Agent Workflow Memory Knowledge Graph Runtime Model Card
这是可索引项目报告证据页：它保留 Agent Workflow Memory Knowledge Graph Runtime Model Card 的源材料入口、机制线索和限制提醒；正文仍需 reader/editor 与 academic public-copy review 后才能当作最终结论引用。
Agent Workflow Memory Knowledge Graph Runtime Model Card
| Field | Value |
|---|---|
| Repository | zorazrw/agent-workflow-memory |
| Category | Agent Workflow Memory Runtime with Knowledge Graph Integration |
| Stars / forks snapshot | 440 / 50 |
| Language | Python |
| License | Apache-2.0 |
| Raw capture | raw-github/zorazrw_agent-workflow-memory.md |
| Updated by | hourly public metadata update, 2026-06-02 01:55 +0800 |
1. Role in Self Evolve
zorazrw/agent-workflow-memory builds an agent workflow memory system with a graph-backed memory manager and FastAPI execution surface. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
link memory manager and knowledge graph into workflow execution -> route task context through persistent memory nodes -> query historical traces to stabilize next-step planning -> reduce drift across multi-step agent workflows
3. Evidence Path
web-observed GitHub page showed 440 stars, 50 forks, 25 commits, Apache-2.0 license, and explicit memory manager plus knowledge-graph workflow framing. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the snapshot as web-observed rather than API-verified.
4. Teaching Use
Use this card to explain Agent Workflow Memory Runtime with Knowledge Graph Integration: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.
5. Limits
The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.