Using open source software without proper attribution or in violation of license terms is not only ethically problematic but may also constitute a legal violation
If you're using code or tools from this repository or GitHub, please ensure you maintain all attribution notices and comply with all applicable licenses.
The license above is a modified MIT LICENSE for the purpose of this project 👆
📄 For full legal terms and enforcement policy, read the Neuron Legal Notice & Enforcement Terms.
Neuron is a composable AI framework that thinks in circuits, not chains.
Traditional AI orchestration tools collapse under real-world complexity—contradictions, sarcasm, conflicting goals, or mixed data formats. Neuron addresses these breakdown zones through modular reasoning circuits inspired by how the brain actually processes information.
┌─────────────────────────────────────────┐
│ NEURON ARCHITECTURE │
└─────────────────────────────────────────┘
│
┌──────────────┬─────────┼─────────┬──────────────┐
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
┌──────────┐ ┌─────────┐ ┌───────┐ ┌─────────┐ ┌─────────┐
│Perception│ │ Memory │ │Synaptic│ │Reasoning│ │Expression│
│ Modules │ │ System │ │ Bus │ │ Modules │ │ Modules │
│ │ │ │ │ │ │ │ │ │
│• Language│◄►│• Episodic│◄►│ Coord │◄►│• Logic │◄►│• Response│
│• Vision │ │• Semantic│ │ Layer │ │• Planning│ │• Adapt │
│• Audio │ │• Working │ │ │ │• Temporal│ │• Format │
│• Multi.. │ │• Context │ │ │ │• Causal │ │• Tone │
└──────────┘ └─────────┘ └───────┘ └─────────┘ └─────────┘
│ │
▼ ▼
┌──────────────────┐ ┌──────────────────┐
│ Self-Monitoring │ │ Adaptability │
│ │ │ │
│• Error Detection │ │• Dynamic Routing │
│• Uncertainty │ │• Context Shift │
│• Explanation │ │• Resource Alloc │
└──────────────────┘ └──────────────────┘
Cognitive Multi-Agent Architecture for Complex AI Systems
Neuron is a research framework exploring cognitive architectures through modular multi-agent coordination. Built on neuroscience-inspired principles, it provides sophisticated agent coordination patterns and memory systems for developing resilient AI applications.
🔬 Current Status: Advanced Research Platform - Production-ready architecture with agent behavior modeling. Suitable for research, prototyping, and foundational development of cognitive AI systems.
Neuron excels in scenarios traditional AI struggles with:
- 🔄 Resilient Processing: Handles ambiguous inputs, contradictory information, and incomplete data without system failure
- 🧠 Persistent Memory: Maintains context across extended interactions for longitudinal reasoning
- ⚡ Selective Activation: Dynamically combines only needed capabilities rather than running every component
- 🔀 Parallel Coordination: Processes multiple tasks simultaneously while maintaining consistency
- 👁️ Complete Observability: Every decision is traceable with full reasoning paths and evidence trails
| Use Case | Traditional AI | Neuron Agent Approach |
|---|---|---|
| Contradictory Customer Requests | Fails or picks one instruction | Agent consensus detects contradiction, requests clarification |
| Multi-Session Medical History | Loses context between visits | Memory agents maintain episodic history with temporal reasoning |
| Emergency Response Triage | Static rule-based priority | Coordination agents provide dynamic multi-modal assessment |
| Regulatory Compliance | Rigid rule checking | Reasoning agents perform contextual interpretation with conflict resolution |
git clone https://github.com/ShaliniAnandaPhD/Neuron.git
cd Neuron
pip install -e .from neuron import initialize, create_agent, CircuitDefinition
from neuron.agents import ReflexAgent, DeliberativeAgent
# Initialize the agent framework
core = initialize()
# Define a cognitive reasoning circuit with agent coordination
circuit_def = CircuitDefinition.create(
name="CustomerSupportCircuit",
description="Handles complex customer issues with agent memory coordination",
