbinghaus), promote when recalled, consolidate with duplicates, link to related concepts. Guardrails are push-based: auto-injected into every memory_context response, immune to decay, min priority 8, max 50 per project. Same memory works across Claude / GPT / Gemini / Ollama / any LLM.

Why? See the doctoral research paper (6 theorems) and the vs Alternatives doc.

The story that hurts

Monday morning. You open Claude. You tell it: "My name is Thomas, I'm building a RAG with Python, FastAPI + Postgres." Claude says: "Got it, I'll remember that."

3 months later. You switch to Cursor + Llama 3.1. Llama: "Hi! Who are you?" Everything Claude "remembered"? Gone. Vendor-locked.

6 months of memory. Wiped in 3 seconds. Because your memory doesn't belong to you.

And the autonomous vehicle:

2:32 PM. The Tesla learns that a yellow pedestrian marker at a crosswalk = slow down. Pattern stored. 2:33 PM. OTA restart. Memory is wiped. Next time, it won't slow down. 2:35 PM. 80 km/h. Zero detection. Zero alerts. Zero memory.

A car that doesn't remember = a car that doesn't understand.

What MATHIR changes:

✅ Memory that follows you everywhere — SQLite local, MIT, zero vendor lock-in. ✅ Memory that improves — +37.8% online learning, not static facts. ✅ Anomaly detected in <1ms — immunological tier, AUC = 1.0. ✅ Runs on edge — 240 MB VRAM, Jetson Orin ✅, Raspberry Pi ⚠️, zero cloud.

🔌 MCP Plug & Play

Add MATHIR to your AI agent (OpenCode, Claude Code, Cursor, MiMo, etc.):

{
  "mcpServers": {
    "mathir": {
      "command": "mathir-mcp"
    }
  }
}

That's it. 26 tools (memory_save, memory_recall, mathir_god_orchestre, mathir_god_agent, etc.) — all your agents.

Full MCP config: mathir_mcp/INSTALL_FOR_AGENT/AGENT.md (50+ agents).

Console Scripts (universal, IDE-agnostic)

Command	What it does
`mathir-mcp`	MCP stdio server (26 tools, 2 prompts)
`mathir-server`	HTTP unified server (port 7338)
`mathir-client`	CLI client: `mathir-client recall "my query"`
`mathir-dashboard`	Stats dashboard (port 7420)
`mathir-migrate`	One-shot legacy→new schema migration
`mathir-brain`	Orchestrator (server + watchdog + proxy)

Install: pip install -e ./mathir_mcp

📚 Documentation Index

Doc	Purpose
mathir_mcp/README.md	Install, MCP setup, all 26 tools
mathir_mcp/INSTALL_FOR_AGENT/AGENT.md	Per-agent MCP config (50+ agents)
mathir_mcp/docs/DAEMON.md	Daemon HTTP API + JSON-RPC protocol
mathir_mcp/docs/DIMENSIONS.md	Embedding model selection
mathir_mcp/docs/DASHBOARD_GUIDE.md	Stats dashboard setup
docs/GOD_MODE.md	God Mode — multi-agent orchestration guide
mathir_mcp/docs/GPU_SETUP.md	GPU/ONNX acceleration
docs/01_MASTER_RESEARCH_PAPER.md	Doctoral research paper (6 theorems)
docs/03_MASTER_QA_GUIDE.md	63 Q&A for defense / evaluation
docs/07_MATHIR_VS_VECTORDB_USE_CASES.md	MATHIR vs FAISS / vector DBs
CHANGELOG.md	Full version history
mathir_mcp/GLOBAL_INSTRUCTIONS.md	Universal AI agent instructions

🛠️ Install Scripts

Cross-platform: python mathir_mcp/INSTALL_FOR_DEV/install_smart.py --autostart-only (Windows / macOS / Linux).

Manual: see INSTALL_FOR_AGENT/INSTALL_WINDOWS.md · INSTALL_FOR_AGENT/INSTALL_LINUX.md · INSTALL_FOR_AGENT/INSTALL_MACOS.md.

🆚 vs Alternatives (2026)

Product	OSS?	LLM-agnostic?	Edge?	Anomaly detection	Cost
🧠 MATHIR	✅ MIT	✅ Any	✅ ~500MB GPU	✅ MCP server: live Mahalanobis detector, AUC-ROC=0.8533	Free
Mem0	⚠️ SDK	✅	❌	❌	Free → $249/mo
Letta	✅ Apache 2.0	✅	⚠️ Heavy	❌	Free
Zep	⚠️	✅	❌	❌	$1,250/yr
Cognee	✅ Apache 2.0	✅	⚠️ Heavy	❌	$35/mo
LangMem	✅ MIT	✅	⚠️ DIY	❌	Free
GraphRAG	✅ MIT	✅	⚠️ DIY	❌	Free
ChatGPT Memory	❌	❌ OpenAI	❌	❌	$20/mo+
Claude Projects	❌	❌ Anthropic	❌	❌	$20/mo+

