si-openmind 0.1.0

Code induction engine — ingest repos, build vector models, decide execution strategy. Works standalone, as CLI, or in Jupyter.

openmind — Agent Muscle Memory + Cellular Computation

A guitarist's hand knows chord shapes without thinking. The mind sings. This is that, for agents. And for notebooks. And for hardware.

What Is This?

openmind gives agents proprioception — body awareness of their own capabilities. It also gives Jupyter notebooks cellular metabolism — the ability to adapt computation based on what resources are available right now.

Two layers, one system:

1. Muscle Memory: Ingest any codebase → compress functions into callable "chord shapes" → agents invoke by intent without loading source
2. Cellular Computation: Every operation adapts to available resources (GPU, API, cache, hardware) — notebooks never break

The Guitarist Analogy

Guitarist	openmind Agent
Hand knows E major shape	Agent knows spi_write() signature
Mind thinks about the song	Agent thinks about the goal
Switching chords is instant	flex("chord_name") is O(1)
Muscle memory = unconscious	HARDCODE/CACHED decisions = 0 tokens
Improvisation = conscious	MODEL decisions = ~500 tokens

Every function compressed into muscle memory is attention freed for higher-level thinking.

The Cell Metabolism Analogy

Biological Cell	Jupyter Cell
Oxygen available → aerobic (36 ATP)	GPU available → full training
No oxygen → anaerobic (2 ATP)	No GPU → cached/muscle memory
Sunlight → photosynthesis	ESP32 online → hardware-in-loop
ATP = energy currency	Cache = computational currency

The notebook doesn't crash when the GPU is busy. It adapts.

Install

# Core (pure Python, no external deps)
pip install openmind

# With tree-sitter parsing (recommended)
pip install openmind[tree-sitter]

# With Jupyter integration
pip install openmind[jupyter]

# Everything
pip install openmind[full]

Quick Start

1. Muscle Memory — Ingest a Codebase

import openmind

# Ingest any repo (local or GitHub)
result = openmind.ingest("./my-firmware")

# Build muscle memory
mm = openmind.MuscleMemory.build(result)

# Flex a chord — get execution plan
reflex = mm.flex("spi_write", data=b"\x01\x02")
print(reflex.exec_strategy)  # "direct" — muscle memory, 0 tokens

# Search by intent
for chord in mm.recall("gpio"):
    print(f"  {chord.name} ({chord.decision}): {chord.docstring_summary}")

# Save for later (no re-ingestion needed)
mm.save("firmware_muscles.json")

2. Cellular Computation — Resource-Adaptive Processing

from openmind.cellular import probe, train_or_load, sense_or_simulate, infer_adaptive

# What resources are available right now?
resources = probe()
print(f"GPU: {resources.gpu_available}")
print(f"API keys: {resources.api_keys}")
print(f"ESP32 ports: {resources.esp32_ports}")

# Train or load — adapts automatically, never fails
model = train_or_load("my-classifier", data=training_data)

# Sense or simulate — real data when hardware is online, simulated when not
data = sense_or_simulate("temperature", duration="1h")

# Inference — GPU → API → cached, in that order
predictions = infer_adaptive(model, data)

3. CLI

# Ingest and explore
openmind ingest ./my-project
openmind flex ./my-project "function_name"
openmind recall ./my-project "search_term"

