Hey HN,

I'm excited to share a significant update to the Evolving Agents Framework. Based on all your valuable feedback, I've completed a substantial refactoring that implements a true agent-centric architecture with embedded strategy logic.

What's New: Pure ReAct SystemAgent

The most significant change is that the SystemAgent is now a pure BeeAI ReActAgent that manages the entire ecosystem through reasoning rather than hardcoded logic. This means:

1. The SystemAgent makes decisions by reasoning about the best approach, not through predefined functions
2. Tools encapsulate their own strategies and decision-making processes
3. The architecture follows a "tools all the way down" approach, where each tool can be independently evolved

This addresses the concerns about the "please improve yourself" approach mentioned in earlier comments. Instead of merely prompting for improvements, each tool now contains embedded strategy logic that can evolve independently.

Evolution Strategies Now Implemented

The `EvolveComponentTool` now implements multiple evolution strategies:

- *Standard*: Balanced evolution preserving core functionality while adding new features
- *Conservative*: Minimal changes focused on compatibility
- *Aggressive*: Significant changes optimizing for new requirements
- *Domain Adaptation*: Specialized for adapting components to new domains

Each strategy contains its own parameters for preservation level, optimization focus, and adaptation method. This is much more sophisticated than the "please improve yourself" approach I initially had.

Similarity-Based Decision Logic

As mentioned by @superb_dev, our initial similarity-based decision logic was quite simple:

"If similarity ≥ 0.8: Reuse an existing agent/tool
If 0.4 ≤ similarity < 0.8: Evolve an existing agent/tool
If similarity < 0.4: Create a new agent/tool"

What's different now is that this logic is encapsulated in the `SearchComponentTool` itself, not hardcoded in the SystemAgent. This means the decision strategy can evolve independently as we discover better approaches, without requiring changes to the SystemAgent.

Agent Bus Instead of "Service Bus"

I've also renamed the "Service Bus" to "Agent Bus" to reinforce the agent-centric philosophy. Everything is an agent with capabilities, not a service. This provides a clearer mental model while still enabling capability-based routing.

The Agent Bus now has three specialized tools:
- `RegisterProviderTool`: For registering agent capabilities
- `RequestServiceTool`: For capability-based routing
- `DiscoverCapabilityTool`: For finding available capabilities

Working Demonstration

I've created a new example (`pure_react_system_agent.py`) that demonstrates all these concepts in action. It shows:

1. How the SystemAgent uses reasoning to choose appropriate tools
2. How tools contain their own decision-making strategies
3. How the entire system works together to create, evolve, and manage agents

The demo is now working correctly and fully implements the architecture described in the README.

Addressing Specific Feedback

@kjhughes asked about the evolution process: The evolution process is now much more robust, with multiple strategies and clear preservation/adaptation parameters. It's no longer just a simple prompt.

@bob1029 pointed out the "please improve yourself" issue: This has been addressed by moving strategy logic into the tools themselves, making the evolution process more sophisticated.

@superb_dev commented on the "sophisticated decision mechanism": While the basic thresholds remain, the implementation is now truly sophisticated because it's embedded in the tools and can evolve independently.

@esafak asked about control and testing: The new architecture makes it easier to checkpoint, test, and govern agent evolution. Each evolution step is clearly tracked with metadata.

@mentalgear suggested better graphics: I've replaced the generated image with proper architectural diagrams that clearly show the system's components and relationships.

Try It Yourself

The new code is now live in the repository. You can run the demonstration with:

python examples/pure_react_system_agent.py

This will show the tools' embedded strategies in action, including semantic search with recommendations, component creation with code generation, and evolution with various strategies.

I'm still working on more comprehensive documentation, benchmarks, and examples, but this update addresses many of the fundamental architectural concerns raised in the comments.

Thank you all for your feedback - it's been invaluable in guiding this refactoring!