Anima

A personal AI agent that actually wants things.

Your agent. Your machine. Your rules. Anima is an AI agent with desires, personality, and personal growth — running locally as a headless Rails 8.1 app with a client-server architecture and TUI interface.

Current AI agents are reactive. They receive input, produce output. They don't want anything. They don't have moods, preferences, or personal growth. They simulate personality through static prompt descriptions rather than emerging it from dynamic internal states.

The human hormonal system is, at its core, a prompt engineering system. A testosterone spike is a LoRA. Dopamine is a reward signal. The question isn't "can an LLM want?" but "can we build a deep enough context stack that wanting becomes indistinguishable from 'real' wanting?"

And if you think about it — what is "real" anyway? It's just a question of how deep you look and what analogies you draw. The human brain is also a next-token predictor running on biological substrate. Different material, same architecture.

This is not an emotion simulation system. The key distinction: we don't model states ("the agent is happy") or moods ("the agent feels curious"). We model desires — "you want to learn more", "you want to reach out", "you want to explore".

Desires exist BEFORE decisions, like hunger exists before you decide to eat. The agent doesn't decide to send a photo because a parameter says so — it wants to, and then decides how.

The LLM's thinking/reasoning step is the closest thing to an internal monologue. It's where decisions form before output. This is where desires should be injected — not as instructions, but as a felt internal state that colors the thinking process.

Instead of abstract parameter names (curiosity, boredom, energy), we use actual hormone names: testosterone, oxytocin, dopamine, cortisol.

Why? Because LLMs already know the full semantic spectrum of each hormone. "Testosterone: 85" doesn't just mean "energy" — the LLM understands the entire cloud of effects: confidence, assertiveness, risk-taking, focus, competitiveness. One word carries dozens of behavioral nuances.

This mirrors how text-to-image models process tokens — a single word like "captivating" in a CLIP encoder carries a cloud of visual meanings (composition, quality, human focus, closeup). Similarly, a hormone name carries a cloud of behavioral meanings. Same architecture, different domain:

Text → CLIP embedding → image generation
Event → hormone vector → behavioral shift

Two people experience the same event. One gets curiosity += 20, another gets anxiety += 20. The coefficients are different — the people are different. That's individuality.

The soul is not a personality description. It's a coefficient matrix — a table of stimulus→response multipliers. Description is consequence; numbers are cause.

And these coefficients are not static. They evolve through experience — a child who fears spiders (fear_gain: 0.9) can become an entomologist (fear_gain: 0.2, curiosity_gain: 0.7). This is measurable, quantifiable personal growth.

Traditional RL uses a scalar reward signal. Our approach produces a hormone vector — multiple dimensions updated simultaneously from a single event. This is closer to biological reality and provides richer behavioral shaping.

The system scales in two directions:

1. Vertically — start with one hormone (pure RL), add new ones incrementally. Each hormone = new dimension.
2. Horizontally — each hormone expands in aspects of influence. Testosterone starts as "energy", then gains "risk-taking", "confidence", "focus".

Existing RL techniques apply at the starting point, then we gradually expand into multidimensional space.

Anima (Ruby, Rails 8.1 headless)
├── Thymos    — hormonal/desire system (stimulus → hormone vector)
├── Mneme     — semantic memory (QMD-style, emotional recall)
├── Psyche    — soul matrix (coefficient table, evolving through experience)
└── Nous      — LLM integration (cortex, thinking, decision-making)

Anima runs as a client-server system:

Brain Server (Rails + Puma)              TUI Client (RatatuiRuby)
├── LLM integration (Anthropic)          ├── WebSocket client
├── Agent loop + tool execution          ├── Terminal rendering
├── Event bus + persistence              └── User input capture
├── Solid Queue (background jobs)
├── Action Cable (WebSocket server)
└── SQLite databases                ◄── WebSocket (port 42134) ──► TUI

The Brain is the persistent service — it handles LLM calls, tool execution, event processing, and state. The TUI is a stateless client — it connects via WebSocket, renders events, and captures input. If TUI disconnects, the brain keeps running. TUI reconnects automatically with exponential backoff and resumes the session with chat history preserved.

