cognition-core 0.5.2

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, tool integration, and API capabilities.

Architecture

cognition-core/
├── src/
│   └── cognition_core/
│       ├── api.py              # Core API implementation
│       ├── crew.py             # Enhanced CrewAI base
│       ├── agent.py            # Enhanced Agent class
│       ├── task.py             # Enhanced Task class
│       ├── llm.py              # Portkey LLM integration
│       ├── logger.py           # Logging system
│       ├── config.py           # Configuration management
│       ├── memory/             # Memory implementations
│       │   ├── entity.py       # Entity memory with Chroma
│       │   ├── long_term.py    # PostgreSQL long-term memory
│       │   ├── short_term.py   # Chroma short-term memory
│       │   ├── storage.py      # ChromaDB storage implementation
│       │   └── mem_svc.py      # Memory service orchestration
│       └── tools/              # Tool management
│           ├── custom_tool.py  # Base for custom tools
│           └── tool_svc.py     # Dynamic tool service

Core Features

1. Enhanced Crew Base

• Automatic API capability through @CognitionCoreCrewBase decorator
• Integrated tool service management
• Memory system initialization
• Configuration management
• Portkey LLM integration

2. Memory Systems

• Short-term Memory: ChromaDB-based implementation
• Long-term Memory: PostgreSQL-based storage
• Entity Memory: Relationship tracking with ChromaDB
• Configurable storage backends
• Embedder configuration support

3. Tool Integration

• Dynamic tool loading from HTTP endpoints
• Tool service with caching
• Async tool operations
• Tool refresh capability
• Structured tool definitions with Pydantic

4. API Integration

• Built-in FastAPI implementation
• Async task processing
• Health check endpoints
• Task status tracking
• Background task execution

5. Configuration Management

• Hot-reloading YAML configuration
• Environment variable integration
• Configurable paths
• Fallback to CrewAI defaults

Environment Variables

Required:

• PORTKEY_API_KEY: API key for Portkey LLM routing
• PORTKEY_VIRTUAL_KEY: Virtual key for Portkey
• COGNITION_CONFIG_DIR: Path to your configuration directory (e.g., "/home/user/.cognition/cognition-config-demo/config")

Optional:

• COGNITION_CONFIG_SOURCE: Git repository URL to clone configuration (e.g., "[e-mail]:user/config-repo.git")
  • If set, will clone the repository to ~/.cognition
  • COGNITION_CONFIG_DIR should then point to the config directory within the cloned repo
• CONFIG_RELOAD_TIMEOUT: Config reload timeout (default: 0.1)
• LONG_TERM_DB_PASSWORD: PostgreSQL database password
• CHROMA_PASSWORD: ChromaDB password
• APP_LOG_LEVEL: Logging level (default: INFO)

Note: When using remote configuration:

1. The repository will be cloned to ~/.cognition
2. Set COGNITION_CONFIG_DIR to point to the config directory within the cloned repository
3. Configuration is managed through a singleton pattern to prevent multiple clones/reloads

Usage Example

from cognition_core import CognitionCoreCrewBase
from crewai import Agent, Task

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

    @task
    def research_task(self) -> Task:
        return CognitionTask(
            config=self.tasks_config["research"],
            tools=["calculator", "search"]
        )

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

# Access API
app = YourCrew().api

Configuration Files

Memory Configuration (memory.yaml)

short_term_memory:
  enabled: true
  external: true
  host: "localhost"
  port: 8000
  collection_name: "short_term"

long_term_memory:
  enabled: true
  external: true
  connection_string: "postgresql://user:${LONG_TERM_DB_PASSWORD}@localhost:5432/db"

entity_memory:
  enabled: true
  external: true
  host: "localhost"
  port: 8000
  collection_name: "entities"

embedder:
  provider: "ollama"
  config:
    model: "nomic-embed-text"
    vector_dimension: 384

Tool Configuration (tools.yaml)

tool_services:
  - name: "primary_service"
    enabled: true
    base_url: "http://localhost:8080/api/v1"
    endpoints:
      - path: "/tools"
        method: "GET"

settings:
  cache:
    enabled: true
    ttl: 3600
  validation:
    response_timeout: 30

Contributing

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

License

MIT