cognition-core 0.3.0

Core integration package for building AI agents with CrewAI, providing configuration management, memory systems, and tool integration

Cognition Core

Core integration package for building AI agents with CrewAI, providing configuration management, memory systems, and tool integration.

Architecture

cognition-core/
├── config/                  # Configuration files
│   ├── agents.yaml         # Agent definitions
│   ├── crew.yaml          # Crew settings
│   ├── memory.yaml        # Memory configuration
│   ├── portkey.yaml       # LLM routing
│   ├── tasks.yaml         # Task definitions
│   └── tools.yaml         # Tool configuration
├── memory/                 # Memory implementations
│   ├── entity.py          # Entity memory
│   ├── long_term.py       # Long-term storage
│   ├── short_term.py      # Short-term memory
│   ├── storage.py         # Storage interfaces
│   └── mem_svc.py         # Memory service
├── tools/                  # Tool management
│   ├── custom_tool.py     # Custom tool base
│   ├── tool_svc.py        # Tool service
│   └── __init__.py
└── crew.py                # Core crew base class

Features

Configuration Management

• Hot-reloading YAML configuration
• Environment variable integration
• Centralized settings management

Memory Systems

• Short-term memory (Redis)
• Long-term memory (Firestore)
• Entity memory for relationship tracking
• Configurable storage backends

Tool Integration

• Tool registry and service
• API wrapper utilities
• Configuration-driven tool management

LLM Integration

• Portkey routing and monitoring
• Multi-model support
• Performance optimization

Installation

pip install cognition-core

Quick Start

from cognition_core import CognitionCoreCrewBase
from crewai import Agent, Task, Crew

class YourCrew(CognitionCoreCrewBase):
    @agent
    def researcher(self) -> Agent:
        return Agent(
            config=self.agents_config["researcher"],
            llm=self.init_portkey_llm(
                model="gpt-4",
                portkey_config=self.portkey_config
            ),
            verbose=True
        )

    @crew
    def crew(self) -> Crew:
        return Crew(
            agents=self.agents,
            tasks=self.tasks,
            process=Process.sequential,
            memory=True,
            verbose=True
        )

Configuration

Example YAML configuration:

# memory.yaml
short_term_memory:
  enabled: true
  external: true
  host: "redis.example.com"
  port: 6379

long_term_memory:
  enabled: true
  external: true
  connection_string: "${LONG_TERM_DB_CONNECTION_STRING}"

Environment Variables

Required variables:

• PORTKEY_API_KEY: Portkey API key
• PORTKEY_VIRTUAL_KEY: Portkey virtual key
• LONG_TERM_DB_PASSWORD: Long-term storage password
• CONFIG_DIR: Configuration directory path (default: src/cognition-core/config)

Contributing

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

License

MIT