MCP Neo4j Agent Memory Model Card

这是可索引项目报告证据页：它保留 MCP Neo4j Agent Memory 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。

MCP Neo4j Agent Memory Model Card

1. Role in Self Evolve

knowall-ai/mcp-neo4j-agent-memory provides an MCP server that gives agents persistent graph memory backed by Neo4j plus vector retrieval. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

capture chat events and tool outputs into a graph memory store -> expose semantic and structural retrieval through MCP endpoints -> keep temporal and entity relations queryable -> feed retrieved memories back into agent planning and execution loops

3. Evidence Path

web-observed GitHub page showed 68 stars, 15 forks, 52 commits, MIT license, and README claims about long-term graph memory for AI agents. 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 Graph-Memory MCP Server for Long-Horizon Agents: 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.