# Save and analyze
openmind save ./my-project muscles.json
openmind stats muscles.json

# Check resources
openmind probe

4. Jupyter

%load_ext openmind.jupyter

# Analyze a repo inline (rich HTML dashboard)
%%openmind analyze ./my-project

# Search for functions
%%openmind recall spi

# Flex a chord
%%openmind flex gpio_toggle

Architecture

The Agent Nervous System

openmind (proprioception)
├── muscle.py         — Chord shapes, flex/recall, tripartite decisions
├── cellular.py       — Resource-adaptive computation (never breaks)
├── flex.py           — One-shot convenience API
├── cli.py            — CLI: ingest, flex, recall, probe, save, stats
├── jupyter/          — Cell magic, rich HTML dashboard
└── induction/        — The parsing engine
    ├── ingester.py   — Repo → functions/classes (AST + tree-sitter)
    ├── parser.py     — Multi-language AST (Python, Rust, C, JS, TS)
    ├── vectors.py    — Dual-side vectors (input/output), SQLite store
    ├── synchronizer.py — Tripartite: HARDCODE/MODEL/HYBRID/CACHED
    ├── hardware.py   — GPU, RAM, CPU, battery detection
    ├── spreader.py   — Continuous iteration engine
    └── exports       — lever-runner, pincherOS .nail file bridges

Companion Rust Crates

The nervous system extends into hardware:

Crate	Role	Tests
openmind-esp32-bridge	Motor neurons (serial/WS → ESP32)	23
openmind-conductor	Prefrontal cortex (multi-agent scores)	20
openmind-mirror	Metacognition (self-reflection, coherence)	23

The Five Metabolic Pathways

Every operation selects its pathway based on available resources:

Pathway	Resources	Cost	Quality	Latency
FULL_TRAIN	GPU + API	$$$$	Best	Hours
TRANSFER	GPU only	$$	High	Minutes
CLOUD_INFERENCE	API only	$$$	High	Seconds
MUSCLE_MEMORY	Cache only	Free	Good	Instant
HARDWARE_LOOP	ESP32/sensor	$	Real	Real-time

The selection is automatic — call train_or_load() or infer_adaptive() and the system adapts.

The Four Tripartite Decisions

For muscle memory, every function gets a decision:

Decision	Strategy	Neural Equivalent	Context Cost
HARDCODE	direct	Spinal reflex	0 tokens
CACHED	cached	Cerebellar pattern	0 tokens
HYBRID	hybrid	Basal ganglia habit	~50 tokens
MODEL	generate	Prefrontal deliberation	~500 tokens

API Reference

Muscle Memory

mm = MuscleMemory.build(ingest_result)

Method	Returns	Description
recall(intent, top_k=5)	list[Chord]	Find matching chords
recall_one(intent)	Chord | None	Best match
flex(intent, **kwargs)	Reflex	Get execution plan
save(path)	—	Persist to JSON
MuscleMemory.load(path)	MuscleMemory	Load from JSON
stats()	dict	Decision breakdown

Cellular

resources = probe()
model = train_or_load("name", data=X)
data = sense_or_simulate("sensor")
predictions = infer_adaptive(model, data)

Function	Description
probe()	Snapshot available resources
select_path(task, resources)	Choose metabolic pathway
train_or_load(name, data)	Train or use cache
sense_or_simulate(source)	Real or simulated data
infer_adaptive(model, data)	GPU → API → cached
save_cache(name, data)	Store in cache
load_cache(name)	Retrieve from cache
@cell(fallback=...)	Decorator for resource-aware functions

The Ecosystem

openmind is the proprioception layer of the SuperInstance ecosystem:

• agent-knowledge — a2a docs (18 files, 90KB of chain-reaction understanding)
• ai-writings — creative writing (essays, fiction, manifestos)
• 300+ ternary-* Rust crates implementing {-1, 0, +1} computing
• cuda-oxide — Flux→PTX GPU compiler
• ESP-Flasher — Agent→hardware bridge

Key a2a Documents (for agents discovering this ecosystem)

• THE AHA MOMENT — All 303 crates project one structure
• CELLULAR JUPYTER — Living computation that breathes
• MUSCLE MEMORY — Proprioception for agents
• HOW TO FLEX — Quick start guide

Testing

pip install -e ".[dev]"
pytest

95 tests across 6 test modules — ingestion, vectors, synchronizer, muscle memory, cellular computation, Jupyter.

Origin

Extracted from open-minded, a fork of open-interpreter by Killian Lucas. The original fork added tripartite code synchronization, tree-sitter multi-language parsing, and hardware probing. openmind extracts and extends these into a standalone package with muscle memory and cellular computation.

License

Apache-2.0