Project COGENTS (COGnitive aGENTic System) is trying to build a comprehensive, modular ecosystem for building intelligent, cognitive agent systems. COGENTS provides foundational abstractions, specialized toolkits, intelligent agents, and powerful search & automation capabilities—all designed to work together seamlessly. It combines theoretical foundations with practical implementations.
COGENTS is supported and implemented by multiple interconnected subprojects, each addressing a specific aspect of cognitive agent development.
1. cogents-core
The foundational layer providing core abstractions and essential modules for all COGENTS projects.
Core Abstractions
- Agent: Base classes for building custom agents (BaseAgent, BaseGraphicAgent, BaseConversationAgent, BaseResearcher)
- Goal Management: Goal decomposition, conflict detection, and dynamic replanning (Goalith)
- Tool Management: Centralized tool registry, execution engine, and repository system (Toolify)
- Memory Management: Persistent memory capabilities (under development)
- Orchestration: Global orchestration system (under development)
Key Features
- Multi-Model LLM Support: OpenAI, Google GenAI, Ollama, LlamaCPP with dynamic routing
- Advanced Routing: Complexity-based and self-assessment routing strategies
- Observability: Built-in token tracking and Opik tracing integration
- Message Bus: Inter-agent communication system
- Extensible Design: Easy to add new providers and capabilities
1
pip install -U cogents-core
Use Cases: Building custom agents, goal-oriented planning, tool integration, LLM management
2. cogents-smith
Complete web intelligence and automation framework: extensive toolkit ecosystem, production-ready agents, multi-engine search, browser automation, and structured data extraction.
Core Capabilities
- Multi-Engine Web Search: Simultaneous search across 9+ engines (Tavily, DuckDuckGo, Google AI, SearXNG, Brave, Baidu, WeChat)
- Deep Research Agent: Autonomous research with iterative query refinement and source aggregation
- Browser Automation: Intelligent browser control with natural language instructions
- Structured Data Extraction: Extract data using Pydantic models and YAML-based schema parser
- LangGraph Workflows: Advanced agent orchestration with state management
Toolkit Ecosystem (18 Specialized Toolkits in 10 Semantic Groups)
- Academic Research: arXiv integration for paper discovery and analysis
- Development Tools: Bash, file editing, GitHub, Python execution
- Media Processing: Image analysis, video processing, audio transcription
- Information Retrieval: Wikipedia, web search, knowledge extraction
- Data Management: Tabular data, memory systems, document handling
- Communication: Gmail integration
- Human Interaction: User communication and feedback collection
Production-Ready Agents
- Askura Agent: Dynamic conversational agent for structured information collection
- Seekra Agent: Deep research agent for comprehensive topic investigation and report generation
Architecture Features
- Semantic Organization: Intuitive grouping for easy discovery
- Lazy Loading: Load only what you need
- Async-First Design: High-performance concurrent operations
- Error Resilience: Graceful handling of missing dependencies
1
pip install -U cogents-smith
Use Cases: Rapid agent development, conversational data collection, deep research, market intelligence, web data extraction, multi-modal processing
3. cogents-browser-use
AI-powered browser automation adapted from browser-use on the COGENTS stack.
Features
- Natural language browser control
- Intelligent web navigation
- Automated interaction with web elements
- Built on COGENTS core abstractions
- Support for headless and headed modes
1
pip install -U cogents-browser-use
Use Cases: Web scraping, automated testing, data extraction, web interaction automation
4. wizsearch
Unified Python library for searching across multiple search engines with a consistent interface.
Features
- Multiple Search Engines: Baidu, Bing, Brave, DuckDuckGo, Google, Google AI, SearxNG, Tavily, WeChat
- Unified Interface: Single API with consistent
SearchResultformat - Multi-Engine Aggregation: Concurrent searches with round-robin result merging
- Page Crawling: Built-in web page content extraction using Crawl4AI
- Semantic Search: Optional vector-based semantic search with local storage
- Full Async/Await Support: High-performance asynchronous operations
1
pip install -U wizsearch
Use Cases: Multi-source search, web content aggregation, semantic search, research automation
Additional Resources
- DeepWiki Documentation:
- Philosophy: Multi-Agent Systems Talk
🌟 Acknowledgments
COGENTS builds upon and integrates with several excellent projects:
- Tencent Youtu-agent for toolkit integration
- browser-use for browser automation foundations
- LangGraph for workflow orchestration
- The open-source community for continuous inspiration and support