agents={
"intake": {
"type": "ReflexAgent",
"role": "INPUT",
"capabilities": ["sentiment_analysis", "intent_detection"]
},
"reasoner": {
"type": "DeliberativeAgent",
"role": "PROCESSOR",
"capabilities": ["contradiction_detection", "memory_retrieval"]
},
"responder": {
"type": "ReflexAgent",
"role": "OUTPUT",
"capabilities": ["response_generation", "tone_adaptation"]
}
},
connections=[
{"source": "intake", "target": "reasoner", "type": "direct"},
{"source": "reasoner", "target": "responder", "type": "conditional"}
]
)
# Deploy and test agent coordination
circuit_id = core.circuit_designer.create_circuit(circuit_def)
core.circuit_designer.deploy_circuit(circuit_id)
# Process a complex request through agent network
response = core.circuit_designer.send_input(circuit_id, {
"customer_id": "12345",
"message": "I love this product but it's completely broken and I want a refund but also keep it",
"context": "third_complaint_this_month"
})
print(response) # Agent network detects contradiction and requests clarificationNeuron provides specialized agents for different cognitive functions:
# Quick response pattern-matching agents
reflex_agent = create_agent(ReflexAgent,
name="IntakeAgent",
capabilities=["sentiment_analysis", "classification"])
# Deep reasoning and analysis agents
deliberative_agent = create_agent(DeliberativeAgent,
name="ReasoningAgent",
capabilities=["logical_inference", "contradiction_detection"])
# Learning and adaptation agents
learning_agent = create_agent(LearningAgent,
name="AdaptiveAgent",
capabilities=["pattern_recognition", "strategy_evolution"])
# Coordination and orchestration agents
coordinator_agent = create_agent(CoordinatorAgent,
name="OrchestratorAgent",
capabilities=["resource_allocation", "priority_management"])Neuron implements sophisticated memory architecture for agent coordination:
# Access different memory types for agents
memory_manager = core.memory_manager
# Immediate context and active processing
working_memory = memory_manager.get_memory_system(MemoryType.WORKING)
# Sequential events and interaction history
episodic_memory = memory_manager.get_memory_system(MemoryType.EPISODIC)
# Conceptual knowledge and relationships
semantic_memory = memory_manager.get_memory_system(MemoryType.SEMANTIC)
# Learned processes and strategies
procedural_memory = memory_manager.get_memory_system(MemoryType.PROCEDURAL)
# Store and retrieve contextual information across agents
episodic_memory.store({
"event": "customer_complaint",
"timestamp": "2024-01-15T10:30:00Z",
"context": {"customer_id": "12345", "sentiment": "frustrated"},
"resolution": "product_replacement_offered"
})
# Retrieve relevant past interactions for agent reasoning
relevant_history = episodic_memory.query(
context={"customer_id": "12345"},
timeframe="last_30_days"
)Agents communicate through a brain-inspired message passing system:
from neuron import Message
# Create and send messages between agents
message = Message.create(
sender="intake_agent",
recipients=["reasoning_agent", "memory_agent"],
content={
"type": "customer_issue",
"data": {"sentiment": "mixed", "intent": "unclear"},
"confidence": 0.75,
"requires_reasoning": True
},
metadata={
"priority": "high",
"timeout": 30,
"fallback_required": True
}
)
await core.synaptic_bus.send(message)Build adaptive processing pipelines that reconfigure agent routing based on context:
# Create circuits that adapt agent routing to complexity
adaptive_circuit = CircuitDefinition.create(
name="AdaptiveReasoningCircuit",
routing_strategy="confidence_based",
fallback_strategy="graceful_degradation",
agents={
"simple_classifier": {
"type": "ReflexAgent",
"activation_threshold": 0.8, # High confidence required
"capabilities": ["quick_classification"]
},
"deep_reasoner": {
"type": "DeliberativeAgent",
"activation_threshold": 0.3, # Handles uncertain cases
