Resources
December 4, 2025 ยท View on GitHub
Agents
1. Agentic Tools & Frameworks
Latest Framework Updates (2025):
- OpenAI Agents SDK (March 2025): Minimalist Python framework for multi-agent workflows with robust tracing
- LangChain v1.0 (2025): Standard tool calling architecture, provider-agnostic design
- LangGraph v1.0 (2025): Node-based architecture for multi-agent systems with structured state management
- Anthropic Claude Skills: New API for building agents with Claude
- Model Context Protocol (MCP): Standard protocol for connecting AI models to external data sources and tools
2. Agents System Design
- Agent Design Patterns
- Core Concepts & Architecture
- Prompting & Development
3. Learning Resources
- Comprehensive Courses
3.5. Evaluation & Testing Tools (2025)
- Evaluation Platforms
- Maxim AI: End-to-end evaluation platform for AI agents
- Langfuse: Open-source observability and evaluation for LLM applications
- Comet Opik: AI agent evaluation and monitoring
- Arize: ML observability platform with agent evaluation
- Evidently AI: AI agent benchmarks and testing frameworks
- Benchmarks
- Web Agent Benchmarks: Evaluate agents' web navigation and interaction capabilities
- OS Agent Benchmarks: Test agents in operating system environments
- Tool-Using Agent Benchmarks: Assess agents' proficiency with external tools
- Multi-Agent Benchmarks: Evaluate collaborative agent systems
4. Applications & Examples
- Awesome LLM Apps - Curated collection of LLM applications
5. Additional Resources
- The Rise of Agentic AI - Comprehensive slide deck
- From LLMs to Stateful Long-Running Multi-Agent Systems
6. Recent Developments (2025)
- Computer-Using Agents (CUA)
- OpenAI's Operator: Computer-Using Agent powered by GPT-4o
- OpenCUA: Open-source framework for computer-use agents (August 2025)
- Microsoft Fara-7B: Efficient agentic model for computer use
- Agentic RAG
- Traditional RAG vs. Agentic RAG (NVIDIA): Why AI agents need dynamic knowledge
- Uber's Enhanced Agentic RAG: Production-scale implementation
- Research Papers
- ARAG: Agentic Retrieval Augmented Generation: Research showing 42.1% improvement over standard RAG
- OpenCUA: Open Foundations for Computer-Use Agents: Framework for transparent research