🔬 v0.2.0: Agent Evaluation System Online!

🎉 Major Release: Comprehensive Agent Evaluation Framework

This release introduces a production-grade evaluation system that sets a new standard for ReAct agent testing and benchmarking.

🔬 Dual Evaluation Framework

Graph Trajectory Evaluation

• LLM-as-Judge methodology with scenario-specific custom rubrics
• Tests agent reasoning patterns and tool usage decisions across multiple scenarios
• Automated scoring and ranking systems for objective performance measurement

Multi-turn Chat Simulation

• Role-persona interaction testing with adversarial scenarios
• Comprehensive conversational capability assessment
• Professional user persona testing including polite and challenging user types

🚀 SiliconFlow Integration

• Complete MaaS Platform Support: Native integration with China's leading open-source model platform
• Multi-Model Benchmarking: Test across Qwen/Qwen3-8B, GLM-4-9B-0414, GLM-Z1-9B-0414 models
• Cost-Effective Evaluation: <10B models provide excellent evaluation capabilities at minimal cost
• Regional API Support: Seamless cn/international endpoint switching

📊 Professional Evaluation Tools

• LangSmith Integration: Complete evaluation tracking with historical analysis
• Structured Reporting: Detailed score extraction and performance analytics
• Trajectory Normalization: JSON serialization-compatible evaluation processing
• Centralized Configuration: Unified evaluation settings via config.py

📚 Comprehensive Documentation

The evaluation system is fully documented in tests/evaluations/README.md with:

• Quick Start Guides: Get running with evaluations in minutes
• Methodology Explanations: Deep dive into LLM-as-Judge approaches
• Configuration References: Complete setup and customization options
• Results Analysis: How to interpret and act on evaluation results

🛠 Enhanced Development Experience

New Make Commands:

make evals                  # Run complete evaluation suite
make eval_graph             # Graph trajectory evaluation
make eval_multiturn         # Multi-turn chat evaluation
make eval_graph_qwen        # Test specific SiliconFlow models
make eval_graph_glm         # GLM model evaluation

Environment Setup:

• New region aliases: cn (China mainland), international (global)
• Added SILICONFLOW_API_KEY for multi-model evaluation
• Enhanced model configuration with provider-specific optimizations

🎯 Production-Ready Features

• Automated CI/CD Integration: Evaluation workflows ready for production pipelines
• Multi-Provider Testing: Compare performance across OpenAI, Anthropic, Qwen, and SiliconFlow
• Security Testing: Adversarial user personas for robust agent validation
• Performance Benchmarking: Quantitative metrics for agent optimization

📈 What's Next

With this evaluation foundation, teams can now:

• Objectively measure agent performance improvements
• Compare different model providers and configurations
• Identify and fix agent reasoning issues before production
• Build confidence in agent reliability through systematic testing

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• Full Documentation: Updated README.md, and README_CN.md with comprehensive v0.2.0 features
• Roadmap: v0.2.0 milestone marked complete in ROADMAP.md with detailed achievements

v0.1.2

🛠️ DevOps & Testing Enhancements - v0.1.2

This release focuses on improving development workflows, testing infrastructure, and MCP server capabilities.

✨ New Features

• MCP Server Filtering: Implemented comprehensive MCP server filtering system with advanced testing capabilities
• Claude Code Integration: Added GitHub Workflow support for Claude Code development assistance
• Project Configuration: Enhanced project configuration and gitignore management

🧪 Testing & Quality Assurance

• Comprehensive MCP Testing: Added extensive test coverage for MCP server filtering and tool integration
• Test Infrastructure: Improved testing framework with better coverage and reliability
• Asset Optimization: Continued image optimization for better performance

🚀 Development Experience

• Workflow Automation: Streamlined CI/CD processes with GitHub Actions integration
• Configuration Management: Better project structure and dependency management
• Developer Tools: Enhanced tooling for local development and debugging

🐛 Improvements

• Build System: Refined build processes and dependency resolution
• Documentation: Updated documentation to reflect new features and workflows

This release strengthens the development infrastructure and testing capabilities, making the project more maintainable and reliable for contributors.

v0.1.1

🔧 Refinements & Optimizations - v0.1.1

This release focuses on performance optimizations, documentation improvements, and developer experience enhancements.

🚀 Performance & Configuration

• Model Optimization: Changed default model from qwen-turbo to qwen-flash for better performance and cost efficiency
• Enhanced Context Metadata: Improved context system with LangGraph node bindings for better state tracking
• Asset Optimization: Optimized images and updated studio UI assets

📝 Documentation & Developer Experience

• LangGraph v0.6.6 Documentation: Updated comprehensive documentation for latest LangGraph integration
• Build System Improvements: Fixed Makefile to prefix Python commands with 'uv run' for consistent environment handling
• Code Quality: Removed trailing whitespace and improved code formatting consistency

🐛 Bug Fixes

• Environment Consistency: Fixed command execution in isolated environments
• Configuration Updates: Improved project configuration and dependency management

This release improves the overall developer experience while maintaining backward compatibility and enhancing performance characteristics.

v0.1.0

🚀 Initial Release - LangGraph ReAct Agent v0.1.0

This is the first stable release of the LangGraph ReAct (Reasoning and Action) Agent template, featuring a complete refactoring into a modular architecture.

✨ New Features

• Qwen Model Support: Native integration with Qwen models including regional API support (PRC/International endpoints)
• MCP Integration: Dynamic tool loading with Model Context Protocol (MCP) servers, including DeepWiki for GitHub repository documentation
• Common Module Architecture: Refactored codebase into reusable common module for better maintainability
• Comprehensive Test Suite: Added pytest-based testing framework with full coverage for LangGraph agent functionality

📝 Documentation

• Bilingual Documentation: Complete English and Chinese README with detailed setup instructions
• Project Roadmap: Clear development roadmap and contribution guidelines
• Configuration Guide: Comprehensive environment setup and model configuration documentation

🛠️ Technical Improvements

• Modular Architecture: Clean separation of concerns with common utilities, models, tools, and prompts
• Enhanced Context System: Improved context management with metadata and LangGraph node bindings
• Dynamic Tool Loading: Runtime tool registration with MCP server integration
• Better Error Handling: Robust error handling and debugging capabilities

This release establishes a solid foundation for building sophisticated ReAct agents with LangGraph, supporting multiple model providers and dynamic tool ecosystems.