cogents-core 0.1.2

Towards a cognitive agentic framework

Cogents-core

A comprehensive collection of essential building blocks for constructing cognitive multi-agent systems (MAS). Rather than building a full agent framework, Cogents provides a lightweight repository of key components designed to bridge the final mile in MAS development. Our philosophy focuses on modular, composable components that can be easily integrated into existing systems or used to build new ones from the ground up. For the underlying philosophy, refer to my talk on MAS (link).

Core Modules

Cogents offers a comprehensive set of modules for creating intelligent agent-based applications:

LLM Integration & Management

• Multi-model support: OpenAI, Google GenAI (via OpenRouter), Ollama, and LlamaCPP
• Advanced routing: Dynamic complexity-based and self-assessment routing strategies
• Tracing & monitoring: Built-in token tracking and Opik tracing integration
• Extensible architecture: Easy to add new LLM providers

Goal Management & Planning

• Goal decomposition: LLM-based and callable goal decomposition strategies
• Conflict detection: Automated goal conflict identification and resolution
• Replanning: Dynamic goal replanning capabilities

Tool Management

• Tool registry: Centralized tool registration and management
• Execution engine: Robust tool execution with error handling
• Repository system: Organized tool storage and retrieval

Memory Management

• Under development

Orchestration

• Under development

Project Structure

cogents/core
├── base/            # Base classes and models
├── goalith/         # Goal management and planning
├── memory/          # Memory management (on plan)
├── orchestrix/      # Global orchestration (on plan)
└── toolify/         # Tool management and execution

Creating a New Agent

From Base Classes

Start with the base agent classes in cogents_core.base to create custom agents with full control over behavior and capabilities.

Base Agent Class Hierarchy

BaseAgent (abstract)
├── Core functionality
│   ├── LLM client management
│   ├── Token usage tracking
│   ├── Logging capabilities
│   └── Configuration management
│
├── BaseGraphicAgent (abstract)
│   ├── LangGraph integration
│   ├── State management
│   ├── Graph visualization
│   └── Error handling patterns
│   │
│   ├── BaseConversationAgent (abstract)
│   │   ├── Session management
│   │   ├── Message handling
│   │   ├── Conversation state
│   │   └── Response generation
│   │
│   └── BaseResearcher (abstract)
│       ├── Research workflow
│       ├── Source management
│       ├── Query generation
│       └── Result compilation
│           └── Uses ResearchOutput model
│               ├── content: str
│               ├── sources: List[Dict]
│               ├── summary: str
│               └── timestamp: datetime

Key Inheritance Paths:

• BaseAgent: Core functionality (LLM client, token tracking, logging)
• BaseGraphicAgent: LangGraph integration and visualization
• BaseConversationAgent: Session management and conversation patterns
• BaseResearcher: Research workflow and structured output patterns

From Existing Agents

Use well-constructed agents like Seekra Agent as templates:

from cogents_core.agents.seekra_agent import SeekraAgent

# Extend Seekra Agent for custom research tasks
class CustomResearchAgent(SeekraAgent):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # Add custom functionality
        
    def custom_research_method(self):
        # Implement custom research logic
        pass

Install

pip install -U cogents-core

License

MIT License - see LICENSE file for details.