Mycelium: A Biologically Inspired Cognitive Architecture for LLM Agents, Integrating Neurochemistry and Active Inference

Mycelium is a biologically inspired cognitive architecture for LLM Agents, integrating persistent associative memory, three-system neurochemistry model, active inference, agent evolution, and peer-to-peer federated learning. It is compatible with any MCP client and marks a key attempt for AI Agents to learn from biological intelligence.

Artificial intelligence has long drawn inspiration from biological nervous systems. The Mycelium project deeply simulates multiple aspects of biological cognition (memory, neurochemical regulation, active learning and adaptation). Its name "Mycelium" metaphorically refers to building a distributed intelligent network for connection, learning, and evolution—just like the underground fungal network that exchanges nutrients and information.

1. Persistent associative memory system: Solves the "amnesia" problem of traditional LLMs, simulates the associative mechanism of the biological brain, builds a semantic network, and supports cross-conversation continuity and similarity-based retrieval reasoning;
2. Three-system neurochemistry model: Drive system (intrinsic motivational goals), regulation system (emotion/attention regulation), cognitive system (reasoning and decision-making). The interaction of these three systems enables the agent to exhibit behavioral characteristics close to biological intelligence.

1. Active inference and predictive coding: With active inference as the core framework, the agent proactively acts to verify predictions of world states, couples perception and action, and improves initiative, learning efficiency, and robustness;
2. Agent evolution mechanism: Records success and failure experiences, optimizes decision-making strategies, accumulates professional skills, and metacognitive ability supports cross-task transfer, which is closely integrated with the memory system.

1. Peer-to-peer federated learning network: Agents form a distributed network, "models move while data stays", share learning outcomes under privacy protection, and eliminate dependence on central servers;
2. MCP compatibility: Compatible with any MCP client, seamlessly integrates into existing AI ecosystems, supports multiple LLM backends (GPT, Claude, Llama, etc.), and lowers the adoption threshold for developers.

Mycelium is suitable for: long-term companion agents (personal assistants, educational tutoring, mental health), adaptive automation systems (dynamic environment optimization), distributed collaborative intelligence (IoT, edge computing), and cognitive science research platforms (verification of cognitive theories).

Current challenges include balancing biological complexity and computational efficiency, security and consistency in multi-point collaboration, and effect evaluation of non-traditional architectures. In the future, Mycelium represents the direction of AI Agents learning from biological intelligence. As understanding of brain mechanisms deepens, bionic methods may bring more breakthroughs and provide references for general artificial intelligence.