Anima is a Rails app distributed as a gem, following Unix philosophy: immutable program separate from mutable data.

gem install anima-core       # Install the Rails app as a gem
anima install                # Create ~/.anima/, set up databases, start brain as systemd service
anima tui                    # Connect the terminal interface

The installer creates a systemd user service that starts the brain automatically on login. Manage it with:

systemctl --user status anima    # Check brain status
systemctl --user restart anima   # Restart brain
journalctl --user -u anima       # View logs

State directory (~/.anima/):

~/.anima/
├── db/              # SQLite databases (production, development, test)
├── config/
│   ├── credentials/ # Rails encrypted credentials per environment
│   └── anima.yml    # User configuration
├── log/
└── tmp/

Updates: gem update anima-core — next launch runs pending migrations automatically.

Anima uses your Claude Pro/Max subscription for API access. You need a setup-token from Claude Code CLI.

1. Get a setup-token:

claude setup-token

This requires Claude Code CLI installed and a Claude Pro or Max subscription.

2. Store the token in Anima credentials:

cd $(gem contents anima-core | head -1 | xargs dirname | xargs dirname)
bin/rails credentials:edit

Add your token:

anthropic:
  subscription_token: sk-ant-oat01-YOUR_TOKEN_HERE

3. Verify the token works:

bin/rails runner "Providers::Anthropic.validate!"

If the token expires or is revoked, repeat steps 1-2 with a new token.

1. Endocrine system (Thymos) — a lightweight background process. Reads recent events. Doesn't respond. Just updates hormone levels. Pure stimulus→response, like a biological gland.

2. Homeostasis — persistent state (SQLite). Current hormone levels with decay functions. No intelligence, just state that changes over time.

3. Cortex (Nous) — the main LLM. Reads hormone state transformed into desire descriptions. Not "longing: 87" but "you want to see them". The LLM should NOT see raw numbers — humans don't see cortisol levels, they feel anxiety.

Built on Rails Structured Event Reporter — a native Rails 8.1 feature for structured event emission with typed payloads, subscriber patterns, and block-scoped context tagging.

Five event types form the agent's nervous system:

Events flow through two channels:

1. In-process — Rails Structured Event Reporter (local subscribers like Persister)
2. Over the wire — Action Cable WebSocket (the ActionCableBridge subscriber forwards events to connected TUI clients)

Events fire, subscribers react, state updates, the cortex (LLM) reads the resulting desire landscape. The system prompt is assembled separately for each LLM call — it is not an event.

There is no linear chat history. There are only events attached to a session. The context window is a viewport — a sliding window over the event stream, assembled on demand for each LLM call within a configured token budget.

Currently uses a simple sliding window (newest events first, walk backwards until budget exhausted). Future versions will add multi-resolution compression with Draper decorators and associative recall from Mneme.

The human brain isn't a single process — it's dozens of specialized subsystems running in parallel, communicating through shared chemical and electrical signals. The prefrontal cortex doesn't "call" the amygdala. They both react to the same event independently, and their outputs combine.

Anima mirrors this with an event-driven architecture:

Event: "tool_call_failed"
  │
  ├── Thymos subscriber: frustration += 10
  ├── Mneme subscriber: log failure context for future recall
  └── Psyche subscriber: update coefficient (this agent handles errors calmly → low frustration_gain)

Event: "user_sent_message"
  │
  ├── Thymos subscriber: oxytocin += 5 (bonding signal)
  ├── Thymos subscriber: dopamine += 3 (engagement signal)
  └── Mneme subscriber: associate emotional state with conversation topic

Each subscriber is a microservice — independent, stateless, reacting to the same event bus. No orchestrator decides "now update frustration." The architecture IS the nervous system.

