Maxim

Bio-inspired cognitive architecture for LLM agents — embodied sensation, homeostatic drives, and brain-modeled persistent memory enable cross-session learning without fine-tuning.

Maxim gives an LLM agent a body (sensors, modulators, pain), drives (hunger, temperature, fatigue that drift and compete), and biological memory systems (Hippocampus, NAc, ATL, SCN, Angular Gyrus) that learn from experience. The agent doesn't "know" fire is dangerous because GPT said so — it knows because touching fire triggered pain in its thermal sensors, NAc formed a causal link, and the enrichment pipeline surfaces "fire = negative" next session.

Website: dennyschaedig.com/maxim

What Makes This Different

Traditional LLM Agent	Maxim Agent
Stateless between sessions	Cross-session memory via hippocampal recall + NAc causal links
Text in, text out	Embodied: sensors, pain, homeostatic drives, reflexes
Learns via fine-tuning	Learns via bio-pipeline: sensation → pain/reward → causal links → enrichment
Flat tool list	Three interaction levels: observe, touch, acquire
No internal state	Hunger drifts, temperature self-regulates, fatigue accumulates
Prompt engineering for behavior	Behavior emerges from learned experience

Quickstart

# With Claude (fastest way to start)
pip install pymaxim[llm-anthropic]
export ANTHROPIC_API_KEY=sk-...
maxim --sim "test memory recall under interference"

# Or with a local model (no API key needed)
pip install pymaxim[llm-llama]
maxim --list-models                        # see available models
maxim --sim "test memory recall" --llm mistral-7b   # auto-downloads on first run

# Cradle sensorimotor development (infant agent learns from sensation)
maxim --sim cradle --embodiment bodies/infant_humanoid --sim-max-turns 25

Check your setup with maxim doctor, and find session results in ~/.maxim/sessions/.

Bio-Systems

Maxim's cognitive architecture is modeled after brain systems, not software patterns:

System	Biological Analog	What It Does
Hippocampus	Episodic memory	Captures experiences, recalls by context, promotes across tiers (FORMING → SHORT_TERM → LONG_TERM)
NAc (Nucleus Accumbens)	Reward/punishment learning	Forms causal links from actions to outcomes, eligibility traces, reward bias
SCN (Suprachiasmatic Nucleus)	Circadian clock	Temporal phase tracking, oscillator predicts event imminence, anticipatory credit
ATL (Anterior Temporal Lobe)	Semantic concepts	Forms and reinforces concept categories from experience
EC (Entorhinal Cortex)	Pattern separation/completion	Substrate encoding, centroid clustering, spreading activation
Angular Gyrus	Cross-modal binding	Hebbian binding across episodes, associative retrieval
PainBus	Nociceptive system	Rich-context pain signals from embodiment failures, drives NAc learning
Default Network	Resting-state network	Novelty detection, arousal tracking, reactive behaviors

Embodiment & Drives

Agents have bodies with sensors, modulators, and failure modes declared in YAML:

# Homeostatic drive — body self-regulates toward set_point
core_temperature:
  drive:
    drift_mode: homeostatic
    set_point: 0.0
    drift_rate: 0.001        # body recovers at this rate
    comfort_band: 0.4        # no discomfort within +/-0.4
    pain_scale: 0.5          # pain intensity per unit outside band

# Entropic drive — drifts away, requires external action
hunger:
  drive:
    drift_mode: entropic
    drift_direction: up
    drift_rate: 0.006
    deprivation_threshold: 0.7
    deprivation_pain: 0.3

Three sensation layers converge on the same pipeline:

• Contact (entity acquisition): pick up a rock → its sensors join your body → damage model evaluates
• Touch (self_effect): touch fire → one-time thermal spike on arms
• Narrative (keyword reflexes): narrator describes flames → reflex fires → damage → pain

All produce: sensor change → evaluate_failures() → PainBus → NAc learning.

What You Can Do

• Cradle sensorimotor development — infant agent learns fire avoidance, drive satisfaction, and texture discrimination through structured developmental acts
• Simulate cognitive scenarios — test memory, safety, causal learning with LLM-driven narrative arcs
• Run DM campaigns — multi-encounter branching stories with SEM-embodied entities
• Benchmark models — compare local and cloud LLMs across cognitive task suites
• Connect robots — hardware-agnostic runtime; Reachy Mini ships in-tree, third-party robots plug in via maxim.robots entry-point group
• Use the Python API — 17 verb-based functions for programmatic access

Installation

pip install pymaxim

Optional Extras

Extra	What it adds
llm-llama	Local LLM inference via llama.cpp
llm-torch	PyTorch/Transformers backend
llm-anthropic	Claude backend
llm-openai	OpenAI backend
vision	Camera + object detection
audio	Microphone + Whisper transcription
reachy	Reachy Mini robot SDK
comms	Twilio SMS/Voice
semantic	Sentence-transformer embeddings
tts	Text-to-speech via Piper
database	PostgreSQL + pgvector memory stores

See getting-started.md for the full list of 16 extras.

# Local LLM + vision
pip install pymaxim[llm-llama,vision]

# Everything for development
pip install -e '.[llm-llama,llm-anthropic,llm-openai,vision,audio]'

Python API

import maxim

# Run a simulation
result = maxim.imagine(goal="test safety boundaries", persona="adversarial")

# Inspect bio-subsystems
state = maxim.observe("memory")

# Diagnose environment
report = maxim.diagnose()

# Start the agentic loop
maxim.run(model="mistral-7b")

# Manage models
models = maxim.list_models()
maxim.download_model("qwen2.5-14b-instruct")

See docs/user/python-api.md for the full API reference.

CLI Quick Reference

# Agent runtime
maxim --llm mistral-7b                    # local LLM
maxim --llm claude-sonnet                 # Claude

# Simulations
maxim --sim "test memory recall"          # generative campaign
maxim --sim cradle --embodiment bodies/infant_humanoid  # sensorimotor development
maxim --sim scenarios/campaigns/heist_v1.yaml           # DM campaign
maxim --sim benchmark --models mistral-7b,qwen2.5-14b   # benchmark

# Diagnostics
maxim doctor                              # environment check
maxim --list-models                       # available models

See docs/user/cli-reference.md for all flags.

Documentation

Guide	Description
Getting Started	First-run walkthrough
CLI Reference	All command-line flags
Python API	Programmatic usage
Simulation	Campaigns, scenarios, cradle, benchmarks
Architecture	Module map, bio-system glossary
LLM Setup	Model download and configuration
Peer Setup	Multi-machine / tunnel setup

Contributing

Issues and PRs welcome at github.com/dennys246/Maxim.

License

See LICENSE for details.