Emoclaw

Emotion Skill

Emotions for AI agents — built from memories, shaped by relationships, always changing.

Emoclaw trains a lightweight model on your agent's identity and conversation history, producing a persistent emotional state that evolves with every interaction. Emotions decay naturally between sessions, shift based on who's talking and what's being said, and gradually recalibrate as your agent grows. The result is injected into the system prompt as an [EMOTIONAL STATE] block, giving your AI a felt sense of its own inner life.

How it works

1. Bootstrap — extract.py reads your agent's identity/memory files. label.py scores each passage via the Claude API (opt-in). train builds a small neural net from those scores. One-time setup.
2. Encode — Each incoming message is turned into a 384-dim vector by a frozen MiniLM sentence encoder. No fine-tuning, no network call — runs from a local cache.
3. Feel — The encoding + context (who's talking, what channel, previous emotion) flows through a GRU and MLP head, outputting an N-dimensional emotion vector (0-1 per dimension). The GRU hidden state persists across sessions — this is the "emotional residue" that carries forward mood.
4. Decay — Between sessions, each dimension drifts back toward its baseline at a configurable half-life (fast for arousal, slow for safety/groundedness). Time apart = cooling off.
5. Inject — The emotion vector is formatted as an [EMOTIONAL STATE] block and inserted into the agent's system prompt, giving the AI a felt sense of its own inner state.

Model is ~2MB, runs on CPU, adds <50ms per message. Network access is only used during bootstrap (opt-in).

Quick Reference

Setup

Quick Setup

python skills/emoclaw/scripts/setup.py

This copies the bundled emotion_model engine to your project root, creates a venv, installs the package, and copies the config template. Then edit emoclaw.yaml to customize for your agent.

Manual Setup

If you prefer to set up manually:

1. Install the package

cd <project-root>
# Copy engine and pyproject.toml from the skill
cp -r skills/emoclaw/engine/emotion_model ./emotion_model
cp skills/emoclaw/engine/pyproject.toml ./pyproject.toml

# Create venv and install
python3 -m venv emotion_model/.venv
source emotion_model/.venv/bin/activate
pip install -e .

Required: Python 3.10+, PyTorch, sentence-transformers, PyYAML.

2. Copy and customize the config

cp skills/emoclaw/assets/emoclaw.yaml ./emoclaw.yaml

Edit emoclaw.yaml to set:

• name — your agent's name
• dimensions — emotional dimensions with baselines and decay rates
• relationships.known — map of relationship names to embedding indices
• channels — communication channels your agent uses
• longing — absence-based desire growth (can be disabled)
• model.device — cpu recommended (MPS has issues with sentence-transformers)

See references/config-reference.md for the full schema.

3. Bootstrap (new agent)

If starting from scratch with identity/memory files:

# Extract passages from your identity files
python scripts/extract.py

# Auto-label passages using Claude API (requires ANTHROPIC_API_KEY)
python scripts/label.py

# Prepare train/val split and train
python -m emotion_model.scripts.prepare_dataset
python -m emotion_model.scripts.train

Or run the full pipeline:

python scripts/bootstrap.py

4. Verify

python -m emotion_model.scripts.status
python -m emotion_model.scripts.diagnose

Usage

Option A: Daemon (Recommended)

The daemon loads the model once and listens on a Unix socket, avoiding the ~2s sentence-transformer load time per message.

# Start
bash scripts/daemon.sh start

# Or directly
python -m emotion_model.daemon
python -m emotion_model.daemon --config path/to/emoclaw.yaml

Option B: Direct Python Import

from emotion_model.inference import EmotionEngine

engine = EmotionEngine(
    model_path="emotion_model/checkpoints/best_model.pt",
    state_path="memory/emotional-state.json",
)

block = engine.process_message(
    message_text="Good morning!",
    sender="alice",        # or None for config default
    channel="chat",        # or None for config default
    recent_context="...",  # optional conversation context
)
print(block)

Option C: One-shot State Injection

For system prompt injection without the daemon:

python -m emotion_model.scripts.inject_state

This reads the persisted state, applies time-based decay, and outputs the [EMOTIONAL STATE] block.

