rulego/rulego-components-ai
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A declarative AI agent framework built on RuleGo
개요
A Declarative AI Agent Development Framework A AI agent development framework built on the RuleGo rule engine. It can be used to develop standalone AI agents or serve as the foundation for building AI coding assistants like Claude Code / OpenClaw. It adopts the design philosophy, embedding LLM reasoning capabilities into event-driven rule chain workflows. Through JSON declarative configuration, 10 AOP aspects, unified abstraction of four tool categories, and multi-dimensional session management, it enables building production-grade agent applications. - — Agents are defined declaratively in JSON and can be freely composed with other rule engine nodes (REST calls, JS filters, message queues, etc.
README
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rulego-components-ai
A Declarative AI Agent Development Framework
A declarative AI agent development framework built on the RuleGo rule engine. It can be used to develop standalone AI agents or serve as the foundation for building AI coding assistants like Claude Code / OpenClaw. It adopts the rule chain as agent design philosophy, embedding LLM reasoning capabilities into event-driven rule chain workflows. Through JSON declarative configuration, 10 AOP aspects, unified abstraction of four tool categories, and multi-dimensional session management, it enables building production-grade agent applications.
Full Documentation: Docs
Differences from Mainstream Frameworks
| Dimension | LangChain / LangGraph | AutoGen / CrewAI | This Framework |
|---|---|---|---|
| Definition | Python code | Python code | JSON declarative, hot-reloadable |
| Runtime Model | Request-response | Request-response | Event-driven, rule chain orchestration |
| Interception | Simple callback list | No unified mechanism | 10 AOP aspects with PointCut condition matching |
| Tool Sources | Functions | Functions | Built-in + Rule chain + Sub-agent + MCP bidirectional |
| Session Management | Basic Memory class | External storage required | 6 scopes, auto-compression/pruning, extensible storage |
| Business Integration | DIY required | DIY required | Native access to rule engine 200+ nodes |
| Model Switching | Build-time binding | Build-time binding | Runtime per-session dynamic switching |
| Deployment | Python runtime | Python runtime | Single binary, low resource usage |
Core Features
- Rule Chain as Agent — Agents are defined declaratively in JSON and can be freely composed with other rule engine nodes (REST calls, JS filters, message queues, etc.) to build hybrid pipelines of AI reasoning and business orchestration
- 10 AOP Aspects — Covering the complete agent execution lifecycle (start/before/around/after/completed/pre-LLM/post-LLM/stream-chunk/pre-tool-call/post-tool-call), with PointCut condition matching and Order priority
- Unified Abstraction of Four Tool Categories — 4 atomic tools (bash/read/write/edit) form the perception-creation-execution-evolution capability loop; rule chain tools (expose any business process as a tool), sub-agent tools (multi-agent collaboration), MCP bidirectional protocol (client + server)
- Skill System — Compatible with the SKILL.md format used by Claude Code / OpenClaw and other AI coding assistants, supporting multi-directory aggregation, fingerprint caching, and automatic hot-reloading
- Multi-dimensional Session Management — 6 scopes (global/per-user/per-channel/per-thread/per-task), automatic LLM compression and intelligent pruning
추천 도구
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설치
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