TencentDB Agent Memory Model Card
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TencentDB Agent Memory Model Card
| Field | Value |
|---|---|
| Repository | Tencent/TencentDB-Agent-Memory |
| Category | Local Long-Term Agent Memory Substrate |
| Stars / forks snapshot | 4300 / 354 |
| Language | TypeScript |
| License | MIT |
| Raw capture | raw-github/tencent_tencentdb-agent-memory.md |
| Updated by | hourly public metadata update, 2026-05-28 04:00 +0800 |
1. Role in Self Evolve
TencentDB Agent Memory provides a local-first long-term memory pipeline for AI agents with plugin integration and benchmarked efficiency gains. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
symbolic short-term memory plus layered long-term memory -> plugin-based integration into agent runtimes -> local-first persistence pipeline -> benchmarked token and pass-rate impact reporting
3. Evidence Path
web-observed GitHub page showed 4.3k stars, 354 forks, 74 commits, license badge indicating MIT, and README claims of a 4-tier local memory pipeline with OpenClaw benchmark improvements. 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 Local Long-Term Agent Memory Substrate: 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.