ii-agent-mcp-universal
ii-agent-mcp-universalは、Pythonで開発された自動化エージェントです。このツールは、さまざまなタスクを効率的に処理するための機能を提供し、ユーザーが手動で行う作業を軽減します。特に、データ処理やAPIとの連携に優れています。
GitHubスター
0
ユーザー評価
未評価
お気に入り
0
閲覧数
11
フォーク
0
イシュー
0
README
II-Agent MCP Universal Connector
This repository contains the prototype for the Universal Dynamic Connector for II-Agent, which builds upon the MVP to create a fully dynamic tool using Crawl4AI for config discovery and local model support.
Long-Term Vision
The Universal Dynamic Connector aims to:
- Auto-discover API configs and rate limits using Crawl4AI
- Install and configure any local model (e.g., Mistral, LLaMA via Hugging Face)
- Dynamically adjust routing based on user-selected APIs and local models
- Add persistent memory ("Borg Memory") across providers and II-Agent tasks
- Include a WebUI for user configuration and monitoring
- Enable scalability (e.g., load balancing, Hetzner instance support)
- Package as a pip-installable module with extensible plugin architecture
Project Status
This repository is currently in the planning and prototype phase. The MVP implementation is available at ii-agent-mcp-mvp.
Planned Features
Crawl4AI Integration
- Scrape API documentation to auto-detect endpoints, rate limits, and models
- Generate provider configurations dynamically
- Update configurations as APIs evolve
Local Model Support
- Install and configure local models via Hugging Face
- Optimize for different hardware configurations
- Support for quantization and efficient inference
Dynamic Routing
- Smart load balancing between cloud APIs and local models
- Cost optimization strategies
- Fallback based on availability, performance, and cost
Persistent Memory
- Cross-provider memory persistence
- Task context preservation across II-Agent sessions
- Memory optimization and pruning strategies
WebUI
- Configuration dashboard
- Performance monitoring
- Cost tracking and optimization suggestions
Development Timeline
- Phase 1: MVP (Complete) - Basic multi-provider support with fallback
- Phase 2: Crawl4AI Integration - Auto-discovery of API configurations
- Phase 3: Local Model Support - Integration with Hugging Face models
- Phase 4: Persistent Memory - Implementation of "Borg Memory"
- Phase 5: WebUI and Monitoring - User interface for configuration and monitoring
- Phase 6: Scalability - Support for distributed deployment and load balancing
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
Open source under the MIT License.