Signet AI Model Card
这是可索引项目报告证据页：它保留 Signet AI 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。
Signet AI Model Card
| Field | Value |
|---|---|
| Repository | Signet-AI/signetai |
| Category | Agent Context and Memory Substrate |
| Stars / forks snapshot | 167 / 33 |
| Language | TypeScript |
| License | Apache-2.0 |
| Raw capture | raw-github/signet-ai_signetai.md |
| Updated by | hourly public metadata update, 2026-05-25 |
1. Role in Self Evolve
Signet 是 local-first agent context layer，把 identity、memory、provenance、secrets、skills 和工作知识放在可检查的 SQLite/文件记录中，支持 Claude Code、OpenCode、OpenClaw、Codex、Gemini CLI 等 harness。
2. Working Principle
raw transcripts/files -> semantic memory -> FTS/vector/graph retrieval -> bounded context injection across harnesses
3. Evidence Path
web GitHub page observed 2,150 commits, Apache-2.0 license, 167 stars and 33 forks; README describes local-first identity, memory and secrets for AI agents, ambient memory, raw transcript provenance, SQLite/FTS/vector/graph retrieval, and 97.6% LongMemEval answer accuracy claim under its MemoryBench rules profile. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the current snapshot as web-observed rather than API-verified.
4. Teaching Use
Use this card to explain Agent Context and Memory Substrate in the raw -> classification -> project card -> site/report pipeline. The key question is whether memory/context evidence improves downstream agent behavior, or only improves retrieval on a local benchmark.
5. Limits
当前未克隆源码，未运行 benchmark、SDK examples、skill install flows、memory experiments、agent evolution loops 或 production deployments；star/fork/commit 快照来自公开 GitHub 页面文本或可见页面片段。