COGNITION

A production-ready virtual interface for building and deploying intelligent agents, powered by CrewAI and enhanced with enterprise-grade features through Cognition Core.

Overview

Cognition serves as an intelligent virtual interface for task orchestration and agent management. It prevents the "agent graveyard" problem by providing:

1. Standardized Agent Development

   • Consistent patterns through Cognition Core
   • Reusable tool ecosystems
   • Unified configuration management

2. Intelligent Task Management

   • Hierarchical task planning with manager agents
   • Dynamic tool allocation
   • Context-aware execution

3. Enterprise Integration

   • Cloud-native deployment options
   • Scalable memory systems
   • Production monitoring

4. Virtual Interface

   • Natural language task submission
   • Execution feedback and monitoring
   • Context-aware responses

Architecture

Core Components

1. Virtual Interface Layer

• Manager Agent: Orchestrates task planning and execution
• Chat LLM: Handles natural language interactions
• Cognitive Architecture: Processes tasks and provides intelligent feedback

2. Task Management

• Task Planning: Dynamic task breakdown and allocation
• Context Management: Maintains execution context
• Tool Selection: Intelligent tool assignment

3. Memory Systems

• Short-term: Quick access to recent context
• Long-term: Persistent knowledge storage
• Entity Memory: Relationship tracking

4. Tool Integration

• Dynamic Loading: Auto-discovery of available tools
• Version Management: Tool compatibility tracking
• Response Validation: Quality assurance

Usage Patterns

1. Container Deployment

from cognition import Cognition

# Initialize with default settings
cognition = Cognition()

# Start API server
app = cognition.api

2. Package Integration

from cognition import Cognition

# Custom configuration
cognition = Cognition(
    config_dir="path/to/config",
    memory_enabled=True,
    tool_discovery=True
)

# Create custom agent
agent = cognition.create_agent(
    role="researcher",
    tools=["web_search", "document_reader"]
)

Configuration

Agent Configuration (agents.yaml)

researcher:
  role: "Research Specialist"
  goal: "Gather and analyze information"
  backstory: "Experienced research analyst with expertise in data analysis"
  llm: "gpt-4"
  tools:
    - web_search
    - document_reader
    - data_analyzer

manager:
  role: "Task Coordinator"
  goal: "Orchestrate and optimize task execution"
  llm: "gpt-4"
  verbose: true

Task Configuration (tasks.yaml)

research_task:
  description: "Conduct comprehensive research on {topic}"
  expected_output: "Detailed analysis report"
  tools:
    - web_search
    - document_reader
  context_required: true

Enterprise Features

1. Scalability

• Horizontal scaling through containerization
• Independent tool scaling
• Distributed memory systems

2. Monitoring

• Task execution metrics
• Agent performance tracking
• Resource utilization

3. Security

• Role-based access control
• Tool usage policies
• Audit logging

Development Workflow

1. Local Development

   # Install dependencies
   pip install cognition-ai[dev]
   
   # Run tests
   pytest tests/
   
   # Start local server
   cognition serve

2. Container Deployment

   # Build container
   docker build -t cognition .
   
   # Run container
   docker run -p 8000:8000 cognition

Environment Variables

Required:

• PORTKEY_API_KEY: Portkey API key
• PORTKEY_VIRTUAL_KEY: Portkey virtual key

Optional:

• COGNITION_CONFIG_DIR: Configuration directory
• MEMORY_ENABLED: Enable memory systems
• TOOL_DISCOVERY: Enable tool discovery
• LOG_LEVEL: Logging level

Contributing

1. Fork the repository
2. Create a feature branch
3. Submit a pull request with tests

License

MIT