NIMA Core 3.2

Noosphere Integrated Memory Architecture — A complete cognitive stack for AI agents: persistent memory, emotional intelligence, dream consolidation, hive mind, and precognitive recall.

Website: https://nima-core.ai · GitHub: https://github.com/lilubot/nima-core

Quick Start

pip install nima-core && nima-core

Your bot now has persistent memory. Zero config needed.

What's New in v3.0

Complete Cognitive Stack

NIMA evolved from a memory plugin into a full cognitive architecture:

v3.0 Highlights

• All cognitive modules unified under a single package

• Installer (install.sh) for zero-friction setup

• All OpenClaw hooks bundled and ready to drop in

• README rewritten, all versions aligned to 3.0.4

Architecture

OPENCLAW HOOKS
├── nima-memory/          Capture hook (3-layer, 4-phase noise filter)
│   ├── index.js          Hook entry point
│   ├── ladybug_store.py  LadybugDB storage backend
│   ├── embeddings.py     Multi-provider embedding (Voyage/OpenAI/Ollama/local)
│   ├── backfill.py       Historical transcript import
│   └── health_check.py   DB integrity checks
├── nima-recall-live/     Recall hook (before_agent_start)
│   ├── lazy_recall.py    Current recall engine
│   └── ladybug_recall.py LadybugDB-native recall
├── nima-affect/          Affect hook (message_received)
│   ├── vader-affect.js   VADER sentiment analyzer
│   └── emotion-lexicon.js Emotion keyword lexicon
└── shared/               Resilient wrappers, error handling

PYTHON CORE (nima_core/)
├── cognition/
│   ├── dynamic_affect.py         Panksepp 7-affect system
│   ├── emotion_detection.py      Text emotion extraction
│   ├── affect_correlation.py     Cross-affect analysis
│   ├── affect_history.py         Temporal affect tracking
│   ├── affect_interactions.py    Affect coupling dynamics
│   ├── archetypes.py             Personality baselines (Guardian, Explorer, etc.)
│   ├── personality_profiles.py   JSON personality configs
│   └── response_modulator_v2.py  Affect → response modulation
├── dream_consolidation.py        Nightly memory synthesis engine
├── memory_pruner.py              Episodic distillation + suppression
├── hive_mind.py                  Multi-agent memory sharing
├── precognition.py               Temporal pattern mining
├── lucid_moments.py              Spontaneous memory surfacing
├── connection_pool.py            SQLite pool (WAL, thread-safe)
├── logging_config.py             Singleton logger
└── metrics.py                    Thread-safe counters/timings

Privacy & Permissions

• ✅ All data stored locally in ~/.nima/

• ✅ Default: local embeddings = zero external calls

• ✅ No NIMA-owned servers, no proprietary tracking, no analytics sent to external services

• ⚠️ Opt-in networking: HiveMind (Redis pub/sub), Precognition (LLM endpoints), LadybugDB migrations — see Optional Features below

• 🔒 Embedding API calls only when explicitly enabling (VOYAGE_API_KEY, OPENAI_API_KEY, etc.)

Optional Features with Network Access

Security

What Gets Installed

Credential Handling

Recommendation: Start with NIMA_EMBEDDER=local (default). Only enable cloud providers when you need better embedding quality.

Safety Features

• Input filtering — System messages, heartbeats, and duplicates are filtered before capture

• FTS5 injection prevention — Parameterized queries prevent SQL injection

• Path traversal protection — All file paths are sanitized

• Temp file cleanup — Automatic cleanup of temporary files

• API timeouts — Network calls have reasonable timeouts (30s Voyage, 10s local)

Best Practices

1. Review before installing — Inspect install.sh and hook files before running

2. Backup config — Backup ~/.openclaw/openclaw.json before adding hooks

3. Don't run as root — Installation writes to user home directories

4. Use containerized envs — Test in a VM or container first if unsure

5. Rotate API keys — If using cloud embeddings, rotate keys periodically

6. Monitor logs — Check ~/.nima/logs/ for suspicious activity

Data Locations

~/.nima/
├── memory/
│   ├── graph.sqlite       # SQLite backend (default)
│   ├── ladybug.lbug       # LadybugDB backend (optional)
│   ├── embedding_cache.db # Cached embeddings
│   └── embedding_index.npy# Vector index
├── affect/
│   └── affect_state.json  # Current emotional state
└── logs/                  # Debug logs (if enabled)

