[Feature]: Suggest Introducing Self-Evolution Capability to Enhance Agent Memory and Adaptive Abilities

## Summary

First and foremost, heartfelt thanks to the QwenPaw development team for their outstanding work! Additionally, I suggest introducing a Hermes Agent-like self-evolution capability to address AI hallucination and memory management issues.

## Component(s) Affected

- [x] Core / Backend (app, agents, config, providers, utils, local_models)
- [ ] Console (frontend web UI)
- [ ] Channels (DingTalk, Feishu, QQ, Discord, iMessage, etc.)
- [ ] Skills
- [ ] CLI
- [ ] Documentation (website)
- [ ] Tests
- [ ] CI/CD
- [ ] Scripts / Deploy

## Problem / Motivation

QwenPaw is lightweight, easy to deploy, and highly customizable—overall user experience is excellent. However, in practical use, AI hallucination and memory management remain core pain points:
- When prompts are not accurately described, AI tends to deviate from expected directions
- Memory retention and context understanding need improvement in long-term conversations
- Currently relying on manual prompt adjustments to correct deviations is inefficient

These issues impact user experience and productivity, especially for scenarios requiring extended multi-turn interactions.

## Proposed Solution

I suggest introducing a **Hermes Agent-like self-evolution capability** that allows agents to:
1. Learn and improve from historical interactions
2. Automatically identify and correct behavioral deviations
3. Build more persistent and accurate memory mechanisms
4. Reduce dependency on precise prompts

This will make QwenPaw more intelligent and adaptive, further enhancing the user experience.

## Alternatives Considered

- More complex prompt engineering (treats symptoms, not root cause)
- Adding manual review processes (reduces efficiency)
- Relying on external memory databases (increases complexity)

Self-evolution capability is the direction that addresses the problem fundamentally.

## Additional Context

- Hermes Agent reference: [Add link here if available]
- QwenPaw's advantages in lightweight deployment and customization are already outstanding
- Hope to enhance agent self-learning capabilities while maintaining the lightweight advantage

## Willing to Contribute

- [ ] I am willing to open a PR for this feature (after discussion).

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## ❤️ Acknowledgments

**Really love QwenPaw!**

Thank you to the development team for your hard work. Looking forward to seeing the project continue to grow and evolve!

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*— A Loyal User*