sensory-memory-adi 0.1.2.1

Sensory Memory ADI: iconic/echoic/haptic short-buffers with attention routing, feeling-aware touch interpretation, and AGI-ready hooks for agentic systems.

sensory-memory-adi (v0.1.2)

A production-ready sensory memory layer for agentic AI systems:

• Visual (Iconic) buffer: ultra-short retention for frames/observations
• Auditory (Echoic) buffer: short retention for audio chunks/ASR snippets
• Touch (Haptic) buffer: short retention for touch events/pressure/vibration
• Attention routing (AGI-ready hooks): salience scoring + event routing
• Feeling-aware touch interpretation: friendly labels (tap/press/hold/swipe) + arousal estimate
• Privacy/Redaction hooks: optional text redaction and safe snapshots
• Optional FastAPI service for deployment

Memory Types & Typical Durations

Type	Duration (typical)
Visual (Iconic)	~0.2–0.5 seconds
Auditory (Echoic)	~2–4 seconds
Touch (Haptic)	~1–2 seconds

Install

pip install sensory-memory-adi

Optional server:

pip install "sensory-memory-adi[server]"
uvicorn sensory_memory_adi.server.app:app --host 0.0.0.0 --port 8081

Quickstart

from sensory_memory_adi import SensoryMemoryADI, SensoryConfig

sm = SensoryMemoryADI(cfg=SensoryConfig(iconic_seconds=0.5, echoic_seconds=3.0, haptic_seconds=1.5))
sm.iconic.add_frame({"camera":"front", "objects":["car","lane"]})
sm.echoic.add_audio_text("User said: turn left at next light")
sm.haptic.add_touch({"kind":"tap", "pressure":0.6, "duration_ms":120})

print(sm.attend(top_k=5).model_dump())

Robotics & Cars (v0.1.2)

v0.1.2 adds real timestamps for all events and lightweight adapters:

• ROS2 adapter (optional): convert ROS2 message dictionaries into iconic/echoic/haptic events
• CAN bus adapter (optional): decode basic CAN frames (id, data) into haptic/echoic events (you can map signals)

The ROS2 adapter is dependency-free by default. If you use rclpy, integrate by passing your decoded data into the adapter functions.

Real timestamps

All add_* methods accept ts (Unix seconds). If omitted, current UTC time is used. The attend() function now uses the true timestamps to compute recency.

Zero-copy frames (v0.1.2)

IconicBuffer.add_frame() supports zero-copy payloads using memoryview or NumPy arrays (optional). If you pass a NumPy array, we store a memoryview of the underlying buffer plus shape/dtype metadata (no copy).

Install NumPy support:

pip install "sensory-memory-adi[numpy]"

High-concurrency ingestion (v0.1.2)

Includes:

• ThreadSafeIngestor (multi-producer via queue.SimpleQueue, centralized drain)
• AsyncIngestor (multi-producer via asyncio.Queue, centralized drain)

Optional C/C++ hot path (v0.1.2)

A pybind11 extension skeleton is included to accelerate the ring buffer. It is OFF by default. Build it only when needed:

macOS/Linux:

export SENSORY_MEMORY_ADI_BUILD_EXT=1
pip install .

Windows PowerShell:

$env:SENSORY_MEMORY_ADI_BUILD_EXT="1"
pip install .

Benchmarks

Run:

python bench/benchmark.py

ROS/ROS2 buffering patterns (optional utilities)

Includes a dependency-free ApproxTimeSynchronizer utility inspired by ROS message_filters.

Legacy compatibility names (v0.1.2.1)

If you have example code using SensoryBuffer and SaliencyFilter, v0.1.2.1 provides them as wrappers:

from sensory_memory_adi import SensoryBuffer, SaliencyFilter
sensory_layer = SensoryBuffer(decay_ms=500)
raw_input = {"type":"log_stream","content":"Critical Error: System Overheat","priority":0.9}
sensory_layer.capture(raw_input)

if SaliencyFilter.is_high_impact(raw_input):
    print("Promoting sensory data to Short-Term Memory...")