Anomaly detection status: the MCP server/daemon (mathir_lib/, what coding agents connect to) now wires its immunological tier to a real, live Mahalanobis-distance detector: /api/memory/save scores every incoming embedding against a running per-project baseline and can write tier='immunological' when it flags an outlier. On a realistic prompt-injection corpus (mathir_mcp/tests/data/anomaly_eval/), the honest result is AUC-ROC=0.8533 for normal-vs-injection separation — good, not perfect. There is no clean separation between "malicious" and "merely unusual" using distance alone: benign-but-unusual text can also score above the threshold and get flagged. Because of this, flagged content is not auto-blocked or silently deleted — it lands in the immunological tier for review via memory_audit_immunological. A separate, simpler (non-Mahalanobis) detector also exists in mathir_dropin/ (the standalone embeddable library for non-MCP apps, see docs/05_SHIPPING_GUIDE.md) — it is a different implementation and its numbers are not the ones quoted above.

Retrieval quality vs FAISS: real BEIR benchmarks (SciFact/ArguAna/NFCorpus, see benchmarks/06_results/current/) currently show plain FAISS dense retrieval outperforming MATHIR's hybrid BM25+dense+cross-encoder pipeline. Any "+14pp vs FAISS" figure you may see elsewhere comes from a 50-query/200-chunk internal evaluation on a single textbook and is not comparable to a standard IR benchmark — see docs/SOTA_RESEARCH_2024_2026.md for the full self-audit.

LoCoMo results (2026-07-03): MATHIR now has LoCoMo numbers — 51.2% on Groq (Llama 3.3 70B, 41/233 judged due to TPM limits), 38.8% on OpenCode Zen free models (67/152 judged). Temporal retrieval is strong (65-73%), multi-hop is weak (8-17%). Full results: benchmarks/06_results/current/README.md.

Full comparison: docs/07_MATHIR_VS_VECTORDB_USE_CASES.md

📊 Tests & Benchmarks

98/98 tests pass (mathir_mcp/tests/). Run yourself:

pytest mathir_mcp/tests/ -v

Benchmark	Result
Multi-agent + MATHIR	0% → 53% avg (free models + shared memory)
INT8 quantization	410 DBs, 1.9 GB → 825 MB, 0% recall loss
Cross-encoder rerank	+20pp hit@10 on NL queries (50% → 70%)
LoCoMo (Groq 70B)	51.2% overall, 73% temporal
e5-small + rerank	52.9% > e5-large 51.0% at 47x less cost
Micro (500 memories)	360 mem/s store, 425 ops/s recall, p50=2.29ms
decay_all	599/599 decayed (100% coverage)
consolidate	99 duplicates merged

Full report: benchmarks/06_results/current/README.md

🏗️ Architecture

┌──────────────────────────────────┐
│  Any LLM (Claude, GPT, Gemini)  │
└──────────────┬───────────────────┘
               │ embeddings 384d
               ▼
┌──────────────────────────────────┐
│  MATHIR Daemon (port 7338)        │
│  Flask+Waitress · FastMCP 3.4.2  │
│  HybridSearch + CrossEncoder rerank│
│  6 tiers · INT8 · Ebbinghaus      │
│                                    │
│  GOD MODE (v8.8.0) + GUARDRAIL     │
│  /api/god/poll · /api/god/agents   │
│  Cross-process multi-agent         │
│  orchestration via shared memory   │
└──────────────┬───────────────────┘
               │
               ▼
        SQLite + sqlite-vec
        (per-project DB)

Full architecture: docs/BRAIN_ARCHITECTURE.md

🛠️ Project Structure

MATHIR/
├── mathir_mcp/         ← Install this (v8.9.0, 26 MCP tools, God Mode + Guardrails)
├── benchmarks/         ← Reproducible benchmarks
├── docs/                ← Doctoral paper, QA, architecture
├── examples/            ← Usage examples
├── stress_test/         ← Stress test web UI
├── vision_testing/      ← Vision/audio testing
├── raspberry_jetson/    ← Edge deployment
├── _deprecated/         ← v1-v7 history (do not use)
└── README.md (you are here)

🗺️ Roadmap

✅ Done (V1–V8.5.1)

✅ V1–V5 Core architecture + KL router ✅ V6 LLM-agnostic plugin API ✅ V7 8 algorithms + 6 theorems + 9.3× compression ✅ V7.5 BEIR benchmarks (0.7441 SOTA on SciFact) ✅ V7.6 Universal Bridge (UNIBRI) ✅ V7.7 Vision & audio testing ✅ V7.7.1 SimpleMemory (FTS5) + UI overhaul ✅ V7.8 GPU embeddings + daemon architecture ✅ V8.0 Cascade architecture ✅ V8.5.0 FastMCP rewrite + auto-injection (20 tools) ✅ V8.5.1 New tools (23 total) + project-aware DB ✅ V8.6.0 INT8 quantization + cross-encoder rerank + multi-agent benchmark ✅ V8.7.0 3-layer auto-cache (L1 embedding, L2 recall, L3 session) ✅ V8.8.0 God Mode — cross-process multi-agent orchestration ✅ V8.9.0 Guardrail tier — push-based always-active rules (6th tier)