~/.openclaw/extensions/
├── nima-memory/           # Capture hook
├── nima-recall-live/     # Recall hook
└── nima-affect/          # Affect hook

Controls:

{
  "plugins": {
    "entries": {
      "nima-memory": {
        "skip_subagents": true,
        "skip_heartbeats": true,
        "noise_filtering": { "filter_system_noise": true }
      }
    }
  }
}

Configuration

Embedding Providers

Database Backend

Upgrade: pip install real-ladybug && python -c "from nima_core.storage import migrate; migrate()"

All Environment Variables

# Embedding (default: local)
NIMA_EMBEDDER=local|voyage|openai|ollama
VOYAGE_API_KEY=pa-xxx
OPENAI_API_KEY=sk-xxx
NIMA_OLLAMA_MODEL=nomic-embed-text

# Data paths
NIMA_DATA_DIR=~/.nima
NIMA_DB_PATH=~/.nima/memory/ladybug.lbug

# Memory pruner
NIMA_DISTILL_MODEL=claude-haiku-4-5
ANTHROPIC_API_KEY=sk-ant-xxx

# Logging
NIMA_LOG_LEVEL=INFO
NIMA_DEBUG_RECALL=1

Hooks

Installation

./install.sh
openclaw gateway restart

Or manual:

cp -r openclaw_hooks/nima-memory ~/.openclaw/extensions/
cp -r openclaw_hooks/nima-recall-live ~/.openclaw/extensions/
cp -r openclaw_hooks/nima-affect ~/.openclaw/extensions/

Advanced Features

Dream Consolidation

Nightly synthesis extracts insights and patterns from episodic memory:

python -m nima_core.dream_consolidation

# Or schedule via OpenClaw cron at 2 AM

Memory Pruner

Distills old conversations into semantic gists, suppresses raw noise:

python -m nima_core.memory_pruner --min-age 14 --live
python -m nima_core.memory_pruner --restore 12345  # undo within 30 days

Hive Mind

Multi-agent memory sharing:

from nima_core import HiveMind
hive = HiveMind(db_path="~/.nima/memory/ladybug.lbug")
context = hive.build_agent_context("research task", max_memories=8)
hive.capture_agent_result("agent-1", "result summary", "model-name")

Precognition

Temporal pattern mining → predictive memory pre-loading:

from nima_core import NimaPrecognition
precog = NimaPrecognition(db_path="~/.nima/memory/ladybug.lbug")
precog.run_mining_cycle()

Lucid Moments

Spontaneous surfacing of emotionally-resonant memories (with safety: trauma filtering, quiet hours, daily caps):

from nima_core import LucidMoments
lucid = LucidMoments(db_path="~/.nima/memory/ladybug.lbug")
moment = lucid.surface_moment()

Affect System

Panksepp 7-affect emotional intelligence with personality archetypes:

from nima_core import DynamicAffectSystem
affect = DynamicAffectSystem(identity_name="my_bot", baseline="guardian")
state = affect.process_input("I'm excited about this!")

# Archetypes: guardian, explorer, trickster, empath, sage

API

from nima_core import (
    DynamicAffectSystem,
    get_affect_system,
    HiveMind,
    NimaPrecognition,
    LucidMoments,
)

# Affect (thread-safe singleton)
affect = get_affect_system(identity_name="lilu")
state = affect.process_input("Hello!")

# Hive Mind
hive = HiveMind()
context = hive.build_agent_context("task description")

# Precognition
precog = NimaPrecognition()
precog.run_mining_cycle()

# Lucid Moments
lucid = LucidMoments()
moment = lucid.surface_moment()

Changelog

See CHANGELOG.md for full version history.

Recent Releases

• v3.0.4 (Feb 23, 2026) — Darwinian memory engine, new CLIs, installer, bug fixes

• v2.5.0 (Feb 21, 2026) — Hive Mind, Precognition, Lucid Moments

• v2.4.0 (Feb 20, 2026) — Dream Consolidation engine

• v2.3.0 (Feb 19, 2026) — Memory Pruner, connection pool, Ollama support

• v2.2.0 (Feb 19, 2026) — VADER Affect, 4-phase noise remediation, ecology scoring

• v2.0.0 (Feb 13, 2026) — LadybugDB backend, security hardening, 348 tests

License

MIT — free for any AI agent, commercial or personal.