"capabilities": ["complex_reasoning", "uncertainty_quantification"]
},
"contradiction_resolver": {
"type": "DeliberativeAgent",
"activation_condition": "contradiction_detected",
"capabilities": ["conflict_resolution", "clarification_generation"]
}
},
decision_rules=[
{
"condition": "confidence > 0.8",
"route": "simple_classifier"
},
{
"condition": "contradiction_detected == True",
"route": "contradiction_resolver"
},
{
"default": True,
"route": "deep_reasoner"
}
]
)# HIPAA-aware healthcare agent circuit
healthcare_circuit = CircuitDefinition.create(
name="ClinicalDecisionSupport",
compliance_modules=["hipaa_monitor", "clinical_guidelines"],
agents={
"triage": {"type": "ReflexAgent", "capabilities": ["symptom_analysis"]},
"risk_scorer": {"type": "DeliberativeAgent", "capabilities": ["risk_assessment"]},
"compliance_checker": {"type": "ValidatorAgent", "capabilities": ["hipaa_validation"]}
}
)# Crisis response with multi-format input handling agents
crisis_circuit = CircuitDefinition.create(
name="EmergencyResponseSystem",
input_types=["text", "voice", "social_media", "sensor_data"],
agents={
"input_processor": {"capabilities": ["multi_modal_parsing"]},
"priority_ranker": {"capabilities": ["urgency_assessment", "resource_allocation"]},
"response_coordinator": {"capabilities": ["dispatch_optimization", "status_tracking"]}
}
)# Customer retention with cross-session memory agents
support_circuit = CircuitDefinition.create(
name="CustomerRetentionIntelligence",
memory_integration=True,
agents={
"relationship_analyzer": {"capabilities": ["sentiment_tracking", "churn_prediction"]},
"issue_resolver": {"capabilities": ["problem_solving", "escalation_management"]},
"retention_strategist": {"capabilities": ["intervention_planning", "satisfaction_optimization"]}
}
)Neuron includes built-in agent reliability mechanisms:
# Configure agent monitoring and fallback behavior
monitoring_config = {
"hallucination_detection": True,
"uncertainty_quantification": True,
"contradiction_detection": True,
"automatic_fallback": True,
"explanation_generation": True
}
# Monitor agent health in real-time
health_status = core.neuro_monitor.get_health_status()
performance_metrics = core.neuro_monitor.get_metrics("circuit.*")# Enable temporal reasoning capabilities in agents
temporal_agent = create_agent(DeliberativeAgent,
capabilities=[
"timeline_reconstruction",
"causal_chain_analysis",
"dependency_tracking",
"scenario_projection"
]
)
# Analyze complex temporal scenarios
timeline_analysis = temporal_agent.process({
"events": mixed_chronological_data,
"analysis_type": "causal_dependencies",
"projection_horizon": "30_days"
})Every agent decision in Neuron is fully traceable:
# Access detailed agent reasoning paths
explanation = core.explainability.get_decision_trace(
circuit_id="customer_support_001",
request_id="req_12345"
)
print(explanation.reasoning_tree) # Step-by-step agent logic
print(explanation.confidence_scores) # Certainty at each agent step
print(explanation.alternative_paths) # Other agent options considered
print(explanation.evidence_sources) # Supporting information used by agentsNeuron's modular architecture supports plug-and-play agent microservices:
- 🔍 Ambiguity Resolution Agent: Detects and handles unclear inputs
- ⚖️ Contradiction Detection Agent: Identifies logical conflicts
- 🧠 Memory Optimization Agent: Manages contextual memory efficiently
- 📊 Performance Analytics Agent: Tracks system-wide agent metrics
- 🔧 Dynamic Reconfiguration Agent: Adapts agent circuits in real-time
from neuron.microservices import BaseAgent
class CustomAnalysisAgent(BaseAgent):
"""Custom domain-specific analysis agent"""
def __init__(self):
super().__init__(name="domain_analyzer")
async def process(self, input_data):
# Your custom agent logic here
analysis_result = self.analyze_domain_specifics(input_data)
return {
"analysis": analysis_result,
"confidence": self.calculate_confidence(analysis_result),
