agent-memory

Requires a running OpenAI-compatible LLM endpoint for extraction and embedding; Docker or Node.js >= 22.16 for the services.

TencentDB Agent Memory gives an agent team a shared, growing memory instead of starting cold every session. Two ways it relates to gm's own flat-file memory (see the gm skill for that): (1) as a fully standalone system (this skill's main content, below) when the ask spans multiple agent frameworks, multiple team members, or needs a human-reviewable panel; (2) as an opt-in storage backend for gm's own memory store (memory.tencentdb_backend in gm.config.json, disabled by default) -- when a namespace is routed to it, gm's memory store writes file-pointer-indexed content compatible with this system's format instead of gm's default 384-dim md-corpus store, with no change to how an agent reads or writes memory. Reach for THIS skill's setup instructions (Docker Compose, panel UI) for the standalone deployment; reach for gm's own docs when the ask is just "make gm's memory use the Tencent-compatible backend."

What it provides

• Chat Memory: retains preferences, facts, decisions, and interaction history per agent. Distilled in layers: L0 raw conversation -> L1 atom -> L2 scenario -> L3 persona.
• Skill library: after complex work, an agent can extract a reusable Skill (versioned, with resource files, trigger boundaries, execution steps, validation rules) from its own conversation/tool-call history, then share it with the team after review.
• Wiki + CodeGraph: turns documentation, specs and runbooks into a linked Wiki; indexes code symbols, files, call relationships, and impact paths into a CodeGraph, both queryable on demand rather than injected wholesale into context.
• Memory Panel: a human-controlled review/control surface (not just a dashboard) for what gets promoted, shared, or pruned.

Assets are portable across agent frameworks and shareable across a team -- a new agent or team member can load existing memory instead of relearning from scratch.

When to use this skill vs. gm's own flat-file memory

• Use agent-memory's standalone setup instructions when: the user explicitly names TencentDB/memory-tencentdb/Memory Hub/team memory, wants memory that survives across different agent frameworks or team members (not just this session), wants a Skill library extracted from past conversations, or wants a Wiki/CodeGraph over a codebase.
• Use the flat-file memory described in the gm skill (no setup) for this session's own local recall. If the user wants gm's own store to be Tencent-format compatible without running the standalone services, point them at gm.config.json's memory.tencentdb_backend block instead of a full standalone install.