Integration

System Prompt Injection

Add the output block to your system prompt. The block format:

[EMOTIONAL STATE]
Valence: 0.55 (balanced)
Arousal: 0.35 (balanced)
Dominance: 0.50 (balanced)
Safety: 0.70 (open)
Desire: 0.20 (neutral)
Connection: 0.50 (balanced)
Playfulness: 0.40 (balanced)
Curiosity: 0.50 (balanced)
Warmth: 0.45 (balanced)
Tension: 0.20 (relaxed)
Groundedness: 0.60 (balanced)

This feels like: present, alive, between one thing and the next
[/EMOTIONAL STATE]

Daemon Protocol

Send JSON over the Unix socket:

{"text": "Good morning!", "sender": "alice", "channel": "chat"}

Special commands:

{"command": "ping"}
{"command": "state"}

Heartbeat Integration

The emotional state decays over time and needs to be refreshed at each session start. Add this entry to your HEARTBEAT.md:

- task: Refresh emotional state
  schedule: session_start
  run: python skills/emoclaw/scripts/inject_state.py
  inject: system_prompt  # append output as [EMOTIONAL STATE] block

Or call the daemon / inject_state script from your heartbeat/cron:

# In your heartbeat script
STATE_BLOCK=$(python -m emotion_model.scripts.inject_state 2>/dev/null)
# Inject $STATE_BLOCK into system prompt

Important: Without heartbeat integration, the emotional state block will go stale between sessions. The inject_state script applies time-based decay and outputs the current state — it must be called at least once per session.

Architecture

The model processes each message through this pipeline:

Message Text ──→ [Frozen MiniLM Encoder] ──→ 384-dim embedding
                                                    │
Conversation Context ──→ [Feature Builder] ──→ context vector
                                                    │
Previous Emotion ──────────────────────────→ emotion vector
                                                    │
                                            ┌───────┴───────┐
                                            │ Input Project  │
                                            │ (Linear+LN+GELU)│
                                            └───────┬───────┘
                                                    │
                                            ┌───────┴───────┐
                                            │     GRU       │
                                            │ (hidden state) │ ← emotional residue
                                            └───────┬───────┘
                                                    │
                                            ┌───────┴───────┐
                                            │ Emotion Head  │
                                            │ (MLP+Sigmoid) │
                                            └───────┬───────┘
                                                    │
                                            N-dim emotion vector [0,1]

The GRU hidden state persists across sessions — this is the "emotional residue" that carries forward mood, context, and relational memory.

See references/architecture.md for full details.

Security & Privacy

Data Flow

1. Extraction (scripts/extract.py) reads markdown files listed in emoclaw.yaml → bootstrap.source_files and bootstrap.memory_patterns. These are configurable and default to identity/memory files within the repo. Extracted passages are written to emotion_model/data/extracted_passages.jsonl.

2. Redaction — Before writing, extracted text is passed through configurable regex patterns (bootstrap.redact_patterns) that replace API keys, tokens, passwords, and other secrets with [REDACTED]. Default patterns cover Anthropic keys, GitHub PATs, bearer tokens, SSH keys, and generic key=value credentials. Add custom patterns in emoclaw.yaml.

3. Labeling (scripts/label.py) — opt-in only. Sends extracted passages to the Anthropic API for emotional scoring. Requires both ANTHROPIC_API_KEY and explicit user consent (interactive prompt before any API call). Use --yes to skip the prompt for automation. Use --dry-run to preview without any network calls.

4. Training runs entirely locally. No data leaves the machine during prepare_dataset or train.

5. Inference runs entirely locally. The daemon and inject_state script make no network calls.