"recommendations": self.generate_recommendations(analysis_result)
}
# Register and deploy your agent
core.agent_manager.register(CustomAnalysisAgent())# Test agent circuit resilience
test_results = core.testing.run_stress_tests(
circuit_id="customer_support_001",
test_scenarios=[
"contradictory_inputs",
"incomplete_information",
"component_failures",
"high_load_conditions"
]
)
# Evaluate agent memory persistence
memory_tests = core.testing.evaluate_memory_systems(
retention_periods=["1_hour", "1_day", "1_week"],
decay_patterns=["importance_weighted", "recency_based"]
)# Compare agent performance against other frameworks
benchmark_results = core.benchmarking.compare_against([
"langchain_equivalent",
"direct_api_calls",
"custom_pipeline"
], test_cases="real_world_scenarios")- Memory: Persistent multi-layered agent memory vs. token-level context
- Reasoning: Parallel multi-agent coordination vs. sequential chains
- Observability: Full agent decision traces vs. execution logs only
- Adaptability: Dynamic agent reconfiguration vs. manual flow updates
- State Management: Rich agent memory systems vs. stateless calls
- Error Handling: Graceful agent degradation vs. hard failures
- Coordination: Multi-agent orchestration vs. single-shot responses
- Explainability: Complete agent reasoning traces vs. black box outputs
- Brain-Inspired Design: Neuroscience-based agent principles vs. generic multi-agent
- Memory Architecture: Sophisticated agent persistence vs. simple conversation history
- Fault Tolerance: Component-level agent resilience vs. system-wide failures
- Observability: Deep agent introspection vs. basic logging
- Install Neuron and run the quick start example
- Go through the Tutorials in Google Colab to understand the architecture
- Read the Evaluation Notebook to understand capabilities
- Join the Community for support and contributions
We welcome contributions! See CONTRIBUTING.md for guidelines.
MIT License - see LICENSE for details.
References
-
Anderson, J. R., & Lebiere, C. (2003). The Newell Test for a theory of cognition. Behavioral and Brain Sciences, 26(5), 587-640. Relevance: Foundational work on cognitive architectures that inspired Neuron's modular agent design and memory integration patterns.
-
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258. Relevance: Provides the theoretical framework for translating neuroscience principles into AI architectures, particularly memory systems and hierarchical processing.
-
Stone, P., & Veloso, M. (2000). Multiagent systems: A survey from a machine learning perspective. Autonomous Robots, 8(3), 345-383. Relevance: Establishes foundational principles for multi-agent coordination and communication protocols that inform Neuron's SynapticBus architecture.
-
Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53(1), 1-25. Relevance: Seminal work on episodic memory systems that directly influenced Neuron's multi-layered memory architecture and temporal reasoning capabilities.
-
Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. International Conference on Machine Learning, 1050-1059. Relevance: Theoretical foundation for uncertainty quantification methods used in Neuron's confidence-based routing and self-monitoring systems.
-
Cox, M. T. (2005). Metacognition in computation: A selected research review. Artificial Intelligence, 169(2), 104-141. Relevance: Provides the conceptual framework for self-monitoring and introspective capabilities that enable Neuron's error detection and adaptive behavior mechanisms.
This software was created and is maintained by Shalini Ananda (GitHub: @ShaliniAnandaPhD). Any use of this framework without attribution, or any commercial repackaging, is a violation of the license terms and may constitute copyright infringement.
Please review the LICENSE.md for conditions. Reach out via GitHub Sponsors for partnership or licensing discussions.