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An Artificial Brain from Biological Blueprints
Maxim is an agentic robotics framework that gives robots something resembling a mind. Not just reflexes. Not just if-then rules. An actual cognitive architecture inspired by how mammalian brains work, adapted for embodied AI agents that can perceive, remember, decide, and learn.
Most robots today are either brilliant or stupid, rarely anything in between. Industrial robots execute perfect movements but can't adapt. LLM-powered robots can reason but lack genuine understanding of their bodies. They don't feel where they are in space. They don't remember what worked before.
Maxim bridges this gap by stealing shamelessly from neuroscience. If evolution spent hundreds of millions of years perfecting certain cognitive architectures, why reinvent them?
Maxim implements computational models of several brain structures, each handling a specific cognitive function:
Episodic memory. What happened, when, and what came of it.
Circadian rhythms. Temporal patterns across hours, days, weeks.
Reward prediction. Learning what leads to good (or bad) outcomes.
Similarity matching. Finding relevant memories for new situations.
Nociception. Detecting and learning from aversive experiences.
Focus. Deciding where to look and what matters.
Dual number system. Fast approximate and slow precise math cognition.
Semantic memory. Lasting knowledge distilled from experience.
External messaging. Reaching users via SMS, voice, and more.
Real-time entity design from novel percepts during low-arousal states.
Base humanoid body with 5 sensors, 8 affordances, and 3 failure modes.
Maxim's behavior is controlled by three independent dimensions: processing states (awake vs sleep), operational modes (passive, active, singularity), and strategies (observe, explore, research, assist, reflect, learn). These combine freely to produce configurations like "active exploration" or "passive reflection."
Live mode is the full embodied runtime with a self-evolving intent system—the agent shapes its own personality through experience. Exploration mode adds curiosity modeling and budget tracking. Sleep isn't a mode but a processing state that enables memory consolidation while monitoring for wake keywords.
The magic isn't in any single component. It's in how they integrate. Perception flows into memory. Memory informs attention. Attention shapes what gets remembered. Reward signals update causal models. Pain teaches avoidance.
A robot is asked to "find the coffee mug." Here's what happens:
Maxim is an agentic robotics framework — a biologically-inspired cognitive architecture that gives robots something resembling a mind. It implements computational models of brain structures (hippocampus, entorhinal cortex, nucleus accumbens, attention networks, proprioception, and more) so embodied AI agents can perceive, remember, decide, and learn.
The Maxim harness is the orchestration layer that wires the biologically-inspired components (memory systems, attention, proprioception, reward prediction, tool use) together with an LLM and an embodied platform like Reachy Mini. It coordinates perception, memory recall, attention, and action in a single agent loop.
Yes — Maxim is open source on GitHub at github.com/dennys246/Maxim. The repository contains the full cognitive architecture, agent harness, simulation environment, and integration code for the Reachy Mini humanoid robot.
Maxim currently runs on the Reachy Mini humanoid robot, processing real visual input, motor commands, and human interactions. The architecture is hardware-agnostic and can be adapted to other embodied platforms via the embodiment layer.
Most AI agents are pure LLM wrappers with stateless tool calls. Maxim adds a persistent bio-substrate — episodic memory (hippocampus), semantic memory (ATL), reward prediction (nucleus accumbens), circadian rhythms (SCN), and proprioception — so the agent learns from every interaction rather than starting fresh each turn.
This isn't just academic curiosity. Biological inspiration provides:
Maxim runs on the Reachy Mini humanoid robot, processing real visual input, real motor commands, real interactions with humans. It's not a simulation. It's not a thought experiment. It's a working system that learns from every interaction.