🔜 Next: 4 validation stages

graph LR
    V91["V9.1<br/>VISION<br/>Model testing<br/>(laptop, T+0)"] --> V92["V9.2<br/>RASPBERRY PI<br/>CPU edge deploy<br/>(Pi 5, T+2 weeks)"]
    V92 --> V93["V9.3<br/>JETSON<br/>GPU edge deploy<br/>(Orin, T+1 month)"]
    V93 --> V94["V9.4<br/>RC CAR<br/>Physical world<br/>(3D-printed, T+2m)"]

    V91 --- |"OpenRouter<br/>26 free models<br/>Vision + audio"| A1
    V92 --- |"MiniLM-L12<br/>CPU + ONNX INT8<br/>&lt;500 MB RAM"| A2
    V93 --- |"bge-large-en-v1.5<br/>CUDA fp16 500MB<br/>&lt;30 ms recall"| A3
    V94 --- |"Real sensors<br/>camera + LIDAR<br/>no internet"| A4

    classDef done fill:#22c55e,color:#fff
    classDef next fill:#3b82f6,color:#fff
    classDef pending fill:#6b7280,color:#fff
    class V91 next
    class V92 pending
    class V93 pending
    class V94 pending

Stage	Hardware	Embedding	Goal
V9.1 Vision (now)	Laptop	OpenRouter free LLM	Verify across LLMs
V9.2 Raspberry Pi	Pi 5 (8GB)	MiniLM CPU + ONNX INT8	CPU edge robustness
V9.3 Jetson	Orin Nano (8GB)	bge-large CUDA fp16	GPU edge acceleration
V9.4 RC Car	3D-printed + Pi/Orin	Same as Jetson	Real-world autonomous

📋 Future

📋 V10 Open-source release (HuggingFace · PyPI)

Why this order?

V9.1 Vision (now): Cheap, fast, validates the memory layer across many LLMs. No hardware risk.
V9.2 Raspberry Pi: Real edge constraints (CPU only, 4GB RAM). If it works here, it works anywhere.
V9.3 Jetson: GPU acceleration, but still bounded power. The "production edge" sweet spot.
V9.4 RC Car: No internet, no reboot, no excuses. Real sensors, real noise, real failures. This is the validation that can't be faked.

🤝 Contributing

We welcome PRs and security reports. Open an issue or pull request.

📄 Citation

@software{mathir2026,
  title  = {MATHIR: Memory-Augmented Tensor Hybrid with Intelligent Routing},
  author = {Mbama Kombila, Prince Gildas},
  year   = {2026},
  url    = {https://github.com/sil3d/MATHIR}
}

Full paper: docs/MATHIR_Research_Paper.tex

📜 License

MIT — free for commercial and research use.

Name		Name	Last commit message	Last commit date
Latest commit History 321 Commits
.github/workflows		.github/workflows
_deprecated		_deprecated
benchmarks		benchmarks
bin/seo		bin/seo
docs		docs
examples		examples
mathir_dropin		mathir_dropin
mathir_mcp		mathir_mcp
raspberry_jetson		raspberry_jetson
stress_test		stress_test
vision_testing		vision_testing
.gitattributes		.gitattributes
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
SEO_TAGS.md		SEO_TAGS.md
check_schema.py		check_schema.py
index.html		index.html
install.bat		install.bat
install.sh		install.sh
requirements.txt		requirements.txt

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🧠 MATHIR

Memory-Augmented Tensor Hybrid with Intelligent Routing

⚡ Quick Start

🔱 Introducing: MATHIR GOD MODE

🆕 Latest: v8.6.0 (2026-07-03)

🏗️ Universal Architecture — How MATHIR runs everywhere

🧭 Project Origin — 2 years, 1 question

🔥 5 real-world problems MATHIR solves

🧠 What is MATHIR?

The story that hurts

🔌 MCP Plug & Play

Console Scripts (universal, IDE-agnostic)

📚 Documentation Index

🛠️ Install Scripts

🆚 vs Alternatives (2026)

📊 Tests & Benchmarks

🏗️ Architecture

🛠️ Project Structure

🗺️ Roadmap

✅ Done (V1–V8.5.1)

🔜 Next: 4 validation stages

📋 Future

Why this order?

🤝 Contributing

📄 Citation

📜 License

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🧠 MATHIR

Memory-Augmented Tensor Hybrid with Intelligent Routing

⚡ Quick Start

🔱 Introducing: MATHIR GOD MODE

🆕 Latest: v8.6.0 (2026-07-03)

🏗️ Universal Architecture — How MATHIR runs everywhere

🧭 Project Origin — 2 years, 1 question

🔥 5 real-world problems MATHIR solves

🧠 What is MATHIR?

The story that hurts

🔌 MCP Plug & Play

Console Scripts (universal, IDE-agnostic)

📚 Documentation Index

🛠️ Install Scripts

🆚 vs Alternatives (2026)

📊 Tests & Benchmarks

🏗️ Architecture

🛠️ Project Structure

🗺️ Roadmap

✅ Done (V1–V8.5.1)

🔜 Next: 4 validation stages

📋 Future

Why this order?

🤝 Contributing

📄 Citation

📜 License

About

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