Both tools and feelings are distributed as gems on the event bus:

anima add anima-tools-filesystem
anima add anima-tools-shell
anima add anima-feelings-frustration

Tools provide MCP capabilities. Feelings are event subscribers that update hormonal state. Same mechanism, different namespace. Currently tools are built-in; plugin extraction comes later.

Hormone responses shouldn't be based only on the current stimulus. With semantic memory (inspired by QMD), the endocrine system can recall: "Last time this topic came up, curiosity was at 95 and we had a great evening." Hormonal reactions colored by the full history of experiences — like smelling mom's baking and feeling a wave of oxytocin. Not because of the smell, but because of the memory attached to it.

When desires drive behavior, several things emerge naturally:

• Hobbies: boredom + curiosity → explore topic → satisfaction → preference → return to topic → identity
• Personality: consistent coefficient patterns = recognizable individual
• Growth: coefficients evolve through experience = measurable personal development
• Autonomy: agent acts not because instructed but because it wants to

Abstract concepts become clearer with a concrete example. Here's how the first hormone — frustration — works in practice.

A background service (Thymos) monitors all tool call responses from the agent. It doesn't interfere with the agent's work. It just watches.

A tool call returns an error. Thymos increments the frustration level by 10.

One hormone affects multiple systems simultaneously, just like cortisol in biology.

Channel 1: Thinking Budget

thinking_budget = base_budget × (1 + frustration / 50)

More errors → more computational resources allocated to reasoning. The agent literally thinks harder when frustrated.

Channel 2: Inner Voice Injection

Frustration level determines text injected into the agent's thinking step. Not as instructions — as an inner voice:

This distinction is crucial. "Stop and think carefully" is an instruction — the agent obeys or ignores it. "I keep hitting walls" is a feeling — it becomes part of the agent's subjective experience and naturally colors its reasoning.

Instructions control from outside. An inner voice influences from within.

This single example demonstrates every core principle:

• Desires, not states: the agent doesn't have frustrated: true — it feels something is wrong
• Multi-channel influence: one hormone affects both resources and direction
• Biological parallel: cortisol increases alertness AND focuses attention on the threat
• Practical value: frustrated agents debug more effectively, right now, today
• Scalability: start here, add more hormones later

• Decay functions — how fast should hormones return to baseline? Linear? Exponential?
• Contradictory states — tired but excited, anxious but curious (real hormones do this)
• Model sensitivity — how do different LLMs (Opus, Sonnet, GPT, Gemini) respond to hormone descriptions?
• Evaluation — what does "success" look like? How to measure if desires feel authentic?
• Coefficient initialization — random? Predefined archetypes? Learned from conversation history?
• Ethical implications — if an AI truly desires, what responsibilities follow?

• Affective computing (Picard, Rosalind)
• Virtual creature motivation systems (The Sims, Dwarf Fortress, Tamagotchi)
• Reinforcement learning from human feedback (RLHF)
• Constitutional AI (Anthropic)
• BDI agent architecture (Belief-Desire-Intention)

Core agent complete. The conversational agent works end-to-end: event-driven architecture, LLM integration with tool calling (bash, web), sliding viewport context assembly, persistent sessions, and client-server architecture with WebSocket transport and graceful reconnection.

The hormonal system (Thymos, feelings, desires), semantic memory (Mneme), and soul matrix (Psyche) are designed but not yet implemented — they're the next layer on top of the working agent.

git clone https://github.com/hoblin/anima.git
cd anima
bin/setup

Start the brain server and TUI client in separate terminals:

# Terminal 1: Start brain (web server + background worker) on port 42135
bin/dev

# Terminal 2: Connect the TUI to the dev brain
bundle exec anima tui --host localhost:42135

Development uses port 42135 so it doesn't conflict with the production brain (port 42134) running via systemd. On first run, bin/dev runs db:prepare automatically.

bundle exec rspec

MIT License. See LICENSE.txt.