Documentation Index Fetch the complete documentation index at: https://mintlify.com/MilesONerd/neurenix/llms.txt
Use this file to discover all available pages before exploring further.
Agents Overview Neurenix provides a powerful agent-based AI system that enables you to build intelligent agents, multi-agent systems, and custom environments. The agent module is designed for reinforcement learning, autonomous agents, and complex multi-agent interactions.
Core Components The Neurenix agent system consists of three main components:
Agent The Agent class is the base class for all AI agents in Neurenix. It provides a foundation for implementing various types of agents, such as:
Reinforcement learning agents
Autonomous agents
Goal-driven agents
Custom intelligent agents
Key Features:
Act based on observations
Learn from experience
Save and load agent state
Reset functionality for episodes
Environment The Environment class defines the world in which agents operate. It:
Provides observations to agents
Processes agent actions
Manages environment state
Supports agent registration
MultiAgent The MultiAgent class coordinates multiple agents in a shared environment, enabling:
Multi-agent interactions
Shared environment coordination
Step-based simulation
Agent management (add/remove)
Quick Start from neurenix.agent import Agent, Environment, MultiAgent # Create a custom agent class MyAgent ( Agent ): def act ( self , observation ): # Implement action selection logic return action def learn ( self , experience ): # Implement learning logic pass # Create a custom environment class MyEnvironment ( Environment ): def step ( self , actions ): # Process actions and return results return { "rewards" : rewards, "done" : done, "info" : info } def observe ( self , agent ): # Return observation for agent return observation # Create and use agents agent = MyAgent( name = "agent-1" ) env = MyEnvironment() # Get observation and act observation = env.observe(agent) action = agent.act(observation)
Architecture The agent system follows a clean, modular architecture:
Agent → act(observation) → Action ↓ learn(experience) ↓ Updated Agent Environment → step(actions) → Results (rewards, done, info) → observe(agent) → Observation MultiAgent → coordinates → Multiple Agents + Environment
Common Use Cases Reinforcement Learning Build RL agents that learn from rewards:
class RLAgent ( Agent ): def __init__ ( self , name = None ): super (). __init__ (name) self .policy = None # Your policy network self .value = None # Your value network def act ( self , observation ): # Use policy to select action return self .policy.forward(observation) def learn ( self , experience ): # Update policy based on experience state, action, reward, next_state = experience # Your learning algorithm (PPO, DQN, etc.)
Multi-Agent Systems Coordinate multiple agents in a shared environment:
agents = [MyAgent( f "agent- { i } " ) for i in range ( 5 )] env = MyEnvironment() mas = MultiAgent(agents, env) # Run simulation observations = mas.reset() for step in range ( 1000 ): results = mas.step() if results[ "done" ]: break
Autonomous Agents Create goal-driven autonomous agents:
class AutonomousAgent ( Agent ): def __init__ ( self , goal , name = None ): super (). __init__ (name) self .goal = goal def act ( self , observation ): # Plan actions based on goal return self .plan_action(observation, self .goal)
Next Steps
Single Agent Learn how to create and customize individual agents
Multi-Agent Build multi-agent systems and coordinate agents
Environments Create custom environments for your agents
Import Reference from neurenix.agent import Agent, Environment, MultiAgent
All agent components are available from the neurenix.agent module.