TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
github.com/tencentcloud/tencentdb-agent-memoryIndexed at 8b86874Up to date with upstream
TencentDB Agent Memory is an end-to-end agent “memory” system: it consumes agent conversations and tool calls (plus product/docs knowledge inputs), builds and persists memory artifacts through a MemoryCore pipeline, enriches them via a knowledge-service index, and exposes them to agents through a context-proxy surface—producing chat memory, skills, and wiki link-graph pages served to the MemoryPanel web UI.
Written by gpt-5.4-nano from the index
1,013 files and 10,477 symbols in 8 modules, led by TypeScript (824 files) and Shell (30).
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Code health
This codebase scores 7.2 out of 10 for code health, which we rate good. It also scores maintainability 6.2 and static performance 9.7 out of 10. The three are scored separately and never blended into one number. Risk is concentrated, as it usually is: 108 of 1,013 files are git hotspots, and they average 5.8 — which is where the fixes pay off most.
On the leaderboards:#21 of 38 TypeScript repos
4 things worth doing this quarter.
Fix 1 broken reference in README_CN.md
The document points readers and agents at a path this repository no longer has.
Fix 1 broken reference in agents/adapter-agent-development.md
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Delete 19 unused symbols and files (189 lines)
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[](https://repowise.dev/repo/tencentcloud/tencentdb-agent-memory?src=badge_card)TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks. This page is a map of the tencentcloud/tencentdb-agent-memory repository, written primarily in TypeScript, rebuilt from the source each time it is indexed. Repowise parses every symbol, computes a dependency graph, scores per-file code health from complexity, duplication, test coverage and churn, mines git history for hotspots and ownership, and lifts the architectural decisions into documentation you can read here or query through MCP.
The codebase has 1,013 files and 10,477 symbols in 8 modules, led by TypeScript, Shell and Python. Code health is 7.2 out of 10, rated good.
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